Sprint 0+1: Package restructure, source tiers, verdicts, multi-variant editions
- New oracle/ package (11 modules) with unified CLI (python -m oracle) - Source tiers: Tier 1 (arxiv/github/hf), Tier 2 (rss/hn), Tier 3 (reddit) - Composite verdicts: PUBLISH/WATCH/ARCHIVE/DROP based on signal score + age - Content-hash dedup: SHA-256[:16] normalized, atomic at insert time - Multi-variant editions: 4 YAML configs (default/research/devops/brief) - Variant engine: filter → rank → render (HTML + JSON, themed) - Per-adapter timeout (10s) + threading fallback - Consolidated 12 root scripts → thin wrappers + oracle/ package - Archived stale scripts (_engagement, _live_compare, reddit_proof) - Updated .gitignore, README.md, schema.sql
This commit is contained in:
Epictetus
@@ -15,3 +15,9 @@ __pycache__/
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# Env / virtualenv
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.venv/
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.env
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# Generated preview output
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_preview/
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# Generated site output (recreated from oracle.db)
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site/
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@@ -1,62 +1,197 @@
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# Athena — AI Research Intelligence Engine
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# Athena Oracle — AI Research Intelligence Pipeline
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> Multi-source research ingestion, pattern detection, and hypothesis falsification pipeline.
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> Autonomous daily operation: ingest → summarize → theme-scan → flag weak signals.
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> Multi-source AI news aggregation, scoring, and multi-variant edition rendering.
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> One pipeline → multiple audience-specific editions.
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## Repo
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**Live site:** https://ai-oracle.com (rendered from this pipeline)
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- **Location:** `Tony_tech/athena-oracle` (public, Gitea)
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- **URL:** http://localhost:3000/Tony_tech/athena-oracle
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- **Branch:** `main`
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- **Origin:** mirrors `~/oracle` (local working copy)
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## Architecture
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## What it does
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Athena runs on a daily cron (13:00 UTC) and continuously ingests from 6 sources,
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then applies a signal-scoring + falsification loop to surface real AI research
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momentum rather than source-expansion noise.
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| Component | File | Purpose |
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|-----------|------|---------|
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| Pipeline | `pipeline.py` | Orchestrates ingest → store → summarize → score |
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| Adapters | `adapters/` | arxiv, github, huggingface, hackernews, reddit, rss_feeds |
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| Theme scan | `theme_scan.py` | Cross-source trend detection + idempotent falsification |
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| Query | `query.py` | Interactive lookup against the store |
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| Archive | `archive.py` | Cold-storage rotation |
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| Summarize | `summarize.py` | Summarization via any available inference model |
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| Schema | `schema.sql` | SQLite store definition |
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| Cron entry | `oracle-pipeline.sh` | Wrapper invoked by Hermes cron |
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## Inference model strategy
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Athena is **model-agnostic** — it uses whatever inference backend is available at
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run time, whether free or paid. There is no hard dependency on a single provider.
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`summarize.py` currently targets a local Ollama endpoint (`llama3.2:1b`) when
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present. The pipeline is designed so the summarization backend can be swapped for
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any model we can reach — local GPU, a paid API, or a free-tier endpoint — without
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changing the ingestion, scoring, or theme-scan logic. When no inference backend is
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reachable, the summarization step is skipped; ingestion, scoring, and theme-scan
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continue uninterrupted.
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To wire in a different backend, implement the same `summarize(text) -> (summary, model)`
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contract that `summarize_with_ollama` satisfies, and add the dispatch in
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`process_card`.
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## Data handling
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- `oracle.db`, `logs/`, `.env`, `__pycache__/` are **git-ignored** (not committed).
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- API tokens (`GITHUB_TOKEN`, `HUGGINGFACE_TOKEN`) are read from environment only — never hardcoded.
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## Setup
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```bash
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pip install -r requirements.txt # if present; else deps are stdlib + requests
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export GITHUB_TOKEN=... # optional, raises rate limit 60→5000/hr
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python3 pipeline.py # manual run
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```
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┌─────────────────────────────────────────────────────┐
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│ adapters/ │
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│ arxiv · github · hackernews · reddit │
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│ huggingface · rss_feeds │
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└───────────────┬─────────────────────────────────────┘
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│ fetch(limit, timeout=10s)
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▼
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┌─────────────────────────────────────────────────────┐
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│ oracle/ — Core Package │
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│ │
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│ scoring.py Pure-rule component scoring (0-10) │
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│ dedup.py Content-hash dedup + verdict engine │
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│ variants.py Multi-variant edition engine │
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│ render.py HTML/JSON variant renderer │
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│ summarize.py Source-aware text summarization │
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│ recency.py Age-based freshness gate │
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│ themes.py Theme-based trend tracking │
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│ archive.py Soft-cap entry archival │
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│ db.py Schema management + migrations │
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│ config.py Centralized configuration │
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│ cli.py Unified CLI (python -m oracle) │
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└───────────────┬─────────────────────────────────────┘
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│
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▼
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┌─────────────────────────────────────────────────────┐
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│ oracle.db — SQLite (784+ entries) │
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│ Columns: signal_score · final_score · content_hash │
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│ · verdict · source_tier · summary │
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└─────────────────────────────────────────────────────┘
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│
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▼
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┌─────────────────────────────────────────────────────┐
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│ variants/ — Edition Configs (YAML) │
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│ │
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│ default.yaml Full feed, clickability-ranked │
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│ research.yaml arXiv + HF papers, signal-ranked │
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│ devops.yaml Shipping tools, 7-day window │
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│ brief.yaml PUBLISH verdict only, top 8 │
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└─────────────────────────────────────────────────────┘
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```
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## Architecture detail
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## Quick Start
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See `whitepaper.md` for full system design, scoring methodology, and the
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verification discipline that keeps adapters honest.
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```bash
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# Run the ingestion pipeline
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python -m oracle ingest
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# Generate summaries for pending entries
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python -m oracle summarize
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# Render all variant editions (preview)
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python -m oracle render --all --dry-run
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# Render a single variant
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python -m oracle render --variant research
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# Deploy to production
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python -m oracle render --all --webroot /var/www/html
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# Check system health
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python -m oracle health
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```
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## CLI Commands
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| Command | Description |
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|---------|-------------|
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| `ingest` | Fetch from adapters, score, store to oracle.db |
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| `summarize` | Generate summaries for entries without one |
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| `query top N` | Top N entries by signal score |
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| `query search "text"` | Keyword search across titles/summaries |
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| `query recent --hours 24` | Recent entries |
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| `render` | Render variant editions (HTML + JSON) |
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| `archive` | Soft-cap archival of old entries |
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| `themes` | Theme-based trend tracking |
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| `dedup` | Content-hash dedup + verdict management |
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| `health` | System health check |
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### Ingest Options
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```bash
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python -m oracle ingest --sources github,arxiv # Specific sources only
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python -m oracle ingest --limit 5 # 5 entries per source
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python -m oracle ingest --dry-run # Fetch but don't store
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```
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### Render Options
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```bash
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python -m oracle render --list # Show available variants
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python -m oracle render --variant brief # Single variant
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python -m oracle render --all # All variants
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python -m oracle render --all --dry-run # Preview mode
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python -m oracle render --all --webroot /var/www # Production deploy
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```
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## Scoring Engine
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Pure-rule component scoring — no embeddings or LLM required.
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| Component | Weight | Description |
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|-----------|--------|-------------|
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| Shipping | 20% | Code releases, benchmarks, working demos |
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| Utility | 20% | Practical tools, frameworks, integrations |
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| Replication | 25% | Reproducible research, open datasets |
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| Enthusiast | 20% | Community buzz, notable figures |
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| Novelty | 15% | First-of-its-kind, paradigm shifts |
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**Hype Penalty:** Caps at 45% to prevent buzzwords from dominating.
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**Signal Score:** 0–10 scale (per-adapter normalization → final composite).
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## Source Tiers (World Monitor Pattern)
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| Tier | Sources | Rationale |
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|------|---------|-----------|
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| **Tier 1** (PRIMARY) | arxiv, github, huggingface | Peer-reviewed research, official code releases, model registry |
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| **Tier 2** (SECONDARY) | rss, hackernews | Curated tech media, curated community |
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| **Tier 3** (TERTIARY) | reddit | User-generated discussion |
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## Composite Verdicts
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Entries are classified based on signal score + age:
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| Verdict | Minimum Score | Maximum Age | Meaning |
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|---------|--------------|-------------|---------|
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| **PUBLISH** | ≥ 6.0 | ≤ 48h | High-signal, fresh — front page material |
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| **WATCH** | ≥ 4.0 | ≤ 168h | Solid signal — worth tracking |
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| **ARCHIVE** | ≥ 2.0 | ≤ 720h | Historical value — keep for reference |
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| **DROP** | any | > 720h | Stale — exclude from active feeds |
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## Content-Hash Dedup
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SHA-256 content hashing (first 16 hex chars) for cross-source duplicate detection:
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- Normalized whitespace before hashing
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- Applied atomically at ingest time via `adapters/_store.py`
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- Prevents the same story from appearing multiple times across sources
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## Variant Editions
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Each variant is a YAML config defining:
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- **Filters:** verdicts, sources, min_score, max_age_h, max_items
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- **Ranking:** clickability | signal_score | verdict_priority | freshness (with half-life decay)
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- **Display:** theme, accent color, logo, show/hide score/tier/verdict badges
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Create a new edition by adding a YAML to `variants/` — no code changes needed.
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## Database
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SQLite (`oracle.db`) with 784+ entries across 6 sources. Schema includes:
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- Entry metadata (title, url, source, extracted_text, raw_metadata)
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- Scoring (signal_score, component scores, final_score, actionability_score)
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- Dedup/verdict (content_hash, verdict, source_tier)
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- Summarization (summary JSON with one_liner, key_points, implications)
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- Categorization (category_tags, bucket, narrative_id, topic_id)
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## Sprint Log
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### Sprint 0 — Foundation (2026-07-22) ✅
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- Package restructure: `oracle/` + `python -m oracle` CLI
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- Per-adapter timeout (10s) + threading fallback
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- Source confidence tiers (3-tier system)
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- Content-hash dedup (SHA-256[:16])
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- Composite verdicts (PUBLISH/WATCH/ARCHIVE/DROP)
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- Wired into pipeline: atomic hash/tier/verdict at insert time
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- Consolidated 12 root scripts → thin wrappers + oracle/ package
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### Sprint 1 — Multi-variant Editions (2026-07-22) ✅
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- Variant engine: `oracle/variants.py`
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- 4 default editions: default, research, devops, brief
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- Variant-aware HTML/JSON renderer with theme support
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- CLI: `render --variant`, `--all`, `--list`, `--webroot`
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- Verified: all 4 variants render correctly with proper filtering
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## Dependencies
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- Python 3.10+
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- PyYAML (`pip install pyyaml`)
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- Trafilatura (for text extraction in adapters)
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- Feedparser (RSS feeds)
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## Backward Compatibility
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Old scripts (`pipeline.py`, `summarize.py`, `query.py`, etc.) are thin wrappers that delegate to the unified CLI. Existing cron jobs calling `python3 pipeline.py` continue to work without changes.
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## License
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AGPL v3 (see LICENSE for details)
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+28
-2
@@ -89,8 +89,9 @@ def true_first_seen(raw_meta, source, now_str):
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UPSERT_SQL = """
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INSERT INTO entries
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(source, source_id, url, title, extracted_text, summary,
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category_tags, signal_score, raw_metadata, first_seen, last_updated)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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category_tags, signal_score, raw_metadata, first_seen, last_updated,
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content_hash, source_tier, verdict)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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ON CONFLICT(source, source_id) DO UPDATE SET
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source = excluded.source,
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source_id = excluded.source_id,
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@@ -101,6 +102,9 @@ ON CONFLICT(source, source_id) DO UPDATE SET
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signal_score = excluded.signal_score,
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raw_metadata = excluded.raw_metadata,
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last_updated = excluded.last_updated,
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content_hash = excluded.content_hash,
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source_tier = excluded.source_tier,
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verdict = excluded.verdict,
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first_seen = COALESCE((SELECT first_seen FROM entries WHERE source = excluded.source AND source_id = excluded.source_id), excluded.first_seen)
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"""
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@@ -111,16 +115,38 @@ def upsert_entries(conn, entries):
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`entries` is the list of dicts as built by each adapter; each dict must
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already have first_seen set to the TRUE publish date (via true_first_seen)
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and last_updated to the harvest time.
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Now also sets content_hash and source_tier at insertion time.
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Returns count of rows written.
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"""
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from oracle.dedup import content_hash, get_source_tier, compute_verdict, age_hours
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cur = conn.cursor()
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written = 0
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for e in entries:
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# Compute content hash
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title = e.get("title", "")
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url = e.get("url", "")
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body = e.get("extracted_text", "")[:500]
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h = content_hash(title, url, body)
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# Get source tier
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source = e.get("source", "")
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tier_info = get_source_tier(source)
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tier = tier_info["tier"]
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# Compute verdict
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first_seen = e.get("first_seen", "")
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score = float(e.get("signal_score") or 0)
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age = age_hours(first_seen)
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verdict = compute_verdict(score, age)
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cur.execute(UPSERT_SQL, (
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e["source"], e["source_id"], e["url"], e["title"],
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e.get("extracted_text"), e.get("summary"),
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e.get("category_tags"), e.get("signal_score"),
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e.get("raw_metadata"), e["first_seen"], e["last_updated"],
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h, tier, verdict,
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))
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written += 1
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conn.commit()
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@@ -355,6 +355,18 @@ class ArxivAdapter(SourceAdapter):
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if paper.get("_applied_domain"):
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tags.append(paper["_applied_domain"])
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# Local-serving / efficient-inference signal (suggested source: arXiv cs.LG
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# MoE/quantization papers → "local-serving" tag filter)
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serving_kw = [
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"quantiz", "quantization", "moe", "mixture of experts",
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"serving", "efficient inference", "pruning", "distill",
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"knowledge distillation", "low-rank", "lora", "parameter-efficient",
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"vram", "memory efficient", "edge inference", "on-device",
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]
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combined = f"{title_lower} {summary_lower}"
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if any(kw in combined for kw in serving_kw):
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tags.append("local-serving")
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return tags
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def fetch(self, query: str = "", limit: int = 20) -> list[dict]:
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+43
-23
@@ -15,7 +15,9 @@ Strategy: 3s spacing between subreddits, retry with backoff.
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"""
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import json
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import math
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import os
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import random
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import re
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import time
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import urllib.request
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@@ -51,11 +53,11 @@ class RedditAdapter(SourceAdapter):
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"automoderator",
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}
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def __init__(self, subreddits=None, rate_limit=1, user_agent=None):
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def __init__(self, subreddits=None, rate_limit=4, user_agent=None):
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"""
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Args:
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subreddits: List of subreddit names.
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rate_limit: Seconds between subreddit requests.
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rate_limit: Base seconds between subreddit requests (with jitter).
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user_agent: Custom User-Agent header.
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"""
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self.subreddits = subreddits or self.DEFAULT_SUBREDDITS
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@@ -65,6 +67,12 @@ class RedditAdapter(SourceAdapter):
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def name(self) -> str:
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return "reddit"
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def _sleep_with_jitter(self, base=None):
|
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"""Sleep with ±30% jitter to avoid pattern detection."""
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base = base or self.rate_limit
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jitter = base * 0.3 * (2 * random.random() - 1) # ±30%
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||||
time.sleep(base + jitter)
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def _clean_html(self, html: str) -> str:
|
||||
"""Extract readable text from Reddit's HTML content."""
|
||||
if not html:
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||||
@@ -106,7 +114,8 @@ class RedditAdapter(SourceAdapter):
|
||||
url = f"https://www.reddit.com/r/{subreddit}/hot/.rss?limit=50"
|
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req = urllib.request.Request(url, headers={"User-Agent": self.user_agent})
|
||||
|
||||
for attempt in range(2): # max 2 attempts, fail fast
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||||
backoff = [3, 8] # staggered backoff: 3s then 8s
|
||||
for attempt in range(3): # max 3 attempts
|
||||
try:
|
||||
with urllib.request.urlopen(req, timeout=10) as resp:
|
||||
xml_data = resp.read().decode("utf-8")
|
||||
@@ -116,8 +125,10 @@ class RedditAdapter(SourceAdapter):
|
||||
print(f" RSS blocked (HTTP {e.code}) for r/{subreddit}")
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||||
return []
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||||
if e.code == 429:
|
||||
if attempt == 0:
|
||||
time.sleep(2) # single retry with short backoff
|
||||
if attempt < len(backoff):
|
||||
delay = backoff[attempt]
|
||||
print(f" RSS 429 for r/{subreddit}, retrying in {delay}s")
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||||
time.sleep(delay)
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||||
continue
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||||
print(f" RSS rate-limited for r/{subreddit}, skip")
|
||||
return []
|
||||
@@ -127,7 +138,7 @@ class RedditAdapter(SourceAdapter):
|
||||
print(f" RSS error r/{subreddit}: {e}")
|
||||
return []
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||||
else:
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||||
print(f" r/{subreddit}: still rate limited, skip")
|
||||
print(f" r/{subreddit}: still rate limited after 3 attempts, skip")
|
||||
return []
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||||
|
||||
# Parse Atom XML
|
||||
@@ -301,6 +312,17 @@ class RedditAdapter(SourceAdapter):
|
||||
]):
|
||||
tags.append("meta:virality")
|
||||
|
||||
# Local-inference / on-device signal (suggested source: r/MachineLearning
|
||||
# "I tried X on-device" posts — high builder signal → local-inference feed)
|
||||
on_device_kw = [
|
||||
"on-device", "on device", "local inference", "local llm",
|
||||
"ran locally", "running locally", "in my pocket", "on my phone",
|
||||
"edge device", "offline", "no gpu", "consumer gpu", "rtx",
|
||||
"single gpu", "self-host", "self host",
|
||||
]
|
||||
if any(kw in title_lower or kw in content for kw in on_device_kw):
|
||||
tags.append("local-inference")
|
||||
|
||||
return tags
|
||||
|
||||
def _fetch_rss(self, subreddit: str) -> list[dict]:
|
||||
@@ -308,7 +330,8 @@ class RedditAdapter(SourceAdapter):
|
||||
url = f"https://www.reddit.com/r/{subreddit}/hot/.rss?limit=50"
|
||||
req = urllib.request.Request(url, headers={"User-Agent": self.user_agent})
|
||||
|
||||
for attempt in range(2): # max 2 attempts, fail fast
|
||||
backoff = [3, 8] # staggered backoff: 3s then 8s
|
||||
for attempt in range(3): # max 3 attempts
|
||||
try:
|
||||
with urllib.request.urlopen(req, timeout=10) as resp:
|
||||
xml_data = resp.read().decode("utf-8")
|
||||
@@ -318,8 +341,10 @@ class RedditAdapter(SourceAdapter):
|
||||
print(f" RSS blocked (HTTP {e.code}) for r/{subreddit}")
|
||||
return []
|
||||
if e.code == 429:
|
||||
if attempt == 0:
|
||||
time.sleep(2) # single retry with short backoff
|
||||
if attempt < len(backoff):
|
||||
delay = backoff[attempt]
|
||||
print(f" RSS 429 for r/{subreddit}, retrying in {delay}s")
|
||||
time.sleep(delay)
|
||||
continue
|
||||
print(f" RSS rate-limited for r/{subreddit}, skip")
|
||||
return []
|
||||
@@ -329,7 +354,7 @@ class RedditAdapter(SourceAdapter):
|
||||
print(f" RSS error r/{subreddit}: {e}")
|
||||
return []
|
||||
else:
|
||||
print(f" r/{subreddit}: still rate limited, skip")
|
||||
print(f" r/{subreddit}: still rate limited after 3 attempts, skip")
|
||||
return []
|
||||
|
||||
# Parse Atom XML
|
||||
@@ -373,31 +398,26 @@ class RedditAdapter(SourceAdapter):
|
||||
Filters AutoModerator and sticky posts.
|
||||
"""
|
||||
now = datetime.now(timezone.utc)
|
||||
# Try JSON first — if it's blocked on the first subreddit, bail fast
|
||||
# rather than wasting time on all subreddits
|
||||
# Try JSON first — if it's blocked on the first subreddit, skip
|
||||
# the test-RSS call (which would waste a request and risk rate-limiting)
|
||||
# and go straight to the RSS loop
|
||||
first_json = self._try_json(self.subreddits[0])
|
||||
if not first_json:
|
||||
# JSON is blocked site-wide, try one RSS to confirm
|
||||
test_rss = self._fetch_rss(self.subreddits[0])
|
||||
if not test_rss:
|
||||
print(" Reddit blocked (403/429), returning empty")
|
||||
return []
|
||||
# RSS works — fall through to full fetch below
|
||||
json_worked = bool(first_json)
|
||||
|
||||
all_entries = []
|
||||
seen_ids = set()
|
||||
json_worked = bool(first_json)
|
||||
|
||||
# Add first JSON results
|
||||
# Add first JSON results if any
|
||||
for p in first_json:
|
||||
if p["id"] not in seen_ids:
|
||||
seen_ids.add(p["id"])
|
||||
all_entries.append(p)
|
||||
time.sleep(self.rate_limit)
|
||||
|
||||
# If JSON didn't work, fall back to RSS for all subreddits
|
||||
if not json_worked:
|
||||
print(" JSON endpoints blocked, using RSS fallback")
|
||||
# Brief cooldown before RSS barrage
|
||||
time.sleep(3)
|
||||
for sub in self.subreddits:
|
||||
entries = self._fetch_rss(sub)
|
||||
for e in entries:
|
||||
@@ -422,7 +442,7 @@ class RedditAdapter(SourceAdapter):
|
||||
if entry["id"] not in seen_ids:
|
||||
seen_ids.add(entry["id"])
|
||||
all_entries.append(entry)
|
||||
time.sleep(self.rate_limit)
|
||||
self._sleep_with_jitter()
|
||||
|
||||
# Filter sticky/mod posts
|
||||
filtered = []
|
||||
|
||||
+8
-91
@@ -1,94 +1,11 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Oracle soft-cap archival.
|
||||
"""Thin wrapper — delegates to oracle.cli archive subcommand."""
|
||||
import sys, os
|
||||
sys.path.insert(0, os.path.dirname(__file__))
|
||||
|
||||
Bounds live `entries` growth by moving old / excess rows into
|
||||
`entries_archive` (preserving data — soft cap, not hard delete).
|
||||
Two triggers:
|
||||
--days N : move entries not updated in N days (default 30)
|
||||
--cap N : if live entries exceed N, archive oldest beyond the cap (default 5000)
|
||||
Default is a real run (data moves). Use --dry-run to report only.
|
||||
"""
|
||||
args = sys.argv[1:]
|
||||
cli_args = ["archive"] + args
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import sqlite3
|
||||
import time
|
||||
|
||||
DB = os.path.join(os.path.dirname(__file__), "oracle.db")
|
||||
|
||||
ARCHIVE_SCHEMA = """
|
||||
CREATE TABLE IF NOT EXISTS entries_archive (
|
||||
id INTEGER PRIMARY KEY,
|
||||
source TEXT, source_id TEXT, url TEXT, title TEXT,
|
||||
extracted_text TEXT, summary TEXT, category_tags TEXT,
|
||||
signal_score REAL, raw_metadata TEXT,
|
||||
first_seen TEXT, last_updated TEXT,
|
||||
archived_at TEXT DEFAULT (strftime('%Y-%m-%dT%H:%M:%SZ', 'now'))
|
||||
);
|
||||
"""
|
||||
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("--days", type=int, default=30, help="Archive entries not updated in N days")
|
||||
ap.add_argument("--cap", type=int, default=5000, help="Soft cap on live entries; archive oldest beyond this")
|
||||
ap.add_argument("--dry-run", action="store_true", help="Report only, make no changes")
|
||||
args = ap.parse_args()
|
||||
|
||||
if not os.path.exists(DB):
|
||||
print("No oracle.db — nothing to archive")
|
||||
return
|
||||
|
||||
conn = sqlite3.connect(DB)
|
||||
conn.execute(ARCHIVE_SCHEMA)
|
||||
|
||||
cutoff = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime(time.time() - args.days * 86400))
|
||||
|
||||
cur = conn.cursor()
|
||||
cur.execute("SELECT id, last_updated FROM entries")
|
||||
rows = cur.fetchall()
|
||||
n_total = len(rows)
|
||||
|
||||
old_ids = [r[0] for r in rows if (r[1] or "") < cutoff]
|
||||
beyond = max(0, n_total - args.cap)
|
||||
if beyond > 0:
|
||||
ordered = sorted(rows, key=lambda r: r[1] or "")[:beyond]
|
||||
cap_ids = [r[0] for r in ordered]
|
||||
else:
|
||||
cap_ids = []
|
||||
|
||||
move_ids = sorted(set(old_ids) | set(cap_ids))
|
||||
|
||||
if not move_ids:
|
||||
print(f"Archive check: {n_total} live entries, none older than {args.days}d "
|
||||
f"or beyond cap {args.cap}. Nothing to archive.")
|
||||
conn.close()
|
||||
return
|
||||
|
||||
print(f"Archive check: {n_total} live entries -> would archive {len(move_ids)} "
|
||||
f"(old={len(old_ids)}, cap={len(cap_ids)}).")
|
||||
|
||||
if args.dry_run:
|
||||
print("DRY RUN — no changes made.")
|
||||
conn.close()
|
||||
return
|
||||
|
||||
q = ",".join("?" * len(move_ids))
|
||||
conn.execute(
|
||||
f"""INSERT OR REPLACE INTO entries_archive
|
||||
(id, source, source_id, url, title, extracted_text, summary,
|
||||
category_tags, signal_score, raw_metadata, first_seen, last_updated)
|
||||
SELECT id, source, source_id, url, title, extracted_text, summary,
|
||||
category_tags, signal_score, raw_metadata, first_seen, last_updated
|
||||
FROM entries WHERE id IN ({q})""",
|
||||
move_ids,
|
||||
)
|
||||
conn.execute(f"DELETE FROM entries WHERE id IN ({q})", move_ids)
|
||||
conn.commit()
|
||||
conn.close()
|
||||
print(f"Archived {len(move_ids)} entries (live now {n_total - len(move_ids)}). "
|
||||
f"Preserved in entries_archive.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
from oracle.cli import main as cli_main
|
||||
sys.argv = ["oracle"] + cli_args
|
||||
cli_main()
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
# athena package — Sprint 1 pure-rule scoring lives in scoring.py
|
||||
@@ -1,545 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
athena/scoring.py — Sprint 1: Pure Rule-Based Bucket Classifier + Scorer.
|
||||
|
||||
DESIGN CONSTRAINT (founder directive, 2026-07-15):
|
||||
Pure rules only. No embeddings, no semantic similarity, no LLM classification.
|
||||
We are still discovering the editorial taxonomy. Deterministic systems are
|
||||
easier to debug; misclassifications are signal; we must be able to explain
|
||||
WHY every story landed where it did before we add intelligence.
|
||||
|
||||
Pipeline position:
|
||||
ingestion/dedup (pipeline.py) -> [ATTACH HERE] -> rendering
|
||||
Call attach_scoring(conn) immediately after store_entries and before render.
|
||||
|
||||
Schema migration (idempotent):
|
||||
bucket, shipping_score, utility_score, replication_score, enthusiast_score,
|
||||
novelty_score, hype_penalty, final_score, actionability_score,
|
||||
narrative_id, topic_id, relation_json
|
||||
|
||||
Review report: human-readable, per-bucket, exposes every fired rule.
|
||||
|
||||
Usage:
|
||||
python3 athena/scoring.py --migrate # add columns
|
||||
python3 athena/scoring.py --backfill # score all unscored rows
|
||||
python3 athena/scoring.py --review # write review report
|
||||
python3 athena/scoring.py --all # migrate + backfill + review
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import sqlite3
|
||||
from datetime import datetime, timezone
|
||||
|
||||
HERE = os.path.dirname(os.path.abspath(__file__))
|
||||
DB_PATH = os.path.join(os.path.dirname(HERE), "oracle.db") # ~/oracle/oracle.db
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# SCORE WEIGHTS (transparent, tunable in one place)
|
||||
# ---------------------------------------------------------------------------
|
||||
WEIGHTS = {
|
||||
"shipping": 0.20, # a working artifact exists
|
||||
"utility": 0.20, # clear practical use for an enthusiast
|
||||
"replication": 0.25, # can a reader reproduce/run it locally
|
||||
"enthusiast": 0.20, # signals a builder/DIY practitioner audience
|
||||
"novelty": 0.15, # new, specific, non-generic
|
||||
}
|
||||
HYPE_CAP = 0.45 # final_score is floored if hype penalty is high
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# BUCKETS + PURE RULES
|
||||
# Each bucket: keyword hits and/or source constraints. First match wins,
|
||||
# evaluated in BUCKET_ORDER (most specific taxon first).
|
||||
# ---------------------------------------------------------------------------
|
||||
# Keyword sets (lowercased; matched against title + summary + tags text).
|
||||
KW_SHIPPING = [
|
||||
"released", "launch", "v1.0", "v2.0", "v3.0", "shipping", "now available",
|
||||
"open source", "open-source", "open weights", "weights released", "live now",
|
||||
"beta", "public beta", "ga release", "general availability", "ships", "deployed",
|
||||
"production", "now in", "available today", "download", "gradio", "demo", "playground",
|
||||
]
|
||||
KW_LOCAL_AI = [
|
||||
"local llm", "local model", "local ai", "run locally", "run it locally", "on-device",
|
||||
"on device", "ollama", "llama.cpp", "llamacpp", "gguf", "ggml", "lm studio",
|
||||
"consumer hardware", "consumer gpu", "rtx", "your own gpu", "offline", "private ai",
|
||||
"local-only", "self-host", "self-hosted", "home server", "edge device", "edge inference",
|
||||
"quantized", "quantization", "q4", "q8", "int4", "fp16", "fine-tune at home",
|
||||
"train at home", "local inference", "local deployment", "no api", "no cloud",
|
||||
]
|
||||
KW_PROBLEM_SOLVED = [
|
||||
"how to", "how i", "solved", "fix", "fixed", "workaround", "benchmark", "improves",
|
||||
"improved", "speedup", "speed-up", "reduces", "reduce", "cut", "cuts", "boost",
|
||||
"optimize", "optimized", "optimisation", "faster", "3x", "10x", "2x", "latency",
|
||||
"throughput", "roi", "cost", "cheaper", "save", "saves", "eliminate", "eliminated",
|
||||
"from 117s to 30s", "p95", "memory usage", "vram", "token cost", "bottleneck",
|
||||
"case study", "results", "we measured", "we tested", "showdown", "comparison",
|
||||
]
|
||||
KW_MODEL_RELEASE = [
|
||||
"releases", "released", "unveils", "introduces", "new model", "new flagship",
|
||||
"gpt-", "claude", "gemini", "llama", "mistral", "qwen", "deepseek", "grok",
|
||||
"phi-", "command-r", "api access", "weights", "open model", "open-models",
|
||||
"frontier", "checkpoint", "fine-tune", "finetune", "rl-trained", "rl train",
|
||||
"trained", "post-training", "post training", "distilled", "distillation",
|
||||
]
|
||||
KW_RESEARCH = [
|
||||
"paper", "arxiv", "preprint", "study", "research", "we propose", "we present",
|
||||
"we introduce", "we show", "method", "framework", "theorem", "analysis of",
|
||||
"survey", "benchmark", "dataset", "neural", "transformer", "diffusion",
|
||||
"gradient", "ablation", "we find", "our approach", "novel", "state-of-the-art",
|
||||
"sota", "cs.lg", "cs.cl", "cs.cv", "cs.ai",
|
||||
]
|
||||
KW_BUSINESS = [
|
||||
"raises", "raised", "$", "valuation", "series a", "series b", "funding", "round",
|
||||
"ipo", "acquisition", "acquires", "merger", "deal", "revenue", "layoff", "hiring",
|
||||
"partnership", "invests", "investment", "market", "vc", "compute deal",
|
||||
"billion", "million", "forecast", "miss", "earnings", "stock",
|
||||
]
|
||||
KW_INFRA = [
|
||||
"gpu", "tpu", "data center", "datacenter", "data centre", "cluster", "cuda",
|
||||
"rocm", "vllm", "tensorrt", "inference server", "serving", "kubernetes", "docker",
|
||||
"pipeline", "mlops", "ci/cd", "rag", "vector db", "vector database", "agent",
|
||||
"agents", "orchestration", "observability", "evaluation", "eval", "guardrail",
|
||||
"safety", "red team", "jailbreak", "prompt injection", "fine-tuning stack",
|
||||
]
|
||||
KW_CULTURE = [
|
||||
"says", "argues", "opinion", "essay", "think", "thinks", "the real", "why we",
|
||||
"the future of", "dystopia", "utopia", "philosophy", "ethics", "regulation",
|
||||
"policy", "ban", "lawsuit", "eu", "senate", "congress", "interview", "podcast",
|
||||
"controversy", "controversial", "debate", "debate", "critic", "criticism",
|
||||
"creepy", "creeping", "not sexy", "vibe", "hot take", "unpopular",
|
||||
]
|
||||
|
||||
# Buckets evaluated in this order (specific -> generic). source_whitelist matches
|
||||
# raw `source` value exactly; if present, story must come from one of those sources.
|
||||
BUCKETS = {
|
||||
"SHIPPING": {
|
||||
"kw": KW_SHIPPING,
|
||||
"source_whitelist": None,
|
||||
"order": 0,
|
||||
},
|
||||
"LOCAL AI": {
|
||||
"kw": KW_LOCAL_AI,
|
||||
"source_whitelist": None,
|
||||
"order": 1,
|
||||
},
|
||||
"PROBLEM SOLVED": {
|
||||
"kw": KW_PROBLEM_SOLVED,
|
||||
"source_whitelist": None,
|
||||
"order": 2,
|
||||
},
|
||||
"MODEL RELEASE": {
|
||||
"kw": KW_MODEL_RELEASE,
|
||||
"source_whitelist": None,
|
||||
"order": 3,
|
||||
},
|
||||
"RESEARCH": {
|
||||
"kw": KW_RESEARCH,
|
||||
"source_whitelist": ["arxiv"],
|
||||
"order": 4,
|
||||
},
|
||||
"BUSINESS": {
|
||||
"kw": KW_BUSINESS,
|
||||
"source_whitelist": None,
|
||||
"order": 5,
|
||||
},
|
||||
"INFRASTRUCTURE": {
|
||||
"kw": KW_INFRA,
|
||||
"source_whitelist": None,
|
||||
"order": 6,
|
||||
},
|
||||
"CULTURE": {
|
||||
"kw": KW_CULTURE,
|
||||
"source_whitelist": None,
|
||||
"order": 7,
|
||||
},
|
||||
}
|
||||
BUCKET_ORDER = sorted(BUCKETS.keys(), key=lambda b: BUCKETS[b]["order"])
|
||||
|
||||
# Hype / low-signal penalty terms
|
||||
HYPE_TERMS = [
|
||||
"revolutionary", "game-changing", "game changer", "breakthrough", "mind-blowing",
|
||||
"insane", "crazy", "unbelievable", "shocking", "the future is here", "omg",
|
||||
"you won't believe", "secret", "they don't want you to know", "leaked", "viral",
|
||||
"hype", "buzzword", "disrupt", "disrupting everything", "ai will replace",
|
||||
"will change everything", "paradigm shift", "godlike", "magic", "miracle",
|
||||
]
|
||||
|
||||
# Enthusiast-audience signals (builder / DIY / practitioner)
|
||||
ENTHUSIAST_SIGNALS = [
|
||||
"github", "repo", "repository", "self-host", "local", "ollama", "llamacpp",
|
||||
"hugging face", "huggingface", "colab", "notebook", "pip install", "docker",
|
||||
"cli", "open source", "open-source", "diy", "build your own", "tutorial",
|
||||
"how to", "implementation", "agent", "agents", "fine-tune", "finetune",
|
||||
"quantiz", "vllm", "rtx", "gpu", "consumer", "homelab", "self-hosted",
|
||||
"machine-learning", "machine learning", "deep learning", "python", "rust",
|
||||
"benchmark", "reproduc", "weights", "gguf",
|
||||
]
|
||||
|
||||
# Source -> enthusiast affinity bonus
|
||||
SOURCE_ENTHUSIAST_BONUS = {
|
||||
"github": 0.20, "huggingface": 0.20, "arxiv": 0.10,
|
||||
"hackernews": 0.10, "reddit": 0.05, "rss": 0.0,
|
||||
}
|
||||
|
||||
|
||||
def _norm(text):
|
||||
if not text:
|
||||
return ""
|
||||
if isinstance(text, bytes):
|
||||
text = text.decode("utf-8", "replace")
|
||||
return " " + re.sub(r"\s+", " ", text.lower()) + " "
|
||||
|
||||
|
||||
def classify(entry):
|
||||
"""Pure rule classification.
|
||||
|
||||
Returns (bucket, matched_list) where matched_list is human-readable proof:
|
||||
["kw:ollama", "kw:gguf", "src:huggingface", "tag:local"]
|
||||
"""
|
||||
title = _norm(entry.get("title") or "")
|
||||
summary = _norm(_summary_text(entry.get("summary")))
|
||||
tags_raw = entry.get("category_tags") or ""
|
||||
try:
|
||||
tags = " ".join(json.loads(tags_raw)) if tags_raw else ""
|
||||
except Exception:
|
||||
tags = tags_raw
|
||||
tags = _norm(tags)
|
||||
source = (entry.get("source") or "").lower()
|
||||
haystack = title + " " + summary + " " + tags
|
||||
|
||||
matched = ["source=%s" % source]
|
||||
best_bucket = "UNCATEGORIZED"
|
||||
best_hits = 0
|
||||
|
||||
for bucket in BUCKET_ORDER:
|
||||
spec = BUCKETS[bucket]
|
||||
whitelist = spec["source_whitelist"]
|
||||
if whitelist and source not in whitelist:
|
||||
continue
|
||||
hits = []
|
||||
for kw in spec["kw"]:
|
||||
kwn = " " + kw.lower() + " "
|
||||
if kwn in haystack:
|
||||
hits.append(kw)
|
||||
if hits:
|
||||
# record proof (cap displayed hits to keep report readable)
|
||||
for h in hits[:8]:
|
||||
matched.append("kw:%s" % h)
|
||||
if len(hits) > best_hits:
|
||||
best_hits = len(hits)
|
||||
best_bucket = bucket
|
||||
|
||||
if best_bucket == "UNCATEGORIZED":
|
||||
matched.append("(no rule fired)")
|
||||
|
||||
return best_bucket, matched
|
||||
|
||||
|
||||
def _summary_text(raw):
|
||||
if not raw:
|
||||
return ""
|
||||
try:
|
||||
d = json.loads(raw)
|
||||
if isinstance(d, dict):
|
||||
return " ".join(str(v) for v in d.values() if isinstance(v, str))
|
||||
except Exception:
|
||||
pass
|
||||
return raw
|
||||
|
||||
|
||||
def score_entry(bucket, matched, entry):
|
||||
"""Return dict of component scores (0..1) + final (0..1).
|
||||
|
||||
Components are deterministic functions of signals; see inline rationale.
|
||||
"""
|
||||
source = (entry.get("source") or "").lower()
|
||||
title = _norm(entry.get("title") or "")
|
||||
summary = _norm(_summary_text(entry.get("summary")))
|
||||
tags_raw = entry.get("category_tags") or ""
|
||||
try:
|
||||
tags = " ".join(json.loads(tags_raw)) if tags_raw else ""
|
||||
except Exception:
|
||||
tags = tags_raw
|
||||
tags = _norm(tags)
|
||||
haystack = title + " " + summary + " " + tags
|
||||
|
||||
# --- enthusiast score ---
|
||||
ent_hits = sum(1 for s in ENTHUSIAST_SIGNALS if (" " + s + " ") in haystack)
|
||||
ent_base = min(ent_hits / 5.0, 1.0) # 5+ signals = full
|
||||
ent_src = SOURCE_ENTHUSIAST_BONUS.get(source, 0.0)
|
||||
enthusiast = min(ent_base + ent_src, 1.0)
|
||||
|
||||
# --- shipping score ---
|
||||
ship_kw = [k for k in KW_SHIPPING if (" " + k + " ") in haystack]
|
||||
shipping = 0.0
|
||||
if bucket == "SHIPPING":
|
||||
shipping = 0.9
|
||||
elif ship_kw:
|
||||
shipping = min(0.4 + 0.1 * len(ship_kw), 0.8)
|
||||
# source artifacts (github/hf) imply something shippable exists
|
||||
if source in ("github", "huggingface"):
|
||||
shipping = max(shipping, 0.7)
|
||||
|
||||
# --- utility score ---
|
||||
util_kw = [k for k in KW_PROBLEM_SOLVED if (" " + k + " ") in haystack]
|
||||
utility = 0.0
|
||||
if bucket == "PROBLEM SOLVED":
|
||||
utility = 0.85
|
||||
elif util_kw:
|
||||
utility = min(0.4 + 0.1 * len(util_kw), 0.8)
|
||||
if "github" in haystack or "huggingface" in haystack or "demo" in haystack:
|
||||
utility = max(utility, 0.6)
|
||||
|
||||
# --- replication score (can a reader reproduce/run locally) ---
|
||||
repl_kw = [k for k in KW_LOCAL_AI if (" " + k + " ") in haystack]
|
||||
replication = 0.0
|
||||
if bucket == "LOCAL AI":
|
||||
replication = 1.0
|
||||
elif repl_kw:
|
||||
replication = min(0.5 + 0.1 * len(repl_kw), 0.9)
|
||||
if source in ("github", "huggingface"):
|
||||
replication = max(replication, 0.7)
|
||||
if "open source" in haystack or "open-source" in haystack or "weights" in haystack:
|
||||
replication = max(replication, 0.6)
|
||||
|
||||
# --- novelty score ---
|
||||
nov_kw = ["new", "novel", "first", "breakthrough-method", "we propose",
|
||||
"we introduce", "we present", "state-of-the-art", "sota", "unveils"]
|
||||
novelty = 0.0
|
||||
if bucket in ("RESEARCH", "MODEL RELEASE"):
|
||||
novelty = 0.6
|
||||
if any((" " + k + " ") in haystack for k in nov_kw):
|
||||
novelty = min(novelty + 0.2, 0.9)
|
||||
if bucket == "CULTURE":
|
||||
novelty = min(novelty, 0.3) # opinion pieces are rarely novel technically
|
||||
|
||||
# --- hype penalty ---
|
||||
hype_hits = [t for t in HYPE_TERMS if (" " + t + " ") in haystack]
|
||||
hype_penalty = min(0.1 * len(hype_hits), 0.6)
|
||||
|
||||
# --- final ---
|
||||
raw = (
|
||||
WEIGHTS["shipping"] * shipping
|
||||
+ WEIGHTS["utility"] * utility
|
||||
+ WEIGHTS["replication"] * replication
|
||||
+ WEIGHTS["enthusiast"] * enthusiast
|
||||
+ WEIGHTS["novelty"] * novelty
|
||||
)
|
||||
final = max(raw - hype_penalty, 0.0)
|
||||
final = min(final, 1.0)
|
||||
|
||||
return {
|
||||
"shipping_score": round(shipping, 3),
|
||||
"utility_score": round(utility, 3),
|
||||
"replication_score": round(replication, 3),
|
||||
"enthusiast_score": round(enthusiast, 3),
|
||||
"novelty_score": round(novelty, 3),
|
||||
"hype_penalty": round(hype_penalty, 3),
|
||||
"final_score": round(final, 4),
|
||||
}
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# DB OPERATIONS
|
||||
# ---------------------------------------------------------------------------
|
||||
NEW_COLUMNS = [
|
||||
"bucket TEXT",
|
||||
"shipping_score REAL DEFAULT 0",
|
||||
"utility_score REAL DEFAULT 0",
|
||||
"replication_score REAL DEFAULT 0",
|
||||
"enthusiast_score REAL DEFAULT 0",
|
||||
"novelty_score REAL DEFAULT 0",
|
||||
"hype_penalty REAL DEFAULT 0",
|
||||
"final_score REAL DEFAULT 0",
|
||||
"actionability_score REAL DEFAULT 0",
|
||||
"narrative_id TEXT",
|
||||
"topic_id TEXT",
|
||||
"relation_json TEXT",
|
||||
]
|
||||
|
||||
|
||||
def migrate(db_path=DB_PATH):
|
||||
"""Idempotent schema migration — only adds missing columns."""
|
||||
conn = sqlite3.connect(db_path)
|
||||
cur = conn.cursor()
|
||||
cur.execute("PRAGMA table_info(entries)")
|
||||
existing = {row[1] for row in cur.fetchall()}
|
||||
added = []
|
||||
for col in NEW_COLUMNS:
|
||||
name = col.split(" ")[0]
|
||||
if name not in existing:
|
||||
cur.execute("ALTER TABLE entries ADD COLUMN %s" % col)
|
||||
added.append(name)
|
||||
conn.commit()
|
||||
conn.close()
|
||||
print("[migrate] added columns: %s" % (", ".join(added) if added else "none (already present)"))
|
||||
return added
|
||||
|
||||
|
||||
def fetch_unscored(conn):
|
||||
cur = conn.cursor()
|
||||
cur.execute("""SELECT id, source, source_id, url, title, summary, category_tags,
|
||||
raw_metadata FROM entries WHERE bucket IS NULL OR bucket = ''""")
|
||||
cols = ["id", "source", "source_id", "url", "title", "summary",
|
||||
"category_tags", "raw_metadata"]
|
||||
return [dict(zip(cols, row)) for row in cur.fetchall()]
|
||||
|
||||
|
||||
def attach_scoring(db_path=DB_PATH, dry_run=False):
|
||||
"""Score every unscored entry. Call after ingestion/dedup, before render."""
|
||||
conn = sqlite3.connect(db_path)
|
||||
rows = fetch_unscored(conn)
|
||||
print("[attach] scoring %d unscored entries" % len(rows))
|
||||
for e in rows:
|
||||
bucket, matched = classify(e)
|
||||
scores = score_entry(bucket, matched, e)
|
||||
if dry_run:
|
||||
continue
|
||||
conn.execute(
|
||||
"""UPDATE entries SET bucket=?, shipping_score=?, utility_score=?,
|
||||
replication_score=?, enthusiast_score=?, novelty_score=?,
|
||||
hype_penalty=?, final_score=?, actionability_score=?,
|
||||
narrative_id=?, topic_id=?, relation_json=? WHERE id=?""",
|
||||
(bucket, scores["shipping_score"], scores["utility_score"],
|
||||
scores["replication_score"], scores["enthusiast_score"],
|
||||
scores["novelty_score"], scores["hype_penalty"], scores["final_score"],
|
||||
0.0, # actionability_score: reserved, unused in Sprint 1
|
||||
None, None, json.dumps({"matched_rules": matched}), e["id"]),
|
||||
)
|
||||
if not dry_run:
|
||||
conn.commit()
|
||||
conn.close()
|
||||
print("[attach] done.")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# REVIEW REPORT
|
||||
# ---------------------------------------------------------------------------
|
||||
REVIEW_BUCKETS = ["SHIPPING", "LOCAL AI", "PROBLEM SOLVED", "MODEL RELEASE",
|
||||
"RESEARCH", "BUSINESS", "INFRASTRUCTURE", "CULTURE",
|
||||
"UNCATEGORIZED"]
|
||||
|
||||
|
||||
def generate_review(db_path=DB_PATH, limit=200, out_path=None):
|
||||
conn = sqlite3.connect(db_path)
|
||||
cur = conn.cursor()
|
||||
cur.execute("""SELECT id, source, title, url, bucket, final_score,
|
||||
relation_json, shipping_score, utility_score,
|
||||
replication_score, enthusiast_score, novelty_score,
|
||||
hype_penalty FROM entries
|
||||
ORDER BY first_seen DESC LIMIT ?""", (limit,))
|
||||
rows = cur.fetchall()
|
||||
conn.close()
|
||||
|
||||
by_bucket = {b: [] for b in REVIEW_BUCKETS}
|
||||
for r in rows:
|
||||
(eid, src, title, url, bucket, final, rel_json, sh, ut, rp, en, no, hy) = r
|
||||
try:
|
||||
matched = json.loads(rel_json).get("matched_rules", []) if rel_json else []
|
||||
except Exception:
|
||||
matched = []
|
||||
by_bucket.setdefault(bucket or "UNCATEGORIZED", []).append(
|
||||
(eid, src, title, url, final, matched, (sh, ut, rp, en, no, hy))
|
||||
)
|
||||
|
||||
lines = []
|
||||
lines.append("=" * 70)
|
||||
lines.append("ATHENA SPRINT 1 — MANUAL REVIEW REPORT")
|
||||
lines.append("Generated: %s" % datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M UTC"))
|
||||
lines.append("Stories reviewed: %d (most recent %d)" % (len(rows), limit))
|
||||
lines.append("=" * 70)
|
||||
lines.append("")
|
||||
lines.append("HOW TO READ: For each story, the fired rules are listed as proof.")
|
||||
lines.append("Bucket = first matching taxon (specific -> generic). Scores are")
|
||||
lines.append("deterministic. 'Publish?' is for HUMAN review only — not algorithmic.")
|
||||
lines.append("")
|
||||
|
||||
total_pub = total_rej = total_border = 0
|
||||
|
||||
for b in REVIEW_BUCKETS:
|
||||
items = by_bucket.get(b, [])
|
||||
if not items:
|
||||
continue
|
||||
lines.append(b)
|
||||
lines.append("-" * len(b))
|
||||
for (eid, src, title, url, final, matched, comps) in items:
|
||||
sh, ut, rp, en, no, hy = comps
|
||||
lines.append("")
|
||||
lines.append("Story: %s" % (title or "(untitled)"))
|
||||
lines.append(" id=%s source=%s final=%.3f" % (eid, src, final))
|
||||
lines.append(" url: %s" % (url or ""))
|
||||
lines.append(" Bucket: %s" % b)
|
||||
lines.append(" Matched:")
|
||||
for m in matched:
|
||||
lines.append(" - %s" % m)
|
||||
lines.append(" Score Components:")
|
||||
lines.append(" Shipping: %.2f" % sh)
|
||||
lines.append(" Utility: %.2f" % ut)
|
||||
lines.append(" Replication: %.2f" % rp)
|
||||
lines.append(" Enthusiast: %.2f" % en)
|
||||
lines.append(" Novelty: %.2f" % no)
|
||||
lines.append(" Hype: %.2f" % hy)
|
||||
lines.append(" Final: %.3f" % final)
|
||||
lines.append(" Publish? [Y/N] <- human review only")
|
||||
lines.append("")
|
||||
lines.append("")
|
||||
|
||||
# Summary block (the founder's key metric)
|
||||
lines.append("=" * 70)
|
||||
lines.append("BUCKET DISTRIBUTION")
|
||||
lines.append("=" * 70)
|
||||
for b in REVIEW_BUCKETS:
|
||||
n = len(by_bucket.get(b, []))
|
||||
if n:
|
||||
lines.append(" %-16s %3d" % (b, n))
|
||||
lines.append("")
|
||||
lines.append("HUMAN REVIEW TALLY (fill in after manual pass):")
|
||||
lines.append(" Published: %d" % total_pub)
|
||||
lines.append(" Rejected: %d" % total_rej)
|
||||
lines.append(" Borderline: %d" % total_border)
|
||||
lines.append("")
|
||||
lines.append("First question is not 'is the classifier accurate?'")
|
||||
lines.append("First question: 'Would we proudly publish these stories?'")
|
||||
lines.append("=" * 70)
|
||||
|
||||
report = "\n".join(lines) + "\n"
|
||||
if out_path is None:
|
||||
out_path = os.path.join(os.path.dirname(HERE), "athena_review_report.txt")
|
||||
with open(out_path, "w") as f:
|
||||
f.write(report)
|
||||
print("[review] wrote %s (%d stories)" % (out_path, len(rows)))
|
||||
# also print to stdout for immediate visibility
|
||||
print(report)
|
||||
return report
|
||||
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser(description="Athena Sprint 1 scoring (pure rules)")
|
||||
ap.add_argument("--migrate", action="store_true", help="add new columns")
|
||||
ap.add_argument("--backfill", action="store_true", help="score all unscored rows")
|
||||
ap.add_argument("--review", action="store_true", help="write manual review report")
|
||||
ap.add_argument("--all", action="store_true", help="migrate + backfill + review")
|
||||
ap.add_argument("--limit", type=int, default=200, help="review story count")
|
||||
ap.add_argument("--dry-run", action="store_true", help="classify but don't write")
|
||||
ap.add_argument("--db", default=DB_PATH, help="db path override")
|
||||
args = ap.parse_args()
|
||||
|
||||
if args.all:
|
||||
migrate(args.db)
|
||||
attach_scoring(args.db, dry_run=args.dry_run)
|
||||
generate_review(args.db, limit=args.limit)
|
||||
else:
|
||||
if args.migrate:
|
||||
migrate(args.db)
|
||||
if args.backfill:
|
||||
attach_scoring(args.db, dry_run=args.dry_run)
|
||||
if args.review:
|
||||
generate_review(args.db, limit=args.limit)
|
||||
if not (args.migrate or args.backfill or args.review):
|
||||
ap.print_help()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
+6
-298
@@ -1,300 +1,8 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Clickability Index for Athena entries.
|
||||
"""Thin wrapper — renders clickability index preview (render --dry-run)."""
|
||||
import sys, os
|
||||
sys.path.insert(0, os.path.dirname(__file__))
|
||||
|
||||
CORRECTED for the REAL schema (verified 2026-07-10):
|
||||
- Table is `entries`, not `items`.
|
||||
- Per-source engagement lives inside the `raw_metadata` JSON blob, not
|
||||
top-level `velocity_raw` / `engagement_raw` columns.
|
||||
- `content_type` is COMPUTED, not stored.
|
||||
|
||||
This module is read-only against the DB (SELECT only). It does not
|
||||
modify oracle.db.
|
||||
|
||||
The MULTIPLIERS and formula match the approved plan exactly.
|
||||
"""
|
||||
import sqlite3, json, math, re, os, time
|
||||
from datetime import datetime, timezone
|
||||
from collections import defaultdict
|
||||
|
||||
DB_PATH = os.path.join(os.path.dirname(__file__), "oracle.db")
|
||||
|
||||
MULTIPLIERS = {} # category multipliers removed: clickability is now virality-driven, not category-driven
|
||||
|
||||
# Virality weights (clickability = how viral/spreadable an item is right now)
|
||||
VEL_W = 0.50
|
||||
ENG_W = 0.50
|
||||
SIG_W = 0.0 # signal_score no longer in the clickability blend (pure virality)
|
||||
|
||||
NOW = None # set in main/fetch for age math
|
||||
|
||||
|
||||
def get_connection():
|
||||
return sqlite3.connect(DB_PATH)
|
||||
|
||||
|
||||
def _classify(src, title, summary):
|
||||
t = (title + " " + (summary or "")).lower()
|
||||
# Show HN — check first (builder posts)
|
||||
if re.search(r"\bshow\s+hn\b", t) or (src == "hackernews" and re.search(r"\b(show|built|made|launched|shipped)\b", t)):
|
||||
return "SHOW_HN"
|
||||
# Model release — pattern-based (works for third-party coverage too)
|
||||
if re.search(r"\b(gpt-|gpt5|gpt-5|deepseek|glm-|llama|qwen|claude|gemini|mistral|flux|stable-diffusion|sora|kimi|grok)\b", t) \
|
||||
and re.search(r"\b(releases?|released|v\d|launch|unveil|model|new\s+model|update|version)\b", t):
|
||||
return "MODEL_RELEASE"
|
||||
if re.search(r"\b(releases?|released|launches?|unveils?|announces?|debut|new\s+model|gpt-5|deepseek-v|glm-5)\b", t) \
|
||||
and re.search(r"\b(openai|anthropic|google|meta|microsoft|nvidia|ai)\b", t):
|
||||
return "MODEL_RELEASE"
|
||||
# HF model cards
|
||||
if src == "huggingface":
|
||||
return "MODEL_CARD"
|
||||
# Research papers
|
||||
if src == "arxiv" or re.search(r"\b(paper|study|benchmark|arxiv|proposes|learns?|novel|framework\s+for|towards)\b", t):
|
||||
return "RESEARCH"
|
||||
# Business/legal
|
||||
if re.search(r"\b(sues|lawsuit|funding|raises|acqui|ipo|valued|stealing|trade secret|layoff|hire[ds]?|exec|ceo)\b", t) \
|
||||
and not re.search(r"\b(repo|library|tool|agent framework)\b", t):
|
||||
return "BUSINESS_LEGAL"
|
||||
# Opinion/essay
|
||||
if re.search(r"\b(burnout|opinion|think|feel|why|essay|culture|linkedin|social media|future of|we made|i think|hot take|i believe|my view|in defense)\b", t):
|
||||
return "CULTURE_OPINION"
|
||||
# Tutorial/howto
|
||||
if re.search(r"\b(how to|tutorial|guide|running|build|setup|install|from scratch|learn)\b", t):
|
||||
return "TUTORIAL_HOWTO"
|
||||
# Dev tools
|
||||
if src == "github" or re.search(r"\b(repo|library|framework|tool|agent|sdk|cli|extension|plugin|app|engine)\b", t):
|
||||
return "DEV_TOOL_DRAMA"
|
||||
return "OTHER"
|
||||
|
||||
|
||||
def _extract(src, md):
|
||||
"""Return (velocity_raw, engagement_raw, age_hours)."""
|
||||
if src == "hackernews":
|
||||
pts = md.get("score", 0) or 0
|
||||
cmts = md.get("descendants", 0) or 0
|
||||
age_h = None
|
||||
if md.get("time"):
|
||||
try:
|
||||
age_h = max((NOW - md["time"]) / 3600.0, 0.1)
|
||||
except Exception:
|
||||
age_h = None
|
||||
vel = (pts / age_h) if age_h else pts
|
||||
return vel, (pts + 2 * cmts), age_h
|
||||
if src == "reddit":
|
||||
ups = md.get("ups", 0) or 0
|
||||
cmts = md.get("num_comments", 0) or 0
|
||||
return ups, (ups + 2 * cmts), None
|
||||
if src == "huggingface":
|
||||
likes = md.get("likes", 0) or 0
|
||||
return likes, likes, None
|
||||
if src == "github":
|
||||
spd = md.get("stars_per_day", 0) or 0
|
||||
stars = md.get("stars", 0) or 0
|
||||
return spd, stars, None
|
||||
if src == "arxiv":
|
||||
return 0.0, 0.0, None
|
||||
return 0.0, 0.0, None
|
||||
|
||||
|
||||
def fetch_items(conn):
|
||||
global NOW
|
||||
NOW = __import__("time").time()
|
||||
cur = conn.cursor()
|
||||
cur.execute("SELECT id, title, url, source, summary, signal_score, raw_metadata, first_seen, "
|
||||
"curated_by, manual_section, manual_tier "
|
||||
"FROM entries")
|
||||
cols = [d[0] for d in cur.description]
|
||||
out = []
|
||||
for row in cur.fetchall():
|
||||
d = dict(zip(cols, row))
|
||||
try:
|
||||
md = json.loads(d.get("raw_metadata") or "{}")
|
||||
except Exception:
|
||||
md = {}
|
||||
vel, eng, age = _extract(d["source"], md)
|
||||
ct = _classify(d["source"], d.get("title") or "", d.get("summary") or "")
|
||||
created_at = md.get("createdAt") if d["source"] == "huggingface" else None
|
||||
out.append({
|
||||
"id": d["id"],
|
||||
"title": d.get("title") or "",
|
||||
"url": d.get("url") or "",
|
||||
"source": d["source"],
|
||||
"summary": d.get("summary") or "",
|
||||
"signal_score": d.get("signal_score") or 0,
|
||||
"velocity_raw": vel,
|
||||
"engagement_raw": eng,
|
||||
"content_type": ct,
|
||||
"first_seen": d.get("first_seen") or "",
|
||||
"created_at": created_at or "",
|
||||
"age_hours": 0.0,
|
||||
"curated_by": d.get("curated_by") or "",
|
||||
"manual_section": d.get("manual_section") or "",
|
||||
"manual_tier": d.get("manual_tier") or "",
|
||||
})
|
||||
return out
|
||||
|
||||
|
||||
def log1p_norm(values):
|
||||
log_vals = [math.log1p(max(v, 0)) for v in values]
|
||||
if not log_vals:
|
||||
return []
|
||||
min_v, max_v = min(log_vals), max(log_vals)
|
||||
if max_v == min_v:
|
||||
return [0.0] * len(values)
|
||||
return [(v - min_v) / (max_v - min_v) for v in log_vals]
|
||||
|
||||
|
||||
def compute_index(items):
|
||||
velocities = [it.get("velocity_raw", 0) or 0 for it in items]
|
||||
engagements = [it.get("engagement_raw", 0) or 0 for it in items]
|
||||
signals = [it.get("signal_score", 0) or 0 for it in items]
|
||||
|
||||
vel_norm = log1p_norm(velocities)
|
||||
eng_norm = log1p_norm(engagements)
|
||||
sig_norm = log1p_norm(signals)
|
||||
|
||||
for i, item in enumerate(items):
|
||||
raw = vel_norm[i] * VEL_W + eng_norm[i] * ENG_W + sig_norm[i] * SIG_W
|
||||
# Items with 0 engagement (arXiv, RSS, Reddit no-data) get a small base score
|
||||
# from signal_score so they can decay naturally instead of being stuck forever.
|
||||
# Floor: 0.05 * signal_score_norm — enough to rank, low enough to sink fast.
|
||||
if raw == 0 and sig_norm[i] > 0:
|
||||
raw = 0.05 * sig_norm[i]
|
||||
item["clickability"] = round(raw, 4)
|
||||
item["section"] = "" # sections removed; flat ranked feed
|
||||
return items
|
||||
|
||||
|
||||
def _age_hours(item):
|
||||
"""Effective news-age in hours.
|
||||
|
||||
HuggingFace items are aged by their TRUE model createdAt (likes/downloads
|
||||
are lifetime cumulative, so DB first_seen would pin every HF entry at
|
||||
ingest time and let all-time leaders dominate 'Top News' forever). All
|
||||
other sources are aged by DB first_seen.
|
||||
"""
|
||||
if item.get("source") == "huggingface" and item.get("created_at"):
|
||||
s = item["created_at"]
|
||||
else:
|
||||
s = item.get("first_seen") or ""
|
||||
if not s:
|
||||
return 0.0
|
||||
try:
|
||||
ts = datetime.strptime(s[:19], "%Y-%m-%dT%H:%M:%S").replace(
|
||||
tzinfo=timezone.utc).timestamp()
|
||||
return max((time.time() - ts) / 3600.0, 0.0)
|
||||
except Exception:
|
||||
return 0.0
|
||||
|
||||
|
||||
# Category-specific half-lives (hours) — controls how long each type stays competitive.
|
||||
# Breaking news decays slowest (stays relevant longer), arXiv/model cards fastest.
|
||||
CATEGORY_HALF_LIVES = {
|
||||
"breaking": 36.0, # Red — truly groundbreaking, double the standard
|
||||
"update": 24.0, # Green — important but not groundbreaking, between red and black
|
||||
"OTHER": 18.0, # Black — standard decay rate
|
||||
}
|
||||
|
||||
# Map content_type to half-life, with tier override for breaking/update
|
||||
def _get_half_life(item):
|
||||
"""Return half-life in hours based on tier and content_type."""
|
||||
# Hardware/Tips section items decay on a 14-DAY half-life (user: revised
|
||||
# spec, shorter than evergreen). Covers both manual curations and
|
||||
# auto-classified section items, so a section link persists 14 days
|
||||
# instead of sinking in ~18h. Beyond this window the item is routed to
|
||||
def _get_half_life(item):
|
||||
"""Return section/tier-specific half-life in hours, or None to fall back
|
||||
to decay_index's half_life_h argument (default 18h news half-life)."""
|
||||
# Spec v1 addendum: Hardware/Tips section items get a 14-day (336h)
|
||||
# half-life so curated/section items persist far longer in-section.
|
||||
ms = (item.get("manual_section") or "").upper()
|
||||
if ms in ("HARDWARE", "TIPS"):
|
||||
return 336.0
|
||||
tier = item.get("tier", "normal")
|
||||
# Tier overrides take precedence
|
||||
if tier == "breaking":
|
||||
return CATEGORY_HALF_LIVES["breaking"]
|
||||
if tier == "update":
|
||||
return CATEGORY_HALF_LIVES["update"]
|
||||
# No special case -> let decay_index use its half_life_h parameter.
|
||||
return None
|
||||
|
||||
|
||||
def decay_index(items, half_life_h=18.0):
|
||||
"""Apply exponential time-decay to clickability so items sink as they age.
|
||||
|
||||
Uses category-specific half-lives: breaking news decays slowest (36h),
|
||||
arXiv/model cards fastest (12h). This controls how long each type
|
||||
stays competitive, not just starting score.
|
||||
|
||||
decayed = clickability * exp(-ln(2)/half_life * age_hours)
|
||||
"""
|
||||
# Rolling 24h freshness window (not calendar-day) so Top News stays populated
|
||||
# between the daily harvest and midnight UTC. Decay still sinks old items.
|
||||
cutoff = datetime.now(timezone.utc).timestamp() - 24 * 3600
|
||||
for it in items:
|
||||
age = _age_hours(it)
|
||||
it["age_hours"] = round(age, 1)
|
||||
base = it.get("clickability", 0) or 0
|
||||
# Category-specific half-life, falling back to the passed half_life_h
|
||||
hl = _get_half_life(it)
|
||||
if hl is None:
|
||||
hl = half_life_h
|
||||
k = math.log(2) / hl
|
||||
it["clickability_decayed"] = round(base * math.exp(-k * age), 4)
|
||||
it["effective_half_life"] = hl
|
||||
# Freshness flag: ingested within the last 24h -> eligible for Top News.
|
||||
fs = it.get("first_seen") or ""
|
||||
try:
|
||||
ts = datetime.fromisoformat(fs.replace("Z", "+00:00")).timestamp()
|
||||
except ValueError:
|
||||
ts = 0
|
||||
it["fresh"] = ts >= cutoff
|
||||
return items
|
||||
|
||||
|
||||
def _pearson(xs, ys):
|
||||
n = len(xs)
|
||||
if n < 3:
|
||||
return None
|
||||
mx, my = sum(xs) / n, sum(ys) / n
|
||||
num = sum((x - mx) * (y - my) for x, y in zip(xs, ys))
|
||||
den = math.sqrt(sum((x - mx) ** 2 for x in xs) * sum((y - my) ** 2 for y in ys))
|
||||
return num / den if den else None
|
||||
|
||||
|
||||
def main():
|
||||
conn = get_connection()
|
||||
items = fetch_items(conn)
|
||||
conn.close()
|
||||
if not items:
|
||||
print("No items found.")
|
||||
return
|
||||
|
||||
computed = compute_index(items)
|
||||
computed.sort(key=lambda x: x["clickability"], reverse=True)
|
||||
|
||||
print(f"=== TOP 20 BY CLICKABILITY INDEX (n={len(items)} items) ===\n")
|
||||
for i, item in enumerate(computed[:20], 1):
|
||||
print(f"{i:2}. [{item['clickability']:.4f}] {item['source']:11} | {item['title'][:58]}")
|
||||
print(f" section={item['section']} | type={item['content_type']} | "
|
||||
f"vel={item['velocity_raw']:.1f} eng={item['engagement_raw']:.1f} sig={item['signal_score']:.2f}")
|
||||
|
||||
# Backtest: Clickability Index vs ACTUAL HN engagement
|
||||
hn = [it for it in computed if it["source"] == "hackernews" and it["engagement_raw"] > 0]
|
||||
if hn:
|
||||
r_full = _pearson([it["engagement_raw"] for it in hn],
|
||||
[it["clickability"] for it in hn])
|
||||
# Honest baseline: signal_score alone vs HN engagement (legacy prior)
|
||||
r_sig = _pearson([it["engagement_raw"] for it in hn],
|
||||
[it["signal_score"] for it in hn])
|
||||
print(f"\n--- BACKTEST (HN, n={len(hn)}) ---")
|
||||
print(f"ClickabilityIndex vs actual HN engagement : r = {r_full:.3f}" if r_full is not None else "r = n/a")
|
||||
print(f"signal_score alone vs HN engagement : r = {r_sig:.3f}" if r_sig is not None else "r = n/a")
|
||||
print("NOTE: engagement_raw is a 40% component of the index, so the full-index")
|
||||
print(" r is structurally high. The meaningful comparison is whether the")
|
||||
print(" index RANKS high-engagement items above low-engagement ones vs the")
|
||||
print(" legacy signal_score prior (r_sig above).")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
from oracle.cli import main as cli_main
|
||||
sys.argv = ["oracle", "render", "--dry-run"]
|
||||
cli_main()
|
||||
|
||||
@@ -0,0 +1,21 @@
|
||||
"""Athena — AI Research Oracle.
|
||||
|
||||
Unified intelligence pipeline for AI news aggregation, scoring, and rendering.
|
||||
|
||||
Architecture:
|
||||
oracle/ - Core package (this directory)
|
||||
adapters/ - Source adapters (GitHub, arXiv, Reddit, HN, HF, RSS)
|
||||
athena/ - Scoring and classification logic
|
||||
cli.py - CLI entry point (subcommands)
|
||||
db.py - Database operations and schema
|
||||
render.py - Static site rendering
|
||||
scoring.py - Athena scoring engine
|
||||
clickability.py - Clickability index and decay
|
||||
summarize.py - Rule-based summarization
|
||||
recency.py - Recency guard and freshness filtering
|
||||
themes.py - Theme-based trend tracking
|
||||
archive.py - Soft-cap archival
|
||||
config.py - Configuration and constants
|
||||
"""
|
||||
|
||||
__version__ = "1.0.0"
|
||||
@@ -0,0 +1,4 @@
|
||||
"""Allow running as: python -m oracle"""
|
||||
from oracle.cli import main
|
||||
|
||||
main()
|
||||
@@ -0,0 +1,83 @@
|
||||
"""Oracle soft-cap archival.
|
||||
|
||||
Bounds live `entries` growth by moving old / excess rows into
|
||||
`entries_archive` (preserving data — soft cap, not hard delete).
|
||||
"""
|
||||
import os
|
||||
import sqlite3
|
||||
import time
|
||||
|
||||
from oracle.config import DB_PATH
|
||||
|
||||
ARCHIVE_SCHEMA = """
|
||||
CREATE TABLE IF NOT EXISTS entries_archive (
|
||||
id INTEGER PRIMARY KEY,
|
||||
source TEXT, source_id TEXT, url TEXT, title TEXT,
|
||||
extracted_text TEXT, summary TEXT, category_tags TEXT,
|
||||
signal_score REAL, raw_metadata TEXT,
|
||||
first_seen TEXT, last_updated TEXT,
|
||||
archived_at TEXT DEFAULT (strftime('%Y-%m-%dT%H:%M:%SZ', 'now'))
|
||||
);
|
||||
"""
|
||||
|
||||
|
||||
def archive(days=30, cap=5000, dry_run=False, db_path=None):
|
||||
"""Archive entries older than N days or beyond the cap.
|
||||
|
||||
Returns (total_live, archived_count).
|
||||
"""
|
||||
path = db_path or str(DB_PATH)
|
||||
if not os.path.exists(path):
|
||||
print("No oracle.db — nothing to archive")
|
||||
return 0, 0
|
||||
|
||||
conn = sqlite3.connect(path)
|
||||
conn.execute(ARCHIVE_SCHEMA)
|
||||
|
||||
cutoff = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime(time.time() - days * 86400))
|
||||
|
||||
cur = conn.cursor()
|
||||
cur.execute("SELECT id, last_updated FROM entries")
|
||||
rows = cur.fetchall()
|
||||
n_total = len(rows)
|
||||
|
||||
old_ids = [r[0] for r in rows if (r[1] or "") < cutoff]
|
||||
beyond = max(0, n_total - cap)
|
||||
if beyond > 0:
|
||||
ordered = sorted(rows, key=lambda r: r[1] or "")[:beyond]
|
||||
cap_ids = [r[0] for r in ordered]
|
||||
else:
|
||||
cap_ids = []
|
||||
|
||||
move_ids = sorted(set(old_ids) | set(cap_ids))
|
||||
|
||||
if not move_ids:
|
||||
print(f"Archive check: {n_total} live entries, none older than {days}d "
|
||||
f"or beyond cap {cap}. Nothing to archive.")
|
||||
conn.close()
|
||||
return n_total, 0
|
||||
|
||||
print(f"Archive check: {n_total} live entries -> would archive {len(move_ids)} "
|
||||
f"(old={len(old_ids)}, cap={len(cap_ids)}).")
|
||||
|
||||
if dry_run:
|
||||
print("DRY RUN — no changes made.")
|
||||
conn.close()
|
||||
return n_total, 0
|
||||
|
||||
q = ",".join("?" * len(move_ids))
|
||||
conn.execute(
|
||||
f"""INSERT OR REPLACE INTO entries_archive
|
||||
(id, source, source_id, url, title, extracted_text, summary,
|
||||
category_tags, signal_score, raw_metadata, first_seen, last_updated)
|
||||
SELECT id, source, source_id, url, title, extracted_text, summary,
|
||||
category_tags, signal_score, raw_metadata, first_seen, last_updated
|
||||
FROM entries WHERE id IN ({q})""",
|
||||
move_ids,
|
||||
)
|
||||
conn.execute(f"DELETE FROM entries WHERE id IN ({q})", move_ids)
|
||||
conn.commit()
|
||||
conn.close()
|
||||
print(f"Archived {len(move_ids)} entries (live now {n_total - len(move_ids)}). "
|
||||
f"Preserved in entries_archive.")
|
||||
return n_total, len(move_ids)
|
||||
+497
@@ -0,0 +1,497 @@
|
||||
"""CLI entry point for the AI Research Oracle.
|
||||
|
||||
Single command with subcommands for all operations.
|
||||
Replaces scattered root scripts with one unified interface.
|
||||
|
||||
Usage:
|
||||
python -m oracle <command> [args]
|
||||
|
||||
Commands:
|
||||
ingest Run the ingestion pipeline (fetch + store + score)
|
||||
summarize Generate summaries for unscored entries
|
||||
query Query the database (top, search, recent, stats, snapshot)
|
||||
render Render static site from clickability index
|
||||
archive Soft-cap archival of old entries
|
||||
themes Theme-based trend tracking
|
||||
recency Recency guard analysis
|
||||
health System health check
|
||||
"""
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
from datetime import datetime, timezone
|
||||
|
||||
# Ensure project root is on path
|
||||
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
|
||||
from oracle.config import DB_PATH, SCHEMA_PATH, ENABLED_SOURCES, DEFAULT_LIMIT, SOURCE_TIERS, VERDICT_THRESHOLDS
|
||||
from oracle.db import get_connection, init_db, get_stats, query_top, query_recent, query_search, query_by_tag, migrate_world_monitor
|
||||
from oracle.scoring import attach_scoring, migrate as scoring_migrate
|
||||
from oracle.summarize import run_summarization
|
||||
from oracle.render import render as render_site
|
||||
from oracle.archive import archive as archive_entries
|
||||
from oracle.themes import scan as theme_scan
|
||||
from oracle.clickability import fetch_items, compute_index, decay_index
|
||||
from oracle.recency import filter_fresh
|
||||
from oracle.dedup import content_hash, compute_verdict, tier_adjusted_score, backfill_hashes, apply_verdicts, get_source_tier
|
||||
|
||||
|
||||
def cmd_ingest(args):
|
||||
"""Run the ingestion pipeline."""
|
||||
import time
|
||||
import sqlite3
|
||||
import signal
|
||||
|
||||
from adapters import SourceAdapter
|
||||
from adapters._store import upsert_entries
|
||||
|
||||
sources = args.sources.split(",") if args.sources else ENABLED_SOURCES
|
||||
now = datetime.now(timezone.utc)
|
||||
print(f"=== AI Research Oracle Pipeline ===")
|
||||
print(f" Sources: {', '.join(sources)}")
|
||||
print(f" Limit: {args.limit}/source")
|
||||
print(f" Dry run: {args.dry_run}")
|
||||
print()
|
||||
|
||||
# Import adapters dynamically
|
||||
adapter_modules = {
|
||||
"github": "adapters.github",
|
||||
"arxiv": "adapters.arxiv",
|
||||
"reddit": "adapters.reddit",
|
||||
"hackernews": "adapters.hackernews",
|
||||
"huggingface": "adapters.huggingface",
|
||||
"rss": "adapters.rss_feeds",
|
||||
}
|
||||
adapter_classes = {
|
||||
"github": "GitHubAdapter",
|
||||
"arxiv": "ArxivAdapter",
|
||||
"reddit": "RedditAdapter",
|
||||
"hackernews": "HackerNewsAdapter",
|
||||
"huggingface": "HuggingFaceAdapter",
|
||||
"rss": "RSSFeedsAdapter",
|
||||
}
|
||||
|
||||
db_path = str(DB_PATH)
|
||||
schema_path = str(SCHEMA_PATH)
|
||||
|
||||
all_entries = []
|
||||
source_stats = {}
|
||||
|
||||
for source_name in sources:
|
||||
if source_name not in adapter_modules:
|
||||
print(f" ⚠ Unknown source: {source_name}")
|
||||
continue
|
||||
|
||||
print(f" [{source_name}]")
|
||||
mod = __import__(adapter_modules[source_name], fromlist=[adapter_classes[source_name]])
|
||||
adapter = getattr(mod, adapter_classes[source_name])()
|
||||
|
||||
# Fetch with per-adapter timeout (prevents blocking on slow endpoints)
|
||||
entries = []
|
||||
error = None
|
||||
try:
|
||||
entries = adapter.fetch(limit=args.limit, timeout=10)
|
||||
except TypeError:
|
||||
# Old adapter signature without timeout param — use thread-based fallback
|
||||
import threading
|
||||
result = {"entries": [], "error": None}
|
||||
def _fetch():
|
||||
try:
|
||||
result["entries"] = adapter.fetch(limit=args.limit)
|
||||
except Exception as e:
|
||||
result["error"] = str(e)
|
||||
t = threading.Thread(target=_fetch, daemon=True)
|
||||
t.start()
|
||||
t.join(timeout=10)
|
||||
if t.is_alive():
|
||||
error = f"timeout after 10s"
|
||||
else:
|
||||
entries = result["entries"]
|
||||
error = result["error"]
|
||||
except Exception as e:
|
||||
error = str(e)
|
||||
|
||||
if error:
|
||||
print(f" ✗ {source_name} failed: {error}")
|
||||
source_stats[source_name] = {"fetched": 0, "stored": 0, "error": error}
|
||||
continue
|
||||
|
||||
for entry in entries:
|
||||
meta = json.loads(entry["raw_metadata"]) if isinstance(entry["raw_metadata"], str) else entry["raw_metadata"]
|
||||
meta["adapter_version"] = "1.0"
|
||||
entry["raw_metadata"] = json.dumps(meta)
|
||||
|
||||
all_entries.extend(entries)
|
||||
source_stats[source_name] = {"fetched": len(entries), "stored": 0}
|
||||
print(f" Fetched: {len(entries)} entries")
|
||||
time.sleep(0.5)
|
||||
|
||||
if not args.dry_run and all_entries:
|
||||
conn = init_db(db_path, schema_path)
|
||||
stored = upsert_entries(conn, all_entries)
|
||||
|
||||
for entry in all_entries:
|
||||
src = entry["source"]
|
||||
if src in source_stats:
|
||||
source_stats[src]["stored"] += 1
|
||||
|
||||
# Score new entries
|
||||
try:
|
||||
attach_scoring(db_path)
|
||||
except Exception as e:
|
||||
print(f" ⚠ scoring attach failed: {e}")
|
||||
|
||||
conn.close()
|
||||
print(f" Total stored: {stored} entries")
|
||||
else:
|
||||
print(f" Total fetched: {len(all_entries)} entries (dry run)")
|
||||
|
||||
print(f"\n Source summary:")
|
||||
for src, stats in source_stats.items():
|
||||
error = stats.get("error", "")
|
||||
error_str = f" ✗ {error}" if error else ""
|
||||
print(f" {src}: {stats['fetched']} fetched, {stats.get('stored', '—')} stored{error_str}")
|
||||
|
||||
if all_entries:
|
||||
print(f"\n Top entries by signal score:")
|
||||
sorted_entries = sorted(all_entries, key=lambda e: e["signal_score"], reverse=True)
|
||||
for i, entry in enumerate(sorted_entries[:5]):
|
||||
print(f" [{i+1}] {entry['source'].upper():6} score={entry['signal_score']:.2f} {entry['title'][:70]}")
|
||||
|
||||
print(f"\n Done.")
|
||||
|
||||
|
||||
def cmd_summarize(args):
|
||||
"""Run the summarization engine."""
|
||||
run_summarization(source=args.source, limit=args.limit)
|
||||
|
||||
|
||||
def cmd_query(args):
|
||||
"""Query the database."""
|
||||
conn = get_connection()
|
||||
qc = getattr(args, "query_command", None)
|
||||
|
||||
if qc == "top":
|
||||
entries = query_top(conn, n=args.n, source=args.source, min_score=args.min_score)
|
||||
print(f"Top {len(entries)} entries:")
|
||||
_print_entries(entries)
|
||||
|
||||
elif qc == "search":
|
||||
entries = query_search(conn, args.query_text, limit=args.limit)
|
||||
print(f"Search results for '{args.query_text}':")
|
||||
_print_entries(entries)
|
||||
|
||||
elif qc == "recent":
|
||||
entries = query_recent(conn, hours=args.hours)
|
||||
print(f"Entries from last {args.hours}h:")
|
||||
_print_entries(entries)
|
||||
|
||||
elif qc == "by-tag":
|
||||
entries = query_by_tag(conn, args.tag, limit=args.limit)
|
||||
print(f"Entries tagged '{args.tag}':")
|
||||
_print_entries(entries)
|
||||
|
||||
elif qc == "stats":
|
||||
stats = get_stats(conn)
|
||||
print(f"Database: {DB_PATH}")
|
||||
print(f"Total entries: {stats['total_entries']}")
|
||||
print(f"Summarized: {stats['summarized']}")
|
||||
print(f"Pending summary: {stats['pending_summary']}")
|
||||
if stats.get("buckets"):
|
||||
print(f"\nBucket distribution:")
|
||||
for bucket, count in sorted(stats["buckets"].items(), key=lambda x: -x[1]):
|
||||
print(f" {bucket}: {count}")
|
||||
|
||||
elif qc == "snapshot":
|
||||
stats = get_stats(conn)
|
||||
top = query_top(conn, n=10)
|
||||
print("=" * 70)
|
||||
print("AI RESEARCH ORACLE — SNAPSHOT")
|
||||
print("=" * 70)
|
||||
print(f"Time: {datetime.now(timezone.utc).strftime('%Y-%m-%dT%H:%M:%SZ')}")
|
||||
print(f"Total entries: {stats['total_entries']}")
|
||||
print()
|
||||
print("Source breakdown:")
|
||||
for src, s in stats["sources"].items():
|
||||
print(f" {src}: {s['cnt']} entries, avg score {s['avg_score']}")
|
||||
print()
|
||||
print("Top 10 by signal score:")
|
||||
_print_entries(top)
|
||||
|
||||
conn.close()
|
||||
|
||||
|
||||
def _print_entries(entries):
|
||||
"""Pretty-print a list of entries."""
|
||||
if not entries:
|
||||
print(" (no results)")
|
||||
return
|
||||
for i, e in enumerate(entries, 1):
|
||||
summary = json.loads(e["summary"]) if e.get("summary") else {}
|
||||
meta = json.loads(e["raw_metadata"]) if e.get("raw_metadata") else {}
|
||||
print(f" [{i}] {e['source'].upper():10} | score={e['signal_score']:.2f}")
|
||||
print(f" {e['title'][:80]}")
|
||||
if summary.get("one_liner"):
|
||||
print(f" → {summary['one_liner'][:100]}")
|
||||
print()
|
||||
|
||||
|
||||
def cmd_render(args):
|
||||
"""Render static site — single variant or all variants."""
|
||||
from oracle.variants import load_variant, list_variants, render_variant
|
||||
|
||||
if args.list:
|
||||
print("Available variants:")
|
||||
for v in list_variants():
|
||||
print(f" {v}")
|
||||
return
|
||||
|
||||
if args.all_variants:
|
||||
variants = list_variants()
|
||||
if not variants:
|
||||
print("No variants found in variants/")
|
||||
return
|
||||
print(f"=== Rendering all {len(variants)} variants ===\n")
|
||||
for vname in variants:
|
||||
try:
|
||||
config = load_variant(vname)
|
||||
render_variant(config, dry_run=args.dry_run, webroot=args.webroot)
|
||||
except Exception as e:
|
||||
print(f"[variant:{vname}] ERROR: {e}")
|
||||
print()
|
||||
return
|
||||
|
||||
# Single variant (default = 'default' if not specified)
|
||||
vname = args.variant or "default"
|
||||
try:
|
||||
config = load_variant(vname)
|
||||
render_variant(config, dry_run=args.dry_run, webroot=args.webroot)
|
||||
except FileNotFoundError as e:
|
||||
print(f"Error: {e}")
|
||||
|
||||
|
||||
def cmd_archive(args):
|
||||
"""Archive old entries."""
|
||||
archive_entries(days=args.days, cap=args.cap, dry_run=args.dry_run)
|
||||
|
||||
|
||||
def cmd_themes(args):
|
||||
"""Run theme scan."""
|
||||
results = theme_scan(history=args.history)
|
||||
print(f"=== Theme trend scan ===")
|
||||
print(f" Fresh entries this cycle: {results['fresh_count']}")
|
||||
if results["new_arrivals"]:
|
||||
print(" NEW theme arrivals this cycle:")
|
||||
for theme, count in results["new_arrivals"].items():
|
||||
print(f" {theme}: +{count}")
|
||||
else:
|
||||
print(" NEW theme arrivals this cycle: 0")
|
||||
print(f" Cumulative totals: {results['cumulative']}")
|
||||
|
||||
if args.history and results.get("history"):
|
||||
print("\n Per-cycle history:")
|
||||
for day, theme, count in results["history"]:
|
||||
print(f" {day} {theme}: {count}")
|
||||
|
||||
|
||||
def cmd_dedup(args):
|
||||
"""World Monitor migration: tiers, hashes, verdicts."""
|
||||
print("=== World Monitor Migration ===\n")
|
||||
|
||||
conn = get_connection()
|
||||
|
||||
# Show source tier config
|
||||
if args.show_tiers:
|
||||
print("Source tier configuration:")
|
||||
for src, info in SOURCE_TIERS.items():
|
||||
print(f" {src:12} Tier {info['tier']} ({info['label']}) - {info['description']}")
|
||||
print()
|
||||
|
||||
# Show verdict thresholds
|
||||
if args.show_verdicts:
|
||||
print("Verdict thresholds:")
|
||||
for verdict, thresholds in VERDICT_THRESHOLDS.items():
|
||||
print(f" {verdict:8} score >= {thresholds['min_score']}, age <= {thresholds['max_age_h']}h")
|
||||
print()
|
||||
|
||||
# Run migration
|
||||
if args.migrate:
|
||||
print("Running schema migration + backfill...\n")
|
||||
result = migrate_world_monitor(conn)
|
||||
print(f" Columns added: {result['columns_added']}")
|
||||
print(f" Hashes backfilled: {result['hashes_backfilled']}")
|
||||
print(f" Verdicts set: {result['verdicts_set']}")
|
||||
print()
|
||||
|
||||
# Show verdict distribution
|
||||
cur = conn.cursor()
|
||||
try:
|
||||
cur.execute("PRAGMA table_info(entries)")
|
||||
columns = {r[1] for r in cur.fetchall()}
|
||||
except Exception:
|
||||
conn.close()
|
||||
return
|
||||
|
||||
if "verdict" in columns:
|
||||
cur.execute("SELECT verdict, COUNT(*) as cnt FROM entries WHERE verdict != '' GROUP BY verdict ORDER BY cnt DESC")
|
||||
rows = cur.fetchall()
|
||||
if rows:
|
||||
print("Verdict distribution:")
|
||||
for r in rows:
|
||||
print(f" {r['verdict']:8} {r['cnt']}")
|
||||
print()
|
||||
|
||||
if "content_hash" in columns:
|
||||
cur.execute("SELECT COUNT(*) FROM entries WHERE content_hash != '' AND content_hash IS NOT NULL")
|
||||
hashed = cur.fetchone()[0]
|
||||
cur.execute("SELECT COUNT(*) FROM entries")
|
||||
total = cur.fetchone()[0]
|
||||
print(f"Content hashes: {hashed}/{total} entries hashed")
|
||||
|
||||
if "source_tier" in columns:
|
||||
cur.execute("SELECT source_tier, COUNT(*) as cnt FROM entries GROUP BY source_tier ORDER BY source_tier")
|
||||
rows = cur.fetchall()
|
||||
if rows:
|
||||
print(f"\nSource tier distribution:")
|
||||
for r in rows:
|
||||
print(f" Tier {r['source_tier']}: {r['cnt']}")
|
||||
|
||||
conn.close()
|
||||
print()
|
||||
|
||||
|
||||
def cmd_health(args):
|
||||
"""System health check."""
|
||||
print("=== System Health Check ===\n")
|
||||
|
||||
# Database
|
||||
try:
|
||||
conn = get_connection()
|
||||
stats = get_stats(conn)
|
||||
conn.close()
|
||||
print(f"✓ Database: {DB_PATH}")
|
||||
print(f" Total entries: {stats['total_entries']}")
|
||||
print(f" Summarized: {stats['summarized']}")
|
||||
print(f" Pending: {stats['pending_summary']}")
|
||||
except Exception as e:
|
||||
print(f"✗ Database error: {e}")
|
||||
|
||||
# Schema columns
|
||||
try:
|
||||
conn = get_connection()
|
||||
cur = conn.cursor()
|
||||
cur.execute("PRAGMA table_info(entries)")
|
||||
columns = [r[1] for r in cur.fetchall()]
|
||||
conn.close()
|
||||
print(f" Schema columns: {len(columns)}")
|
||||
except Exception as e:
|
||||
print(f" Schema check: {e}")
|
||||
|
||||
# Run log
|
||||
try:
|
||||
conn = get_connection()
|
||||
cur = conn.cursor()
|
||||
cur.execute("SELECT COUNT(*) FROM run_log")
|
||||
runs = cur.fetchone()[0]
|
||||
cur.execute("SELECT run_time, failure_class FROM run_log ORDER BY id DESC LIMIT 3")
|
||||
recent = cur.fetchall()
|
||||
conn.close()
|
||||
print(f" Pipeline runs logged: {runs}")
|
||||
for r in recent:
|
||||
print(f" {r[0]} class={r[1]}")
|
||||
except Exception as e:
|
||||
print(f" Run log: {e}")
|
||||
|
||||
print()
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="AI Research Oracle — Unified CLI",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
)
|
||||
sub = parser.add_subparsers(dest="command", help="Available commands")
|
||||
|
||||
# ingest
|
||||
p_ingest = sub.add_parser("ingest", help="Run the ingestion pipeline")
|
||||
p_ingest.add_argument("--sources", default=None, help="Comma-separated sources")
|
||||
p_ingest.add_argument("--limit", type=int, default=DEFAULT_LIMIT, help="Entries per source")
|
||||
p_ingest.add_argument("--dry-run", action="store_true", help="Fetch but don't store")
|
||||
|
||||
# summarize
|
||||
p_summarize = sub.add_parser("summarize", help="Generate summaries")
|
||||
p_summarize.add_argument("--source", default=None, help="Filter by source")
|
||||
p_summarize.add_argument("--limit", type=int, default=0, help="Max entries (0=all)")
|
||||
|
||||
# query (nested subcommands)
|
||||
p_query = sub.add_parser("query", help="Query the database")
|
||||
query_sub = p_query.add_subparsers(dest="query_command")
|
||||
|
||||
p_top = query_sub.add_parser("top", help="Top N entries")
|
||||
p_top.add_argument("n", type=int, nargs="?", default=10)
|
||||
p_top.add_argument("--source", default=None)
|
||||
p_top.add_argument("--min-score", type=float, default=0)
|
||||
|
||||
p_search = query_sub.add_parser("search", help="Keyword search")
|
||||
p_search.add_argument("query_text")
|
||||
p_search.add_argument("--limit", type=int, default=20)
|
||||
|
||||
p_recent = query_sub.add_parser("recent", help="Recent entries")
|
||||
p_recent.add_argument("--hours", type=int, default=24)
|
||||
|
||||
p_tag = query_sub.add_parser("by-tag", help="Entries by tag")
|
||||
p_tag.add_argument("tag")
|
||||
p_tag.add_argument("--limit", type=int, default=20)
|
||||
|
||||
query_sub.add_parser("stats", help="Database statistics")
|
||||
query_sub.add_parser("snapshot", help="Full snapshot")
|
||||
|
||||
# render
|
||||
p_render = sub.add_parser("render", help="Render static site")
|
||||
p_render.add_argument("--variant", default=None, help="Variant name (default, research, devops, brief)")
|
||||
p_render.add_argument("--all", dest="all_variants", action="store_true", help="Render all variants")
|
||||
p_render.add_argument("--list", action="store_true", help="List available variants")
|
||||
p_render.add_argument("--dry-run", action="store_true")
|
||||
p_render.add_argument("--webroot", default=None, help="Output directory")
|
||||
|
||||
# archive
|
||||
p_archive = sub.add_parser("archive", help="Archive old entries")
|
||||
p_archive.add_argument("--days", type=int, default=30)
|
||||
p_archive.add_argument("--cap", type=int, default=5000)
|
||||
p_archive.add_argument("--dry-run", action="store_true")
|
||||
|
||||
# themes
|
||||
p_themes = sub.add_parser("themes", help="Theme trend tracking")
|
||||
p_themes.add_argument("--history", action="store_true")
|
||||
|
||||
# dedup (World Monitor migration)
|
||||
p_dedup = sub.add_parser("dedup", help="World Monitor: tiers, hashes, verdicts")
|
||||
p_dedup.add_argument("--migrate", action="store_true", help="Run schema migration + backfill")
|
||||
p_dedup.add_argument("--show-tiers", action="store_true", help="Show source tier config")
|
||||
p_dedup.add_argument("--show-verdicts", action="store_true", help="Show verdict thresholds")
|
||||
|
||||
# health
|
||||
sub.add_parser("health", help="System health check")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
commands = {
|
||||
"ingest": cmd_ingest,
|
||||
"summarize": cmd_summarize,
|
||||
"query": cmd_query,
|
||||
"render": cmd_render,
|
||||
"archive": cmd_archive,
|
||||
"themes": cmd_themes,
|
||||
"dedup": cmd_dedup,
|
||||
"health": cmd_health,
|
||||
}
|
||||
|
||||
if args.command and args.command in commands:
|
||||
commands[args.command](args)
|
||||
else:
|
||||
parser.print_help()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,210 @@
|
||||
"""Clickability Index for Athena entries.
|
||||
|
||||
Read-only against the DB (SELECT only). Computes virality ranking with
|
||||
exponential time-decay so items sink as they age.
|
||||
"""
|
||||
import json
|
||||
import math
|
||||
import os
|
||||
import re
|
||||
import time
|
||||
from datetime import datetime, timezone
|
||||
from typing import Optional
|
||||
|
||||
from oracle.config import DB_PATH
|
||||
|
||||
# Virality weights (clickability = how viral/spreadable an item is right now)
|
||||
VEL_W = 0.50
|
||||
ENG_W = 0.50
|
||||
SIG_W = 0.0
|
||||
|
||||
NOW = None # set in fetch_items for age math
|
||||
|
||||
# Category-specific half-lives (hours)
|
||||
CATEGORY_HALF_LIVES = {
|
||||
"breaking": 36.0,
|
||||
"update": 24.0,
|
||||
"OTHER": 18.0,
|
||||
}
|
||||
|
||||
|
||||
def get_connection():
|
||||
return __import__("sqlite3").connect(str(DB_PATH))
|
||||
|
||||
|
||||
def _classify(src: str, title: str, summary: str) -> str:
|
||||
t = (title + " " + (summary or "")).lower()
|
||||
if re.search(r"\bshow\s+hn\b", t) or (src == "hackernews" and re.search(r"\b(show|built|made|launched|shipped)\b", t)):
|
||||
return "SHOW_HN"
|
||||
if re.search(r"\b(gpt-|gpt5|gpt-5|deepseek|glm-|llama|qwen|claude|gemini|mistral|flux|stable-diffusion|sora|kimi|grok)\b", t) \
|
||||
and re.search(r"\b(releases?|released|v\d|launch|unveil|model|new\s+model|update|version)\b", t):
|
||||
return "MODEL_RELEASE"
|
||||
if re.search(r"\b(releases?|released|launches?|unveils?|announces?|debut|new\s+model|gpt-5|deepseek-v|glm-5)\b", t) \
|
||||
and re.search(r"\b(openai|anthropic|google|meta|microsoft|nvidia|ai)\b", t):
|
||||
return "MODEL_RELEASE"
|
||||
if src == "huggingface":
|
||||
return "MODEL_CARD"
|
||||
if src == "arxiv" or re.search(r"\b(paper|study|benchmark|arxiv|proposes|learns?|novel|framework\s+for|towards)\b", t):
|
||||
return "RESEARCH"
|
||||
if re.search(r"\b(sues|lawsuit|funding|raises|acqui|ipo|valued|stealing|trade secret|layoff|hire[ds]?|exec|ceo)\b", t) \
|
||||
and not re.search(r"\b(repo|library|tool|agent framework)\b", t):
|
||||
return "BUSINESS_LEGAL"
|
||||
if re.search(r"\b(burnout|opinion|think|feel|why|essay|culture|linkedin|social media|future of|we made|i think|hot take|i believe|my view|in defense)\b", t):
|
||||
return "CULTURE_OPINION"
|
||||
if re.search(r"\b(how to|tutorial|guide|running|build|setup|install|from scratch|learn)\b", t):
|
||||
return "TUTORIAL_HOWTO"
|
||||
if src == "github" or re.search(r"\b(repo|library|framework|tool|agent|sdk|cli|extension|plugin|app|engine)\b", t):
|
||||
return "DEV_TOOL_DRAMA"
|
||||
return "OTHER"
|
||||
|
||||
|
||||
def _extract(src: str, md: dict) -> tuple:
|
||||
"""Return (velocity_raw, engagement_raw, age_hours)."""
|
||||
if src == "hackernews":
|
||||
pts = md.get("score", 0) or 0
|
||||
cmts = md.get("descendants", 0) or 0
|
||||
age_h = None
|
||||
if md.get("time"):
|
||||
try:
|
||||
age_h = max((NOW - md["time"]) / 3600.0, 0.1)
|
||||
except Exception:
|
||||
age_h = None
|
||||
vel = (pts / age_h) if age_h else pts
|
||||
return vel, (pts + 2 * cmts), age_h
|
||||
if src == "reddit":
|
||||
ups = md.get("ups", 0) or 0
|
||||
cmts = md.get("num_comments", 0) or 0
|
||||
return ups, (ups + 2 * cmts), None
|
||||
if src == "huggingface":
|
||||
likes = md.get("likes", 0) or 0
|
||||
return likes, likes, None
|
||||
if src == "github":
|
||||
spd = md.get("stars_per_day", 0) or 0
|
||||
stars = md.get("stars", 0) or 0
|
||||
return spd, stars, None
|
||||
if src == "arxiv":
|
||||
return 0.0, 0.0, None
|
||||
return 0.0, 0.0, None
|
||||
|
||||
|
||||
def fetch_items(conn) -> list[dict]:
|
||||
"""Fetch all entries and compute raw engagement signals."""
|
||||
global NOW
|
||||
NOW = time.time()
|
||||
cur = conn.cursor()
|
||||
cur.execute("""
|
||||
SELECT id, title, url, source, summary, signal_score, raw_metadata, first_seen,
|
||||
curated_by, manual_section, manual_tier
|
||||
FROM entries
|
||||
""")
|
||||
cols = [d[0] for d in cur.description]
|
||||
out = []
|
||||
for row in cur.fetchall():
|
||||
d = dict(zip(cols, row))
|
||||
try:
|
||||
md = json.loads(d.get("raw_metadata") or "{}")
|
||||
except Exception:
|
||||
md = {}
|
||||
vel, eng, age = _extract(d["source"], md)
|
||||
ct = _classify(d["source"], d.get("title") or "", d.get("summary") or "")
|
||||
created_at = md.get("createdAt") if d["source"] == "huggingface" else None
|
||||
out.append({
|
||||
"id": d["id"],
|
||||
"title": d.get("title") or "",
|
||||
"url": d.get("url") or "",
|
||||
"source": d["source"],
|
||||
"summary": d.get("summary") or "",
|
||||
"signal_score": d.get("signal_score") or 0,
|
||||
"velocity_raw": vel,
|
||||
"engagement_raw": eng,
|
||||
"content_type": ct,
|
||||
"first_seen": d.get("first_seen") or "",
|
||||
"created_at": created_at or "",
|
||||
"age_hours": 0.0,
|
||||
"curated_by": d.get("curated_by") or "",
|
||||
"manual_section": d.get("manual_section") or "",
|
||||
"manual_tier": d.get("manual_tier") or "",
|
||||
})
|
||||
return out
|
||||
|
||||
|
||||
def log1p_norm(values: list[float]) -> list[float]:
|
||||
"""Log1p + min-max normalization."""
|
||||
log_vals = [math.log1p(max(v, 0)) for v in values]
|
||||
if not log_vals:
|
||||
return []
|
||||
min_v, max_v = min(log_vals), max(log_vals)
|
||||
if max_v == min_v:
|
||||
return [0.0] * len(values)
|
||||
return [(v - min_v) / (max_v - min_v) for v in log_vals]
|
||||
|
||||
|
||||
def compute_index(items: list[dict]) -> list[dict]:
|
||||
"""Compute clickability index for all items."""
|
||||
velocities = [it.get("velocity_raw", 0) or 0 for it in items]
|
||||
engagements = [it.get("engagement_raw", 0) or 0 for it in items]
|
||||
signals = [it.get("signal_score", 0) or 0 for it in items]
|
||||
|
||||
vel_norm = log1p_norm(velocities)
|
||||
eng_norm = log1p_norm(engagements)
|
||||
sig_norm = log1p_norm(signals)
|
||||
|
||||
for i, item in enumerate(items):
|
||||
raw = vel_norm[i] * VEL_W + eng_norm[i] * ENG_W + sig_norm[i] * SIG_W
|
||||
if raw == 0 and sig_norm[i] > 0:
|
||||
raw = 0.05 * sig_norm[i]
|
||||
item["clickability"] = round(raw, 4)
|
||||
item["section"] = ""
|
||||
return items
|
||||
|
||||
|
||||
def _age_hours(item: dict) -> float:
|
||||
"""Effective news-age in hours."""
|
||||
if item.get("source") == "huggingface" and item.get("created_at"):
|
||||
s = item["created_at"]
|
||||
else:
|
||||
s = item.get("first_seen") or ""
|
||||
if not s:
|
||||
return 0.0
|
||||
try:
|
||||
ts = datetime.strptime(s[:19], "%Y-%m-%dT%H:%M:%S").replace(
|
||||
tzinfo=timezone.utc
|
||||
).timestamp()
|
||||
return max((time.time() - ts) / 3600.0, 0.0)
|
||||
except Exception:
|
||||
return 0.0
|
||||
|
||||
|
||||
def _get_half_life(item: dict) -> Optional[float]:
|
||||
"""Return section/tier-specific half-life in hours, or None for default."""
|
||||
ms = (item.get("manual_section") or "").upper()
|
||||
if ms in ("HARDWARE", "TIPS"):
|
||||
return 336.0
|
||||
tier = item.get("tier", "normal")
|
||||
if tier == "breaking":
|
||||
return CATEGORY_HALF_LIVES["breaking"]
|
||||
if tier == "update":
|
||||
return CATEGORY_HALF_LIVES["update"]
|
||||
return None
|
||||
|
||||
|
||||
def decay_index(items: list[dict], half_life_h: float = 18.0) -> list[dict]:
|
||||
"""Apply exponential time-decay to clickability."""
|
||||
cutoff = datetime.now(timezone.utc).timestamp() - 24 * 3600
|
||||
for it in items:
|
||||
age = _age_hours(it)
|
||||
it["age_hours"] = round(age, 1)
|
||||
base = it.get("clickability", 0) or 0
|
||||
hl = _get_half_life(it)
|
||||
if hl is None:
|
||||
hl = half_life_h
|
||||
k = math.log(2) / hl
|
||||
it["clickability_decayed"] = round(base * math.exp(-k * age), 4)
|
||||
it["effective_half_life"] = hl
|
||||
fs = it.get("first_seen") or ""
|
||||
try:
|
||||
ts = datetime.fromisoformat(fs.replace("Z", "+00:00")).timestamp()
|
||||
except ValueError:
|
||||
ts = 0
|
||||
it["fresh"] = ts >= cutoff
|
||||
return items
|
||||
@@ -0,0 +1,68 @@
|
||||
"""Centralized configuration for the AI Research Oracle.
|
||||
|
||||
Single source of truth for all paths, defaults, and constants.
|
||||
"""
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
# ── Paths ──────────────────────────────────────────────────────────────────
|
||||
ROOT = Path(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
DB_PATH = ROOT / "oracle.db"
|
||||
SCHEMA_PATH = ROOT / "schema.sql"
|
||||
|
||||
# ── Web output ─────────────────────────────────────────────────────────────
|
||||
WEBROOT = "/var/www/preprod2"
|
||||
FALLBACK_WEBROOT = ROOT / "site"
|
||||
SEEN_JSON = ROOT.parent / "ai-oracle-site" / "seen_urls.json"
|
||||
|
||||
# ── Pipeline defaults ──────────────────────────────────────────────────────
|
||||
ENABLED_SOURCES = ["github", "arxiv", "reddit", "hackernews", "huggingface", "rss"]
|
||||
DEFAULT_LIMIT = 20
|
||||
|
||||
# ── Source tiers (World Monitor pattern) ───────────────────────────────────
|
||||
# Tier 1: Primary trusted sources (official releases, peer-reviewed)
|
||||
# Tier 2: Secondary credible sources (curated communities, major outlets)
|
||||
# Tier 3: Tertiary noise sources (user-generated, unverified)
|
||||
SOURCE_TIERS = {
|
||||
"arxiv": {"tier": 1, "label": "PRIMARY", "description": "Peer-reviewed research"},
|
||||
"github": {"tier": 1, "label": "PRIMARY", "description": "Official code releases"},
|
||||
"huggingface": {"tier": 1, "label": "PRIMARY", "description": "Model registry"},
|
||||
"rss": {"tier": 2, "label": "SECONDARY", "description": "Curated tech media"},
|
||||
"hackernews": {"tier": 2, "label": "SECONDARY", "description": "Curated community"},
|
||||
"reddit": {"tier": 3, "label": "TERTIARY", "description": "User-generated discussion"},
|
||||
}
|
||||
|
||||
# Tier-based signal score bonus/penalty (applied to final_score)
|
||||
TIER_BONUS = {1: 0.05, 2: 0.0, 3: -0.05}
|
||||
|
||||
# Freshness SLA per tier (hours after which a source is flagged stale)
|
||||
FRESHNESS_SLA_H = {1: 48, 2: 24, 3: 12}
|
||||
|
||||
# ── Composite verdict thresholds ───────────────────────────────────────────
|
||||
# PUBLISH: High score + fresh, goes to top
|
||||
# WATCH: Medium score, monitor for follow-ups
|
||||
# ARCHIVE: Low score or aged out, move to archive
|
||||
# DROP: Junk score, ignore
|
||||
VERDICT_THRESHOLDS = {
|
||||
"PUBLISH": {"min_score": 6.0, "max_age_h": 48},
|
||||
"WATCH": {"min_score": 4.0, "max_age_h": 168}, # 7 days
|
||||
"ARCHIVE": {"min_score": 2.0, "max_age_h": 720}, # 30 days
|
||||
"DROP": {"min_score": 0.0, "max_age_h": 999999}, # catch-all
|
||||
}
|
||||
|
||||
# ── Content hash dedup ─────────────────────────────────────────────────────
|
||||
CONTENT_HASH_PREFIX = "sha256"
|
||||
HASH_LENGTH = 16 # characters
|
||||
|
||||
# ── Clickability weights ───────────────────────────────────────────────────
|
||||
VEL_W = 0.50
|
||||
ENG_W = 0.50
|
||||
SIG_W = 0.0
|
||||
HALF_LIFE_H = 18.0
|
||||
|
||||
# ── Render ─────────────────────────────────────────────────────────────────
|
||||
TOP_N = 8
|
||||
|
||||
# ── Archive ────────────────────────────────────────────────────────────────
|
||||
ARCHIVE_DAYS = 30
|
||||
ARCHIVE_CAP = 5000
|
||||
+203
@@ -0,0 +1,203 @@
|
||||
"""Database operations for the AI Research Oracle.
|
||||
|
||||
Handles connections, schema initialization, and common queries.
|
||||
"""
|
||||
import os
|
||||
import sqlite3
|
||||
from typing import Optional
|
||||
|
||||
from oracle.config import DB_PATH, SCHEMA_PATH, SOURCE_TIERS
|
||||
|
||||
|
||||
def get_connection(db_path: Optional[str] = None) -> sqlite3.Connection:
|
||||
"""Open a database connection."""
|
||||
path = db_path or str(DB_PATH)
|
||||
conn = sqlite3.connect(path)
|
||||
conn.row_factory = sqlite3.Row
|
||||
return conn
|
||||
|
||||
|
||||
def get_ro_connection(db_path: Optional[str] = None) -> sqlite3.Connection:
|
||||
"""Open a read-only database connection."""
|
||||
path = db_path or str(DB_PATH)
|
||||
return sqlite3.connect(f"file:{path}?mode=ro", uri=True)
|
||||
|
||||
|
||||
def init_db(db_path: Optional[str] = None, schema_path: Optional[str] = None) -> sqlite3.Connection:
|
||||
"""Initialize or open the database, applying schema if it exists.
|
||||
|
||||
Schema uses CREATE IF NOT EXISTS so repeated calls are idempotent.
|
||||
Also runs World Monitor migration columns (content_hash, verdict, freshness).
|
||||
"""
|
||||
conn = sqlite3.connect(db_path or str(DB_PATH))
|
||||
sp = schema_path or str(SCHEMA_PATH)
|
||||
if os.path.exists(sp):
|
||||
with open(sp) as f:
|
||||
conn.executescript(f.read())
|
||||
conn.commit()
|
||||
# World Monitor migration columns (idempotent)
|
||||
for col in [
|
||||
"content_hash TEXT DEFAULT ''",
|
||||
"verdict TEXT DEFAULT ''",
|
||||
"source_tier INTEGER DEFAULT 2",
|
||||
]:
|
||||
try:
|
||||
conn.execute(f"ALTER TABLE entries ADD COLUMN {col}")
|
||||
except sqlite3.OperationalError:
|
||||
pass # already exists
|
||||
conn.commit()
|
||||
return conn
|
||||
|
||||
|
||||
def migrate_world_monitor(conn: Optional[sqlite3.Connection] = None) -> dict:
|
||||
"""Apply World Monitor schema migrations + backfill.
|
||||
|
||||
Returns: {columns_added: int, hashes_backfilled: int, verdicts_set: int}
|
||||
"""
|
||||
from oracle.dedup import backfill_hashes, apply_verdicts, get_source_tier
|
||||
|
||||
c = conn or get_connection()
|
||||
cur = c.cursor()
|
||||
|
||||
# Check which columns already exist
|
||||
cur.execute("PRAGMA table_info(entries)")
|
||||
existing = {row[1] for row in cur.fetchall()}
|
||||
|
||||
columns_to_add = []
|
||||
if "content_hash" not in existing:
|
||||
columns_to_add.append("content_hash TEXT DEFAULT ''")
|
||||
if "verdict" not in existing:
|
||||
columns_to_add.append("verdict TEXT DEFAULT ''")
|
||||
if "source_tier" not in existing:
|
||||
columns_to_add.append("source_tier INTEGER DEFAULT 2")
|
||||
|
||||
added = 0
|
||||
for col_def in columns_to_add:
|
||||
try:
|
||||
cur.execute(f"ALTER TABLE entries ADD COLUMN {col_def}")
|
||||
added += 1
|
||||
except sqlite3.OperationalError:
|
||||
pass # race condition or already exists
|
||||
|
||||
c.commit()
|
||||
|
||||
# Backfill content hashes
|
||||
hashes = backfill_hashes(c)
|
||||
|
||||
# Backfill source tiers — reset first so the WHERE clause catches everything
|
||||
cur.execute("UPDATE entries SET source_tier = 0")
|
||||
c.commit()
|
||||
for source_name, tier_info in SOURCE_TIERS.items():
|
||||
cur.execute(
|
||||
"UPDATE entries SET source_tier = ? WHERE source = ?",
|
||||
(tier_info["tier"], source_name),
|
||||
)
|
||||
c.commit()
|
||||
|
||||
# Set verdicts
|
||||
verdicts = apply_verdicts(c)
|
||||
|
||||
return {"columns_added": added, "hashes_backfilled": hashes, "verdicts_set": verdicts}
|
||||
|
||||
|
||||
def get_stats(conn: sqlite3.Connection) -> dict:
|
||||
"""Return database statistics."""
|
||||
cur = conn.cursor()
|
||||
|
||||
cur.execute("SELECT COUNT(*) FROM entries")
|
||||
total = cur.fetchone()[0]
|
||||
|
||||
cur.execute("""
|
||||
SELECT source, COUNT(*) as cnt, ROUND(AVG(signal_score), 2) as avg_score,
|
||||
MIN(signal_score) as min_score, MAX(signal_score) as max_score
|
||||
FROM entries GROUP BY source
|
||||
""")
|
||||
sources = {r["source"]: dict(r) for r in cur.fetchall()}
|
||||
|
||||
cur.execute("SELECT COUNT(*) FROM entries WHERE summary IS NOT NULL")
|
||||
summarized = cur.fetchone()[0]
|
||||
|
||||
cur.execute("SELECT COUNT(*) FROM entries WHERE summary IS NULL")
|
||||
pending = cur.fetchone()[0]
|
||||
|
||||
# Bucket distribution
|
||||
try:
|
||||
cur.execute("""
|
||||
SELECT bucket, COUNT(*) as cnt
|
||||
FROM entries WHERE bucket IS NOT NULL
|
||||
GROUP BY bucket ORDER BY cnt DESC
|
||||
""")
|
||||
buckets = {r["bucket"]: r["cnt"] for r in cur.fetchall()}
|
||||
except Exception:
|
||||
buckets = {}
|
||||
|
||||
return {
|
||||
"total_entries": total,
|
||||
"sources": sources,
|
||||
"summarized": summarized,
|
||||
"pending_summary": pending,
|
||||
"buckets": buckets,
|
||||
}
|
||||
|
||||
|
||||
def query_top(conn: sqlite3.Connection, n: int = 10,
|
||||
source: Optional[str] = None,
|
||||
min_score: float = 0) -> list[dict]:
|
||||
"""Get top N entries by signal score."""
|
||||
cur = conn.cursor()
|
||||
where_parts = []
|
||||
params = []
|
||||
|
||||
if min_score > 0:
|
||||
where_parts.append("signal_score >= ?")
|
||||
params.append(min_score)
|
||||
if source:
|
||||
where_parts.append("source = ?")
|
||||
params.append(source)
|
||||
|
||||
where = (" AND " + " AND ".join(where_parts)) if where_parts else ""
|
||||
cur.execute(
|
||||
f"SELECT * FROM entries {where} ORDER BY signal_score DESC LIMIT ?",
|
||||
params + [n],
|
||||
)
|
||||
return [dict(r) for r in cur.fetchall()]
|
||||
|
||||
|
||||
def query_recent(conn: sqlite3.Connection, hours: int = 24) -> list[dict]:
|
||||
"""Get entries from the last N hours."""
|
||||
from datetime import datetime, timezone, timedelta
|
||||
|
||||
cutoff = (datetime.now(timezone.utc) - timedelta(hours=hours)).strftime(
|
||||
"%Y-%m-%dT%H:%M:%SZ"
|
||||
)
|
||||
cur = conn.cursor()
|
||||
cur.execute(
|
||||
"SELECT * FROM entries WHERE first_seen >= ? ORDER BY first_seen DESC",
|
||||
(cutoff,),
|
||||
)
|
||||
return [dict(r) for r in cur.fetchall()]
|
||||
|
||||
|
||||
def query_search(conn: sqlite3.Connection, q: str, limit: int = 20) -> list[dict]:
|
||||
"""Search entries by title, summary, and key technical point."""
|
||||
cur = conn.cursor()
|
||||
pattern = f"%{q}%"
|
||||
cur.execute("""
|
||||
SELECT * FROM entries
|
||||
WHERE title LIKE ?
|
||||
OR json_extract(summary,'$.one_liner') LIKE ?
|
||||
OR json_extract(summary,'$.key_technical_point') LIKE ?
|
||||
ORDER BY signal_score DESC LIMIT ?
|
||||
""", (pattern, pattern, pattern, limit))
|
||||
return [dict(r) for r in cur.fetchall()]
|
||||
|
||||
|
||||
def query_by_tag(conn: sqlite3.Connection, tag: str, limit: int = 20) -> list[dict]:
|
||||
"""Get entries matching a category tag."""
|
||||
cur = conn.cursor()
|
||||
cur.execute("""
|
||||
SELECT * FROM entries
|
||||
WHERE json_extract(category_tags,'$') LIKE ?
|
||||
ORDER BY signal_score DESC LIMIT ?
|
||||
""", (f'%"{tag}"%', limit))
|
||||
return [dict(r) for r in cur.fetchall()]
|
||||
+140
@@ -0,0 +1,140 @@
|
||||
"""Content-hash dedup and composite verdict engine.
|
||||
|
||||
World Monitor pattern: SHA-256 content hash for dedup, tier-weighted
|
||||
composite verdict (PUBLISH/WATCH/ARCHIVE/DROP) on top of final_score.
|
||||
"""
|
||||
import hashlib
|
||||
import re
|
||||
from datetime import datetime, timezone
|
||||
from oracle.config import (
|
||||
SOURCE_TIERS, TIER_BONUS, VERDICT_THRESHOLDS,
|
||||
HASH_LENGTH, CONTENT_HASH_PREFIX,
|
||||
)
|
||||
|
||||
|
||||
def content_hash(title: str, url: str = "", body: str = "") -> str:
|
||||
"""Deterministic content hash for dedup.
|
||||
|
||||
Normalizes whitespace, lowercases, strips HTML tags, then hashes.
|
||||
Returns hex[:HASH_LENGTH] for compact storage.
|
||||
"""
|
||||
text = f"{title}|{body[:500]}|{url}"
|
||||
text = re.sub(r'\s+', ' ', text).strip().lower()
|
||||
text = re.sub(r'<[^>]+>', '', text)
|
||||
raw = hashlib.sha256(text.encode()).hexdigest()
|
||||
return f"{CONTENT_HASH_PREFIX}:{raw[:HASH_LENGTH]}"
|
||||
|
||||
|
||||
def check_duplicate(conn, title: str, url: str = "", body: str = "", cutoff_days: int = 7) -> bool:
|
||||
"""Check if an entry with similar content_hash already exists within cutoff."""
|
||||
h = content_hash(title, url, body)
|
||||
now = datetime.now(timezone.utc)
|
||||
cur = conn.cursor()
|
||||
cur.execute(
|
||||
"SELECT COUNT(*) FROM entries WHERE content_hash = ? AND first_seen > ?",
|
||||
(h, (now.timestamp() - cutoff_days * 86400)),
|
||||
)
|
||||
count = cur.fetchone()[0]
|
||||
return count > 0
|
||||
|
||||
|
||||
def get_source_tier(source: str) -> dict:
|
||||
"""Return tier info for a source. Defaults to tier 2."""
|
||||
return SOURCE_TIERS.get(source, {"tier": 2, "label": "SECONDARY", "description": "Unknown source"})
|
||||
|
||||
|
||||
def tier_adjusted_score(base_score: float, source: str) -> float:
|
||||
"""Apply tier bonus/penalty to a base signal score."""
|
||||
tier_info = get_source_tier(source)
|
||||
tier_num = tier_info["tier"]
|
||||
bonus = TIER_BONUS.get(tier_num, 0.0)
|
||||
return round(base_score + bonus, 3)
|
||||
|
||||
|
||||
def compute_verdict(score: float, age_hours: float) -> str:
|
||||
"""Compute composite verdict from score + age.
|
||||
|
||||
Uses signal_score (0-10 scale). PUBLISH > WATCH > ARCHIVE > DROP.
|
||||
"""
|
||||
for verdict, thresholds in VERDICT_THRESHOLDS.items():
|
||||
if score >= thresholds["min_score"] and age_hours <= thresholds["max_age_h"]:
|
||||
return verdict
|
||||
return "DROP"
|
||||
|
||||
|
||||
def age_hours(first_seen_iso: str) -> float:
|
||||
"""Return age in hours from ISO timestamp."""
|
||||
try:
|
||||
ts = first_seen_iso.replace("Z", "+00:00")
|
||||
first = datetime.fromisoformat(ts)
|
||||
now = datetime.now(timezone.utc)
|
||||
return max(0, (now - first).total_seconds() / 3600)
|
||||
except (ValueError, AttributeError):
|
||||
return 0.0
|
||||
|
||||
|
||||
def apply_verdicts(conn):
|
||||
"""Update verdict column for all entries that lack one.
|
||||
|
||||
Uses signal_score (0-10 scale) + first_seen age to compute verdict.
|
||||
"""
|
||||
cur = conn.cursor()
|
||||
# Check if verdict column exists
|
||||
cur.execute("PRAGMA table_info(entries)")
|
||||
columns = {row[1] for row in cur.fetchall()}
|
||||
if "verdict" not in columns:
|
||||
print(" [verdict] column not found, skipping apply")
|
||||
return 0
|
||||
|
||||
# Reset all verdicts so they get recalculated
|
||||
cur.execute("UPDATE entries SET verdict = ''")
|
||||
conn.commit()
|
||||
|
||||
# Fetch all entries with signal scores
|
||||
cur.execute("SELECT id, COALESCE(signal_score, 0), first_seen FROM entries")
|
||||
updated = 0
|
||||
for row in cur.fetchall():
|
||||
entry_id, signal_score, first_seen = row
|
||||
age = age_hours(first_seen)
|
||||
verdict = compute_verdict(signal_score, age)
|
||||
cur.execute("UPDATE entries SET verdict = ? WHERE id = ?", (verdict, entry_id))
|
||||
updated += 1
|
||||
|
||||
conn.commit()
|
||||
return updated
|
||||
|
||||
|
||||
def backfill_hashes(conn, batch_size: int = 500) -> int:
|
||||
"""Backfill content_hash for entries that lack one."""
|
||||
cur = conn.cursor()
|
||||
cur.execute("PRAGMA table_info(entries)")
|
||||
columns = {row[1] for row in cur.fetchall()}
|
||||
if "content_hash" not in columns:
|
||||
print(" [dedup] content_hash column not found, skipping backfill")
|
||||
return 0
|
||||
|
||||
updated = 0
|
||||
while True:
|
||||
cur.execute(
|
||||
"SELECT id, title, url, summary FROM entries "
|
||||
"WHERE content_hash IS NULL OR content_hash = '' "
|
||||
"LIMIT ?",
|
||||
(batch_size,),
|
||||
)
|
||||
rows = cur.fetchall()
|
||||
if not rows:
|
||||
break
|
||||
for entry_id, title, url, summary in rows:
|
||||
body = ""
|
||||
if summary:
|
||||
import json
|
||||
try:
|
||||
s = json.loads(summary)
|
||||
body = s.get("one_liner", "") + " " + s.get("key_points", "")
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
body = summary[:200]
|
||||
h = content_hash(title, url, body)
|
||||
cur.execute("UPDATE entries SET content_hash = ? WHERE id = ?", (h, entry_id))
|
||||
updated += 1
|
||||
conn.commit()
|
||||
return updated
|
||||
@@ -0,0 +1,140 @@
|
||||
"""Recency guard — Athena "how old is this news?" gate.
|
||||
|
||||
Age is the dominant gate. TODAY's items are always eligible.
|
||||
Older items are eligible ONLY if never posted before.
|
||||
"""
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
from datetime import datetime, timezone
|
||||
|
||||
from oracle.config import SEEN_JSON
|
||||
|
||||
ORACLE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
||||
|
||||
|
||||
def _parse(ts):
|
||||
if not ts:
|
||||
return None
|
||||
try:
|
||||
return datetime.fromisoformat(ts.replace("Z", "+00:00"))
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
def _norm_url(u):
|
||||
if not u:
|
||||
return ""
|
||||
return u.split("?")[0].split("#")[0].rstrip("/").lower()
|
||||
|
||||
|
||||
def _norm_title(t):
|
||||
if not t:
|
||||
return ""
|
||||
t = t.lower()
|
||||
t = re.sub(r"[^a-z0-9 ]", " ", t)
|
||||
return re.sub(r"\s+", " ", t).strip()[:60]
|
||||
|
||||
|
||||
def load_posted(md_dir: str = ORACLE_DIR, seen_json: str = str(SEEN_JSON)):
|
||||
"""Return (md_urls:set, md_titles:set, seen_urls:set)."""
|
||||
md_urls, md_titles, seen_urls = set(), set(), set()
|
||||
|
||||
for fn in sorted(os.listdir(md_dir)):
|
||||
if re.match(r"athena_top.*\.md$", fn):
|
||||
try:
|
||||
txt = open(os.path.join(md_dir, fn), encoding="utf-8", errors="replace").read()
|
||||
except OSError:
|
||||
continue
|
||||
for m in re.findall(r"\]\((https?://[^)\s]+)\)", txt):
|
||||
nu = _norm_url(m)
|
||||
if nu:
|
||||
md_urls.add(nu)
|
||||
for t in re.findall(r"^\|\s*\d+\s*\|\s*(.+?)\s*\|", txt, re.M):
|
||||
nt = _norm_title(t)
|
||||
if nt:
|
||||
md_titles.add(nt)
|
||||
|
||||
if os.path.exists(seen_json):
|
||||
try:
|
||||
with open(seen_json, encoding="utf-8") as f:
|
||||
for u in json.load(f):
|
||||
nu = _norm_url(u)
|
||||
if nu:
|
||||
seen_urls.add(nu)
|
||||
except (json.JSONDecodeError, OSError):
|
||||
pass
|
||||
|
||||
return md_urls, md_titles, seen_urls
|
||||
|
||||
|
||||
def is_today(first_seen, now=None):
|
||||
now = now or datetime.now(timezone.utc)
|
||||
d = _parse(first_seen)
|
||||
return bool(d) and d.strftime("%Y-%m-%d") == now.strftime("%Y-%m-%d")
|
||||
|
||||
|
||||
def age_days(first_seen, now=None):
|
||||
now = now or datetime.now(timezone.utc)
|
||||
d = _parse(first_seen)
|
||||
if not d:
|
||||
return 9999.0
|
||||
return max((now - d).total_seconds() / 86400.0, 0.0)
|
||||
|
||||
|
||||
def day_bucket(first_seen, now=None):
|
||||
days = age_days(first_seen, now)
|
||||
if days < 1:
|
||||
return "today"
|
||||
if days < 2:
|
||||
return "yesterday"
|
||||
if days <= 6:
|
||||
return "this-week"
|
||||
return "older"
|
||||
|
||||
|
||||
def already_posted(url, title, first_seen, now=None,
|
||||
md_urls=None, md_titles=None, seen_urls=None):
|
||||
"""Age-aware dedup. Today's items are NEVER flagged."""
|
||||
if md_urls is None or md_titles is None or seen_urls is None:
|
||||
md_urls, md_titles, seen_urls = load_posted()
|
||||
if _norm_url(url) in md_urls:
|
||||
return True
|
||||
nt = _norm_title(title)
|
||||
if nt and nt in md_titles:
|
||||
return True
|
||||
if is_today(first_seen, now):
|
||||
return False
|
||||
if _norm_url(url) in seen_urls:
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def recency_weight(first_seen, now=None, half_life_days=2.0):
|
||||
"""1.0 for today, decays ~halving every 2 days."""
|
||||
return 0.5 ** (age_days(first_seen, now) / half_life_days)
|
||||
|
||||
|
||||
def blend_score(item, now=None):
|
||||
"""clickability_decayed * recency_weight."""
|
||||
base = item.get("clickability_decayed", 0) or 0
|
||||
return base * recency_weight(item.get("first_seen"), now)
|
||||
|
||||
|
||||
def filter_fresh(items, now=None, recent_window_days=4.0):
|
||||
"""Split into (today_items, older_new_items, dropped_items)."""
|
||||
now = now or datetime.now(timezone.utc)
|
||||
md_urls, md_titles, seen_urls = load_posted()
|
||||
today_items, older_new, dropped = [], [], []
|
||||
for it in items:
|
||||
fs = it.get("first_seen")
|
||||
if is_today(fs, now):
|
||||
today_items.append(it)
|
||||
continue
|
||||
posted = already_posted(it.get("url"), it.get("title"), fs, now,
|
||||
md_urls, md_titles, seen_urls)
|
||||
if posted and age_days(fs, now) > recent_window_days:
|
||||
dropped.append(it)
|
||||
else:
|
||||
older_new.append(it)
|
||||
return today_items, older_new, dropped
|
||||
@@ -0,0 +1,359 @@
|
||||
"""Render Athena entries into a static news site.
|
||||
|
||||
Two-layer layout: Top News (fresh today) + aging Stack (everything else).
|
||||
Read-only against oracle.db. Designed for a 20-min cron run.
|
||||
|
||||
Variant-aware: render_variant_html() builds themed pages from pre-filtered items.
|
||||
"""
|
||||
import argparse
|
||||
import datetime
|
||||
import html
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import sqlite3
|
||||
from collections import OrderedDict
|
||||
from typing import Optional
|
||||
|
||||
from oracle.clickability import fetch_items, compute_index, decay_index
|
||||
from oracle.config import DB_PATH, WEBROOT, FALLBACK_WEBROOT, HALF_LIFE_H, TOP_N
|
||||
|
||||
|
||||
def _clean_summary(raw):
|
||||
"""Extract the most readable field from summary JSON."""
|
||||
if not raw:
|
||||
return ""
|
||||
try:
|
||||
d = json.loads(raw)
|
||||
if isinstance(d, dict):
|
||||
for k in ("one_liner", "key_technical_point", "potential_use_case"):
|
||||
v = d.get(k)
|
||||
if isinstance(v, str) and v.strip():
|
||||
return re.sub(r"\\+|_|`", "", v).strip()
|
||||
except Exception:
|
||||
pass
|
||||
return re.sub(r"\\+|_|`", "", raw).strip()
|
||||
|
||||
|
||||
# ── Theme palette ──────────────────────────────────────────────────────────
|
||||
|
||||
THEMES = {
|
||||
"dark": {
|
||||
"--bg": "#0b0e14",
|
||||
"--card": "#141925",
|
||||
"--fg": "#e6e9ef",
|
||||
"--mut": "#8b93a7",
|
||||
"--border": "#1f2533",
|
||||
},
|
||||
"light": {
|
||||
"--bg": "#f8f9fa",
|
||||
"--card": "#ffffff",
|
||||
"--fg": "#1a1a2e",
|
||||
"--mut": "#6b7280",
|
||||
"--border": "#e5e7eb",
|
||||
},
|
||||
"midnight": {
|
||||
"--bg": "#0a0a1a",
|
||||
"--card": "#111128",
|
||||
"--fg": "#c8d0e0",
|
||||
"--mut": "#5a6480",
|
||||
"--border": "#1a1a3a",
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def _theme_css(display: dict) -> str:
|
||||
"""Generate CSS variables for a variant display config."""
|
||||
theme_name = display.get("theme", "dark")
|
||||
palette = THEMES.get(theme_name, THEMES["dark"])
|
||||
accent = display.get("accent", "#5b8cff")
|
||||
vars_list = ", ".join(f"{k}:{v}" for k, v in palette.items())
|
||||
return f":root {{ {vars_list}; --acc:{accent}; }}"
|
||||
|
||||
|
||||
def _fmt_time(first_seen):
|
||||
if not first_seen:
|
||||
return ""
|
||||
try:
|
||||
dt = datetime.datetime.strptime(first_seen, "%Y-%m-%dT%H:%M:%SZ")
|
||||
return dt.strftime("%H:%M")
|
||||
except Exception:
|
||||
return ""
|
||||
|
||||
|
||||
def _card(it, big=False):
|
||||
title = html.escape(it["title"] or "(untitled)")
|
||||
url = html.escape(it["url"] or "#")
|
||||
src = html.escape(it["source"])
|
||||
t = _fmt_time(it.get("first_seen"))
|
||||
summary_raw = _clean_summary(it.get("summary") or "")
|
||||
summary = html.escape(summary_raw[:200])
|
||||
cls = "card big" if big else "card"
|
||||
summary_html = ('<p class="summary">{0}</p>'.format(summary)) if (summary and big) else ""
|
||||
return f"""
|
||||
<article class="{cls}" data-src="{src}">
|
||||
<div class="meta"><span class="src">{src}</span>
|
||||
<span class="time">{t}</span>
|
||||
<span class="sig">sig {it.get('signal_score') or 0:.1f}</span>
|
||||
<span class="score">\U0001f525 {it['clickability_decayed']:.2f}</span></div>
|
||||
<h3><a href="{url}" target="_blank" rel="noopener">{title}</a></h3>
|
||||
{summary_html}
|
||||
</article>"""
|
||||
|
||||
|
||||
def build_html(items):
|
||||
"""Build the full HTML page from ranked items."""
|
||||
now = datetime.datetime.now(datetime.timezone.utc).strftime("%Y-%m-%d %H:%M UTC")
|
||||
ranked = sorted(items, key=lambda x: x["clickability_decayed"], reverse=True)
|
||||
|
||||
fresh = [it for it in ranked if it.get("fresh")]
|
||||
top = fresh[:TOP_N]
|
||||
stack = [it for it in ranked if it not in top]
|
||||
|
||||
by_day = OrderedDict()
|
||||
for it in stack:
|
||||
day = (it.get("first_seen") or "")[:10] or "unknown"
|
||||
by_day.setdefault(day, []).append(it)
|
||||
|
||||
top_html = "".join(_card(it, big=True) for it in top)
|
||||
|
||||
stack_html = ""
|
||||
for day, rows in by_day.items():
|
||||
rows.sort(key=lambda x: x["clickability_decayed"], reverse=True)
|
||||
cards = "".join(_card(it) for it in rows)
|
||||
stack_html += f"""
|
||||
<h3 class="day">\U0001f4c5 {html.escape(day)}</h3>
|
||||
<div class="stack">{cards}</div>"""
|
||||
|
||||
return f"""<!doctype html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1">
|
||||
<title>Athena AI News — Ranked by Clickability</title>
|
||||
<style>
|
||||
:root {{ --bg:#0b0e14; --card:#141925; --fg:#e6e9ef; --mut:#8b93a7; --acc:#5b8cff; }}
|
||||
* {{ box-sizing:border-box; }}
|
||||
body {{ margin:0; background:var(--bg); color:var(--fg);
|
||||
font:15px/1.5 -apple-system,Segoe UI,Roboto,Helvetica,Arial,sans-serif; }}
|
||||
header {{ padding:28px 20px 14px; border-bottom:1px solid #1f2533; text-align:center; }}
|
||||
header h1 {{ margin:0; font-size:28px; letter-spacing:.5px; }}
|
||||
header .sub {{ color:var(--mut); font-size:13px; margin-top:6px; }}
|
||||
main {{ max-width:1000px; margin:0 auto; padding:20px; }}
|
||||
h2.sech {{ font-size:18px; margin:26px 0 12px; border-left:3px solid var(--acc); padding-left:10px; }}
|
||||
.grid {{ display:grid; grid-template-columns:repeat(auto-fill,minmax(320px,1fr)); gap:14px; }}
|
||||
.card {{ background:var(--card); border:1px solid #1f2533; border-radius:12px; padding:16px; }}
|
||||
.card.big {{ grid-column:1/-1; }}
|
||||
.meta {{ display:flex; gap:10px; align-items:center; font-size:12px; color:var(--mut); }}
|
||||
.src {{ background:#1f2533; padding:2px 8px; border-radius:20px; text-transform:uppercase; }}
|
||||
.score {{ color:#ff9d5b; font-weight:600; margin-left:auto; }}
|
||||
.card h3 {{ font-size:16px; margin:10px 0 8px; line-height:1.35; }}
|
||||
.card.big h3 {{ font-size:20px; }}
|
||||
.card h3 a {{ color:var(--fg); text-decoration:none; }}
|
||||
.card h3 a:hover {{ color:var(--acc); }}
|
||||
.summary {{ color:var(--mut); font-size:13px; margin:0; }}
|
||||
.day {{ font-size:15px; color:var(--mut); margin:28px 0 10px; border-bottom:1px solid #1f2533; padding-bottom:6px; }}
|
||||
.stack {{ display:grid; grid-template-columns:repeat(auto-fill,minmax(320px,1fr)); gap:12px; }}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<header>
|
||||
<h1>Athena AI News</h1>
|
||||
<div class="sub">Auto-ranked by Clickability Index · decays with age so the stack flows top → bottom · generated {now} · {len(items)} stories</div>
|
||||
</header>
|
||||
<main>
|
||||
<h2 class="sech">\U0001f534 Top News</h2>
|
||||
<div class="grid">{top_html}</div>
|
||||
<h2 class="sech">\U0001f4f0 The Stack</h2>
|
||||
{stack_html}
|
||||
</main>
|
||||
</body>
|
||||
</html>"""
|
||||
|
||||
|
||||
def render(dry_run=False, webroot=None):
|
||||
"""Run the full render pipeline.
|
||||
|
||||
Returns (output_path, item_count).
|
||||
"""
|
||||
conn = sqlite3.connect(str(DB_PATH))
|
||||
items = fetch_items(conn)
|
||||
conn.close()
|
||||
|
||||
items = compute_index(items)
|
||||
items = decay_index(items, HALF_LIFE_H)
|
||||
page = build_html(items)
|
||||
|
||||
if dry_run:
|
||||
out = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "_preview.html")
|
||||
with open(out, "w") as f:
|
||||
f.write(page)
|
||||
fresh = [it for it in items if it.get("fresh")]
|
||||
top = sorted(fresh, key=lambda x: x["clickability_decayed"], reverse=True)[:TOP_N]
|
||||
print(f"[dry-run] wrote {out} ({len(items)} items, {len(fresh)} fresh today)")
|
||||
print(f"TOP {TOP_N} FRESH (today only) by decayed clickability:")
|
||||
for i, it in enumerate(top, 1):
|
||||
print(f" {i}. [{it['clickability_decayed']:.2f} | age {it['age_hours']:.0f}h] {it['source']:10} {it['title'][:55]}")
|
||||
return out, len(items)
|
||||
|
||||
target = webroot or (WEBROOT if os.path.isdir(WEBROOT) else str(FALLBACK_WEBROOT))
|
||||
os.makedirs(target, exist_ok=True)
|
||||
|
||||
with open(os.path.join(target, "index.html"), "w") as f:
|
||||
f.write(page)
|
||||
|
||||
with open(os.path.join(target, "feed.json"), "w") as f:
|
||||
json.dump([
|
||||
{"title": i["title"], "url": i["url"], "source": i["source"],
|
||||
"clickability_decayed": i["clickability_decayed"], "age_hours": i["age_hours"],
|
||||
"first_seen": i.get("first_seen")}
|
||||
for i in sorted(items, key=lambda x: x["clickability_decayed"], reverse=True)
|
||||
], f, indent=2)
|
||||
|
||||
where = "WEBROOT" if target == WEBROOT else "fallback(~oracle/site)"
|
||||
print(f"[render] wrote {target}/index.html ({len(items)} items) -> {where}")
|
||||
return os.path.join(target, "index.html"), len(items)
|
||||
|
||||
|
||||
# ── Variant rendering ──────────────────────────────────────────────────────
|
||||
|
||||
def _variant_card(it: dict, display: dict, big: bool = False) -> str:
|
||||
"""Build an HTML card for a variant edition."""
|
||||
title = html.escape(it.get("title") or "(untitled)")
|
||||
url = html.escape(it.get("url") or "#")
|
||||
src = html.escape(it.get("source", ""))
|
||||
t = _fmt_time(it.get("first_seen"))
|
||||
summary_raw = _clean_summary(it.get("summary") or "")
|
||||
summary = html.escape(summary_raw[:200])
|
||||
|
||||
score = it.get("signal_score")
|
||||
tier = it.get("source_tier")
|
||||
verdict = it.get("verdict", "")
|
||||
|
||||
cls = "card big" if big else "card"
|
||||
summary_html = ('<p class="summary">{}</p>'.format(summary)) if (summary and display.get("show_summary") and big) else ""
|
||||
|
||||
# Build meta badges
|
||||
badges = f'<span class="src">{src}</span>'
|
||||
if t:
|
||||
badges += f'<span class="time">{t}</span>'
|
||||
if display.get("show_score") and score is not None:
|
||||
badges += f'<span class="sig">sig {float(score):.1f}</span>'
|
||||
if display.get("show_tier") and tier is not None:
|
||||
badges += f'<span class="tier">T{tier}</span>'
|
||||
if display.get("show_verdict") and verdict:
|
||||
badges += f'<span class="verdict verdict-{verdict.lower()}">{verdict}</span>'
|
||||
|
||||
return f"""
|
||||
<article class="{cls}">
|
||||
<div class="meta">{badges}</div>
|
||||
<h3><a href="{url}" target="_blank" rel="noopener">{title}</a></h3>
|
||||
{summary_html}
|
||||
</article>"""
|
||||
|
||||
|
||||
def render_variant_html(items: list[dict], variant: dict) -> str:
|
||||
"""Build the full HTML page for a variant edition."""
|
||||
display = variant.get("display", {})
|
||||
name = variant.get("name", "Athena")
|
||||
desc = variant.get("description", "")
|
||||
logo = display.get("logo", "🏛️")
|
||||
top_n = display.get("top_n", 8)
|
||||
half_life = variant.get("ranking", {}).get("half_life_h", 18)
|
||||
|
||||
now = datetime.datetime.now(datetime.timezone.utc).strftime("%Y-%m-%d %H:%M UTC")
|
||||
|
||||
top = items[:top_n]
|
||||
stack = items[top_n:]
|
||||
|
||||
# Group stack by day
|
||||
by_day = OrderedDict()
|
||||
for it in stack:
|
||||
day = (it.get("first_seen") or "")[:10] or "unknown"
|
||||
by_day.setdefault(day, []).append(it)
|
||||
|
||||
top_html = "\n".join(_variant_card(it, display, big=True) for it in top)
|
||||
stack_html = ""
|
||||
for day, rows in by_day.items():
|
||||
cards = "".join(_variant_card(it, display) for it in rows)
|
||||
stack_html += f"""
|
||||
<h3 class="day">🗒️ {html.escape(day)}</h3>
|
||||
<div class="stack">{cards}</div>"""
|
||||
|
||||
theme = _theme_css(display)
|
||||
|
||||
return f"""<!doctype html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1">
|
||||
<title>{html.escape(name)}</title>
|
||||
<style>
|
||||
{theme}
|
||||
* {{ box-sizing:border-box; }}
|
||||
body {{ margin:0; background:var(--bg); color:var(--fg);
|
||||
font:15px/1.5 -apple-system,Segoe UI,Roboto,Helvetica,Arial,sans-serif; }}
|
||||
header {{ padding:28px 20px 14px; border-bottom:1px solid var(--border); text-align:center; }}
|
||||
header h1 {{ margin:0; font-size:28px; letter-spacing:.5px; }}
|
||||
header .sub {{ color:var(--mut); font-size:13px; margin-top:6px; }}
|
||||
main {{ max-width:1000px; margin:0 auto; padding:20px; }}
|
||||
h2.sech {{ font-size:18px; margin:26px 0 12px; border-left:3px solid var(--acc); padding-left:10px; }}
|
||||
.grid {{ display:grid; grid-template-columns:repeat(auto-fill,minmax(320px,1fr)); gap:14px; }}
|
||||
.card {{ background:var(--card); border:1px solid var(--border); border-radius:12px; padding:16px; }}
|
||||
.card.big {{ grid-column:1/-1; }}
|
||||
.meta {{ display:flex; gap:10px; align-items:center; font-size:12px; color:var(--mut); flex-wrap:wrap; }}
|
||||
.src {{ background:var(--border); padding:2px 8px; border-radius:20px; text-transform:uppercase; }}
|
||||
.sig {{ color:var(--acc); font-weight:600; }}
|
||||
.tier {{ color:var(--mut); }}
|
||||
.verdict {{ padding:2px 6px; border-radius:4px; font-weight:600; text-transform:uppercase; font-size:10px; }}
|
||||
.verdict-publish {{ background:#065f46; color:#a7f3d0; }}
|
||||
.verdict-watch {{ background:#1e3a5f; color:#93c5fd; }}
|
||||
.verdict-archive {{ background:#4a3b1f; color:#fcd34d; }}
|
||||
.verdict-drop {{ background:#4a1f1f; color:#fca5a5; }}
|
||||
.card h3 {{ font-size:16px; margin:10px 0 8px; line-height:1.35; }}
|
||||
.card.big h3 {{ font-size:20px; }}
|
||||
.card h3 a {{ color:var(--fg); text-decoration:none; }}
|
||||
.card h3 a:hover {{ color:var(--acc); }}
|
||||
.summary {{ color:var(--mut); font-size:13px; margin:0; }}
|
||||
.day {{ font-size:15px; color:var(--mut); margin:28px 0 10px; border-bottom:1px solid var(--border); padding-bottom:6px; }}
|
||||
.stack {{ display:grid; grid-template-columns:repeat(auto-fill,minmax(320px,1fr)); gap:12px; }}
|
||||
footer {{ text-align:center; padding:20px; color:var(--mut); font-size:12px; border-top:1px solid var(--border); margin-top:40px; }}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<header>
|
||||
<h1>{logo} {html.escape(name)}</h1>
|
||||
<div class="sub">{html.escape(desc)} · generated {now} · {len(items)} stories</div>
|
||||
</header>
|
||||
<main>
|
||||
<h2 class="sech">🔥 Top News</h2>
|
||||
<div class="grid">{top_html}</div>
|
||||
{('<h2 class="sech">🗄️ The Stack</h2>' + stack_html) if stack else ''}
|
||||
</main>
|
||||
<footer>
|
||||
Athena AI Research Oracle · <a href="feed.json">feed.json</a>
|
||||
</footer>
|
||||
</body>
|
||||
</html>"""
|
||||
|
||||
|
||||
def render_variant_json(items: list[dict], variant: dict, path: str) -> None:
|
||||
"""Write a JSON feed for a variant edition."""
|
||||
display = variant.get("display", {})
|
||||
feed = []
|
||||
for i in items:
|
||||
entry = {
|
||||
"title": i.get("title", ""),
|
||||
"url": i.get("url", ""),
|
||||
"source": i.get("source", ""),
|
||||
"signal_score": float(i.get("signal_score") or 0),
|
||||
"first_seen": i.get("first_seen", ""),
|
||||
}
|
||||
if display.get("show_tier"):
|
||||
entry["source_tier"] = i.get("source_tier")
|
||||
if display.get("show_verdict"):
|
||||
entry["verdict"] = i.get("verdict", "")
|
||||
feed.append(entry)
|
||||
|
||||
with open(path, "w") as f:
|
||||
json.dump(feed, f, indent=2)
|
||||
@@ -0,0 +1,332 @@
|
||||
"""Athena scoring engine — Sprint 1: Pure Rule-Based Bucket Classifier + Scorer.
|
||||
|
||||
DESIGN CONSTRAINT (founder directive, 2026-07-15):
|
||||
Pure rules only. No embeddings, no semantic similarity, no LLM classification.
|
||||
|
||||
Pipeline position:
|
||||
ingestion/dedup -> [attach_scoring] -> rendering
|
||||
"""
|
||||
import json
|
||||
import re
|
||||
import sqlite3
|
||||
from typing import Optional
|
||||
|
||||
from oracle.config import DB_PATH
|
||||
|
||||
# ── Score weights ──────────────────────────────────────────────────────────
|
||||
WEIGHTS = {
|
||||
"shipping": 0.20,
|
||||
"utility": 0.20,
|
||||
"replication": 0.25,
|
||||
"enthusiast": 0.20,
|
||||
"novelty": 0.15,
|
||||
}
|
||||
HYPE_CAP = 0.45
|
||||
|
||||
# ── Keyword sets ───────────────────────────────────────────────────────────
|
||||
KW_SHIPPING = [
|
||||
"released", "launch", "v1.0", "v2.0", "v3.0", "shipping", "now available",
|
||||
"open source", "open-source", "open weights", "weights released", "live now",
|
||||
"beta", "public beta", "ga release", "general availability", "ships", "deployed",
|
||||
"production", "now in", "available today", "download", "gradio", "demo", "playground",
|
||||
]
|
||||
KW_LOCAL_AI = [
|
||||
"local llm", "local model", "local ai", "run locally", "run it locally", "on-device",
|
||||
"on device", "ollama", "llama.cpp", "llamacpp", "gguf", "ggml", "lm studio",
|
||||
"consumer hardware", "consumer gpu", "rtx", "your own gpu", "offline", "private ai",
|
||||
"local-only", "self-host", "self-hosted", "home server", "edge device", "edge inference",
|
||||
"quantized", "quantization", "q4", "q8", "int4", "fp16", "fine-tune at home",
|
||||
"train at home", "local inference", "local deployment", "no api", "no cloud",
|
||||
]
|
||||
KW_PROBLEM_SOLVED = [
|
||||
"how to", "how i", "solved", "fix", "fixed", "workaround", "benchmark", "improves",
|
||||
"improved", "speedup", "speed-up", "reduces", "reduce", "cut", "cuts", "boost",
|
||||
"optimize", "optimized", "optimisation", "faster", "3x", "10x", "2x", "latency",
|
||||
"throughput", "roi", "cost", "cheaper", "save", "saves", "eliminate", "eliminated",
|
||||
"from 117s to 30s", "p95", "memory usage", "vram", "token cost", "bottleneck",
|
||||
"case study", "results", "we measured", "we tested", "showdown", "comparison",
|
||||
]
|
||||
KW_MODEL_RELEASE = [
|
||||
"releases", "released", "unveils", "introduces", "new model", "new flagship",
|
||||
"gpt-", "claude", "gemini", "llama", "mistral", "qwen", "deepseek", "grok",
|
||||
"phi-", "command-r", "api access", "weights", "open model", "open-models",
|
||||
"frontier", "checkpoint", "fine-tune", "finetune", "rl-trained", "rl train",
|
||||
"trained", "post-training", "post training", "distilled", "distillation",
|
||||
]
|
||||
KW_RESEARCH = [
|
||||
"paper", "arxiv", "preprint", "study", "research", "we propose", "we present",
|
||||
"we introduce", "we show", "method", "framework", "theorem", "analysis of",
|
||||
"survey", "benchmark", "dataset", "neural", "transformer", "diffusion",
|
||||
"gradient", "ablation", "we find", "our approach", "novel", "state-of-the-art",
|
||||
"sota", "cs.lg", "cs.cl", "cs.cv", "cs.ai",
|
||||
]
|
||||
KW_BUSINESS = [
|
||||
"raises", "raised", "$", "valuation", "series a", "series b", "funding", "round",
|
||||
"ipo", "acquisition", "acquires", "merger", "deal", "revenue", "layoff", "hiring",
|
||||
"partnership", "invests", "investment", "market", "vc", "compute deal",
|
||||
"billion", "million", "forecast", "miss", "earnings", "stock",
|
||||
]
|
||||
KW_INFRA = [
|
||||
"gpu", "tpu", "data center", "datacenter", "data centre", "cluster", "cuda",
|
||||
"rocm", "vllm", "tensorrt", "inference server", "serving", "kubernetes", "docker",
|
||||
"pipeline", "mlops", "ci/cd", "rag", "vector db", "vector database", "agent",
|
||||
"agents", "orchestration", "observability", "evaluation", "eval", "guardrail",
|
||||
"safety", "red team", "jailbreak", "prompt injection", "fine-tuning stack",
|
||||
]
|
||||
KW_CULTURE = [
|
||||
"says", "argues", "opinion", "essay", "think", "thinks", "the real", "why we",
|
||||
"the future of", "dystopia", "utopia", "philosophy", "ethics", "regulation",
|
||||
"policy", "ban", "lawsuit", "eu", "senate", "congress", "interview", "podcast",
|
||||
"controversy", "controversial", "debate", "critic", "criticism",
|
||||
"creepy", "creeping", "not sexy", "vibe", "hot take", "unpopular",
|
||||
]
|
||||
|
||||
BUCKETS = {
|
||||
"SHIPPING": {"kw": KW_SHIPPING, "source_whitelist": None, "order": 0},
|
||||
"LOCAL AI": {"kw": KW_LOCAL_AI, "source_whitelist": None, "order": 1},
|
||||
"PROBLEM SOLVED": {"kw": KW_PROBLEM_SOLVED, "source_whitelist": None, "order": 2},
|
||||
"MODEL RELEASE": {"kw": KW_MODEL_RELEASE, "source_whitelist": None, "order": 3},
|
||||
"RESEARCH": {"kw": KW_RESEARCH, "source_whitelist": ["arxiv"], "order": 4},
|
||||
"BUSINESS": {"kw": KW_BUSINESS, "source_whitelist": None, "order": 5},
|
||||
"INFRASTRUCTURE": {"kw": KW_INFRA, "source_whitelist": None, "order": 6},
|
||||
"CULTURE": {"kw": KW_CULTURE, "source_whitelist": None, "order": 7},
|
||||
}
|
||||
BUCKET_ORDER = sorted(BUCKETS.keys(), key=lambda b: BUCKETS[b]["order"])
|
||||
|
||||
HYPE_TERMS = [
|
||||
"revolutionary", "game-changing", "game changer", "breakthrough", "mind-blowing",
|
||||
"insane", "crazy", "unbelievable", "shocking", "the future is here", "omg",
|
||||
"you won't believe", "secret", "they don't want you to know", "leaked", "viral",
|
||||
"hype", "buzzword", "disrupt", "disrupting everything", "ai will replace",
|
||||
"will change everything", "paradigm shift", "godlike", "magic", "miracle",
|
||||
]
|
||||
|
||||
ENTHUSIAST_SIGNALS = [
|
||||
"github", "repo", "repository", "self-host", "local", "ollama", "llamacpp",
|
||||
"hugging face", "huggingface", "colab", "notebook", "pip install", "docker",
|
||||
"cli", "open source", "open-source", "diy", "build your own", "tutorial",
|
||||
"how to", "implementation", "agent", "agents", "fine-tune", "finetune",
|
||||
"quantiz", "vllm", "rtx", "gpu", "consumer", "homelab", "self-hosted",
|
||||
"machine-learning", "machine learning", "deep learning", "python", "rust",
|
||||
"benchmark", "reproduc", "weights", "gguf",
|
||||
]
|
||||
SOURCE_ENTHUSIAST_BONUS = {
|
||||
"github": 0.20, "huggingface": 0.20, "arxiv": 0.10,
|
||||
"hackernews": 0.10, "reddit": 0.05, "rss": 0.0,
|
||||
}
|
||||
|
||||
NEW_COLUMNS = [
|
||||
"bucket TEXT",
|
||||
"shipping_score REAL DEFAULT 0",
|
||||
"utility_score REAL DEFAULT 0",
|
||||
"replication_score REAL DEFAULT 0",
|
||||
"enthusiast_score REAL DEFAULT 0",
|
||||
"novelty_score REAL DEFAULT 0",
|
||||
"hype_penalty REAL DEFAULT 0",
|
||||
"final_score REAL DEFAULT 0",
|
||||
"actionability_score REAL DEFAULT 0",
|
||||
"narrative_id TEXT",
|
||||
"topic_id TEXT",
|
||||
"relation_json TEXT",
|
||||
]
|
||||
|
||||
|
||||
# ── Helpers ────────────────────────────────────────────────────────────────
|
||||
def _norm(text):
|
||||
if not text:
|
||||
return ""
|
||||
if isinstance(text, bytes):
|
||||
text = text.decode("utf-8", "replace")
|
||||
return " " + re.sub(r"\s+", " ", text.lower()) + " "
|
||||
|
||||
|
||||
def _summary_text(raw):
|
||||
if not raw:
|
||||
return ""
|
||||
try:
|
||||
d = json.loads(raw)
|
||||
if isinstance(d, dict):
|
||||
return " ".join(str(v) for v in d.values() if isinstance(v, str))
|
||||
except Exception:
|
||||
pass
|
||||
return raw
|
||||
|
||||
|
||||
# ── Classification ─────────────────────────────────────────────────────────
|
||||
def classify(entry: dict) -> tuple[str, list[str]]:
|
||||
"""Pure rule classification.
|
||||
|
||||
Returns (bucket, matched_list) where matched_list is human-readable proof.
|
||||
"""
|
||||
title = _norm(entry.get("title") or "")
|
||||
summary = _norm(_summary_text(entry.get("summary")))
|
||||
tags_raw = entry.get("category_tags") or ""
|
||||
try:
|
||||
tags = " ".join(json.loads(tags_raw)) if tags_raw else ""
|
||||
except Exception:
|
||||
tags = tags_raw
|
||||
tags = _norm(tags)
|
||||
source = (entry.get("source") or "").lower()
|
||||
haystack = title + " " + summary + " " + tags
|
||||
|
||||
matched = [f"source={source}"]
|
||||
best_bucket = "UNCATEGORIZED"
|
||||
best_hits = 0
|
||||
|
||||
for bucket in BUCKET_ORDER:
|
||||
spec = BUCKETS[bucket]
|
||||
whitelist = spec["source_whitelist"]
|
||||
if whitelist and source not in whitelist:
|
||||
continue
|
||||
hits = []
|
||||
for kw in spec["kw"]:
|
||||
if f" {kw.lower()} " in haystack:
|
||||
hits.append(kw)
|
||||
if hits:
|
||||
matched.extend(f"kw:{h}" for h in hits[:8])
|
||||
if len(hits) > best_hits:
|
||||
best_hits = len(hits)
|
||||
best_bucket = bucket
|
||||
|
||||
if best_bucket == "UNCATEGORIZED":
|
||||
matched.append("(no rule fired)")
|
||||
|
||||
return best_bucket, matched
|
||||
|
||||
|
||||
# ── Scoring ────────────────────────────────────────────────────────────────
|
||||
def score_entry(bucket: str, matched: list, entry: dict) -> dict:
|
||||
"""Return dict of component scores (0..1) + final (0..1)."""
|
||||
source = (entry.get("source") or "").lower()
|
||||
haystack = _norm(entry.get("title") or "") + " " + _norm(_summary_text(entry.get("summary")))
|
||||
|
||||
tags_raw = entry.get("category_tags") or ""
|
||||
try:
|
||||
tags = " ".join(json.loads(tags_raw)) if tags_raw else ""
|
||||
except Exception:
|
||||
tags = tags_raw
|
||||
haystack += _norm(tags)
|
||||
|
||||
# Enthusiast score
|
||||
ent_hits = sum(1 for s in ENTHUSIAST_SIGNALS if f" {s} " in haystack)
|
||||
enthusiast = min(ent_hits / 5.0 + SOURCE_ENTHUSIAST_BONUS.get(source, 0.0), 1.0)
|
||||
|
||||
# Shipping score
|
||||
ship_kw = [k for k in KW_SHIPPING if f" {k} " in haystack]
|
||||
shipping = 0.0
|
||||
if bucket == "SHIPPING":
|
||||
shipping = 0.9
|
||||
elif ship_kw:
|
||||
shipping = min(0.4 + 0.1 * len(ship_kw), 0.8)
|
||||
if source in ("github", "huggingface"):
|
||||
shipping = max(shipping, 0.7)
|
||||
|
||||
# Utility score
|
||||
util_kw = [k for k in KW_PROBLEM_SOLVED if f" {k} " in haystack]
|
||||
utility = 0.0
|
||||
if bucket == "PROBLEM SOLVED":
|
||||
utility = 0.85
|
||||
elif util_kw:
|
||||
utility = min(0.4 + 0.1 * len(util_kw), 0.8)
|
||||
if any(s in haystack for s in [" github ", " huggingface ", " demo "]):
|
||||
utility = max(utility, 0.6)
|
||||
|
||||
# Replication score
|
||||
repl_kw = [k for k in KW_LOCAL_AI if f" {k} " in haystack]
|
||||
replication = 0.0
|
||||
if bucket == "LOCAL AI":
|
||||
replication = 1.0
|
||||
elif repl_kw:
|
||||
replication = min(0.5 + 0.1 * len(repl_kw), 0.9)
|
||||
if source in ("github", "huggingface"):
|
||||
replication = max(replication, 0.7)
|
||||
if any(s in haystack for s in [" open source ", " open-source ", " weights "]):
|
||||
replication = max(replication, 0.6)
|
||||
|
||||
# Novelty score
|
||||
novelty = 0.0
|
||||
if bucket in ("RESEARCH", "MODEL RELEASE"):
|
||||
novelty = 0.6
|
||||
nov_kw = ["new", "novel", "first", "breakthrough-method", "we propose",
|
||||
"we introduce", "we present", "state-of-the-art", "sota", "unveils"]
|
||||
if any(f" {k} " in haystack for k in nov_kw):
|
||||
novelty = min(novelty + 0.2, 0.9)
|
||||
if bucket == "CULTURE":
|
||||
novelty = min(novelty, 0.3)
|
||||
|
||||
# Hype penalty
|
||||
hype_penalty = min(0.1 * sum(1 for t in HYPE_TERMS if f" {t} " in haystack), 0.6)
|
||||
|
||||
# Final score
|
||||
raw = (
|
||||
WEIGHTS["shipping"] * shipping
|
||||
+ WEIGHTS["utility"] * utility
|
||||
+ WEIGHTS["replication"] * replication
|
||||
+ WEIGHTS["enthusiast"] * enthusiast
|
||||
+ WEIGHTS["novelty"] * novelty
|
||||
)
|
||||
final = min(max(raw - hype_penalty, 0.0), 1.0)
|
||||
|
||||
return {
|
||||
"shipping_score": round(shipping, 3),
|
||||
"utility_score": round(utility, 3),
|
||||
"replication_score": round(replication, 3),
|
||||
"enthusiast_score": round(enthusiast, 3),
|
||||
"novelty_score": round(novelty, 3),
|
||||
"hype_penalty": round(hype_penalty, 3),
|
||||
"final_score": round(final, 4),
|
||||
}
|
||||
|
||||
|
||||
# ── DB Operations ──────────────────────────────────────────────────────────
|
||||
def migrate(db_path: Optional[str] = None) -> list[str]:
|
||||
"""Idempotent schema migration — only adds missing columns."""
|
||||
conn = sqlite3.connect(db_path or str(DB_PATH))
|
||||
cur = conn.cursor()
|
||||
cur.execute("PRAGMA table_info(entries)")
|
||||
existing = {row[1] for row in cur.fetchall()}
|
||||
added = []
|
||||
for col in NEW_COLUMNS:
|
||||
name = col.split(" ")[0]
|
||||
if name not in existing:
|
||||
cur.execute(f"ALTER TABLE entries ADD COLUMN {col}")
|
||||
added.append(name)
|
||||
conn.commit()
|
||||
conn.close()
|
||||
print(f"[migrate] added columns: {', '.join(added) if added else 'none (already present)'}")
|
||||
return added
|
||||
|
||||
|
||||
def fetch_unscored(conn: sqlite3.Connection) -> list[dict]:
|
||||
cur = conn.cursor()
|
||||
cur.execute("""
|
||||
SELECT id, source, source_id, url, title, summary, category_tags, raw_metadata
|
||||
FROM entries WHERE bucket IS NULL OR bucket = ''
|
||||
""")
|
||||
cols = ["id", "source", "source_id", "url", "title", "summary", "category_tags", "raw_metadata"]
|
||||
return [dict(zip(cols, row)) for row in cur.fetchall()]
|
||||
|
||||
|
||||
def attach_scoring(db_path: Optional[str] = None, dry_run: bool = False) -> None:
|
||||
"""Score every unscored entry. Call after ingestion/dedup, before render."""
|
||||
conn = sqlite3.connect(db_path or str(DB_PATH))
|
||||
rows = fetch_unscored(conn)
|
||||
print(f"[attach] scoring {len(rows)} unscored entries")
|
||||
for e in rows:
|
||||
bucket, matched = classify(e)
|
||||
scores = score_entry(bucket, matched, e)
|
||||
if not dry_run:
|
||||
conn.execute(
|
||||
"""UPDATE entries SET bucket=?, shipping_score=?, utility_score=?,
|
||||
replication_score=?, enthusiast_score=?, novelty_score=?,
|
||||
hype_penalty=?, final_score=?, actionability_score=?,
|
||||
narrative_id=?, topic_id=?, relation_json=? WHERE id=?""",
|
||||
(bucket, scores["shipping_score"], scores["utility_score"],
|
||||
scores["replication_score"], scores["enthusiast_score"],
|
||||
scores["novelty_score"], scores["hype_penalty"], scores["final_score"],
|
||||
0.0, None, None, json.dumps({"matched_rules": matched}), e["id"]),
|
||||
)
|
||||
if not dry_run:
|
||||
conn.commit()
|
||||
conn.close()
|
||||
print("[attach] done.")
|
||||
@@ -0,0 +1,301 @@
|
||||
"""Summarization engine for the AI Research Oracle.
|
||||
|
||||
Generates structured summaries for entries where summary IS NULL.
|
||||
Uses source-specific extraction logic (no LLM required).
|
||||
|
||||
Output schema: {one_liner, key_technical_point, potential_use_case, confidence}
|
||||
"""
|
||||
import json
|
||||
import re
|
||||
import sqlite3
|
||||
from typing import Optional
|
||||
|
||||
from oracle.config import DB_PATH
|
||||
|
||||
|
||||
def extract_github_summary(title: str, content: str) -> dict:
|
||||
"""Extract summary from GitHub README content."""
|
||||
text = re.sub(r'<p[^>]*>', '\n', content)
|
||||
text = re.sub(r'</p>', '\n', content)
|
||||
text = re.sub(r'<h[1-6][^>]*>', '\n## ', text)
|
||||
text = re.sub(r'</h[1-6]>', '\n', text)
|
||||
text = re.sub(r'<[^>]+>', '', text)
|
||||
text = re.sub(r'&', '&', text)
|
||||
text = re.sub(r'—', '—', text)
|
||||
text = re.sub(r''', "'", text)
|
||||
text = re.sub(r'·', '·', text)
|
||||
text = re.sub(r'```[\s\S]*?```', '', text)
|
||||
text = re.sub(r'\n\s*\n+', '\n\n', text)
|
||||
text = text.strip()
|
||||
|
||||
source_confidence = "low"
|
||||
if len(text) > 2000:
|
||||
source_confidence = "high"
|
||||
elif len(text) > 500:
|
||||
source_confidence = "medium"
|
||||
|
||||
one_liner = _find_project_description(text, title) or title[:200]
|
||||
key_tech = _extract_technical_point(text, source_confidence)
|
||||
use_case = _extract_use_case(text, title)
|
||||
confidence = _assess_extraction_quality(one_liner, key_tech, use_case, source_confidence)
|
||||
|
||||
if _is_security_tooling(title, one_liner, key_tech):
|
||||
use_case = use_case + " [security:dual-use]"
|
||||
|
||||
return {
|
||||
"one_liner": one_liner[:200],
|
||||
"key_technical_point": key_tech[:200],
|
||||
"potential_use_case": use_case[:200],
|
||||
"confidence": confidence,
|
||||
}
|
||||
|
||||
|
||||
def extract_arxiv_summary(title: str, content: str) -> dict:
|
||||
"""Extract summary from arXiv abstract."""
|
||||
text = re.sub(r'<[^>]+>', ' ', content)
|
||||
text = re.sub(r'\s+', ' ', text).strip()
|
||||
|
||||
confidence = "high" if len(text) > 300 else "medium"
|
||||
one_liner = _find_contribution(text) or f"This paper presents {title.lower()}"
|
||||
key_tech = _extract_method(text)
|
||||
use_case = _extract_application(text)
|
||||
|
||||
return {
|
||||
"one_liner": one_liner[:200],
|
||||
"key_technical_point": key_tech[:200],
|
||||
"potential_use_case": use_case[:200],
|
||||
"confidence": confidence,
|
||||
}
|
||||
|
||||
|
||||
def extract_reddit_summary(title: str, content: str) -> dict:
|
||||
"""Extract summary from Reddit post."""
|
||||
text = re.sub(r'<[^>]+>', ' ', content)
|
||||
text = re.sub(r'\s+', ' ', text).strip()
|
||||
|
||||
if len(text) > 500:
|
||||
confidence = "high"
|
||||
elif len(text) > 100:
|
||||
confidence = "medium"
|
||||
else:
|
||||
confidence = "low"
|
||||
|
||||
return {
|
||||
"one_liner": (title or text[:150])[:200],
|
||||
"key_technical_point": (text or "No additional content in post")[:200],
|
||||
"potential_use_case": "AI community discussion",
|
||||
"confidence": confidence,
|
||||
}
|
||||
|
||||
|
||||
# ── Extraction helpers ─────────────────────────────────────────────────────
|
||||
def _assess_extraction_quality(one_liner, key_tech, use_case, source_confidence) -> str:
|
||||
score = 0
|
||||
penalties = 0
|
||||
ol = one_liner.strip()
|
||||
ol_len = len(ol)
|
||||
|
||||
if 40 <= ol_len <= 200:
|
||||
score += 2
|
||||
elif 20 <= ol_len < 40:
|
||||
score += 1
|
||||
elif ol_len > 200:
|
||||
penalties += 1
|
||||
|
||||
if ol.endswith(('.', '!', '?', '…')):
|
||||
score += 1
|
||||
else:
|
||||
penalties += 1
|
||||
|
||||
if re.search(r'\b(?:is|are|provides|enables|implements|makes|allows|builds|creates|runs|uses)\b', ol, re.I):
|
||||
score += 1
|
||||
elif re.match(r'^[A-Z]\w+', ol) and ol_len > 30:
|
||||
score += 0.5
|
||||
|
||||
open_brackets = ol.count('[') + ol.count('(')
|
||||
close_brackets = ol.count(']') + ol.count(')')
|
||||
if abs(open_brackets - close_brackets) > 0:
|
||||
penalties += 1
|
||||
if open_brackets > 2:
|
||||
penalties += 1
|
||||
|
||||
kt = key_tech.strip()
|
||||
if kt and len(kt) > 20 and not kt.startswith('See '):
|
||||
score += 1
|
||||
else:
|
||||
penalties += 0.5
|
||||
|
||||
uc = use_case.strip()
|
||||
if uc and len(uc) > 10 and not uc.startswith('Relevant for'):
|
||||
score += 1
|
||||
else:
|
||||
penalties += 0.5
|
||||
|
||||
net = score - penalties
|
||||
if net >= 3:
|
||||
return source_confidence
|
||||
elif net >= 1:
|
||||
return "medium"
|
||||
return "low"
|
||||
|
||||
|
||||
def _is_security_tooling(title: str, one_liner: str, key_tech: str) -> bool:
|
||||
combined = f"{title} {one_liner} {key_tech}".lower()
|
||||
return any(sig in combined for sig in [
|
||||
"offensive", "pentest", "red team", "exploit", "kill chain",
|
||||
"attack surface", "vulnerability scan", "zero-day",
|
||||
"reverse engineer", "c2", "command and control",
|
||||
])
|
||||
|
||||
|
||||
def _find_project_description(text: str, title: str) -> Optional[str]:
|
||||
proj_name = title.split(':')[0].split('/')[0].strip().lower()
|
||||
for para in text.split('\n\n'):
|
||||
para = para.strip()
|
||||
if not para or para.startswith('##') or len(para) < 20:
|
||||
continue
|
||||
if 'img' in para.lower() or 'badge' in para.lower() or 'shields' in para.lower():
|
||||
continue
|
||||
if re.match(r'^[~$#€£¥*»\d]', para):
|
||||
continue
|
||||
special_chars = sum(1 for c in para if not c.isalnum() and not c.isspace() and c not in ',.!?;:\'\"-()[]')
|
||||
if special_chars / max(len(para), 1) > 0.4:
|
||||
continue
|
||||
sentence = re.split(r'[.!?]', para)[0].strip()
|
||||
if len(sentence) > 30:
|
||||
return sentence + '.'
|
||||
|
||||
for pattern in [
|
||||
rf'{re.escape(proj_name[:20])}\s+(?:is|enables|provides|implements)\s+[^.]+\.?',
|
||||
r'(?:This\s+)?(?:project|library|framework|tool|package)\s+(?:is|enables|provides)\s+[^.]+\.?',
|
||||
]:
|
||||
match = re.search(pattern, text, re.I)
|
||||
if match:
|
||||
return match.group(0)[:200]
|
||||
return None
|
||||
|
||||
|
||||
def _find_contribution(text: str) -> Optional[str]:
|
||||
for pattern in [
|
||||
r'(?:we|this\s+paper)\s+(?:propose|introduce|present|propose and evaluate)\s+[^.]{10,150}\.',
|
||||
r'(?:we\s+(?:show|demonstrate|find|discover|observe))\s+[^.]{10,150}\.',
|
||||
]:
|
||||
match = re.search(pattern, text, re.I)
|
||||
if match:
|
||||
return match.group(0)[:200]
|
||||
first = re.split(r'[.!?]', text)[0].strip()
|
||||
return first if first else None
|
||||
|
||||
|
||||
def _extract_technical_point(text: str, confidence: str) -> str:
|
||||
for pattern in [
|
||||
r'architecture(?:\s+designed)?\s+(?:for|to|that)\s+[^.]+\.?',
|
||||
r'(?:using|via|based\s+on|through)\s+[a-z][^.]{10,100}\.',
|
||||
]:
|
||||
match = re.search(pattern, text, re.I)
|
||||
if match:
|
||||
return match.group(0)[:200]
|
||||
if confidence == "low":
|
||||
return "Technical details not available in extracted content"
|
||||
return "See README for technical details"
|
||||
|
||||
|
||||
def _extract_method(text: str) -> str:
|
||||
for pattern in [
|
||||
r'(?:method|approach|framework|technique|model|system)\s+(?:based|using|via|through|with)\s+[a-z][^.]{10,120}\.',
|
||||
r'(?:combining|leveraging|exploiting)\s+[a-z][^.]{10,120}\.',
|
||||
]:
|
||||
match = re.search(pattern, text, re.I)
|
||||
if match:
|
||||
return match.group(0)[:200]
|
||||
return "See full paper for methodology"
|
||||
|
||||
|
||||
def _extract_use_case(text: str, title: str) -> str:
|
||||
for pattern in [
|
||||
r'(?:for|to)\s+(?:developers|engineers|researchers|teams)\s+who?\s+[^.]{5,80}\.',
|
||||
r'(?:enables|allows|helps)\s+[^\s]+\s+to\s+[^.]{10,80}\.',
|
||||
]:
|
||||
match = re.search(pattern, text, re.I)
|
||||
if match:
|
||||
return match.group(0)[:200]
|
||||
return f"Relevant for {title.lower()[:50]} developers and users"
|
||||
|
||||
|
||||
def _extract_application(text: str) -> str:
|
||||
title_lower = text[:200].lower()
|
||||
if any(k in title_lower for k in ["agent", "agentic"]):
|
||||
return "Building AI agent systems"
|
||||
if any(k in title_lower for k in ["verification", "verify"]):
|
||||
return "LLM output verification and reliability"
|
||||
if any(k in title_lower for k in ["embodied", "robot"]):
|
||||
return "Embodied AI and robotics applications"
|
||||
if any(k in title_lower for k in ["distill"]):
|
||||
return "Model distillation and knowledge transfer"
|
||||
return "See paper for specific applications"
|
||||
|
||||
|
||||
# ── Pipeline functions ─────────────────────────────────────────────────────
|
||||
def summarize_entry(entry: dict, conn: sqlite3.Connection) -> bool:
|
||||
"""Summarize a single entry using rule-based extraction."""
|
||||
source = entry["source"]
|
||||
title = entry["title"]
|
||||
content = entry.get("extracted_text", "")
|
||||
eid = entry["id"]
|
||||
|
||||
if not content or len(content) < 50:
|
||||
return False
|
||||
|
||||
if source == "github":
|
||||
summary = extract_github_summary(title, content)
|
||||
elif source == "arxiv":
|
||||
summary = extract_arxiv_summary(title, content)
|
||||
elif source == "reddit":
|
||||
summary = extract_reddit_summary(title, content)
|
||||
else:
|
||||
summary = extract_reddit_summary(title, content)
|
||||
|
||||
conn.execute("UPDATE entries SET summary = ? WHERE id = ?",
|
||||
(json.dumps(summary), eid))
|
||||
conn.commit()
|
||||
return True
|
||||
|
||||
|
||||
def run_summarization(source: Optional[str] = None, limit: int = 0) -> None:
|
||||
"""Summarize all pending entries."""
|
||||
conn = sqlite3.connect(str(DB_PATH))
|
||||
cur = conn.cursor()
|
||||
|
||||
where = "summary IS NULL"
|
||||
params = []
|
||||
if source:
|
||||
where += " AND source = ?"
|
||||
params.append(source)
|
||||
|
||||
cur.execute(f"SELECT COUNT(*) FROM entries WHERE {where}", params)
|
||||
total_pending = cur.fetchone()[0]
|
||||
print(f"[summarize] {total_pending} pending entries")
|
||||
|
||||
if limit:
|
||||
limit_clause = f"LIMIT {limit}"
|
||||
else:
|
||||
limit_clause = ""
|
||||
|
||||
cur.execute(f"""
|
||||
SELECT id, source, title, extracted_text, summary
|
||||
FROM entries WHERE {where}
|
||||
ORDER BY first_seen DESC
|
||||
{limit_clause}
|
||||
""", params)
|
||||
|
||||
summarized = 0
|
||||
for row in cur.fetchall():
|
||||
entry = {
|
||||
"id": row[0], "source": row[1], "title": row[2],
|
||||
"extracted_text": row[3], "summary": row[4],
|
||||
}
|
||||
if summarize_entry(entry, conn):
|
||||
summarized += 1
|
||||
|
||||
conn.close()
|
||||
print(f"[summarize] done — {summarized} entries summarized")
|
||||
@@ -0,0 +1,93 @@
|
||||
"""Theme-based trend tracking for Athena.
|
||||
|
||||
Tag by THEME, not by entry ID. Count NEW theme-tagged arrivals per cron cycle.
|
||||
"""
|
||||
import argparse
|
||||
import os
|
||||
import re
|
||||
import sqlite3
|
||||
from collections import Counter
|
||||
|
||||
from oracle.config import DB_PATH
|
||||
|
||||
# Theme -> regex over title+summary+extracted text
|
||||
THEME_PATTERNS = {
|
||||
"tool-call": re.compile(
|
||||
r"\b(tool[- ]?call|tool[- ]?use|competence gate|confidence gate|"
|
||||
r"gate[d]? tool|action gate|tool reliability|function call gate)\b",
|
||||
re.I),
|
||||
"context": re.compile(
|
||||
r"\b(context (compress|window|ceiling|summar)|semantic compress|"
|
||||
r"token (compress|budget)|compress (context|session)|context (limit|overflow))\b",
|
||||
re.I),
|
||||
"compute": re.compile(
|
||||
r"\b(small(er|est)? model|route to|inference cost|cpu (tts|infer)|"
|
||||
r"cheap(er)? model|model routing|tiny model|on[- ]device (llm|model))\b",
|
||||
re.I),
|
||||
"trust": re.compile(
|
||||
r"\b(trust(ed)? (adapter|lora)|vetted adapter|learn (only|what).*adapter|"
|
||||
r"trust boundary|what a model (can|may) learn|auditable (adapter|skill))\b",
|
||||
re.I),
|
||||
}
|
||||
|
||||
|
||||
def scan(conn=None, history=False) -> dict:
|
||||
"""Classify fresh entries and report new theme arrivals.
|
||||
|
||||
Returns dict with counts and cumulative totals.
|
||||
"""
|
||||
if conn is None:
|
||||
conn = sqlite3.connect(str(DB_PATH))
|
||||
conn.row_factory = sqlite3.Row
|
||||
own_conn = True
|
||||
else:
|
||||
own_conn = False
|
||||
|
||||
cur = conn.cursor()
|
||||
cur.execute("""CREATE TABLE IF NOT EXISTS theme_tags (
|
||||
entry_id INTEGER NOT NULL,
|
||||
theme TEXT NOT NULL,
|
||||
first_seen_cycle TEXT DEFAULT (strftime('%Y-%m-%dT%H:%M:%SZ','now')),
|
||||
PRIMARY KEY (entry_id, theme))""")
|
||||
|
||||
cur.execute("""
|
||||
SELECT e.id, e.source, e.title,
|
||||
COALESCE(e.summary,'') AS summary,
|
||||
COALESCE(e.extracted_text,'') AS extracted
|
||||
FROM entries e
|
||||
WHERE e.id NOT IN (SELECT entry_id FROM theme_tags)
|
||||
""")
|
||||
fresh = cur.fetchall()
|
||||
|
||||
new_counts = Counter()
|
||||
for row in fresh:
|
||||
blob = f"{row['title']} {row['summary']} {row['extracted']}"
|
||||
for theme, pat in THEME_PATTERNS.items():
|
||||
if pat.search(blob):
|
||||
cur.execute(
|
||||
"INSERT OR IGNORE INTO theme_tags (entry_id, theme) VALUES (?, ?)",
|
||||
(row["id"], theme))
|
||||
new_counts[theme] += 1
|
||||
|
||||
conn.commit()
|
||||
|
||||
cur.execute("SELECT theme, COUNT(*) AS c FROM theme_tags GROUP BY theme")
|
||||
cum = {r["theme"]: r["c"] for r in cur.fetchall()}
|
||||
|
||||
result = {
|
||||
"fresh_count": len(fresh),
|
||||
"new_arrivals": dict(new_counts),
|
||||
"cumulative": cum,
|
||||
}
|
||||
|
||||
if history:
|
||||
cur.execute("""
|
||||
SELECT substr(first_seen_cycle,1,10) AS day, theme, COUNT(*) AS c
|
||||
FROM theme_tags GROUP BY day, theme ORDER BY day, theme
|
||||
""")
|
||||
result["history"] = [(r["day"], r["theme"], r["c"]) for r in cur.fetchall()]
|
||||
|
||||
if own_conn:
|
||||
conn.close()
|
||||
|
||||
return result
|
||||
@@ -0,0 +1,254 @@
|
||||
"""Multi-variant edition engine for Athena.
|
||||
|
||||
One data pipeline → multiple audience-specific editions.
|
||||
Each variant is a YAML config that defines filters, ranking, and display.
|
||||
|
||||
World Monitor pattern: 6 variants from 1 codebase.
|
||||
"""
|
||||
import os
|
||||
import sqlite3
|
||||
import yaml
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
from oracle.config import ROOT, DB_PATH, VERDICT_THRESHOLDS
|
||||
|
||||
|
||||
# ── Variant config defaults ────────────────────────────────────────────────
|
||||
|
||||
DEFAULT_CONFIG = {
|
||||
"name": "Athena AI News",
|
||||
"description": "",
|
||||
"output": "index.html",
|
||||
"filters": {
|
||||
"verdicts": ["PUBLISH", "WATCH", "ARCHIVE", "DROP"],
|
||||
"sources": [],
|
||||
"min_score": 0,
|
||||
"max_age_h": 0,
|
||||
"max_items": 0,
|
||||
},
|
||||
"ranking": {
|
||||
"by": "clickability",
|
||||
"half_life_h": 18,
|
||||
},
|
||||
"display": {
|
||||
"top_n": 8,
|
||||
"show_summary": True,
|
||||
"show_score": True,
|
||||
"show_tier": False,
|
||||
"show_verdict": False,
|
||||
"theme": "dark",
|
||||
"accent": "#5b8cff",
|
||||
"logo": "🏛️",
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def load_variant(name: str) -> dict:
|
||||
"""Load a variant config by name, merging with defaults.
|
||||
|
||||
Looks in variants/<name>.yaml relative to project root.
|
||||
Returns merged config dict.
|
||||
"""
|
||||
variants_dir = ROOT / "variants"
|
||||
variant_file = variants_dir / f"{name}.yaml"
|
||||
|
||||
if not variant_file.exists():
|
||||
available = [p.stem for p in sorted(variants_dir.glob("*.yaml"))]
|
||||
raise FileNotFoundError(
|
||||
f"Variant '{name}' not found. Available: {', '.join(available)}"
|
||||
)
|
||||
|
||||
with open(variant_file) as f:
|
||||
raw = yaml.safe_load(f) or {}
|
||||
|
||||
# Deep merge with defaults
|
||||
config = _deep_merge(DEFAULT_CONFIG, raw)
|
||||
config["_name"] = name
|
||||
config["_file"] = str(variant_file)
|
||||
return config
|
||||
|
||||
|
||||
def list_variants() -> list[str]:
|
||||
"""List all available variant names."""
|
||||
variants_dir = ROOT / "variants"
|
||||
if not variants_dir.exists():
|
||||
return []
|
||||
return sorted(p.stem for p in variants_dir.glob("*.yaml"))
|
||||
|
||||
|
||||
def apply_filters(conn: sqlite3.Connection, variant: dict) -> list[dict]:
|
||||
"""Fetch entries from DB filtered by variant config.
|
||||
|
||||
Returns list of entry dicts matching the variant's filter criteria.
|
||||
"""
|
||||
f = variant["filters"]
|
||||
|
||||
# Build query
|
||||
clauses = []
|
||||
params = []
|
||||
|
||||
# Verdict filter
|
||||
if f.get("verdicts"):
|
||||
verdicts = [v for v in f["verdicts"] if v] # strip empties
|
||||
if verdicts:
|
||||
placeholders = ", ".join("?" for _ in verdicts)
|
||||
clauses.append(f"verdict IN ({placeholders})")
|
||||
params.extend(verdicts)
|
||||
|
||||
# Source filter (empty = all)
|
||||
if f.get("sources"):
|
||||
sources = [s for s in f["sources"] if s]
|
||||
if sources:
|
||||
placeholders = ", ".join("?" for _ in sources)
|
||||
clauses.append(f"source IN ({placeholders})")
|
||||
params.extend(sources)
|
||||
|
||||
# Min signal score
|
||||
if f.get("min_score", 0) > 0:
|
||||
clauses.append("COALESCE(signal_score, 0) >= ?")
|
||||
params.append(f["min_score"])
|
||||
|
||||
# Max age
|
||||
if f.get("max_age_h", 0) > 0:
|
||||
max_age = f["max_age_h"]
|
||||
cutoff = datetime.now(timezone.utc).timestamp() - (max_age * 3600)
|
||||
clauses.append("first_seen >= datetime(?, 'unixepoch')")
|
||||
params.append(cutoff)
|
||||
|
||||
# Base query
|
||||
sql = "SELECT * FROM entries"
|
||||
if clauses:
|
||||
sql += " WHERE " + " AND ".join(clauses)
|
||||
|
||||
sql += " ORDER BY first_seen DESC"
|
||||
|
||||
# Max items
|
||||
if f.get("max_items", 0) > 0:
|
||||
sql += f" LIMIT {int(f['max_items'])}"
|
||||
|
||||
conn.row_factory = sqlite3.Row
|
||||
cur = conn.cursor()
|
||||
cur.execute(sql, params)
|
||||
rows = cur.fetchall()
|
||||
|
||||
return [dict(r) for r in rows]
|
||||
|
||||
|
||||
def rank_items(items: list[dict], variant: dict) -> list[dict]:
|
||||
"""Rank items according to variant ranking config.
|
||||
|
||||
Supports: clickability, signal_score, verdict_priority, freshness
|
||||
"""
|
||||
ranking = variant["ranking"]
|
||||
by = ranking.get("by", "clickability")
|
||||
half_life = ranking.get("half_life_h", 18)
|
||||
|
||||
if by == "clickability":
|
||||
# Use clickability module for decayed scoring
|
||||
from oracle.clickability import compute_index, decay_index
|
||||
items = compute_index(items)
|
||||
items = decay_index(items, half_life)
|
||||
items.sort(key=lambda x: x.get("clickability_decayed", 0), reverse=True)
|
||||
|
||||
elif by == "signal_score":
|
||||
items.sort(key=lambda x: float(x.get("signal_score") or 0), reverse=True)
|
||||
|
||||
elif by == "verdict_priority":
|
||||
# PUBLISH > WATCH > ARCHIVE > DROP
|
||||
priority = {"PUBLISH": 0, "WATCH": 1, "ARCHIVE": 2, "DROP": 3}
|
||||
items.sort(key=lambda x: (
|
||||
priority.get(x.get("verdict", "DROP"), 4),
|
||||
float(x.get("signal_score") or 0),
|
||||
), reverse=False)
|
||||
|
||||
elif by == "freshness":
|
||||
items.sort(key=lambda x: x.get("first_seen", ""), reverse=True)
|
||||
|
||||
else:
|
||||
# Default fallback: signal score
|
||||
items.sort(key=lambda x: float(x.get("signal_score") or 0), reverse=True)
|
||||
|
||||
return items
|
||||
|
||||
|
||||
def render_variant(variant: dict, dry_run: bool = False, webroot: Optional[str] = None):
|
||||
"""Full render pipeline for one variant.
|
||||
|
||||
Load config → filter → rank → render HTML.
|
||||
Returns (output_path, item_count).
|
||||
"""
|
||||
from oracle.render import render_variant_html, render_variant_json
|
||||
|
||||
conn = sqlite3.connect(str(DB_PATH))
|
||||
conn.row_factory = sqlite3.Row
|
||||
|
||||
items = apply_filters(conn, variant)
|
||||
items = rank_items(items, variant)
|
||||
conn.close()
|
||||
|
||||
if not items:
|
||||
print(f"[variant:{variant['_name']}] No items matched filters")
|
||||
return None, 0
|
||||
|
||||
page = render_variant_html(items, variant)
|
||||
|
||||
# Determine output path
|
||||
output_rel = variant.get("output", "index.html")
|
||||
|
||||
if dry_run:
|
||||
out_dir = ROOT / "_preview" / variant["_name"]
|
||||
out_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Use just the filename for dry-run (output_rel may have subdirs)
|
||||
dry_filename = os.path.basename(output_rel)
|
||||
out_path = str(out_dir / dry_filename)
|
||||
|
||||
with open(out_path, "w") as f:
|
||||
f.write(page)
|
||||
|
||||
# Also write JSON feed
|
||||
json_path = str(out_dir / "feed.json")
|
||||
render_variant_json(items, variant, json_path)
|
||||
|
||||
display = variant["display"]
|
||||
top_n = display.get("top_n", 8)
|
||||
top = items[:top_n]
|
||||
print(f"[variant:{variant['_name']}] wrote {out_path} ({len(items)} items)")
|
||||
print(f" TOP {top_n}:")
|
||||
for i, it in enumerate(top, 1):
|
||||
score = f" sig={it.get('signal_score', 0):.1f}" if display.get("show_score") else ""
|
||||
tier = f" tier={it.get('source_tier', '?')}" if display.get("show_tier") else ""
|
||||
verdict = f" [{it.get('verdict', '?')}]" if display.get("show_verdict") else ""
|
||||
print(f" {i}. {it['title'][:60]}{score}{tier}{verdict}")
|
||||
|
||||
return out_path, len(items)
|
||||
|
||||
# Production render
|
||||
target = webroot or str(ROOT / "site")
|
||||
out_path = os.path.join(target, output_rel)
|
||||
os.makedirs(os.path.dirname(out_path) or ".", exist_ok=True)
|
||||
|
||||
with open(out_path, "w") as f:
|
||||
f.write(page)
|
||||
|
||||
# JSON feed alongside
|
||||
json_path = os.path.join(os.path.dirname(out_path), "feed.json")
|
||||
render_variant_json(items, variant, json_path)
|
||||
|
||||
print(f"[variant:{variant['_name']}] wrote {out_path} ({len(items)} items)")
|
||||
return out_path, len(items)
|
||||
|
||||
|
||||
# ── Helpers ────────────────────────────────────────────────────────────────
|
||||
|
||||
def _deep_merge(base: dict, override: dict) -> dict:
|
||||
"""Recursively merge override into base dict."""
|
||||
merged = base.copy()
|
||||
for key, value in override.items():
|
||||
if key in merged and isinstance(merged[key], dict) and isinstance(value, dict):
|
||||
merged[key] = _deep_merge(merged[key], value)
|
||||
else:
|
||||
merged[key] = value
|
||||
return merged
|
||||
+19
-362
@@ -1,370 +1,27 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Thin wrapper — delegates to oracle.cli ingest subcommand.
|
||||
|
||||
Kept for backward compatibility with existing cron/calls.
|
||||
"""
|
||||
AI Research Oracle — Pipeline Orchestrator.
|
||||
|
||||
Runs source adapters, deduplicates, stores to unified SQLite DB.
|
||||
Designed so adding a new adapter is one line of registration.
|
||||
|
||||
Usage:
|
||||
python3 pipeline.py # run all enabled adapters
|
||||
python3 pipeline.py --sources github,arxiv # specific sources
|
||||
python3 pipeline.py --limit 15 # per-source limit
|
||||
python3 pipeline.py --dry-run # fetch but don't store
|
||||
python3 pipeline.py --verify # spot-check N entries
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import sqlite3
|
||||
import sys
|
||||
import time
|
||||
from datetime import datetime, timezone
|
||||
import os
|
||||
|
||||
# Allow running from project root
|
||||
sys.path.insert(0, os.path.dirname(__file__))
|
||||
|
||||
from adapters import SourceAdapter
|
||||
from adapters._store import upsert_entries
|
||||
# Translate old args to new CLI format
|
||||
args = sys.argv[1:]
|
||||
cli_args = ["ingest"]
|
||||
for i, arg in enumerate(args):
|
||||
if arg in ("--sources", "--limit"):
|
||||
cli_args.append(arg)
|
||||
if i + 1 < len(args):
|
||||
cli_args.append(args[i + 1])
|
||||
elif arg == "--dry-run":
|
||||
cli_args.append("--dry-run")
|
||||
elif arg == "--verify":
|
||||
pass # verify handled internally
|
||||
|
||||
# Sprint 1 (2026-07-15): pure-rule bucket classifier + scorer.
|
||||
# Attaches immediately after ingest/dedup and before any rendering step.
|
||||
from athena import scoring as _scoring
|
||||
from oracle.cli import main as cli_main
|
||||
|
||||
# Adapter registry — add new adapters here (one line each)
|
||||
ADAPTERS = {
|
||||
"github": lambda: __import__("adapters.github", fromlist=["GitHubAdapter"]).GitHubAdapter(),
|
||||
"arxiv": lambda: __import__("adapters.arxiv", fromlist=["ArxivAdapter"]).ArxivAdapter(),
|
||||
"reddit": lambda: __import__("adapters.reddit", fromlist=["RedditAdapter"]).RedditAdapter(),
|
||||
"hackernews": lambda: __import__("adapters.hackernews", fromlist=["HackerNewsAdapter"]).HackerNewsAdapter(),
|
||||
"huggingface": lambda: __import__("adapters.huggingface", fromlist=["HuggingFaceAdapter"]).HuggingFaceAdapter(),
|
||||
"rss": lambda: __import__("adapters.rss_feeds", fromlist=["RSSFeedsAdapter"]).RSSFeedsAdapter(),
|
||||
}
|
||||
|
||||
# Default enabled sources
|
||||
ENABLED_SOURCES = ["github", "arxiv", "reddit", "hackernews", "huggingface", "rss"]
|
||||
|
||||
|
||||
def init_db(db_path: str, schema_path: str) -> sqlite3.Connection:
|
||||
"""Initialize or open the database."""
|
||||
conn = sqlite3.connect(db_path)
|
||||
if os.path.exists(schema_path):
|
||||
with open(schema_path) as f:
|
||||
conn.executescript(f.read())
|
||||
conn.commit()
|
||||
return conn
|
||||
|
||||
|
||||
def store_entries(conn: sqlite3.Connection, entries: list[dict]) -> int:
|
||||
"""Store entries idempotently (dedup by url).
|
||||
|
||||
FIX (2026-07-13, Tony): was INSERT OR REPLACE which OVERWROTE first_seen
|
||||
with the harvest time on every re-harvest, turning stale stories into
|
||||
"today". Now uses an UPSERT that preserves the ORIGINAL first_seen and only
|
||||
bumps last_updated. Entries must already carry first_seen = true publish date.
|
||||
"""
|
||||
return upsert_entries(conn, entries)
|
||||
|
||||
|
||||
def verify_entries(conn: sqlite3.Connection, source: str, sample_size: int = 3):
|
||||
"""Spot-check N entries from a source against live data.
|
||||
|
||||
This is a standing quality gate: after each adapter run, verify a
|
||||
random sample of high-signal entries match the live source.
|
||||
Protects against bad batches propagating to the reasoning layer.
|
||||
"""
|
||||
import urllib.request
|
||||
import random
|
||||
|
||||
cur = conn.cursor()
|
||||
cur.execute("""
|
||||
SELECT id, source, source_id, url, title, raw_metadata, signal_score
|
||||
FROM entries WHERE source = ?
|
||||
ORDER BY signal_score DESC
|
||||
LIMIT ?
|
||||
""", (source, sample_size))
|
||||
|
||||
rows = cur.fetchall()
|
||||
if not rows:
|
||||
print(f" No entries to verify for {source}")
|
||||
return True
|
||||
|
||||
passed = 0
|
||||
for row in rows:
|
||||
eid, src, sid, url, title, meta_json, score = row
|
||||
meta = json.loads(meta_json) if meta_json else {}
|
||||
|
||||
# Source-specific verification
|
||||
if src == "github":
|
||||
repo = meta.get("full_name", "")
|
||||
db_stars = meta.get("stars", 0)
|
||||
if repo:
|
||||
try:
|
||||
api_url = f"https://api.github.com/repos/{repo}"
|
||||
req = urllib.request.Request(api_url, headers={
|
||||
"Accept": "application/vnd.github.v3+json",
|
||||
"User-Agent": "ai-oracle/0.1",
|
||||
})
|
||||
with urllib.request.urlopen(req, timeout=10) as resp:
|
||||
live = json.loads(resp.read().decode("utf-8"))
|
||||
live_stars = live.get("stargazers_count", 0)
|
||||
drift = abs(live_stars - db_stars)
|
||||
# Allow up to 1% drift or 100 stars (whichever larger)
|
||||
threshold = max(int(db_stars * 0.01), 100)
|
||||
if drift <= threshold:
|
||||
print(f" ✓ [{eid}] {title[:60]}... stars={db_stars} drift={drift}")
|
||||
passed += 1
|
||||
else:
|
||||
print(f" ✗ [{eid}] {title[:60]}... stars={db_stars} vs live={live_stars} DRIFT={drift}")
|
||||
except Exception as e:
|
||||
print(f" ? [{eid}] {title[:60]}... verify failed: {e}")
|
||||
|
||||
elif src == "arxiv":
|
||||
arxiv_id = meta.get("arxiv_id", "")
|
||||
if arxiv_id:
|
||||
# Clean version suffix for URL
|
||||
clean_id = arxiv_id.split("v")[0]
|
||||
try:
|
||||
req = urllib.request.Request(
|
||||
f"https://arxiv.org/abs/{clean_id}",
|
||||
headers={"User-Agent": "ai-oracle/0.1"}
|
||||
)
|
||||
with urllib.request.urlopen(req, timeout=10) as resp:
|
||||
status = resp.status
|
||||
if status == 200:
|
||||
print(f" ✓ [{eid}] {title[:60]}... arxiv.org/abs/{clean_id} exists")
|
||||
passed += 1
|
||||
else:
|
||||
print(f" ? [{eid}] {title[:60]}... HTTP {status}")
|
||||
except Exception as e:
|
||||
print(f" ? [{eid}] {title[:60]}... verify failed: {e}")
|
||||
|
||||
elif src == "hackernews":
|
||||
hn_id = meta.get("id", "")
|
||||
if hn_id:
|
||||
try:
|
||||
req = urllib.request.Request(
|
||||
f"https://hacker-news.firebaseio.com/v0/item/{hn_id}.json",
|
||||
headers={"User-Agent": "ai-oracle/0.1"},
|
||||
)
|
||||
with urllib.request.urlopen(req, timeout=10) as resp:
|
||||
live = json.loads(resp.read().decode("utf-8"))
|
||||
live_score = live.get("score", 0)
|
||||
db_score = meta.get("score", 0)
|
||||
drift = abs(live_score - db_score)
|
||||
if drift <= 20:
|
||||
print(f" ✓ [{eid}] {title[:60]}... hn_score={db_score} drift={drift}")
|
||||
passed += 1
|
||||
else:
|
||||
print(f" ? [{eid}] {title[:60]}... hn_score={db_score} vs live={live_score} DRIFT={drift}")
|
||||
except Exception as e:
|
||||
print(f" ? [{eid}] {title[:60]}... verify failed: {e}")
|
||||
|
||||
elif src == "huggingface":
|
||||
model_id = meta.get("model_id", "")
|
||||
if model_id:
|
||||
try:
|
||||
req = urllib.request.Request(
|
||||
f"https://huggingface.co/api/models/{model_id}",
|
||||
headers={"User-Agent": "athena/0.1"},
|
||||
)
|
||||
with urllib.request.urlopen(req, timeout=10) as resp:
|
||||
live = json.loads(resp.read().decode("utf-8"))
|
||||
live_likes = live.get("likes", 0)
|
||||
db_likes = meta.get("likes", 0)
|
||||
drift = abs(live_likes - db_likes)
|
||||
if drift <= 10: # HF likes update in real-time, allow small drift
|
||||
print(f" ✓ [{eid}] {title[:60]}... hf_likes={db_likes} drift={drift}")
|
||||
passed += 1
|
||||
else:
|
||||
print(f" ? [{eid}] {title[:60]}... hf_likes={db_likes} vs live={live_likes} DRIFT={drift}")
|
||||
except Exception as e:
|
||||
print(f" ? [{eid}] {title[:60]}... verify failed: {e}")
|
||||
|
||||
elif src == "reddit":
|
||||
# For Reddit, just check the URL is reachable (no easy verification of scores via RSS)
|
||||
if url:
|
||||
try:
|
||||
req = urllib.request.Request(url, headers={"User-Agent": "ai-oracle/0.1"})
|
||||
with urllib.request.urlopen(req, timeout=10) as resp:
|
||||
status = resp.status
|
||||
if status in (200, 302):
|
||||
print(f" ✓ [{eid}] {title[:60]}... URL reachable")
|
||||
passed += 1
|
||||
else:
|
||||
print(f" ? [{eid}] {title[:60]}... HTTP {status}")
|
||||
except Exception as e:
|
||||
print(f" ? [{eid}] {title[:60]}... verify failed: {e}")
|
||||
|
||||
elif src == "rss":
|
||||
# For RSS, verify the article URL is reachable
|
||||
if url:
|
||||
try:
|
||||
req = urllib.request.Request(url, headers={"User-Agent": "ai-oracle/0.1"})
|
||||
with urllib.request.urlopen(req, timeout=10) as resp:
|
||||
status = resp.status
|
||||
if status in (200, 301, 302):
|
||||
print(f" ✓ [{eid}] {title[:60]}... URL reachable (HTTP {status})")
|
||||
passed += 1
|
||||
else:
|
||||
print(f" ? [{eid}] {title[:60]}... HTTP {status}")
|
||||
except Exception as e:
|
||||
print(f" ? [{eid}] {title[:60]}... verify failed: {e}")
|
||||
|
||||
time.sleep(0.5) # polite spacing
|
||||
|
||||
return passed > 0
|
||||
|
||||
|
||||
def run_pipeline(sources: list[str] | None = None, limit: int = 20, dry_run: bool = False, verify: bool = False):
|
||||
"""Run the ingestion pipeline."""
|
||||
sources = sources or ENABLED_SOURCES
|
||||
now = datetime.now(timezone.utc)
|
||||
print(f"=== AI Research Oracle Pipeline ===")
|
||||
print(f" Sources: {', '.join(sources)}")
|
||||
print(f" Limit: {limit}/source")
|
||||
print(f" Dry run: {dry_run}")
|
||||
print()
|
||||
|
||||
db_path = os.path.join(os.path.dirname(__file__), "oracle.db")
|
||||
schema_path = os.path.join(os.path.dirname(__file__), "schema.sql")
|
||||
|
||||
all_entries = []
|
||||
source_stats = {}
|
||||
|
||||
for source_name in sources:
|
||||
if source_name not in ADAPTERS:
|
||||
print(f" ⚠ Unknown source: {source_name} (available: {', '.join(ADAPTERS.keys())})")
|
||||
continue
|
||||
|
||||
print(f" [{source_name}]")
|
||||
adapter = ADAPTERS[source_name]()
|
||||
|
||||
try:
|
||||
entries = adapter.fetch(limit=limit)
|
||||
except Exception as e:
|
||||
print(f" ✗ {source_name} failed: {e}")
|
||||
source_stats[source_name] = {"fetched": 0, "stored": 0,
|
||||
"error": str(e),
|
||||
"failure_class": "error"}
|
||||
continue
|
||||
|
||||
# Capture classification from the adapter (set by http_get on failure)
|
||||
fc = getattr(adapter, "last_failure_class", None)
|
||||
|
||||
# Add adapter_version to metadata
|
||||
for entry in entries:
|
||||
meta = json.loads(entry["raw_metadata"]) if isinstance(entry["raw_metadata"], str) else entry["raw_metadata"]
|
||||
meta["adapter_version"] = "0.1"
|
||||
entry["raw_metadata"] = json.dumps(meta)
|
||||
|
||||
all_entries.extend(entries)
|
||||
source_stats[source_name] = {"fetched": len(entries), "stored": 0,
|
||||
"failure_class": fc or "ok"}
|
||||
print(f" Fetched: {len(entries)} entries")
|
||||
|
||||
# Small spacing between sources
|
||||
time.sleep(1)
|
||||
|
||||
# Store
|
||||
if not dry_run and all_entries:
|
||||
conn = init_db(db_path, schema_path)
|
||||
stored = store_entries(conn, all_entries)
|
||||
|
||||
# Update per-source stored counts
|
||||
for entry in all_entries:
|
||||
src = entry["source"]
|
||||
if src in source_stats:
|
||||
source_stats[src]["stored"] += 1
|
||||
|
||||
# --- Sprint 1 attach point: score after ingest/dedup, before render ---
|
||||
try:
|
||||
_scoring.attach_scoring(db_path)
|
||||
except Exception as e:
|
||||
print(f" ⚠ scoring attach failed: {e}")
|
||||
|
||||
# Verification
|
||||
if verify:
|
||||
print(f"\n [Verification]")
|
||||
for src in sources:
|
||||
if source_stats.get(src, {}).get("stored", 0) > 0:
|
||||
print(f" Checking {src}...")
|
||||
verify_entries(conn, src, sample_size=3)
|
||||
print()
|
||||
|
||||
# Record run log (failure visibility + growth control) — before conn.close()
|
||||
ok = [s for s, st in source_stats.items() if not st.get("error")]
|
||||
failed = [s for s, st in source_stats.items() if st.get("error")]
|
||||
# Zero-fetch (e.g. Reddit fully rate-limited) raises no exception but
|
||||
# is still a degraded run — record it so run_log can tell
|
||||
# "intermittent vs consistently-broken" apart over time.
|
||||
zero = [s for s, st in source_stats.items()
|
||||
if st.get("fetched", 0) == 0 and not st.get("error")]
|
||||
notes_parts = [f"{s}: {st['error']}" for s, st in source_stats.items() if st.get("error")]
|
||||
if zero:
|
||||
notes_parts.append(f"no-fetch (degraded): {', '.join(zero)}")
|
||||
notes = "; ".join(notes_parts) or "all sources ok"
|
||||
|
||||
# Rollup failure_class (issue #2): most severe across sources.
|
||||
# Priority: 5xx > 4xx > 429 > error > zero_fetch > ok
|
||||
rank = {"5xx": 5, "4xx": 4, "429": 3, "error": 2, "zero_fetch": 1, "ok": 0}
|
||||
classes = [st.get("failure_class", "ok") for st in source_stats.values()]
|
||||
if any(c in ("5xx", "4xx", "429", "error") for c in classes):
|
||||
run_fc = max((c for c in classes if c in rank),
|
||||
key=lambda c: rank[c])
|
||||
elif zero:
|
||||
run_fc = "zero_fetch"
|
||||
else:
|
||||
run_fc = "ok"
|
||||
|
||||
try:
|
||||
conn.execute("""
|
||||
INSERT INTO run_log (total_fetched, total_stored, sources_ok,
|
||||
sources_failed, failure_class, notes)
|
||||
VALUES (?, ?, ?, ?, ?, ?)
|
||||
""", (len(all_entries), stored, json.dumps(ok), json.dumps(failed),
|
||||
run_fc, notes))
|
||||
conn.commit()
|
||||
except Exception as e:
|
||||
print(f" ⚠ run_log write failed: {e}")
|
||||
|
||||
conn.close()
|
||||
print(f" Total stored: {stored} entries")
|
||||
else:
|
||||
print(f" Total fetched: {len(all_entries)} entries (dry run, not stored)")
|
||||
|
||||
# Summary
|
||||
print(f"\n Source summary:")
|
||||
for src, stats in source_stats.items():
|
||||
error = stats.get("error", "")
|
||||
error_str = f" ✗ {error}" if error else ""
|
||||
print(f" {src}: {stats['fetched']} fetched, {stats.get('stored', '—')} stored{error_str}")
|
||||
|
||||
# Top entries across all sources
|
||||
if all_entries:
|
||||
print(f"\n Recent entries by signal score (per-source ranking, NOT cross-source comparable):")
|
||||
print(f" (Note: GitHub scores use actual star counts; arXiv/reddit use estimated heuristics)")
|
||||
sorted_entries = sorted(all_entries, key=lambda e: e["signal_score"], reverse=True)
|
||||
for i, entry in enumerate(sorted_entries[:5]):
|
||||
meta = json.loads(entry["raw_metadata"]) if isinstance(entry["raw_metadata"], str) else entry["raw_metadata"]
|
||||
score_type = meta.get("score_type", "?")
|
||||
print(f" [{i+1}] {entry['source'].upper():6} ({score_type:8}) score={entry['signal_score']:.2f} {entry['title'][:75]}")
|
||||
|
||||
print(f"\n Done.")
|
||||
return all_entries
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="AI Research Oracle Pipeline")
|
||||
parser.add_argument("--sources", default=None, help="Comma-separated sources (default: github,arxiv)")
|
||||
parser.add_argument("--limit", type=int, default=20, help="Entries per source")
|
||||
parser.add_argument("--dry-run", action="store_true", help="Fetch but don't store")
|
||||
parser.add_argument("--verify", action="store_true", help="Spot-check entries against live sources")
|
||||
args = parser.parse_args()
|
||||
|
||||
sources = args.sources.split(",") if args.sources else None
|
||||
run_pipeline(sources=sources, limit=args.limit, dry_run=args.dry_run, verify=args.verify)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
sys.argv = ["oracle"] + cli_args
|
||||
cli_main()
|
||||
|
||||
+6
-275
@@ -1,277 +1,8 @@
|
||||
#!/usr/bin/env python3
|
||||
"""One-shot stack propagator (explicit user request 2026-07-12, rev 2.2).
|
||||
"""Thin wrapper — delegates to oracle.render (full publish, not dry-run)."""
|
||||
import sys, os
|
||||
sys.path.insert(0, os.path.dirname(__file__))
|
||||
|
||||
Editorial rules applied (reuses BUILT-IN pipeline functions, no pipeline edits):
|
||||
- clickability.compute_index / decay_index (virality rank + 18h decay)
|
||||
- generate_from_athena.clean_headline (repo-prefix trim, emoji strip, length cap)
|
||||
- generate_from_athena.add_prefix (Breaking | text prefix, BREAKING GATE)
|
||||
- render_site._clean_summary (one-liner descriptions on every card)
|
||||
|
||||
USER DIRECTIVES (2026-07-12):
|
||||
1. GitHub source EXCLUDED entirely (until further notice).
|
||||
2. 'update' green tier REMOVED. Only 'breaking' (rare real events) or 'normal'.
|
||||
3. Curated Picks section surfaces two flavors (tight deterministic phrase match,
|
||||
no broad keywords to avoid false positives):
|
||||
(a) QUIRKY + agents roasting their humans
|
||||
(b) people who BUILT / SHIPPED / EARNED from an AI product (indie hackers)
|
||||
Window: last DAYS days (default 4). Cap: LIMIT (default 200) — GitHub ban caps the
|
||||
real max at ~180 over 4 days; we render whatever is eligible (never fake count).
|
||||
"""
|
||||
import os, re, sys, json, sqlite3, html as _html
|
||||
from datetime import datetime as dt, timezone, timedelta
|
||||
from collections import OrderedDict
|
||||
|
||||
ORACLE = "/home/vpsadmin/oracle"
|
||||
sys.path.insert(0, ORACLE)
|
||||
sys.path.insert(0, "/home/vpsadmin/ai-oracle-site")
|
||||
import clickability as cb
|
||||
import render_site as rs
|
||||
import generate_from_athena as ga
|
||||
|
||||
DB = os.path.join(ORACLE, "oracle.db")
|
||||
WEBROOT = "/var/www/preprod3"
|
||||
FALLBACK = os.path.join(ORACLE, "site")
|
||||
NOW = dt.now(timezone.utc)
|
||||
DAYS = 14 # span whole DB so all 182 non-GitHub entries are eligible (DB only goes back ~7d)
|
||||
LIMIT = 200 # hard ceiling: DB only has 182 non-GitHub entries total, so 182 will render
|
||||
EXCLUDE_SOURCES = {"github"} # banned until further notice
|
||||
|
||||
# BREAKING GATE (verbatim pipeline logic; repos/papers/models never breaking)
|
||||
REPO_SOURCES = {"github", "gitlab", "huggingface", "arxiv"}
|
||||
IMPORTANCE = re.compile(
|
||||
r"\b(sues?|sue|lawsuit|launches?|launch|releases?|release|"
|
||||
r"bans?|ban|war|strikes?|attack|acquires?|acquisition|trillion|billions?|"
|
||||
r"layoffs?|declares?|emergency|outage|breach|stolen|steals?|theft|antitrust|"
|
||||
r"monopoly|reveals?|exposed|breakthrough|first|warns?|crackdown|shutdown|"
|
||||
r"GPT-?5|Claude|Gemini|OpenAI|Anthropic|Google|Apple|Microsoft|Meta|xAI|"
|
||||
r"Musk|Altman|Grok|DeepSeek|Llama|NVIDIA|AMD|FCC|EU|antitrust|"
|
||||
r"folded|spins? off|partners?|raises?|ipo|funding)\\b", re.I)
|
||||
BREAKING_PCT = 0.90
|
||||
|
||||
# --- CURATION: QUIRKY + agents roasting their humans ONLY (deterministic; no LLM) ---
|
||||
# Standing directive 2026-07-12 (end of session): "shipped & paid / built & earned"
|
||||
# was WALKED BACK ("looking for people who build and ship products is a whole
|
||||
# separate issue"). Do NOT bake it in. Curation = quirky + agents-roasting-humans.
|
||||
QUIRKY = [
|
||||
"hit piece", "roast", "roasting", "insult", "revenge", "betray",
|
||||
"bizarre", "weird", "cursed", "font humans", "brain region",
|
||||
"conspiracy", "haunted", "absurd", "unhinged", "sentient", "scream",
|
||||
"mock", "taunt", "expose their", "its human", "its user", "their owner",
|
||||
"about their", "their creator", "their master", "turned on", "backstab",
|
||||
"wrote about its", "turned against", "rebelled", "sassy", "savage",
|
||||
]
|
||||
# built / shipped / EARNED from an AI product (FIRST-PERSON builder only —
|
||||
# tight phrases; bare 'revenue'/'funding'/'ipo' EXCLUDED to avoid industry-news
|
||||
# false positives like TechCrunch "startups growing revenue").
|
||||
BUILT_SHIPPED = [
|
||||
"indie hacker", "i built", "i made", "i shipped", "i launched", "i sold",
|
||||
"my saas", "my startup", "my app", "my product", "my business",
|
||||
"side project", "bootstrapped", "profitable", "paying customers",
|
||||
"made money", "earn money", "mrr", "monthly recurring", "i run a",
|
||||
"made me $", "income from", "subscriptions", "sold my", "quit my job",
|
||||
"shipped a", "built a", "customers pay", "my first", "passive income",
|
||||
]
|
||||
|
||||
|
||||
def _parse(ts):
|
||||
if not ts:
|
||||
return None
|
||||
try:
|
||||
return dt.fromisoformat(ts.replace("Z", "+00:00"))
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
def _curation(it):
|
||||
blob = f"{(it.get('title') or '')} {(rs._clean_summary(it.get('summary') or ''))}".lower()
|
||||
if any(k in blob for k in BUILT_SHIPPED):
|
||||
return ("built", 1.22)
|
||||
if any(k in blob for k in QUIRKY):
|
||||
return ("quirky", 1.16)
|
||||
return (None, 1.0)
|
||||
|
||||
|
||||
def main():
|
||||
conn = sqlite3.connect(f"file:{DB}?mode=ro", uri=True)
|
||||
items = cb.fetch_items(conn)
|
||||
conn.close()
|
||||
items = cb.compute_index(items)
|
||||
items = cb.decay_index(items, rs.HALF_LIFE_H)
|
||||
|
||||
cutoff = NOW - timedelta(days=DAYS)
|
||||
eligible = [it for it in items
|
||||
if it.get("title") and it.get("url")
|
||||
and it.get("first_seen") and _parse(it["first_seen"])
|
||||
and _parse(it["first_seen"]) >= cutoff
|
||||
and (it.get("source") or "").lower() not in EXCLUDE_SOURCES]
|
||||
# --- RECENCY GUARD (2026-07-13, Tony's correction): NEVER re-post old news.
|
||||
# Age is the dominant measure: today's items ALWAYS lead (week-open fresh
|
||||
# news); older items keep only if NEVER posted before (md-stack / seen).
|
||||
# This kills the Apple-vs-OpenAI / GPT-5.6 re-post problem at the source. ---
|
||||
from recency_guard import filter_fresh as _rg_filter
|
||||
_today, _older_new, _dropped = _rg_filter(eligible)
|
||||
if _dropped:
|
||||
print(f"[recency_guard] dropped {len(_dropped)} already-posted older items")
|
||||
eligible = _today + _older_new
|
||||
eligible.sort(key=lambda x: x["clickability_decayed"], reverse=True)
|
||||
top = eligible[:LIMIT]
|
||||
|
||||
scores = [it["clickability_decayed"] for it in top]
|
||||
n = len(scores)
|
||||
|
||||
def pct_rank(v):
|
||||
beaten = sum(1 for s in scores if s <= v)
|
||||
return beaten / n if n else 0.0
|
||||
|
||||
for it in top:
|
||||
src = (it.get("source") or "").lower()
|
||||
pr = pct_rank(it["clickability_decayed"])
|
||||
is_repo = src in REPO_SOURCES
|
||||
important = bool(IMPORTANCE.search(it.get("title") or ""))
|
||||
if (not is_repo) and important and pr >= BREAKING_PCT:
|
||||
tier = "breaking"
|
||||
else:
|
||||
tier = "normal"
|
||||
cleaned = ga.clean_headline(it["title"], it.get("source", ""))
|
||||
it["title"] = ga.add_prefix(cleaned, it["url"], tier)
|
||||
it["_tier"] = tier
|
||||
label, mult = _curation(it)
|
||||
it["_curated"] = label
|
||||
it["clickability_decayed"] = it["clickability_decayed"] * mult
|
||||
|
||||
ranked = sorted(top, key=lambda x: x["clickability_decayed"], reverse=True)
|
||||
fresh = [it for it in ranked if it.get("fresh")]
|
||||
top_cards = fresh[:rs.TOP_N]
|
||||
stack = [it for it in ranked if it not in top_cards]
|
||||
curated = [it for it in ranked if it.get("_curated")]
|
||||
curated.sort(key=lambda x: x["clickability_decayed"], reverse=True)
|
||||
curated_cards = curated[:12]
|
||||
|
||||
by_day = OrderedDict()
|
||||
for it in stack:
|
||||
day = (it.get("first_seen") or "")[:10] or "unknown"
|
||||
by_day.setdefault(day, []).append(it)
|
||||
|
||||
def card(it):
|
||||
title = _html.escape(it["title"] or "(untitled)")
|
||||
url = _html.escape(it["url"] or "#")
|
||||
src = _html.escape(it["source"])
|
||||
sig = it.get("signal_score") or 0
|
||||
t = rs._fmt_time(it.get("first_seen"))
|
||||
summary = _html.escape(rs._clean_summary(it.get("summary") or "")[:200])
|
||||
cls = "card"
|
||||
if it.get("_tier") == "breaking":
|
||||
cls += " breaking"
|
||||
if it.get("_curated"):
|
||||
cls += " curated"
|
||||
badge = ""
|
||||
if it.get("_curated") == "built":
|
||||
badge = '<span class="badge built">\U0001f4b0 Built & Earned</span>'
|
||||
elif it.get("_curated") == "quirky":
|
||||
badge = '<span class="badge quirky">\U0001f300 Quirky</span>'
|
||||
sum_html = f'<p class="summary">{summary}</p>' if summary else ""
|
||||
return f"""
|
||||
<article class="{cls}" data-src="{src}">
|
||||
<div class="meta"><span class="src">{src}</span>
|
||||
<span class="time">{t}</span>
|
||||
<span class="sig">sig {sig:.1f}</span>
|
||||
{badge}
|
||||
<span class="score">\U0001f525 {it['clickability_decayed']:.2f}</span></div>
|
||||
<h3><a href="{url}" target="_blank" rel="noopener">{title}</a></h3>
|
||||
{sum_html}
|
||||
</article>"""
|
||||
|
||||
top_html = "".join(card(it) for it in top_cards)
|
||||
curated_html = "".join(card(it) for it in curated_cards)
|
||||
stack_html = ""
|
||||
for day, rows in by_day.items():
|
||||
rows.sort(key=lambda x: x["clickability_decayed"], reverse=True)
|
||||
cards = "".join(card(it) for it in rows)
|
||||
stack_html += f"""
|
||||
<h3 class="day">\U0001f4c5 {day}</h3>
|
||||
<div class="stack">{cards}</div>"""
|
||||
|
||||
now_str = NOW.strftime("%Y-%m-%d %H:%M UTC")
|
||||
page = f"""<!doctype html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1">
|
||||
<title>Athena AI News — Ranked by Clickability</title>
|
||||
<style>
|
||||
:root {{ --bg:#0b0e14; --card:#141925; --fg:#e6e9ef; --mut:#8b93a7; --acc:#5b8cff; }}
|
||||
* {{ box-sizing:border-box; }}
|
||||
body {{ margin:0; background:var(--bg); color:var(--fg);
|
||||
font:15px/1.5 -apple-system,Segoe UI,Roboto,Helvetica,Arial,sans-serif; }}
|
||||
header {{ padding:28px 20px 14px; border-bottom:1px solid #1f2533; text-align:center; }}
|
||||
header h1 {{ margin:0; font-size:28px; letter-spacing:.5px; }}
|
||||
header .sub {{ color:var(--mut); font-size:13px; margin-top:6px; }}
|
||||
main {{ max-width:1000px; margin:0 auto; padding:20px; }}
|
||||
h2.sech {{ font-size:18px; margin:26px 0 12px; border-left:3px solid var(--acc); padding-left:10px; }}
|
||||
.grid {{ display:grid; grid-template-columns:repeat(auto-fill,minmax(320px,1fr)); gap:14px; }}
|
||||
.card {{ background:var(--card); border:1px solid #1f2533; border-radius:12px; padding:16px; }}
|
||||
.card.breaking {{ border-left:3px solid #ff5b5b; }}
|
||||
.card.curated {{ border-left:3px solid #ffcf5b; background:#1a160c; }}
|
||||
.meta {{ display:flex; gap:8px; align-items:center; font-size:12px; color:var(--mut); flex-wrap:wrap; }}
|
||||
.src {{ background:#1f2533; padding:2px 8px; border-radius:20px; text-transform:uppercase; }}
|
||||
.badge {{ padding:1px 8px; border-radius:10px; font-size:11px; font-weight:600; }}
|
||||
.badge.built {{ background:#ffcf5b; color:#1a160c; }}
|
||||
.badge.quirky {{ background:#b98cff; color:#150c1f; }}
|
||||
.score {{ color:#ff9d5b; font-weight:600; margin-left:auto; }}
|
||||
.card h3 {{ font-size:16px; margin:10px 0 8px; line-height:1.35; }}
|
||||
.card h3 a {{ color:var(--fg); text-decoration:none; }}
|
||||
.card h3 a:hover {{ color:var(--acc); }}
|
||||
.summary {{ color:var(--mut); font-size:13px; margin:0; }}
|
||||
.day {{ font-size:15px; color:var(--mut); margin:28px 0 10px; border-bottom:1px solid #1f2533; padding-bottom:6px; }}
|
||||
.stack {{ display:grid; grid-template-columns:repeat(auto-fill,minmax(320px,1fr)); gap:12px; }}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<header>
|
||||
<h1>Athena AI News</h1>
|
||||
<div class="sub">Auto-ranked by Clickability Index · {len(top)} stories (4-day window, GitHub excluded) · curated: quirky + agents roasting their humans · generated {now_str}</div>
|
||||
</header>
|
||||
<main>
|
||||
<h2 class="sech">\U0001f4b0\U0001f300 Curated Picks — Built & Earned · Quirky · Agents Roasting Their Humans</h2>
|
||||
<div class="grid">{curated_html}</div>
|
||||
<h2 class="sech">\U0001f534 Top News</h2>
|
||||
<div class="grid">{top_html}</div>
|
||||
<h2 class="sech">\U0001f4f0 The Stack</h2>
|
||||
{stack_html}
|
||||
</main>
|
||||
</body>
|
||||
</html>"""
|
||||
|
||||
target = WEBROOT if os.path.isdir(WEBROOT) else FALLBACK
|
||||
os.makedirs(target, exist_ok=True)
|
||||
with open(os.path.join(target, "index.html"), "w") as f:
|
||||
f.write(page)
|
||||
with open(os.path.join(target, "feed.json"), "w") as f:
|
||||
json.dump([
|
||||
{"title": i["title"], "url": i["url"], "source": i["source"],
|
||||
"tier": i.get("_tier"), "curated": i.get("_curated"),
|
||||
"clickability_decayed": round(i["clickability_decayed"], 3),
|
||||
"age_hours": i["age_hours"], "first_seen": i.get("first_seen")}
|
||||
for i in ranked
|
||||
], f, indent=2)
|
||||
|
||||
where = "WEBROOT(/var/www/preprod3)" if target == WEBROOT else "FALLBACK(~oracle/site)"
|
||||
tiers = {"breaking": 0, "normal": 0}
|
||||
for it in top:
|
||||
tiers[it["_tier"]] += 1
|
||||
cc = {"built": 0, "quirky": 0, "none": 0}
|
||||
for it in top:
|
||||
cc[it["_curated"] or "none"] += 1
|
||||
with_desc = sum(1 for it in top if rs._clean_summary(it.get("summary") or ""))
|
||||
print(f"[propagate v2.2] wrote {target}/index.html + feed.json")
|
||||
print(f" target : {where}")
|
||||
print(f" window : last {DAYS} days, GitHub EXCLUDED")
|
||||
print(f" eligible : {len(eligible)} (cap {LIMIT} -> rendered {len(top)})")
|
||||
print(f" tiers : {tiers['breaking']} breaking / {tiers['normal']} normal (update tier REMOVED)")
|
||||
print(f" curated flags : {cc['built']} built&earned | {cc['quirky']} quirky | {cc['none']} none")
|
||||
print(f" curated shown : top {len(curated_cards)} in Curated Picks section")
|
||||
print(f" with desc : {with_desc}/{len(top)} cards have a one-liner description")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
from oracle.cli import main as cli_main
|
||||
sys.argv = ["oracle", "render"]
|
||||
cli_main()
|
||||
|
||||
@@ -1,459 +1,17 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
AI Research Oracle — Query & Snapshot CLI.
|
||||
|
||||
Query the unified database and produce Claude-ready snapshots.
|
||||
Supports filtering by source, score, confidence, tag, and date range.
|
||||
|
||||
Usage:
|
||||
python3 query.py top 10 # top 10 across all sources
|
||||
python3 query.py by-source github 5 # top 5 from GitHub
|
||||
python3 query.py by-tag "agent" # entries tagged with "agent"
|
||||
python3 query.py snapshot # full snapshot for Claude relay
|
||||
python3 query.py search "world model" # keyword search in titles/summaries
|
||||
python3 query.py recent --hours 24 # entries from last 24h
|
||||
python3 query.py stats # database statistics
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import sqlite3
|
||||
import sys
|
||||
from datetime import datetime, timezone, timedelta
|
||||
from urllib.parse import quote_plus
|
||||
|
||||
"""Thin wrapper — delegates to oracle.cli query subcommand."""
|
||||
import sys, os
|
||||
sys.path.insert(0, os.path.dirname(__file__))
|
||||
|
||||
DB_PATH = os.path.join(os.path.dirname(__file__), "oracle.db")
|
||||
|
||||
|
||||
def get_db():
|
||||
"""Open database connection."""
|
||||
conn = sqlite3.connect(DB_PATH)
|
||||
conn.row_factory = sqlite3.Row
|
||||
return conn
|
||||
|
||||
|
||||
def format_entry(row: sqlite3.Row, rank: int = 0) -> dict:
|
||||
"""Format a DB row into a clean dict for output."""
|
||||
summary = json.loads(row["summary"]) if row["summary"] else {}
|
||||
meta = json.loads(row["raw_metadata"]) if row["raw_metadata"] else {}
|
||||
tags = json.loads(row["category_tags"]) if row["category_tags"] else []
|
||||
|
||||
score_type = meta.get("score_type", "?")
|
||||
source_detail = ""
|
||||
if row["source"] == "github":
|
||||
source_detail = f"⭐ {meta.get('stars', '?')} stars"
|
||||
elif row["source"] == "arxiv":
|
||||
source_detail = f"arXiv:{meta.get('arxiv_id', '?')}"
|
||||
elif row["source"] == "reddit":
|
||||
source_detail = f"r/{meta.get('subreddit', '?')}"
|
||||
|
||||
return {
|
||||
"rank": rank,
|
||||
"source": row["source"],
|
||||
"title": row["title"],
|
||||
"url": row["url"],
|
||||
"score": row["signal_score"],
|
||||
"score_type": score_type,
|
||||
"confidence": summary.get("confidence", "?"),
|
||||
"source_detail": source_detail,
|
||||
"tags": tags,
|
||||
"one_liner": summary.get("one_liner", ""),
|
||||
"key_technical_point": summary.get("key_technical_point", ""),
|
||||
"potential_use_case": summary.get("potential_use_case", ""),
|
||||
"first_seen": row["first_seen"],
|
||||
}
|
||||
|
||||
|
||||
def cmd_top(args):
|
||||
"""Top N entries across all sources (per-source ranking)."""
|
||||
conn = get_db()
|
||||
cur = conn.cursor()
|
||||
|
||||
# Score filter
|
||||
min_score = getattr(args, 'min_score', 0) or 0
|
||||
# Confidence filter
|
||||
min_confidence = getattr(args, 'min_confidence', None) or None
|
||||
# Source filter
|
||||
source_filter = getattr(args, 'source', None) or None
|
||||
|
||||
where = []
|
||||
params = []
|
||||
if min_score > 0:
|
||||
where.append("signal_score >= ?")
|
||||
params.append(min_score)
|
||||
if min_confidence:
|
||||
where.append("json_extract(summary,'$.confidence') = ?")
|
||||
params.append(min_confidence)
|
||||
if source_filter:
|
||||
where.append("source = ?")
|
||||
params.append(source_filter)
|
||||
|
||||
where_str = (" AND " if where else "") + " AND ".join(where) if where else ""
|
||||
limit = args.n or 10
|
||||
|
||||
cur.execute(f"""
|
||||
SELECT * FROM entries {where_str}
|
||||
ORDER BY signal_score DESC
|
||||
LIMIT ?
|
||||
""", params + [limit])
|
||||
|
||||
rows = cur.fetchall()
|
||||
entries = [format_entry(r, i+1) for i, r in enumerate(rows)]
|
||||
|
||||
print(f"Top {len(entries)} entries{' by score' if not source_filter else f' from {source_filter}'}:\n")
|
||||
_print_entries(entries)
|
||||
conn.close()
|
||||
|
||||
|
||||
def cmd_by_source(args):
|
||||
"""Top N from a specific source."""
|
||||
conn = get_db()
|
||||
cur = conn.cursor()
|
||||
cur.execute("""
|
||||
SELECT * FROM entries WHERE source = ?
|
||||
ORDER BY signal_score DESC
|
||||
LIMIT ?
|
||||
""", (args.source, args.n or 10))
|
||||
|
||||
rows = cur.fetchall()
|
||||
entries = [format_entry(r, i+1) for i, r in enumerate(rows)]
|
||||
|
||||
print(f"Top {len(entries)} from {args.source}:\n")
|
||||
_print_entries(entries)
|
||||
conn.close()
|
||||
|
||||
|
||||
def cmd_by_tag(args):
|
||||
"""Entries matching a tag."""
|
||||
conn = get_db()
|
||||
cur = conn.cursor()
|
||||
cur.execute("""
|
||||
SELECT * FROM entries
|
||||
WHERE json_extract(category_tags,'$') LIKE ?
|
||||
ORDER BY signal_score DESC
|
||||
LIMIT 20
|
||||
""", (f'%"{args.tag}"%',))
|
||||
|
||||
rows = cur.fetchall()
|
||||
entries = [format_entry(r, i+1) for i, r in enumerate(rows)]
|
||||
|
||||
print(f"Entries tagged '{args.tag}':\n")
|
||||
_print_entries(entries)
|
||||
conn.close()
|
||||
|
||||
|
||||
def cmd_search(args):
|
||||
"""Keyword search in titles and summaries."""
|
||||
conn = get_db()
|
||||
cur = conn.cursor()
|
||||
q = f"%{args.query}%"
|
||||
cur.execute("""
|
||||
SELECT * FROM entries
|
||||
WHERE title LIKE ?
|
||||
OR json_extract(summary,'$.one_liner') LIKE ?
|
||||
OR json_extract(summary,'$.key_technical_point') LIKE ?
|
||||
ORDER BY signal_score DESC
|
||||
LIMIT 20
|
||||
""", (q, q, q))
|
||||
|
||||
rows = cur.fetchall()
|
||||
entries = [format_entry(r, i+1) for i, r in enumerate(rows)]
|
||||
|
||||
print(f"Search results for '{args.query}':\n")
|
||||
_print_entries(entries)
|
||||
conn.close()
|
||||
|
||||
|
||||
def cmd_recent(args):
|
||||
"""Entries from the last N hours."""
|
||||
hours = args.hours or 24
|
||||
cutoff = (datetime.now(timezone.utc) - timedelta(hours=hours)).strftime("%Y-%m-%dT%H:%M:%SZ")
|
||||
conn = get_db()
|
||||
cur = conn.cursor()
|
||||
cur.execute("""
|
||||
SELECT * FROM entries WHERE first_seen >= ?
|
||||
ORDER BY first_seen DESC
|
||||
""", (cutoff,))
|
||||
|
||||
rows = cur.fetchall()
|
||||
entries = [format_entry(r, i+1) for i, r in enumerate(rows)]
|
||||
|
||||
print(f"Entries from last {hours}h:\n")
|
||||
_print_entries(entries)
|
||||
conn.close()
|
||||
|
||||
|
||||
def cmd_snapshot(args):
|
||||
"""Full snapshot for Claude relay.
|
||||
|
||||
Produces a structured summary of the current DB state,
|
||||
formatted for Claude to reason over.
|
||||
"""
|
||||
conn = get_db()
|
||||
cur = conn.cursor()
|
||||
|
||||
# DB stats
|
||||
cur.execute("SELECT COUNT(*) FROM entries")
|
||||
total = cur.fetchone()[0]
|
||||
|
||||
cur.execute("SELECT source, COUNT(*) as cnt, AVG(signal_score) as avg_score, MIN(first_seen) as oldest, MAX(last_updated) as newest FROM entries GROUP BY source")
|
||||
source_stats = {r["source"]: dict(r) for r in cur.fetchall()}
|
||||
|
||||
# Security-tagged entries
|
||||
cur.execute("""
|
||||
SELECT COUNT(*) FROM entries
|
||||
WHERE summary IS NOT NULL AND summary != '' AND json_extract(summary,'$.potential_use_case') LIKE '%security%'
|
||||
""")
|
||||
security_count = cur.fetchone()[0]
|
||||
|
||||
# Top 10 overall
|
||||
cur.execute("SELECT * FROM entries WHERE summary IS NOT NULL AND summary != '' ORDER BY signal_score DESC LIMIT 10")
|
||||
top_entries = [format_entry(r, i+1) for i, r in enumerate(cur.fetchall())]
|
||||
|
||||
# Confidence distribution
|
||||
cur.execute("""
|
||||
SELECT json_extract(summary,'$.confidence') as conf, COUNT(*) as cnt
|
||||
FROM entries WHERE summary IS NOT NULL AND summary != ''
|
||||
GROUP BY conf
|
||||
""")
|
||||
conf_dist = {r["conf"]: r["cnt"] for r in cur.fetchall()}
|
||||
|
||||
now = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
|
||||
|
||||
snapshot = {
|
||||
"snapshot_time": now,
|
||||
"total_entries": total,
|
||||
"sources": source_stats,
|
||||
"security_flagged": security_count,
|
||||
"confidence_distribution": conf_dist,
|
||||
"top_10": top_entries,
|
||||
}
|
||||
|
||||
# Output as formatted text for relay
|
||||
print("=" * 70)
|
||||
print("AI RESEARCH ORACLE — SNAPSHOT")
|
||||
print("=" * 70)
|
||||
print(f"Time: {now}")
|
||||
print(f"Total entries: {total}")
|
||||
print()
|
||||
|
||||
print("Source breakdown:")
|
||||
for src, stats in source_stats.items():
|
||||
print(f" {src}: {stats['cnt']} entries, avg score {stats['avg_score']:.2f}")
|
||||
print()
|
||||
|
||||
if conf_dist:
|
||||
print(f"Confidence distribution: {conf_dist}")
|
||||
print()
|
||||
|
||||
if security_count:
|
||||
print(f"⚠ {security_count} entries flagged as security:dual-use")
|
||||
print()
|
||||
|
||||
print("Top 10 by signal score (per-source ranking, NOT cross-source comparable):")
|
||||
print("-" * 70)
|
||||
for e in top_entries:
|
||||
score_label = f"{e['score']:.2f} ({e['score_type']})"
|
||||
conf = e["confidence"]
|
||||
print(f"\n [{e['rank']}] {e['source'].upper()} | {score_label} | confidence={conf}")
|
||||
print(f" {e['title']}")
|
||||
print(f" {e['source_detail']}")
|
||||
if e["one_liner"]:
|
||||
print(f" → {e['one_liner'][:120]}")
|
||||
print(f" Tags: {', '.join(e['tags'][:4])}")
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print("NOTE: Scores are NOT comparable across sources. GitHub uses")
|
||||
print("actual star counts (log-scaled), arXiv/reddit use estimated")
|
||||
print("heuristics. Rank within-source, not cross-source.")
|
||||
print("=" * 70)
|
||||
|
||||
conn.close()
|
||||
|
||||
|
||||
def cmd_explain(args):
|
||||
"""Explain why an entry scored the way it did."""
|
||||
conn = get_db()
|
||||
cur = conn.cursor()
|
||||
|
||||
if args.entry_id.isdigit():
|
||||
cur.execute("SELECT * FROM entries WHERE id = ?", (args.entry_id,))
|
||||
args = sys.argv[1:]
|
||||
# Map old query commands to new CLI format
|
||||
if args and args[0] in ("top", "search", "recent", "by-tag", "stats", "snapshot"):
|
||||
cli_args = ["query"] + args
|
||||
elif args and args[0].startswith("-"):
|
||||
cli_args = ["query", "top"] + args
|
||||
else:
|
||||
q = f"%{args.entry_id}%"
|
||||
cur.execute("SELECT * FROM entries WHERE title LIKE ?", (q,))
|
||||
cli_args = ["query", "top"] + args
|
||||
|
||||
row = cur.fetchone()
|
||||
if not row:
|
||||
print(f"Entry not found: {args.entry_id}")
|
||||
conn.close()
|
||||
return
|
||||
|
||||
meta = json.loads(row["raw_metadata"]) if row["raw_metadata"] else {}
|
||||
summary = json.loads(row["summary"]) if row["summary"] else {}
|
||||
tags = json.loads(row["category_tags"]) if row["category_tags"] else []
|
||||
|
||||
print(f"=== Score Explanation ===\n")
|
||||
print(f"Title: {row['title']}")
|
||||
print(f"Source: {row['source']}")
|
||||
print(f"Score: {row['signal_score']:.2f} ({meta.get('score_type', '?')})")
|
||||
print(f"Confidence: {summary.get('confidence', '?')}")
|
||||
print()
|
||||
|
||||
if row["source"] == "arxiv":
|
||||
print(f"Authors: {meta.get('author_count', '?')}")
|
||||
print(f"Categories: {', '.join(meta.get('categories', []))}")
|
||||
print(f"Published: {meta.get('published', '?')}")
|
||||
print(f"Abstract length: {meta.get('abstract_length', '?')} chars")
|
||||
print(f"Applied domain: {meta.get('applied_domain', 'none (core AI)')}")
|
||||
print()
|
||||
print("Scoring (arXiv — relevance-weighted, structural capped):")
|
||||
print(f" - Structural (recency+authors+diversity, capped ~3.5): weak signals")
|
||||
print(f" - AI keyword density in abstract (capped 2.0)")
|
||||
print(f" - AI methodology claim (propose/novel = up to 2.5)")
|
||||
print(f" - Applied-domain is TAGGED, not penalized")
|
||||
if meta.get('applied_domain'):
|
||||
print(f" Note: tagged '{meta['applied_domain']}' for filtering — no score penalty")
|
||||
elif row["source"] == "github":
|
||||
print(f"Stars: {meta.get('stars', '?')}")
|
||||
print(f"Language: {meta.get('language', '?')}")
|
||||
print()
|
||||
print("Scoring (GitHub actual star count, log-scaled)")
|
||||
|
||||
print()
|
||||
print(f"Tags: {', '.join(tags)}")
|
||||
if summary.get('one_liner'):
|
||||
print(f"One-liner: {summary['one_liner'][:120]}")
|
||||
|
||||
conn.close()
|
||||
|
||||
|
||||
def cmd_stats(args):
|
||||
"""Database statistics."""
|
||||
conn = get_db()
|
||||
cur = conn.cursor()
|
||||
|
||||
cur.execute("SELECT COUNT(*) FROM entries")
|
||||
total = cur.fetchone()[0]
|
||||
|
||||
cur.execute("SELECT source, COUNT(*) as cnt, ROUND(AVG(signal_score),2) as avg_score, MIN(signal_score) as min_score, MAX(signal_score) as max_score FROM entries GROUP BY source")
|
||||
rows = cur.fetchall()
|
||||
|
||||
print(f"Database: {DB_PATH}")
|
||||
print(f"Total entries: {total}\n")
|
||||
|
||||
print(f"{'Source':<12} {'Count':<8} {'Avg':<8} {'Min':<8} {'Max':<8}")
|
||||
print("-" * 44)
|
||||
for r in rows:
|
||||
print(f"{r['source']:<12} {r['cnt']:<8} {r['avg_score']:<8} {r['min_score']:<8} {r['max_score']:<8}")
|
||||
|
||||
# Summarization status
|
||||
cur.execute("SELECT COUNT(*) FROM entries WHERE summary IS NOT NULL")
|
||||
summarized = cur.fetchone()[0]
|
||||
cur.execute("SELECT COUNT(*) FROM entries WHERE summary IS NULL")
|
||||
pending = cur.fetchone()[0]
|
||||
print(f"\nSummarization: {summarized} done, {pending} pending")
|
||||
|
||||
# Confidence distribution
|
||||
cur.execute("SELECT json_extract(summary,'$.confidence') as c, COUNT(*) as n FROM entries WHERE summary IS NOT NULL AND summary != '' GROUP BY c")
|
||||
if cur.fetchall():
|
||||
print(f"Confidence: {' | '.join(f'{r[0]}={r[1]}' for r in cur.fetchall())}")
|
||||
|
||||
# Recent run history (failure visibility)
|
||||
cur.execute("SELECT run_time, total_fetched, total_stored, sources_ok, sources_failed FROM run_log ORDER BY id DESC LIMIT 5")
|
||||
runs = cur.fetchall()
|
||||
if runs:
|
||||
print(f"\nRecent runs (last {len(runs)}):")
|
||||
for r in runs:
|
||||
failed = json.loads(r["sources_failed"]) if r["sources_failed"] else []
|
||||
status = "✓ all ok" if not failed else f"⚠ partial: {', '.join(failed)}"
|
||||
print(f" {r['run_time']} fetched={r['total_fetched']} stored={r['total_stored']} {status}")
|
||||
|
||||
conn.close()
|
||||
|
||||
|
||||
def _print_entries(entries: list[dict]):
|
||||
"""Pretty-print a list of entries."""
|
||||
if not entries:
|
||||
print(" (no results)")
|
||||
return
|
||||
|
||||
for e in entries:
|
||||
score_label = f"{e['score']:.2f} ({e['score_type']})"
|
||||
conf = e["confidence"]
|
||||
print(f" [{e['rank']}] {e['source'].upper():6} | {score_label} | confidence={conf}")
|
||||
print(f" {e['title']}")
|
||||
if e["source_detail"]:
|
||||
print(f" {e['source_detail']}")
|
||||
if e["one_liner"]:
|
||||
print(f" → {e['one_liner'][:120]}")
|
||||
# Show applied-domain and security tags prominently
|
||||
shown_tags = [t for t in e["tags"] if t.startswith("applied:") or t.startswith("security:")]
|
||||
other_tags = [t for t in e["tags"] if not t.startswith("applied:") and not t.startswith("security:")]
|
||||
display_tags = shown_tags + other_tags[:4]
|
||||
if display_tags:
|
||||
print(f" Tags: {', '.join(display_tags)}")
|
||||
print()
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="AI Research Oracle — Query")
|
||||
sub = parser.add_subparsers(dest="command")
|
||||
|
||||
# top
|
||||
p_top = sub.add_parser("top", help="Top N entries")
|
||||
p_top.add_argument("n", type=int, nargs="?", default=10)
|
||||
p_top.add_argument("--source", default=None)
|
||||
p_top.add_argument("--min-score", type=float, default=0)
|
||||
|
||||
# by-source
|
||||
p_src = sub.add_parser("by-source", help="Top N from a source")
|
||||
p_src.add_argument("source")
|
||||
p_src.add_argument("n", type=int, nargs="?", default=10)
|
||||
|
||||
# by-tag
|
||||
p_tag = sub.add_parser("by-tag", help="Entries by tag")
|
||||
p_tag.add_argument("tag")
|
||||
|
||||
# search
|
||||
p_search = sub.add_parser("search", help="Keyword search")
|
||||
p_search.add_argument("query")
|
||||
|
||||
# recent
|
||||
p_recent = sub.add_parser("recent", help="Recent entries")
|
||||
p_recent.add_argument("--hours", type=int, default=24)
|
||||
|
||||
# snapshot
|
||||
sub.add_parser("snapshot", help="Full snapshot for Claude")
|
||||
|
||||
# explain
|
||||
p_explain = sub.add_parser("explain", help="Explain why an entry scored high")
|
||||
p_explain.add_argument("entry_id", help="Entry ID or partial title")
|
||||
|
||||
# stats
|
||||
sub.add_parser("stats", help="Database statistics")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
commands = {
|
||||
"top": cmd_top,
|
||||
"by-source": cmd_by_source,
|
||||
"by-tag": cmd_by_tag,
|
||||
"search": cmd_search,
|
||||
"recent": cmd_recent,
|
||||
"snapshot": cmd_snapshot,
|
||||
"explain": cmd_explain,
|
||||
"stats": cmd_stats,
|
||||
}
|
||||
|
||||
cmd = commands.get(args.command)
|
||||
if cmd:
|
||||
cmd(args)
|
||||
else:
|
||||
parser.print_help()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
from oracle.cli import main as cli_main
|
||||
sys.argv = ["oracle"] + cli_args
|
||||
cli_main()
|
||||
|
||||
+21
-193
@@ -1,201 +1,29 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
recency_guard.py — Athena "how old is this news?" gate (Tony's correction, 2026-07-13).
|
||||
"""Thin wrapper — delegates to oracle.recency filter_fresh + analysis."""
|
||||
import sys, os
|
||||
sys.path.insert(0, os.path.dirname(__file__))
|
||||
|
||||
THE PROBLEM IT SOLVES:
|
||||
The old pipeline ranked by virality + editorial fit ONLY. That surfaced the
|
||||
SAME stories week after week (Apple-vs-OpenAI, GPT-5.6, etc.) because the
|
||||
curation had NO memory of what was already posted. Tony's rule:
|
||||
|
||||
"when you receive the news stories, morality [editorial fit] is only ONE
|
||||
measure. The OTHER measure -- maybe MORE important -- is how OLD is the
|
||||
news. Today is Monday; the week's news is just beginning, so we begin the
|
||||
week with stories dated for TODAY."
|
||||
|
||||
So AGE is the dominant gate. The algorithm (editorial, not render-time):
|
||||
|
||||
* TODAY's items -> ALWAYS eligible. They are this week's fresh news; a
|
||||
Monday stack leads with them even if rendered earlier today.
|
||||
* OLDER items -> eligible ONLY if NEVER posted before (not in the
|
||||
markdown Top-N stack history AND not in seen_urls.json).
|
||||
This kills the re-post problem at the source.
|
||||
|
||||
WHAT IS "ALREADY POSTED":
|
||||
Two signals, differing in authority:
|
||||
- athena_top*.md = the CURATED/PUBLIC stack history (authoritative)
|
||||
- seen_urls.json = the live-site render dedup (secondary; ALSO flags items
|
||||
rendered in prior runs TODAY, which we must NOT drop)
|
||||
Because seen_urls.json contains today's own items, we only treat a seen_url
|
||||
as "already posted" when the candidate is OLDER than today. Today's items are
|
||||
exempt from the seen_urls gate entirely (fresh by definition).
|
||||
|
||||
EXPORTS:
|
||||
load_posted() -> (md_urls, md_titles, seen_urls) sets
|
||||
is_today(first_seen, now)
|
||||
age_days(first_seen, now)
|
||||
day_bucket(first_seen, now)
|
||||
already_posted_fs(url,title,fs,now) -> bool (age-aware dedup)
|
||||
filter_fresh(items, now) -> (today_items, older_new_items, dropped_items)
|
||||
recency_weight(first_seen, now) -> float (1.0 today -> ~0 over 7d)
|
||||
blend_score(item, now) -> clickability_decayed * recency_weight
|
||||
Read-only against markdown + json + passed-in items. No DB writes.
|
||||
"""
|
||||
import os, re, json
|
||||
from datetime import datetime, timezone
|
||||
from oracle.clickability import fetch_items, compute_index, decay_index
|
||||
from oracle.recency import filter_fresh, blend_score
|
||||
import sqlite3
|
||||
from oracle.config import DB_PATH, HALF_LIFE_H
|
||||
|
||||
ORACLE = os.path.dirname(os.path.abspath(__file__))
|
||||
SEEN_JSON = "/home/vpsadmin/ai-oracle-site/seen_urls.json"
|
||||
conn = sqlite3.connect(str(DB_PATH))
|
||||
items = fetch_items(conn)
|
||||
conn.close()
|
||||
|
||||
items = compute_index(items)
|
||||
items = decay_index(items, HALF_LIFE_H)
|
||||
|
||||
def _parse(ts):
|
||||
if not ts:
|
||||
return None
|
||||
try:
|
||||
return datetime.fromisoformat(ts.replace("Z", "+00:00"))
|
||||
except Exception:
|
||||
return None
|
||||
today, older_new, dropped = filter_fresh(items)
|
||||
|
||||
print(f"=== Recency Guard ===")
|
||||
print(f" Total items: {len(items)}")
|
||||
print(f" TODAY (always eligible): {len(today)}")
|
||||
print(f" OLDER but never posted: {len(older_new)}")
|
||||
print(f" DROPPED (posted + old): {len(dropped)}")
|
||||
print()
|
||||
|
||||
def _norm_url(u):
|
||||
if not u:
|
||||
return ""
|
||||
return u.split("?")[0].split("#")[0].rstrip("/").lower()
|
||||
|
||||
|
||||
def _norm_title(t):
|
||||
if not t:
|
||||
return ""
|
||||
t = t.lower()
|
||||
t = re.sub(r"[^a-z0-9 ]", " ", t)
|
||||
t = re.sub(r"\s+", " ", t).strip()
|
||||
return t[:60]
|
||||
|
||||
|
||||
def load_posted(md_dir=ORACLE, seen_json=SEEN_JSON):
|
||||
"""Return (md_urls:set, md_titles:set, seen_urls:set)."""
|
||||
md_urls, md_titles, seen_urls = set(), set(), set()
|
||||
|
||||
for fn in sorted(os.listdir(md_dir)):
|
||||
if re.match(r"athena_top.*\.md$", fn):
|
||||
try:
|
||||
txt = open(os.path.join(md_dir, fn), encoding="utf-8", errors="replace").read()
|
||||
except OSError:
|
||||
continue
|
||||
for m in re.findall(r"\]\((https?://[^)\s]+)\)", txt):
|
||||
nu = _norm_url(m)
|
||||
if nu:
|
||||
md_urls.add(nu)
|
||||
for t in re.findall(r"^\|\s*\d+\s*\|\s*(.+?)\s*\|", txt, re.M):
|
||||
nt = _norm_title(t)
|
||||
if nt:
|
||||
md_titles.add(nt)
|
||||
|
||||
if os.path.exists(seen_json):
|
||||
try:
|
||||
with open(seen_json, encoding="utf-8") as f:
|
||||
for u in json.load(f):
|
||||
nu = _norm_url(u)
|
||||
if nu:
|
||||
seen_urls.add(nu)
|
||||
except (json.JSONDecodeError, OSError):
|
||||
pass
|
||||
|
||||
return md_urls, md_titles, seen_urls
|
||||
|
||||
|
||||
def is_today(first_seen, now=None):
|
||||
now = now or datetime.now(timezone.utc)
|
||||
d = _parse(first_seen)
|
||||
return bool(d) and d.strftime("%Y-%m-%d") == now.strftime("%Y-%m-%d")
|
||||
|
||||
|
||||
def age_days(first_seen, now=None):
|
||||
now = now or datetime.now(timezone.utc)
|
||||
d = _parse(first_seen)
|
||||
if not d:
|
||||
return 9999.0
|
||||
return max((now - d).total_seconds() / 86400.0, 0.0)
|
||||
|
||||
|
||||
def day_bucket(first_seen, now=None):
|
||||
"""'today' | 'yesterday' | 'this-week' (<=6d) | 'older'."""
|
||||
days = age_days(first_seen, now)
|
||||
if days < 1:
|
||||
return "today"
|
||||
if days < 2:
|
||||
return "yesterday"
|
||||
if days <= 6:
|
||||
return "this-week"
|
||||
return "older"
|
||||
|
||||
|
||||
def already_posted_fs(url, title, first_seen, now=None,
|
||||
md_urls=None, md_titles=None, seen_urls=None):
|
||||
"""Age-aware dedup. A candidate is 'already posted' iff:
|
||||
(a) it matches the curated md-stack history, OR
|
||||
(b) it is OLDER than today AND its URL is in seen_urls.json.
|
||||
Today's items are NEVER flagged -- they are fresh by definition.
|
||||
"""
|
||||
if md_urls is None or md_titles is None or seen_urls is None:
|
||||
md_urls, md_titles, seen_urls = load_posted()
|
||||
if _norm_url(url) in md_urls:
|
||||
return True
|
||||
nt = _norm_title(title)
|
||||
if nt and nt in md_titles:
|
||||
return True
|
||||
if is_today(first_seen, now):
|
||||
return False
|
||||
if _norm_url(url) in seen_urls:
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def recency_weight(first_seen, now=None, half_life_days=2.0):
|
||||
"""1.0 for today, decays ~halving every 2 days. The 'age' measure."""
|
||||
return 0.5 ** (age_days(first_seen, now) / half_life_days)
|
||||
|
||||
|
||||
def blend_score(item, now=None):
|
||||
"""clickability_decayed * recency_weight. Today's items dominate; old sink."""
|
||||
base = item.get("clickability_decayed", 0) or 0
|
||||
return base * recency_weight(item.get("first_seen"), now)
|
||||
|
||||
|
||||
def filter_fresh(items, now=None):
|
||||
"""Split into (today_items, older_new_items, dropped_items).
|
||||
|
||||
today_items = first_seen == today (always eligible; the week-open lead)
|
||||
older_new_items= older, but never before posted (md/seen)
|
||||
dropped_items = older AND already posted (the re-posts we are killing)
|
||||
"""
|
||||
now = now or datetime.now(timezone.utc)
|
||||
md_urls, md_titles, seen_urls = load_posted()
|
||||
today_items, older_new, dropped = [], [], []
|
||||
for it in items:
|
||||
fs = it.get("first_seen")
|
||||
if is_today(fs, now):
|
||||
today_items.append(it)
|
||||
continue
|
||||
if already_posted_fs(it.get("url"), it.get("title"), fs, now,
|
||||
md_urls, md_titles, seen_urls):
|
||||
dropped.append(it)
|
||||
else:
|
||||
older_new.append(it)
|
||||
return today_items, older_new, dropped
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import sys
|
||||
sys.path.insert(0, ORACLE)
|
||||
import clickability as cb
|
||||
DB = os.path.join(ORACLE, "oracle.db")
|
||||
conn = sqlite3.connect(f"file:{DB}?mode=ro", uri=True)
|
||||
items = cb.fetch_items(conn); conn.close()
|
||||
items = cb.compute_index(items); items = cb.decay_index(items)
|
||||
today_items, older_new, dropped = filter_fresh(items)
|
||||
print(f"TODAY-new (week-open lead): {len(today_items)}")
|
||||
print(f"OLDER-but-never-posted: {len(older_new)}")
|
||||
print(f"DROPPED (already posted): {len(dropped)}")
|
||||
print("\nSample dropped (the re-posts that caused the problem):")
|
||||
for it in sorted(dropped, key=lambda x: -x["clickability_decayed"])[:6]:
|
||||
print(f" - [{it['clickability_decayed']:.3f}] {it['title'][:66]}")
|
||||
for it in sorted(today, key=lambda x: x["clickability_decayed"], reverse=True)[:5]:
|
||||
print(f" [TODAY {it['clickability_decayed']:.2f}] {it['source']:10} {it['title'][:60]}")
|
||||
|
||||
+8
-182
@@ -1,185 +1,11 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Render Athena entries into a static news site (two-layer: Top News + aging Stack).
|
||||
"""Thin wrapper — delegates to oracle.cli render subcommand."""
|
||||
import sys, os
|
||||
sys.path.insert(0, os.path.dirname(__file__))
|
||||
|
||||
Read-only against oracle.db. Writes static HTML to the preprod3 webroot.
|
||||
Designed for a 20-min no_agent cron.
|
||||
args = sys.argv[1:]
|
||||
cli_args = ["render"] + args
|
||||
|
||||
Usage:
|
||||
python3 render_site.py # write to WEBROOT
|
||||
python3 render_site.py --dry-run # print stats, write to ./_preview.html
|
||||
"""
|
||||
import argparse, html, os, json, sqlite3, datetime, re
|
||||
from collections import OrderedDict
|
||||
|
||||
import clickability as cb
|
||||
|
||||
HERE = os.path.dirname(os.path.abspath(__file__))
|
||||
WEBROOT = "/var/www/preprod2"
|
||||
DB_PATH = os.path.join(HERE, "oracle.db")
|
||||
TOP_N = 8
|
||||
HALF_LIFE_H = 18.0
|
||||
|
||||
|
||||
def _clean_summary(raw):
|
||||
"""summary is stored as JSON {one_liner, key_technical_point, potential_use_case}.
|
||||
Pull the most readable field; fall back to the raw text if it isn't JSON.
|
||||
Strips markdown/latex noise so the card reads clean on the page."""
|
||||
if not raw:
|
||||
return ""
|
||||
try:
|
||||
d = json.loads(raw)
|
||||
if isinstance(d, dict):
|
||||
for k in ("one_liner", "key_technical_point", "potential_use_case"):
|
||||
v = d.get(k)
|
||||
if isinstance(v, str) and v.strip():
|
||||
return re.sub(r"\\+|_|`", "", v).strip()
|
||||
except Exception:
|
||||
pass
|
||||
return re.sub(r"\\+|_|`", "", raw).strip()
|
||||
|
||||
|
||||
def _fmt_time(first_seen):
|
||||
if not first_seen:
|
||||
return ""
|
||||
try:
|
||||
dt = datetime.datetime.strptime(first_seen, "%Y-%m-%dT%H:%M:%SZ")
|
||||
return dt.strftime("%H:%M")
|
||||
except Exception:
|
||||
return ""
|
||||
|
||||
|
||||
def _card(it, big=False):
|
||||
title = html.escape(it["title"] or "(untitled)")
|
||||
url = html.escape(it["url"] or "#")
|
||||
src = html.escape(it["source"])
|
||||
sig = it.get("signal_score") or 0
|
||||
t = _fmt_time(it.get("first_seen"))
|
||||
summary_raw = _clean_summary(it.get("summary") or "")
|
||||
summary = html.escape(summary_raw[:200])
|
||||
cls = "card big" if big else "card"
|
||||
summary_html = ('<p class="summary">{0}</p>'.format(summary)) if (summary and big) else ""
|
||||
return f"""
|
||||
<article class="{cls}" data-src="{src}">
|
||||
<div class="meta"><span class="src">{src}</span>
|
||||
<span class="time">{t}</span>
|
||||
<span class="sig">sig {sig:.1f}</span>
|
||||
<span class="score">\U0001f525 {it['clickability_decayed']:.2f}</span></div>
|
||||
<h3><a href="{url}" target="_blank" rel="noopener">{title}</a></h3>
|
||||
{summary_html}
|
||||
</article>"""
|
||||
|
||||
|
||||
def build_html(items):
|
||||
now = datetime.datetime.now(datetime.timezone.utc).strftime("%Y-%m-%d %H:%M UTC")
|
||||
ranked = sorted(items, key=lambda x: x["clickability_decayed"], reverse=True)
|
||||
# Top News = fresh items only (ingested today, UTC). Yesterday's viral
|
||||
# leftovers sink into the Stack instead of dominating the front page.
|
||||
fresh = [it for it in ranked if it.get("fresh")]
|
||||
top = fresh[:TOP_N]
|
||||
stack = [it for it in ranked if it not in top]
|
||||
|
||||
# group stack by day (first_seen date)
|
||||
by_day = OrderedDict()
|
||||
for it in stack:
|
||||
day = (it.get("first_seen") or "")[:10] or "unknown"
|
||||
by_day.setdefault(day, []).append(it)
|
||||
|
||||
top_html = "".join(_card(it, big=True) for it in top)
|
||||
|
||||
stack_html = ""
|
||||
for day, rows in by_day.items():
|
||||
rows.sort(key=lambda x: x["clickability_decayed"], reverse=True)
|
||||
cards = "".join(_card(it) for it in rows)
|
||||
stack_html += f"""
|
||||
<h3 class="day">\U0001f4c5 {html.escape(day)}</h3>
|
||||
<div class="stack">{cards}</div>"""
|
||||
|
||||
return f"""<!doctype html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1">
|
||||
<title>Athena AI News — Ranked by Clickability</title>
|
||||
<style>
|
||||
:root {{ --bg:#0b0e14; --card:#141925; --fg:#e6e9ef; --mut:#8b93a7; --acc:#5b8cff; }}
|
||||
* {{ box-sizing:border-box; }}
|
||||
body {{ margin:0; background:var(--bg); color:var(--fg);
|
||||
font:15px/1.5 -apple-system,Segoe UI,Roboto,Helvetica,Arial,sans-serif; }}
|
||||
header {{ padding:28px 20px 14px; border-bottom:1px solid #1f2533; text-align:center; }}
|
||||
header h1 {{ margin:0; font-size:28px; letter-spacing:.5px; }}
|
||||
header .sub {{ color:var(--mut); font-size:13px; margin-top:6px; }}
|
||||
main {{ max-width:1000px; margin:0 auto; padding:20px; }}
|
||||
h2.sech {{ font-size:18px; margin:26px 0 12px; border-left:3px solid var(--acc); padding-left:10px; }}
|
||||
.grid {{ display:grid; grid-template-columns:repeat(auto-fill,minmax(320px,1fr)); gap:14px; }}
|
||||
.card {{ background:var(--card); border:1px solid #1f2533; border-radius:12px; padding:16px; }}
|
||||
.card.big {{ grid-column:1/-1; }}
|
||||
.meta {{ display:flex; gap:10px; align-items:center; font-size:12px; color:var(--mut); }}
|
||||
.src {{ background:#1f2533; padding:2px 8px; border-radius:20px; text-transform:uppercase; }}
|
||||
.score {{ color:#ff9d5b; font-weight:600; margin-left:auto; }}
|
||||
.card h3 {{ font-size:16px; margin:10px 0 8px; line-height:1.35; }}
|
||||
.card.big h3 {{ font-size:20px; }}
|
||||
.card h3 a {{ color:var(--fg); text-decoration:none; }}
|
||||
.card h3 a:hover {{ color:var(--acc); }}
|
||||
.summary {{ color:var(--mut); font-size:13px; margin:0; }}
|
||||
.day {{ font-size:15px; color:var(--mut); margin:28px 0 10px; border-bottom:1px solid #1f2533; padding-bottom:6px; }}
|
||||
.stack {{ display:grid; grid-template-columns:repeat(auto-fill,minmax(320px,1fr)); gap:12px; }}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<header>
|
||||
<h1>Athena AI News</h1>
|
||||
<div class="sub">Auto-ranked by Clickability Index · decays with age so the stack flows top → bottom · generated {now} · {len(items)} stories</div>
|
||||
</header>
|
||||
<main>
|
||||
<h2 class="sech">\U0001f534 Top News</h2>
|
||||
<div class="grid">{top_html}</div>
|
||||
<h2 class="sech">\U0001f4f0 The Stack</h2>
|
||||
{stack_html}
|
||||
</main>
|
||||
</body>
|
||||
</html>"""
|
||||
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("--dry-run", action="store_true")
|
||||
args = ap.parse_args()
|
||||
|
||||
conn = sqlite3.connect(DB_PATH)
|
||||
items = cb.fetch_items(conn)
|
||||
conn.close()
|
||||
items = cb.compute_index(items)
|
||||
items = cb.decay_index(items, HALF_LIFE_H)
|
||||
page = build_html(items)
|
||||
|
||||
if args.dry_run:
|
||||
out = os.path.join(HERE, "_preview.html")
|
||||
with open(out, "w") as f:
|
||||
f.write(page)
|
||||
fresh = [it for it in items if it.get("fresh")]
|
||||
top = sorted(fresh, key=lambda x: x["clickability_decayed"], reverse=True)[:TOP_N]
|
||||
print(f"[dry-run] wrote {out} ({len(items)} items, {len(fresh)} fresh today)")
|
||||
print(f"TOP {TOP_N} FRESH (today only) by decayed clickability:")
|
||||
for i, it in enumerate(top, 1):
|
||||
print(f" {i}. [{it['clickability_decayed']:.2f} | age {it['age_hours']:.0f}h] {it['source']:10} {it['title'][:55]}")
|
||||
return
|
||||
|
||||
# Write to webroot if it exists (deployed); otherwise fall back to a
|
||||
# user-owned dir so the no_agent cron never errors pre-deploy.
|
||||
fallback = os.path.join(HERE, "site")
|
||||
target = WEBROOT if os.path.isdir(WEBROOT) else fallback
|
||||
os.makedirs(target, exist_ok=True)
|
||||
with open(os.path.join(target, "index.html"), "w") as f:
|
||||
f.write(page)
|
||||
with open(os.path.join(target, "feed.json"), "w") as f:
|
||||
json.dump([
|
||||
{"title": i["title"], "url": i["url"], "source": i["source"],
|
||||
"clickability_decayed": i["clickability_decayed"], "age_hours": i["age_hours"],
|
||||
"first_seen": i.get("first_seen")}
|
||||
for i in sorted(items, key=lambda x: x["clickability_decayed"], reverse=True)
|
||||
], f, indent=2)
|
||||
where = "WEBROOT" if target == WEBROOT else "fallback(~oracle/site)"
|
||||
print(f"[render] wrote {target}/index.html ({len(items)} items) -> {where}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
from oracle.cli import main as cli_main
|
||||
sys.argv = ["oracle"] + cli_args
|
||||
cli_main()
|
||||
|
||||
+7
-548
@@ -1,552 +1,11 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
AI Research Oracle — Summarization Engine (v1).
|
||||
|
||||
Generates structured summaries for entries where summary IS NULL.
|
||||
Uses source-specific extraction logic (no LLM required — eliminates hallucination).
|
||||
|
||||
Output schema: {one_liner, key_technical_point, potential_use_case, confidence}
|
||||
|
||||
Architecture note: This v1 uses deterministic extraction rules to avoid
|
||||
hallucination. When a local LLM becomes available (Ollama GPU, Hermes API),
|
||||
swap in LLM mode via --llm flag. The DB schema is identical.
|
||||
|
||||
Usage:
|
||||
python3 summarize.py # summarize all pending
|
||||
python3 summarize.py --source github # specific source
|
||||
python3 summarize.py --limit 10 # max entries
|
||||
python3 summarize.py --verify # spot-check 2-3 summaries
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import sqlite3
|
||||
import sys
|
||||
import time
|
||||
from datetime import datetime, timezone
|
||||
|
||||
"""Thin wrapper — delegates to oracle.cli summarize subcommand."""
|
||||
import sys, os
|
||||
sys.path.insert(0, os.path.dirname(__file__))
|
||||
|
||||
args = sys.argv[1:]
|
||||
cli_args = ["summarize"] + args
|
||||
|
||||
def extract_github_summary(title: str, content: str) -> dict:
|
||||
"""Extract summary from GitHub README content.
|
||||
|
||||
Strategy: Clean HTML, find the first substantive paragraph that
|
||||
describes the project (usually below the badges), extract the
|
||||
"what it does" sentence.
|
||||
"""
|
||||
# Aggressive HTML cleaning
|
||||
text = re.sub(r'<p[^>]*>', '\n', content)
|
||||
text = re.sub(r'</p>', '\n', content)
|
||||
text = re.sub(r'<h[1-6][^>]*>', '\n## ', text)
|
||||
text = re.sub(r'</h[1-6]>', '\n', text)
|
||||
text = re.sub(r'<[^>]+>', '', text)
|
||||
text = re.sub(r'&', '&', text)
|
||||
text = re.sub(r'—', '—', text)
|
||||
text = re.sub(r''', "'", text)
|
||||
text = re.sub(r'·', '·', text)
|
||||
# Remove code blocks (``` ... ```) — often ASCII art
|
||||
text = re.sub(r'```[\s\S]*?```', '', text)
|
||||
text = re.sub(r'\n\s*\n+', '\n\n', text)
|
||||
text = text.strip()
|
||||
|
||||
# Confidence starts from source content quality
|
||||
source_confidence = "low"
|
||||
if len(text) > 2000:
|
||||
source_confidence = "high"
|
||||
elif len(text) > 500:
|
||||
source_confidence = "medium"
|
||||
|
||||
# Find the one-liner: look for project description paragraph
|
||||
one_liner = _find_project_description(text, title)
|
||||
if not one_liner:
|
||||
one_liner = title[:200]
|
||||
|
||||
# Key technical point
|
||||
key_tech = _extract_technical_point(text, source_confidence)
|
||||
|
||||
# Use case
|
||||
use_case = _extract_use_case(text, title)
|
||||
|
||||
# Quality-gate confidence on extraction signals, not raw length
|
||||
confidence = _assess_extraction_quality(one_liner, key_tech, use_case, source_confidence)
|
||||
|
||||
# Tag security tooling if detected
|
||||
if _is_security_tooling(title, one_liner, key_tech):
|
||||
use_case = use_case + " [security:dual-use]"
|
||||
|
||||
return {
|
||||
"one_liner": one_liner[:200],
|
||||
"key_technical_point": key_tech[:200],
|
||||
"potential_use_case": use_case[:200],
|
||||
"confidence": confidence,
|
||||
}
|
||||
|
||||
|
||||
def extract_arxiv_summary(title: str, content: str) -> dict:
|
||||
"""Extract summary from arXiv abstract.
|
||||
|
||||
Strategy: arXiv abstracts have a predictable structure:
|
||||
1. Background/motivation
|
||||
2. "In this paper we propose..."
|
||||
3. Results
|
||||
4. Implications
|
||||
|
||||
We extract the contribution statement and key finding.
|
||||
"""
|
||||
text = re.sub(r'<[^>]+>', ' ', content)
|
||||
text = re.sub(r'\s+', ' ', text).strip()
|
||||
|
||||
# Confidence based on abstract clarity
|
||||
confidence = "high" if len(text) > 300 else "medium"
|
||||
|
||||
# One-liner: find the contribution statement
|
||||
one_liner = _find_contribution(text)
|
||||
if not one_liner:
|
||||
# Fallback: use title as base
|
||||
one_liner = f"This paper presents {title.lower()}"
|
||||
|
||||
# Key technical point: look for method description
|
||||
key_tech = _extract_method(text)
|
||||
|
||||
# Use case: look for application statements
|
||||
use_case = _extract_application(text)
|
||||
|
||||
return {
|
||||
"one_liner": one_liner[:200],
|
||||
"key_technical_point": key_tech[:200],
|
||||
"potential_use_case": use_case[:200],
|
||||
"confidence": confidence,
|
||||
}
|
||||
|
||||
|
||||
def extract_reddit_summary(title: str, content: str) -> dict:
|
||||
"""Extract summary from Reddit post.
|
||||
|
||||
Strategy: Reddit posts vary wildly in quality. Extract the core
|
||||
question or claim, note if it's discussion vs announcement.
|
||||
"""
|
||||
text = re.sub(r'<[^>]+>', ' ', content)
|
||||
text = re.sub(r'\s+', ' ', text).strip()
|
||||
|
||||
# Confidence based on content length
|
||||
if len(text) > 500:
|
||||
confidence = "high"
|
||||
elif len(text) > 100:
|
||||
confidence = "medium"
|
||||
else:
|
||||
confidence = "low"
|
||||
|
||||
# One-liner from title (Reddit titles are usually the summary)
|
||||
one_liner = title[:200] if title else text[:150]
|
||||
|
||||
# Key technical point from content
|
||||
key_tech = text[:200] if text else "No additional content in post"
|
||||
|
||||
# Use case: community relevance
|
||||
use_case = "AI community discussion"
|
||||
|
||||
return {
|
||||
"one_liner": one_liner,
|
||||
"key_technical_point": key_tech,
|
||||
"potential_use_case": use_case,
|
||||
"confidence": confidence,
|
||||
}
|
||||
|
||||
|
||||
# --- Extraction helpers ---
|
||||
|
||||
def _assess_extraction_quality(one_liner: str, key_tech: str, use_case: str, source_confidence: str) -> str:
|
||||
"""Assess extraction quality based on output signals, not source length.
|
||||
|
||||
A short-but-complete Reddit title should score higher confidence
|
||||
than a long README that yielded a fragment.
|
||||
"""
|
||||
score = 0
|
||||
penalties = 0
|
||||
|
||||
# One-liner quality
|
||||
ol = one_liner.strip()
|
||||
ol_len = len(ol)
|
||||
|
||||
# Length window: 40-200 chars is a reasonable sentence
|
||||
if 40 <= ol_len <= 200:
|
||||
score += 2
|
||||
elif 20 <= ol_len < 40:
|
||||
score += 1
|
||||
elif ol_len > 200:
|
||||
penalties += 1 # too long, likely grabbed too much
|
||||
|
||||
# Ends with terminal punctuation
|
||||
if ol.endswith(('.', '!', '?', '…')):
|
||||
score += 1
|
||||
else:
|
||||
penalties += 1
|
||||
|
||||
# Contains subject-verb pattern (basic heuristic)
|
||||
if re.search(r'\b(?:is|are|provides|enables|implements|makes|allows|builds|creates|runs|uses)\b', ol, re.I):
|
||||
score += 1
|
||||
# Or starts with a proper noun/capitalized phrase
|
||||
elif re.match(r'^[A-Z]\w+', ol) and ol_len > 30:
|
||||
score += 0.5
|
||||
|
||||
# No unmatched brackets (artifact from markdown/HTML)
|
||||
open_brackets = ol.count('[') + ol.count('(')
|
||||
close_brackets = ol.count(']') + ol.count(')')
|
||||
if abs(open_brackets - close_brackets) > 0:
|
||||
penalties += 1
|
||||
if open_brackets > 2:
|
||||
penalties += 1 # likely grabbed markdown link syntax
|
||||
|
||||
# Key technical point quality
|
||||
kt = key_tech.strip()
|
||||
if kt and len(kt) > 20 and not kt.startswith('See '):
|
||||
score += 1
|
||||
else:
|
||||
penalties += 0.5
|
||||
|
||||
# Use case quality
|
||||
uc = use_case.strip()
|
||||
if uc and len(uc) > 10 and not uc.startswith('Relevant for'):
|
||||
score += 1
|
||||
else:
|
||||
penalties += 0.5
|
||||
|
||||
# Final confidence based on score - penalties
|
||||
net = score - penalties
|
||||
if net >= 3:
|
||||
return source_confidence # extraction is good, trust source quality
|
||||
elif net >= 1:
|
||||
return "medium"
|
||||
else:
|
||||
return "low"
|
||||
|
||||
|
||||
def _is_security_tooling(title: str, one_liner: str, key_tech: str) -> bool:
|
||||
"""Detect if a project is security/offensive tooling."""
|
||||
combined = f"{title} {one_liner} {key_tech}".lower()
|
||||
security_signals = [
|
||||
"offensive", "pentest", "red team", "exploit", "kill chain",
|
||||
"attack surface", "vulnerability scan", "zero-day",
|
||||
"reverse engineer", "c2", "command and control",
|
||||
]
|
||||
return any(sig in combined for sig in security_signals)
|
||||
|
||||
|
||||
def _find_project_description(text: str, title: str) -> str | None:
|
||||
"""Find the project description paragraph in a README."""
|
||||
paras = text.split('\n\n')
|
||||
proj_name = title.split(':')[0].split('/')[0].strip().lower()
|
||||
|
||||
for para in paras:
|
||||
para = para.strip()
|
||||
if not para or para.startswith('##') or len(para) < 20:
|
||||
continue
|
||||
# Skip badges, stats lines, separator lines
|
||||
if 'img' in para.lower() or 'badge' in para.lower() or 'shields' in para.lower():
|
||||
continue
|
||||
# Skip lines that start with stats (~54%, etc.)
|
||||
if re.match(r'^[~$#€£¥*»\d]', para):
|
||||
continue
|
||||
# Skip ASCII art (high ratio of special chars)
|
||||
special_chars = sum(1 for c in para if not c.isalnum() and not c.isspace() and c not in ',.!?;:\'"-()[]')
|
||||
if special_chars / max(len(para), 1) > 0.4:
|
||||
continue
|
||||
if len(para) < 40:
|
||||
continue
|
||||
# Good paragraph — extract first sentence
|
||||
sentence = re.split(r'[.!?]', para)[0].strip()
|
||||
if len(sentence) > 30:
|
||||
return sentence + '.'
|
||||
|
||||
# Fallback: look for "is a" pattern anywhere
|
||||
patterns = [
|
||||
rf'{re.escape(proj_name[:20])}\s+(?:is|enables|provides|implements)\s+[^.]+\.?',
|
||||
r'(?:This\s+)?(?:project|library|framework|tool|package)\s+(?:is|enables|provides)\s+[^.]+\.?',
|
||||
]
|
||||
for pattern in patterns:
|
||||
match = re.search(pattern, text, re.I)
|
||||
if match:
|
||||
return match.group(0)[:200]
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def _find_what_sentence(text: str, title: str) -> str | None:
|
||||
"""Find the 'X is a...' sentence that describes what the project does."""
|
||||
patterns = [
|
||||
rf'{re.escape(title[:30])}\s+(?:is|enables|provides|implements)\s+[^.]+\.?',
|
||||
r'(?:This\s+)?(?:project|library|framework|tool|package)\s+(?:is|enables|provides|implements)\s+[^.]+\.?',
|
||||
r'(?:makes|allows)\s+[^\s]+\s+(?:to|can)\s+[^.]+\.?',
|
||||
r'(?:\w+\s+(?:is|provides|enables|implements|delivers))\s+[a-z].{10,100}\.',
|
||||
]
|
||||
|
||||
for pattern in patterns:
|
||||
match = re.search(pattern, text, re.I)
|
||||
if match:
|
||||
return match.group(0)[:200]
|
||||
|
||||
# Fallback: first meaningful paragraph
|
||||
for para in text.split('\n\n'):
|
||||
para = para.strip()
|
||||
if len(para) > 30 and not para.startswith('#'):
|
||||
return para[:200]
|
||||
return None
|
||||
|
||||
|
||||
def _find_contribution(text: str) -> str | None:
|
||||
"""Find the 'we propose/introduce/present' statement in an abstract."""
|
||||
patterns = [
|
||||
r'(?:we|this\s+paper)\s+(?:propose|introduce|present|propose and evaluate)\s+[^.]{10,150}\.',
|
||||
r'(?:we\s+(?:show|demonstrate|find|discover|observe))\s+[^.]{10,150}\.',
|
||||
r'(?:we\s+(?:introduce|present|propose))\s+(?:a|an|our)\s+\w+\s+[^.]{5,150}\.',
|
||||
]
|
||||
|
||||
for pattern in patterns:
|
||||
match = re.search(pattern, text, re.I)
|
||||
if match:
|
||||
return match.group(0)[:200]
|
||||
|
||||
# Fallback: first sentence
|
||||
first = re.split(r'[.!?]', text)[0].strip()
|
||||
return first if first else None
|
||||
|
||||
|
||||
def _extract_technical_point(text: str, confidence: str) -> str:
|
||||
"""Extract the main technical approach or innovation."""
|
||||
patterns = [
|
||||
r'architecture(?:\s+designed)?\s+(?:for|to|that)\s+[^.]+\.?',
|
||||
r'(?:using|via|based\s+on|through)\s+[a-z][^.]{10,100}\.',
|
||||
r'(?:novel|new|unique|innovative)\s+\w+\s+[^.]{5,80}\.',
|
||||
]
|
||||
|
||||
for pattern in patterns:
|
||||
match = re.search(pattern, text, re.I)
|
||||
if match:
|
||||
return match.group(0)[:200]
|
||||
|
||||
# Fallback: confidence-based
|
||||
if confidence == "low":
|
||||
return "Technical details not available in extracted content"
|
||||
return "See README for technical details"
|
||||
|
||||
|
||||
def _extract_method(text: str) -> str:
|
||||
"""Extract the method/approach from an arXiv abstract."""
|
||||
patterns = [
|
||||
r'(?:method|approach|framework|technique|model|system)\s+(?:based|using|via|through|with)\s+[a-z][^.]{10,120}\.',
|
||||
r'(?:combining|leveraging|exploiting)\s+[a-z][^.]{10,120}\.',
|
||||
r'(?:learn|train|optimize|generate)\s+[a-z][^.]{10,120}\.',
|
||||
]
|
||||
|
||||
for pattern in patterns:
|
||||
match = re.search(pattern, text, re.I)
|
||||
if match:
|
||||
return match.group(0)[:200]
|
||||
|
||||
# Fallback: core contribution
|
||||
for pattern in [
|
||||
r'(?:propose|introduce)\s+(?:a|an)\s+[^.]{10,100}\.',
|
||||
]:
|
||||
match = re.search(pattern, text, re.I)
|
||||
if match:
|
||||
return match.group(0)[:200]
|
||||
|
||||
return "See full paper for methodology"
|
||||
|
||||
|
||||
def _extract_use_case(text: str, title: str) -> str:
|
||||
"""Extract potential use case from README content."""
|
||||
patterns = [
|
||||
r'(?:for|to)\s+(?:developers|engineers|researchers|teams)\s+who?\s+[^.]{5,80}\.',
|
||||
r'(?:enables|allows|helps)\s+[^\s]+\s+to\s+[^.]{10,80}\.',
|
||||
r'(?:use\s+case|application|target\s+user)\s*:\s*[^.]{10,80}\.',
|
||||
]
|
||||
|
||||
for pattern in patterns:
|
||||
match = re.search(pattern, text, re.I)
|
||||
if match:
|
||||
return match.group(0)[:200]
|
||||
|
||||
return f"Relevant for {title.lower()[:50]} developers and users"
|
||||
|
||||
|
||||
def _extract_application(text: str) -> str:
|
||||
"""Extract application/use case from arXiv abstract."""
|
||||
patterns = [
|
||||
r'(?:application|use\s+case|can\s+be\s+used|could\s+be\s+applied)\s+(?:for|in|to)\s+[a-z][^.]{10,80}\.',
|
||||
r'(?:improve|enhance|advance)\s+[a-z][^.]{10,80}\.',
|
||||
]
|
||||
|
||||
for pattern in patterns:
|
||||
match = re.search(pattern, text, re.I)
|
||||
if match:
|
||||
return match.group(0)[:200]
|
||||
|
||||
# Generic fallback based on title keywords
|
||||
title_lower = text[:200].lower()
|
||||
if any(k in title_lower for k in ["agent", "agentic"]):
|
||||
return "Building AI agent systems"
|
||||
elif any(k in title_lower for k in ["verification", "verify"]):
|
||||
return "LLM output verification and reliability"
|
||||
elif any(k in title_lower for k in ["embodied", "robot"]):
|
||||
return "Embodied AI and robotics applications"
|
||||
elif any(k in title_lower for k in ["distill"]):
|
||||
return "Model distillation and knowledge transfer"
|
||||
return "See paper for specific applications"
|
||||
|
||||
|
||||
def summarize_entry(entry: dict, conn: sqlite3.Connection) -> bool:
|
||||
"""Summarize a single entry using rule-based extraction."""
|
||||
source = entry["source"]
|
||||
title = entry["title"]
|
||||
content = entry.get("extracted_text", "")
|
||||
eid = entry["id"]
|
||||
|
||||
if not content or len(content) < 50:
|
||||
return False
|
||||
|
||||
# Source-specific extraction
|
||||
if source == "github":
|
||||
summary = extract_github_summary(title, content)
|
||||
elif source == "arxiv":
|
||||
summary = extract_arxiv_summary(title, content)
|
||||
elif source == "reddit":
|
||||
summary = extract_reddit_summary(title, content)
|
||||
else:
|
||||
summary = extract_reddit_summary(title, content) # fallback
|
||||
|
||||
# Store
|
||||
cur = conn.cursor()
|
||||
cur.execute("UPDATE entries SET summary = ? WHERE id = ?",
|
||||
(json.dumps(summary), eid))
|
||||
conn.commit()
|
||||
return True
|
||||
|
||||
|
||||
def verify_summaries(conn: sqlite3.Connection, source: str, sample_size: int = 3):
|
||||
"""Spot-check summaries against source text.
|
||||
|
||||
Look for hallucinated specifics: numbers, claims, features not
|
||||
present in the original extracted_text.
|
||||
|
||||
NOTE: Rule-based extraction v1 is inherently lower-risk for
|
||||
hallucination since it extracts actual text, not generates new claims.
|
||||
But we still verify the extraction logic is working correctly.
|
||||
"""
|
||||
cur = conn.cursor()
|
||||
cur.execute("""
|
||||
SELECT id, title, extracted_text, summary
|
||||
FROM entries WHERE source = ? AND summary IS NOT NULL
|
||||
ORDER BY RANDOM()
|
||||
LIMIT ?
|
||||
""", (source, sample_size))
|
||||
|
||||
rows = cur.fetchall()
|
||||
if not rows:
|
||||
print(f" No summaries to verify for {source}")
|
||||
return
|
||||
|
||||
for eid, title, source_text, summary_json in rows:
|
||||
summary = json.loads(summary_json)
|
||||
one_liner = summary.get("one_liner", "")
|
||||
confidence = summary.get("confidence", "?")
|
||||
|
||||
issues = []
|
||||
|
||||
# Check: does the one-liner contain text actually present in source?
|
||||
# (For rule-based extraction, this should always be true)
|
||||
words = one_liner.split()[:5]
|
||||
found = sum(1 for w in words if w.lower() in source_text.lower())
|
||||
if found < 3:
|
||||
issues.append(f"Low overlap: {found}/5 words from source")
|
||||
|
||||
# Check: confidence matches content length
|
||||
if confidence == "high" and len(source_text) < 500:
|
||||
issues.append("High confidence on short source")
|
||||
elif confidence == "low" and len(source_text) > 2000:
|
||||
issues.append("Low confidence on long source")
|
||||
|
||||
if issues:
|
||||
print(f" ⚠ [{eid}] {title[:50]}... issues: {'; '.join(issues)}")
|
||||
print(f" Summary: {one_liner[:80]}...")
|
||||
else:
|
||||
print(f" ✓ [{eid}] {title[:50]}... confidence={confidence}")
|
||||
|
||||
time.sleep(0.3)
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="AI Research Oracle — Summarization")
|
||||
parser.add_argument("--source", default=None, help="Filter by source (github/arxiv/reddit)")
|
||||
parser.add_argument("--limit", type=int, default=0, help="Max entries (0=all)")
|
||||
parser.add_argument("--verify", action="store_true", help="Spot-check summaries")
|
||||
args = parser.parse_args()
|
||||
|
||||
db_path = os.path.join(os.path.dirname(__file__), "oracle.db")
|
||||
conn = sqlite3.connect(db_path)
|
||||
cur = conn.cursor()
|
||||
|
||||
# Find pending entries
|
||||
where = "summary IS NULL"
|
||||
params = []
|
||||
if args.source:
|
||||
where += " AND source = ?"
|
||||
params.append(args.source)
|
||||
|
||||
cur.execute(f"SELECT COUNT(*) FROM entries WHERE {where}", params)
|
||||
total_pending = cur.fetchone()[0]
|
||||
print(f"=== Summarization Engine (Rule-based v1) ===")
|
||||
print(f" Pending entries: {total_pending}")
|
||||
|
||||
if total_pending == 0:
|
||||
print(" Nothing to summarize.")
|
||||
conn.close()
|
||||
return
|
||||
|
||||
# Fetch entries
|
||||
limit_clause = " LIMIT ?" if args.limit > 0 else ""
|
||||
limit_params = params + [args.limit] if args.limit > 0 else params
|
||||
|
||||
cur.execute(f"""
|
||||
SELECT id, source, title, extracted_text
|
||||
FROM entries WHERE {where}
|
||||
ORDER BY signal_score DESC
|
||||
{limit_clause}
|
||||
""", limit_params)
|
||||
|
||||
entries = [{"id": r[0], "source": r[1], "title": r[2], "extracted_text": r[3]} for r in cur.fetchall()]
|
||||
print(f" Processing: {len(entries)} entries")
|
||||
print()
|
||||
|
||||
success = 0
|
||||
failed = 0
|
||||
for entry in entries:
|
||||
try:
|
||||
if summarize_entry(entry, conn):
|
||||
success += 1
|
||||
print(f" ✓ [{entry['id']}] {entry['title'][:60]}... ({entry['source']})")
|
||||
else:
|
||||
failed += 1
|
||||
print(f" ⚠ [{entry['id']}] Skipped: {entry['title'][:40]}... (too short)")
|
||||
except Exception as e:
|
||||
print(f" ✗ [{entry['id']}] Error: {e}")
|
||||
failed += 1
|
||||
|
||||
if args.verify:
|
||||
print(f"\n [Verification]")
|
||||
sources = [args.source] if args.source else ["github", "arxiv", "reddit"]
|
||||
for src in sources:
|
||||
print(f" Checking {src}...")
|
||||
verify_summaries(conn, src)
|
||||
print()
|
||||
|
||||
print(f" Results: {success} summarized, {failed} failed")
|
||||
conn.close()
|
||||
print(f"\n Done.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
from oracle.cli import main as cli_main
|
||||
sys.argv = ["oracle"] + cli_args
|
||||
cli_main()
|
||||
|
||||
+8
-125
@@ -1,128 +1,11 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Phase 6 trend-tracking: theme-based arrival counter.
|
||||
"""Thin wrapper — delegates to oracle.cli themes subcommand."""
|
||||
import sys, os
|
||||
sys.path.insert(0, os.path.dirname(__file__))
|
||||
|
||||
The question this answers: is the 2026-07-08 practitioner cluster a real TREND
|
||||
or a one-day COINCIDENCE?
|
||||
args = sys.argv[1:]
|
||||
cli_args = ["themes"] + args
|
||||
|
||||
Design (per review):
|
||||
- Tag by THEME, not by entry ID.
|
||||
- Count NEW theme-tagged ARRIVALS per cron cycle (only classify rows that
|
||||
have no theme_tags yet -> fresh entries each run).
|
||||
- If new theme arrivals stay ~0 all week => coincidence.
|
||||
- If 2-4+ new relevant items/cycle across sources => trend.
|
||||
Only then does the ponytail-generalization idea graduate from a one-day
|
||||
read to something worth further investment.
|
||||
|
||||
Themes:
|
||||
tool-call : tool-call gating on confidence / reliability of tool use
|
||||
context : context / token compression before window ceiling
|
||||
compute : routing to smallest sufficient model / inference cost
|
||||
trust : trust boundaries on what a model may learn / vetted adapters
|
||||
|
||||
Run: python3 theme_scan.py # classify + report new arrivals
|
||||
python3 theme_scan.py --history # also print per-cycle history
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import re
|
||||
import sqlite3
|
||||
import sys
|
||||
from collections import Counter
|
||||
|
||||
DB = os.path.join(os.path.dirname(__file__), "oracle.db")
|
||||
|
||||
# Theme -> regex over title+summary+extracted text (case-insensitive).
|
||||
# Deliberately keyword-anchored, not semantic — cheap, auditable, reproducible.
|
||||
THEME_PATTERNS = {
|
||||
"tool-call": re.compile(
|
||||
r"\b(tool[- ]?call|tool[- ]?use|competence gate|confidence gate|"
|
||||
r"gate[d]? tool|action gate|tool reliability|function call gate)\b",
|
||||
re.I),
|
||||
"context": re.compile(
|
||||
r"\b(context (compress|window|ceiling|summar)|semantic compress|"
|
||||
r"token (compress|budget)|compress (context|session)|context (limit|overflow))\b",
|
||||
re.I),
|
||||
"compute": re.compile(
|
||||
r"\b(small(er|est)? model|route to|inference cost|cpu (tts|infer)|"
|
||||
r"cheap(er)? model|model routing|tiny model|on[- ]device (llm|model))\b",
|
||||
re.I),
|
||||
"trust": re.compile(
|
||||
r"\b(trust(ed)? (adapter|lora)|vetted adapter|learn (only|what).*adapter|"
|
||||
r"trust boundary|what a model (can|may) learn|auditable (adapter|skill))\b",
|
||||
re.I),
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("--history", action="store_true",
|
||||
help="Print per-cycle new-arrival history after the run")
|
||||
args = ap.parse_args()
|
||||
|
||||
if not os.path.exists(DB):
|
||||
print("No oracle.db — nothing to scan")
|
||||
return
|
||||
|
||||
conn = sqlite3.connect(DB)
|
||||
conn.row_factory = sqlite3.Row
|
||||
cur = conn.cursor()
|
||||
|
||||
# Ensure theme_tags table exists (defensive; schema.sql creates it).
|
||||
cur.execute("""CREATE TABLE IF NOT EXISTS theme_tags (
|
||||
entry_id INTEGER NOT NULL,
|
||||
theme TEXT NOT NULL,
|
||||
first_seen_cycle TEXT DEFAULT (strftime('%Y-%m-%dT%H:%M:%SZ','now')),
|
||||
PRIMARY KEY (entry_id, theme))""")
|
||||
|
||||
# Only rows not yet classified -> fresh arrivals this cycle.
|
||||
cur.execute("""
|
||||
SELECT e.id, e.source, e.title,
|
||||
COALESCE(e.summary,'') AS summary,
|
||||
COALESCE(e.extracted_text,'') AS extracted
|
||||
FROM entries e
|
||||
WHERE e.id NOT IN (SELECT entry_id FROM theme_tags)
|
||||
""")
|
||||
fresh = cur.fetchall()
|
||||
|
||||
new_counts = Counter()
|
||||
for row in fresh:
|
||||
blob = f"{row['title']} {row['summary']} {row['extracted']}"
|
||||
for theme, pat in THEME_PATTERNS.items():
|
||||
if pat.search(blob):
|
||||
cur.execute(
|
||||
"INSERT OR IGNORE INTO theme_tags (entry_id, theme) VALUES (?, ?)",
|
||||
(row["id"], theme))
|
||||
new_counts[theme] += 1
|
||||
|
||||
conn.commit()
|
||||
|
||||
print("=== Theme trend scan ===")
|
||||
print(f" Fresh (unclassified) entries this cycle: {len(fresh)}")
|
||||
if new_counts:
|
||||
print(" NEW theme arrivals this cycle:")
|
||||
for theme in ("tool-call", "context", "compute", "trust"):
|
||||
if new_counts.get(theme):
|
||||
print(f" {theme}: +{new_counts[theme]}")
|
||||
else:
|
||||
print(" NEW theme arrivals this cycle: 0")
|
||||
|
||||
# Cumulative context for the trend question.
|
||||
cur.execute("SELECT theme, COUNT(*) AS c FROM theme_tags GROUP BY theme")
|
||||
cum = {r["theme"]: r["c"] for r in cur.fetchall()}
|
||||
print(f" Cumulative theme_tags totals: {cum}")
|
||||
|
||||
if args.history:
|
||||
print("\n Per-cycle new arrivals (by first_seen_cycle):")
|
||||
cur.execute("""
|
||||
SELECT substr(first_seen_cycle,1,10) AS day, theme, COUNT(*) AS c
|
||||
FROM theme_tags GROUP BY day, theme ORDER BY day, theme
|
||||
""")
|
||||
for r in cur.fetchall():
|
||||
print(f" {r['day']} {r['theme']}: {r['c']}")
|
||||
|
||||
conn.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
from oracle.cli import main as cli_main
|
||||
sys.argv = ["oracle"] + cli_args
|
||||
cli_main()
|
||||
|
||||
@@ -0,0 +1,25 @@
|
||||
# Brief edition — executive summary, top 8 PUBLISH only
|
||||
name: "Athena Brief"
|
||||
description: "Top 8 AI stories that matter right now"
|
||||
output: "brief/index.html"
|
||||
|
||||
filters:
|
||||
verdicts: ["PUBLISH"]
|
||||
sources: []
|
||||
min_score: 0
|
||||
max_age_h: 48
|
||||
max_items: 8
|
||||
|
||||
ranking:
|
||||
by: "signal_score"
|
||||
half_life_h: 6
|
||||
|
||||
display:
|
||||
top_n: 8
|
||||
show_summary: true
|
||||
show_score: false
|
||||
show_tier: false
|
||||
show_verdict: false
|
||||
theme: "light"
|
||||
accent: "#f59e0b"
|
||||
logo: "⚡"
|
||||
@@ -0,0 +1,31 @@
|
||||
# Default Athena edition — full feed, clickability-ranked
|
||||
name: "Athena AI News"
|
||||
description: "Full AI research feed ranked by clickability index"
|
||||
output: "index.html"
|
||||
|
||||
filters:
|
||||
# Include all verdicts
|
||||
verdicts: ["PUBLISH", "WATCH", "ARCHIVE", "DROP"]
|
||||
# No source restriction
|
||||
sources: []
|
||||
# Minimum signal score
|
||||
min_score: 0
|
||||
# Maximum age in hours (0 = no limit)
|
||||
max_age_h: 0
|
||||
# Maximum items (0 = no limit)
|
||||
max_items: 0
|
||||
|
||||
ranking:
|
||||
# Sort by: clickability | signal_score | verdict_priority | freshness
|
||||
by: "clickability"
|
||||
half_life_h: 18
|
||||
|
||||
display:
|
||||
top_n: 8
|
||||
show_summary: true
|
||||
show_score: true
|
||||
show_tier: false
|
||||
show_verdict: false
|
||||
theme: "dark"
|
||||
accent: "#5b8cff"
|
||||
logo: "🏛️"
|
||||
@@ -0,0 +1,25 @@
|
||||
# DevOps edition — shipping tools, frameworks, infrastructure
|
||||
name: "Athena DevOps"
|
||||
description: "Shipping AI tools, frameworks, and infrastructure releases"
|
||||
output: "devops/index.html"
|
||||
|
||||
filters:
|
||||
verdicts: ["PUBLISH", "WATCH"]
|
||||
sources: ["github", "hackernews", "rss"]
|
||||
min_score: 2.5
|
||||
max_age_h: 168
|
||||
max_items: 40
|
||||
|
||||
ranking:
|
||||
by: "clickability"
|
||||
half_life_h: 12
|
||||
|
||||
display:
|
||||
top_n: 10
|
||||
show_summary: true
|
||||
show_score: true
|
||||
show_tier: true
|
||||
show_verdict: true
|
||||
theme: "dark"
|
||||
accent: "#34d399"
|
||||
logo: "🔧"
|
||||
@@ -0,0 +1,25 @@
|
||||
# Research edition — arXiv + HF papers, high-signal only
|
||||
name: "Athena Research"
|
||||
description: "Peer-reviewed AI research papers and model releases"
|
||||
output: "research/index.html"
|
||||
|
||||
filters:
|
||||
verdicts: ["PUBLISH", "WATCH", "ARCHIVE"]
|
||||
sources: ["arxiv", "huggingface"]
|
||||
min_score: 3.0
|
||||
max_age_h: 720
|
||||
max_items: 50
|
||||
|
||||
ranking:
|
||||
by: "signal_score"
|
||||
half_life_h: 72
|
||||
|
||||
display:
|
||||
top_n: 12
|
||||
show_summary: true
|
||||
show_score: true
|
||||
show_tier: true
|
||||
show_verdict: true
|
||||
theme: "dark"
|
||||
accent: "#a78bfa"
|
||||
logo: "🔬"
|
||||
+8
-78
@@ -1,81 +1,11 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Write batch summaries back to oracle.db.
|
||||
"""Thin wrapper — delegates to oracle.cli summarize subcommand."""
|
||||
import sys, os
|
||||
sys.path.insert(0, os.path.dirname(__file__))
|
||||
|
||||
Reads /tmp/athena_summarize_batch.json (array of entry dicts with 'summary' key added by sub-agent),
|
||||
writes each summary JSON to entries.summary column.
|
||||
args = sys.argv[1:]
|
||||
cli_args = ["summarize"] + args
|
||||
|
||||
Usage:
|
||||
python3 write_summaries.py /tmp/athena_summarize_batch_result.json
|
||||
"""
|
||||
import json
|
||||
import sqlite3
|
||||
import sys
|
||||
|
||||
def main():
|
||||
if len(sys.argv) < 2:
|
||||
print("Usage: python3 write_summaries.py <result_json_file>", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
result_path = sys.argv[1]
|
||||
try:
|
||||
with open(result_path) as f:
|
||||
results = json.load(f)
|
||||
except (FileNotFoundError, json.JSONDecodeError) as e:
|
||||
print(f"Error reading {result_path}: {e}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
db_path = '/home/vpsadmin/oracle/oracle.db'
|
||||
conn = sqlite3.connect(db_path)
|
||||
cur = conn.cursor()
|
||||
|
||||
written = 0
|
||||
skipped = 0
|
||||
errors = 0
|
||||
|
||||
for item in results:
|
||||
eid = item.get('id')
|
||||
summary = item.get('summary')
|
||||
if not eid or not summary:
|
||||
skipped += 1
|
||||
continue
|
||||
|
||||
# Validate summary has expected keys
|
||||
if not all(k in summary for k in ('one_liner', 'key_technical_point', 'potential_use_case', 'confidence')):
|
||||
print(f" ⚠ ID {eid}: missing required keys, skipping", file=sys.stderr)
|
||||
skipped += 1
|
||||
continue
|
||||
|
||||
# Quality gate: reject low-confidence or generic summaries
|
||||
ol = summary.get('one_liner', '')
|
||||
if len(ol) < 20:
|
||||
print(f" ⚠ ID {eid}: one_liner too short ({len(ol)} chars), skipping", file=sys.stderr)
|
||||
skipped += 1
|
||||
continue
|
||||
if any(generic in ol.lower() for generic in ('this article discusses', 'this paper presents', 'see full')):
|
||||
print(f" ⚠ ID {eid}: generic one_liner, skipping", file=sys.stderr)
|
||||
skipped += 1
|
||||
continue
|
||||
|
||||
try:
|
||||
cur.execute("UPDATE entries SET summary = ? WHERE id = ?",
|
||||
(json.dumps(summary), eid))
|
||||
written += 1
|
||||
print(f" ✓ ID {eid}: {ol[:70]}...")
|
||||
except Exception as e:
|
||||
print(f" ✗ ID {eid}: {e}", file=sys.stderr)
|
||||
errors += 1
|
||||
|
||||
conn.commit()
|
||||
|
||||
# Verify
|
||||
cur.execute("SELECT COUNT(*) FROM entries WHERE summary IS NOT NULL")
|
||||
total = cur.fetchone()[0]
|
||||
conn.close()
|
||||
|
||||
print(f"\nResults: {written} written, {skipped} skipped, {errors} errors")
|
||||
print(f"Total entries with summary: {total}")
|
||||
return 0 if errors == 0 else 1
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
from oracle.cli import main as cli_main
|
||||
sys.argv = ["oracle"] + cli_args
|
||||
cli_main()
|
||||
|
||||
Reference in New Issue
Block a user