Vision Lock v1: AI News Aggregator -> AI Enthusiast Daily (ADR-0007)
- docs/vision/VISION_LOCK_V1.md: permanent editorial mission, audience (builders/tinkerers/
local-AI users/home-lab/agent devs), Editorial Test ('what would an enthusiast DO?'),
architectual constraint (editorial vision is primary system), priority order.
- docs/vision/ADR-0007_EDITORIAL_PIVOT.md: accepted pivot record, before/after table,
Sprint 1 evidence (high-signal = actionable, low-signal = spectator content).
- docs/vision/EDITORIAL_GUIDELINES.md: operating rules; score is draft, taste overrides;
hand-before-machine; memory must store 'what can be done' not 'what happened'.
- docs/vision/SECTION_DEFINITIONS.md: 5 sections (What Shipped / Run It Locally /
Benchmarks & Builds / Problem Solved / Worth Trying Tonight).
- docs/vision/SPRINT_1_FINDINGS.md: reference evidence, 45/200 UNCATEGORIZED taxonomy gap.
- Issue_001.md: handcrafted prototype edition from Sprint 1 stories; benchmark for all future automation.
This is the architectural directive. No scoring/narrative/memory work proceeds except in
service of the Vision Lock. Sprint 2 (Lens) stays blocked pending manual review tallies.
This commit is contained in:
Epictetus
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# Issue 001 — Prototype Edition: "If We Launched Tomorrow Morning"
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**Type:** Handcrafted prototype edition
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**Automation:** NONE. No new architecture. No additional scoring. Curated by hand from Sprint 1 stories (athena_review_report.txt) against the Editorial Test (VISION_LOCK_V1.md).
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**Purpose:** The benchmark against which all future automation is measured. If a future pipeline can't reproduce the judgment shown here, it isn't ready.
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---
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## What Shipped Today
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**Juggler — open-source GUI coding agent** `(id 2225, HN)`
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A visual, open-source coding agent from the creator of JUCE. Install and drive it.
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→ *Do: install it. GitHub: github.com/juggler-ai/juggler*
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**Open-Source Local LLM Training Tool (consumer hardware)** `(id 2315, Reddit)`
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A tool for training/fine-tuning local models on consumer GPUs.
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→ *Do: run it on your hardware.*
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**Zer0Fit — MCP server for zero-shot ML, 100% local** `(id 2427, Reddit)`
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Wraps Google's TabFM/TimesFM as an MCP server for forecasts/classifications/regressions. Fully local.
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→ *Do: wire it into your agent as an MCP tool.*
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**Hundreds of arXiv papers hit daily — an open-source triage tool** `(id 2430, Reddit)`
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Built because only ~3 of hundreds matter to one researcher's work.
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→ *Do: use it to cut your own paper noise.*
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---
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## Run It Locally
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**Quad 5060Tis benchmarked for code-gen with Qwen3.6-27B** `(id 1558, Reddit, final 0.50)`
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Real numbers, consumer hardware, reproducible method.
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→ *Do: replicate the bench on your GPU.*
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**Voodoo Quant beats Unsloth Dynamic 2.0 KLD by 95% (Qwen3.5 0.8B/2B)** `(id 1977, Reddit, 0.45)`
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Quantization-quality showdown with measured delta.
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→ *Do: try Voodoo on your next quant.*
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**Nemotron Puzzle 75B on a 64GB M2 Max** `(id 1573, Reddit)`
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Runs smoothly — a concrete ceiling number for Apple-silicon home labs.
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→ *Do: compare against your own max-model-on-Mac result.*
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**Your $80 Tesla P100 has been doing silently noisy math in llama.cpp for years** `(id 1975, Infra, 0.32)`
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Three-line fix, free. The most "worth trying tonight" infrastructure post in the set.
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→ *Do: apply the 3-line fix tonight.*
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---
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## Benchmarks & Builds
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**GPUHedge: serverless GPU hedging drops cold-start p95 from 117s → 30s** `(id 2438, Problem Solved, 0.41)`
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Measured latency win with a concrete method.
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→ *Do: steal the hedging pattern for your own GPU jobs.*
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**Migrating a production AI agent to GPT-5.6: 2.2x faster, 27% cheaper** `(id 2089, HN, 0.41)`
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Real cost/latency numbers from a production cutover.
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→ *Do: benchmark your own agent migration against these.*
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**5090 (475/600W) vs 6000 Pro MaxQ / WS (600W) concurrency** `(id 1559, Infra, 0.34)`
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Full compute comparison with methodology.
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→ *Do: map your workload onto their numbers.*
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**Dual-GPU PCIe transfer under llama.cpp (tensor + pipeline)** `(id 1565, Reddit, 0.34)`
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Measured interconnect behavior for multi-GPU home builds.
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→ *Do: check your own PCIe scaling.*
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---
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## Problem Solved
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**The real bottleneck for AI agents may be proving who they are** `(id 2314, Infra, 0.19)`
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Identity before intelligence — a production lesson, not a feature drop.
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→ *Do: learn the pattern before you scale agents.*
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**AI agent crawlers now need permission** `(id 2134, RSS, 0.25)`
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Cloudflare-style rules for agent access. A workflow decision, not news.
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→ *Do: set the same rule for your own agents.*
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**GPUHedge / Migrating** (see Benchmarks) — both are *problem-solved-with-numbers*, the canonical section fit.
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---
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## Worth Trying Tonight
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1. **Apply the Tesla P100 llama.cpp 3-line fix** `(id 1975)` — free, immediate, local.
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2. **Install Juggler and drive one real coding task** `(id 2225)` — open-source, now.
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3. **Replicate the quad-5060Ti Qwen3.6 bench on your GPU** `(id 1558)` — 20 minutes, real numbers.
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4. **Try Voodoo Quant on your next local model** `(id 1977)` — measured 95% KLD win.
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5. **Wire Zer0Fit as an MCP tool in your agent** `(id 2427)` — 100% local forecasting.
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---
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## What This Edition Proves
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Every story above passes the Editorial Test: a reader can **install, run, benchmark, replicate, or learn** something. Nothing here is a lawsuit, a funding round, a CEO take, or a valuation. That's the entire publication in one morning — and the bar every automated edition must clear.
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*Rejected from this prototype (representative): PixVerse $439M raise, Apple/OpenAI trade-secret suit, Altman job-creation take, Spotify AI assistant, Lorde-on-AI-glasses. Reader would do NOTHING with those.*
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# ADR-0007: Editorial Pivot — AI News Aggregator → AI Enthusiast Daily
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**Status:** ACCEPTED · 2026-07-15
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**Supersedes:** implicit "AI news aggregation" assumption (never written down — that was the bug)
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**Superseded by:** nothing yet
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---
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## Title
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Editorial Pivot: AI News Aggregator → AI Enthusiast Daily
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## Context
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Athena Oracle was built as a multi-source AI research pipeline: ingest → score → pattern/falsification. Sprint 1 (pure rule-based bucket classifier + scorer) was the first time we put 200 real stories in front of a human with the question "would we proudly publish these?"
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The classifier accuracy was never the discovery. The discovery was **what the stories were actually for**. Scanning the 200, the ones we'd proudly ship were uniformly *actionable by a builder*: local-model how-tos, benchmarks with real numbers, reproducible tooling, agent infra with code. The ones we'd reject were uniformly *spectator content*: lawsuits, funding rounds, CEO opinions, valuations, corporate announcements.
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We had been optimizing the system for the wrong question.
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## Decision
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Athena Oracle is **no longer optimizing for:**
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> "What happened in AI today?"
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Athena Oracle is **optimizing for:**
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> "What can an AI enthusiast DO after reading this?"
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This changes every layer: scoring, classification, editorial review, rendering, narratives, memory. Everything.
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## Before / After
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| Dimension | Before (implicit) | After (locked) |
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|-----------|------------------|-----------------|
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| Optimized for | Recency | Actionability |
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| | Volume | Reproducibility |
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| | Virality | Enthusiast value |
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| | | Builder usefulness |
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| Unit of value | "was published" | "can be done" |
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| Audience | anyone interested in AI | builders / tinkerers / local-AI users |
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| Reject signal | none | "reader would do NOTHING" |
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## Sprint 1 Evidence
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From the 200-story manual review (athena_review_report.txt):
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**High signal (would ship):**
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- LOCAL AI bucket — gguf / rtx / quantization / llama.cpp stories scored highest on enthusiast + replication
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- Open-source tools (Juggler GUI coding agent, open-source arXiv tool, Zer0Fit MCP server)
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- Benchmarks with real numbers (GPUHedge: 117s→30s p95; Migrating to GPT-5.6: 2.2x faster, 27% cheaper)
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- Infrastructure with code (Your $80 Tesla P100 llama.cpp fix)
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- Production learnings (agent identity, permissioned crawlers)
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**Low signal (would reject):**
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- Funding rounds (PixVerse $439M, DeepSeek $7B)
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- Lawsuits (Apple/OpenAI trade secret, Google training suit)
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- CEO opinions (Altman, Hassabis, Mosseri takes)
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- Valuations (PixVerse $2B)
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- Corporate announcements (Waze features, Spotify assistant)
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**Taxonomy gap found:** 45 / 200 (22.5%) were UNCATEGORIZED — mostly Reddit/HN opinion + culture-adjacent pieces with no keyword hit. This is exactly the discovery data we wanted: the editorial boundary is sharper than any keyword set yet.
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## Consequences
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- All future scoring work measures actionability, not recency.
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- The Lens (Sprint 2) is **blocked** until manual review tallies are returned and the taxonomy is tuned by hand.
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- Narratives and memory engines, when built, must encode "what can be done," not "what happened."
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- This ADR is the reference anchor for every later ADR.
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## Confirmation
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Accepted by founder directive 2026-07-15. Locked in `/docs/vision/VISION_LOCK_V1.md`.
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# EDITORIAL GUIDELINES — AI Enthusiast Daily
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Derived from VISION_LOCK_V1.md and ADR-0007. These are the operating rules for every edition, human- or machine-assisted.
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---
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## The One Test
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> **What would an AI enthusiast DO after reading this?**
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If the honest answer is **Nothing**, the story does not belong in the publication. No exceptions for "it's a big deal" or "everyone's talking about it."
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## Publish Lean
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Default toward stories where the reader walks away able to:
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- **install** something (open-source tool, model, agent)
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- **benchmark** something (real numbers, reproducible method)
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- **run** something locally (Ollama, llama.cpp, GGUF, self-host)
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- **replicate** a result (code, weights, config)
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- **learn** a technique (production war story, failure post-mortem)
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- **build with** it (agent infra, framework, pipeline)
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## Reject Default
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Reject by default when the story is:
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- A lawsuit or legal filing (unless it changes what a builder can ship)
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- A funding round or valuation (money moved, no capability changed)
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- A CEO / exec opinion or take (someone said something)
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- A celebrity or consumer AI culture piece (glasses, ads, vibes)
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- A corporate feature announcement with no reproducible substance
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- A pure market / stock / IPO item
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## Section Fit (see SECTION_DEFINITIONS.md)
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Every published story must map to exactly one section. If it fits none, it's either UNCATEGORIZED-by-design (hold) or a reject.
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## Score Is Not Taste
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The `final_score` from Sprint 1 is a **draft signal only**. It was built to be debugged, not trusted. Editorial judgment — the One Test — overrides any number. A 0.50 local-AI benchmark may ship; a 0.29 culture piece may not.
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## Hand-Before-Machine
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No automation may publish. Automation may *propose*; the human editor *confirms*. The founder rule stands: "Publish? [Y/N] — Not algorithmic. Human review only."
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## Memory Rule
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When future memory / knowledge-graph components land, they must record **what a reader can do** with a development — not a summary of the development. "Qwen3.6-27B runs on 4×5060Ti at INT8" is memory. "Qwen released a model" is not.
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# SECTION DEFINITIONS — AI Enthusiast Daily
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The publication is organized into five working sections. Every published story maps to exactly one. These are editorial sections, not the Sprint 1 classifier buckets (those are a separate, internal taxonomy — see athena_review_report.txt).
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---
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## 1. What Shipped Today
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**Things people built.**
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New open-source tools, models released with weights, agents shipped, MCP servers, GitHub projects that a reader can go use right now.
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*Editorial test:* "Can I install or run it today?" → Yes = in.
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## 2. Run It Locally
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**Local models, GGUF, Ollama, inference, benchmarks on consumer hardware.**
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Anything about running AI on your own machine — quantization, VRAM budgets, llama.cpp tricks, Mac/Linux/RTX setups, home-lab builds.
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*Editorial test:* "Could I replicate this on hardware I own?" → Yes = in.
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## 3. Benchmarks & Builds
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**Hardware and performance experimentation.**
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Head-to-head GPU comparisons, concurrency tests, latency/throughput numbers, quantization-quality showdowns, build logs with measurable outcomes.
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*Editorial test:* "Are there real numbers I can compare against my own setup?" → Yes = in.
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## 4. Problem Solved
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**Real-world outcomes and workflows.**
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Production war stories, failure post-mortems, "we fixed X and here's the method," agent-identity/permissioning, cost-reduction playbooks.
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*Editorial test:* "Did someone actually ship something and learn a transferable lesson?" → Yes = in.
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## 5. Worth Trying Tonight
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**High-action recommendations.**
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The short list — 3 to 5 items max — that a reader could act on *this evening*. Curated, not comprehensive. The "if you only do one thing" section.
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*Editorial test:* "Would a builder drop what they're doing and try this tonight?" → Yes = in.
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---
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## Mapping note (Sprint 1 → sections)
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The internal classifier buckets (LOCAL AI, PROBLEM SOLVED, RESEARCH, INFRASTRUCTURE, SHIPPING, MODEL RELEASE, BUSINESS, CULTURE, UNCATEGORIZED) feed these sections but are NOT identical to them. Section assignment is an editorial act, not a mechanical copy. A RESEARCH paper with a clear builder angle may land in Benchmarks & Builds; a MODEL RELEASE with no reproducible substance may be rejected entirely.
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# SPRINT 1 FINDINGS — Reference Material
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Raw evidence from the Sprint 1 manual review (200 stories, athena_review_report.txt). This document is **reference material for future decisions** — it records what the data actually showed so later tuning has a baseline.
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---
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## What We Learned
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Sprint 1 was pure rules (no embeddings, no LLM, no semantic similarity) by founder directive: *discover the taxonomy before adding intelligence*. The classifier's job was not accuracy — it was to surface misclassifications as signal. It did.
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## High Signal (ship)
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Stories the editorial test would pass — reader can DO something:
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- **Local AI** — gguf / rtx / quantization / llama.cpp. Highest enthusiast + replication scores.
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- e.g. "I benched quad 5060Tis for code generation with Qwen3.6-27B" (final 0.50)
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- "Voodoo Quant beats Unsloth Dynamic 2.0 KLD by 95%" (0.45)
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- **Open-source tools** — things to install/run.
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- "Show HN: Juggler – open-source GUI coding agent" (0.57, highest in set)
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- "Open Source Local LLM Training Tool (consumer hardware)" (0.46)
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- "Zer0Fit — MCP server for zero-shot ML, 100% local" (0.30–0.46)
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- **Benchmarks with real numbers** — reproducible, comparable.
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- "GPUHedge: cold-start p95 117s→30s" (0.41)
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- "Migrating to GPT-5.6: 2.2x faster, 27% cheaper" (0.41)
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- **Infrastructure with code** — fixable, runnable.
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- "Your $80 Tesla P100 silently noisy math in llama.cpp — 3-line fix, free" (0.32)
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- **Production learnings** — transferable lessons.
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- "The real bottleneck for AI agents may be proving who they are" (0.19)
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- "AI agent crawlers now need permission" (0.25)
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## Low Signal (reject)
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Stories the editorial test would fail — reader does NOTHING:
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- **Funding rounds** — PixVerse $439M, DeepSeek $7B. Money moved, no capability changed.
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- **Lawsuits** — Apple/OpenAI trade secret, Google training suit. Spectator content.
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- **CEO opinions** — Altman, Hassabis, Mosseri takes. Someone said something.
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- **Valuations** — PixVerse $2B. Number, no action.
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- **Corporate announcements** — Waze features, Spotify assistant, Siri beta. No reproducible substance.
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## Taxonomy Gap (the real discovery)
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- **45 / 200 (22.5%) UNCATEGORIZED.** Mostly Reddit/HN opinion + culture-adjacent pieces with no keyword hit (e.g. "Stop Telling Me to Ask an LLM", "Mesh LLM", "Ghost Font").
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- This is **not a failure** — it's the editorial boundary made visible. The keyword set is narrower than taste. Tuning Sprint 2 (Lens) must start from these 45, not from classifier precision.
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## Bucket distribution (200 reviewed)
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| Bucket | n |
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|--------|---|
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| RESEARCH | 40 |
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| INFRASTRUCTURE | 23 |
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| UNCATEGORIZED | 45 |
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| MODEL RELEASE | 24 |
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| PROBLEM SOLVED | 19 |
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| SHIPPING | 12 |
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| CULTURE | 15 |
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| BUSINESS | 13 |
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| LOCAL AI | 9 |
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## Open question for Sprint 2
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When the Lens arrives, its first job is **not** to classify better. It's to explain *why* a story is or isn't actionable — and to propose a section (§SECTION_DEFINITIONS) for human confirmation. The 45 UNCATEGORIZED are the training set for that judgment, not the 155 that already matched.
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# VISION LOCK V1
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**Status:** PERMANENT · LOCKED 2026-07-15
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**Authority:** Primary system constraint. No future scoring, narrative, memory, knowledge-graph, or editorial-automation work may contradict this document.
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---
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## Mission
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Find the **small number of developments that change what AI enthusiasts can DO today** — not what happened, but what became possible.
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## Audience
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NOT:
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- General readers
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- Investors
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- Executives
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- Policymakers
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IS:
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- **Builders** — people shipping things with AI
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- **Tinkerers** — people modifying, extending, breaking
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- **Local AI users** — people running models on their own hardware
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- **Home-lab operators** — people running inference, agents, pipelines at home
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- **Workflow creators** — people automating with agents
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- **Agent developers** — people building agent systems
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## Editorial Test
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For **every** story, ask one question:
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> **What would an AI enthusiast DO after reading this?**
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Acceptable answers:
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- `install it`
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- `benchmark it`
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- `run it`
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- `replicate it`
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- `learn from it`
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- `build with it`
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If the answer is:
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> **Nothing.**
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→ **Reject.**
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## Architectural Constraint
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The editorial vision is now the **primary system**.
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The architecture serves it. Not the other way around.
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Any future component — scorer, classifier, narrative engine, memory engine, knowledge graph, rendering layer, editorial automation — must be justified against the Editorial Test above. If a component optimizes for recency, volume, virality, or engagement instead of *actionability*, it contradicts the Lock and must be rejected or re-scoped.
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## Architectural Priority Order
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1. **Vision** — the Editorial Test wins all conflicts
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2. **Editorial Quality** — would we proudly publish this?
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3. **Reader Value** — can the reader DO something?
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4. **Automation** — only after 1–3 are proven by hand
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5. **Technical Elegance** — last. Never traded against 1–4.
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If any two levels conflict, the higher level wins.
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Reference in New Issue
Block a user