AthenaV2/PRD.md

AthenaV2 PRD + Design Document v2.1

Project: AI News Daily Engine (AthenaV2) Owner: Ty tech Repo: Ty/AthenaV2 Date: 2026-07-15

1. Executive Summary

AthenaV2 powers AI News Daily — a curated daily operating system for serious AI practitioners who run local models, build agents, and tinker with hardware.

Guiding Principle: Optimize for helping readers do things, not just know things.

Strategic Focus: Builder Outcome Acquisition (real production wins, cost savings, shipped tools) to improve editorial quality and Keep Rate.

2. User Stories (Extracted from Original Scope)

• As a builder, I want curated “what shipped” + local setups (GGUF, Ollama, benchmarks) so I can install/run tonight.
• As a tinkerer, I want benchmarks, builds, and production stories (problem → solution → result).
• As a reader, I want strict filtering so only high-signal, actionable items reach me.
• As operator, I want the machine to handle ingestion, deduplication, classification, ranking, memory, trends, velocity/impact/hype scoring.
• Human Taste Layer retains final judgment, taste, and spotlights (Setup of the Day).
• As premium user, I want deeper agentic access via MCP beyond the static web page.
• Long-term: Builder Intelligence to surface verifiable outcomes (saved time/money, shipped tools, production wins) for higher Keep Rate.

3. Success Metrics

Primary Health Metric: Keep Rate = Stories Ingested → Candidates Clearing Editorial Test → Published Example: 840 ingested → 73 candidates → 18 published (2.1% keep rate). Goal: Improve via outcome-focused sourcing.

4. Full Architecture (Tight & Consolidated — No Redundancies)

Base Layer

• Docker Compose on fresh colo Ubuntu 24.04 LTS
• Gitea (Git repo for content, PRs/issues for human review, Actions for CI) Why: Single lightweight hub for versioning, collaboration, audit, and human taste workflow. Self-hosted, low resource.

Ingestion Layer

• Python scripts / n8n (GitHub, Hugging Face APIs, webhooks, polite polling) + Redis queue
• Early outcome signal hunting (Builder Intelligence seeds) Why: Fast, controllable, respects “what shipped today” while feeding outcome discovery.

AI Processing Layer

• Ollama (Nomic embeddings + strong open LLM) + LlamaIndex pipelines Why: Local-first, efficient batch inference; unified service for classification, scoring (actionability + outcome strength + hype vs substance), summarization.

Knowledge Layer

• Postgres + pgvector (vectors, graph edges, similarity, trends, clustering)
• Structured Markdown in Gitea (human-readable drafts, history, direct site export) Why: Handles semantic search/clustering/memory without extra Neo4j bloat. Markdown ensures readability and easy export.

Orchestration Layer

• Prefect (self-hosted server + workers) Why: Single tool for DAGs, scheduling, monitoring, retries, and human approval gates. Eliminates cron redundancy.

Public Output Layer

• Astro/Hugo static site generation Why: Fast, secure, low-cost daily editions matching the 80/15/5 organizational structure.

Premium Output Layer

• MCP Server (agentic API endpoint) Why: Turns the static site into an interactive daily OS for paying users. Recurring revenue + deeper value.

5. MCP Premium Tier — Detailed Use Cases

MCP gives subscribers agentic, personalized, interactive access to the full knowledge graph, embeddings, and pipelines.

Use Case 1: Personalized Research & Synthesis Query: “Summarize production wins for agent frameworks on RTX 50-series GPUs this month with benchmarks and outcome metrics.” Output: Graph traversal + synthesized summary with links and verifiable results. Value: Saves hours of manual searching; surfaces tailored, high-signal insights.

Use Case 2: Hardware-Specific Setup Guidance Query: “Best GGUF quantization for my dual 5060 Ti setup from latest releases, with one-command install and expected throughput.” Output: Hardware-aware recommendations + direct install commands. Value: Direct “evening experiment” support with personalized performance expectations.

Use Case 3: Workflow Stealing & Adaptation Query: “Find local RAG production stories and adapt for my WooCommerce inventory automation.” Output: Problem-solution-result templates + customized code snippets. Value: Accelerates building by stealing proven patterns safely.

Use Case 4: Outcome & Trend Deep Dives (Builder Intelligence Powered) Query: “Real cost savings examples from switching to local LLMs in SaaS or agent products.” Output: Builder Intelligence-enriched results with metrics and sources. Value: Informs business decisions with evidence, not hype.

Use Case 5: Proactive Monitoring & Alerts Scheduled or conversational: “Alert me to new high-impact quantization releases matching my hardware profile.” Follow-up: “Build on yesterday’s analysis with the latest llama.cpp PR.” Value: Turns daily edition into always-on companion.

Use Case 6: Seamless Integration & Export Query: “Export top 3 setups as Markdown ready for my Obsidian vault or Hermes agent.” Output: Native integration with user’s local stack. Value: Embeds the engine directly into existing workflows.

MCP Technical Notes: Authenticated, rate-limited, sandboxed. Starts read-heavy; expands to light tools. Tiered pricing. Reuses existing Ollama/Postgres/Gitea resources.

6. Builder Intelligence Module (Week 5+ Priority)

Purpose: Solve the hidden supply-chain risk — most signals are junk; outcomes create memorable, high-value stories that readers remember and act on.

Specific Outcome Signal Sources:

• X/Twitter (advanced search with practitioner keywords + outcome verbs: shipped, launched, cut costs, production, customers, workflow)
• Reddit (r/LocalLLaMA, r/selfhosted, r/SaaS success threads)
• GitHub issues/PRs (showcase, production labels, linked blogs)
• Discord showcase channels (Ollama, llama.cpp, agent frameworks)
• Indie Hackers / Product Hunt launches with revenue/usage metrics
• Hugging Face discussions & model card comments

How it Works: Targeted crawlers + LLM outcome classifier → feeds main scoring pipeline (boosts actionability score). Enriches graph with outcome edges. MCP Bonus: Premium users can query “find verified production wins for X on my hardware.”

7. The Real System Flow

Athena Engine
  ├─ Ingestion (+ Builder Outcome Signals)
  ├─ Scoring (Actionability + Outcome Strength + Hype/Substance)
  ├─ Memory / Knowledge Layer (pgvector + Gitea Markdown)
  ├─ Trends / Builder Intelligence
  └─ MCP (Premium Agentic Access)

         ↓ (Human Taste Layer — Editorial Review & Judgment)

      Daily Edition (Public Site)

8. Why This Design (Consolidations & Rationale)

• No Redundancies: Single orchestrator (Prefect), unified knowledge store (Markdown + pgvector), Gitea as central nervous system. Removed Neo4j, raw cron, dual schedulers.
• Colo Practical: Low idle footprint; reuses services; GPU optional for Ollama batches.
• Scalable & Maintainable: Start simple, add Builder Intelligence iteratively.
• Strategic Moat: Builder Intelligence + MCP premium directly addresses quality and monetization.
• Risk-Managed: Early filtering + human layer prevents junk; real editions drive learning.
• Aligned with Architect Feedback: Architecture approved; Builder Outcome Acquisition elevated as key future component.

9. Roadmap

• Week 1: Infrastructure, Ingestion, Storage.
• Week 2: Scoring, Classification, Draft generation.
• Week 3: Edition building, Human review workflow + Keep Rate dashboards.
• Week 4: Site launch, MCP foundation, Monitoring, Backups.
• Week 5+: Builder Intelligence (outcome pipelines) — prioritized over advanced graphs/narratives.

Next Actions: Build the pipeline. Generate real editions. Measure Keep Rate. Tighten the lens every week.

Status: Architecture approved. Captured in Ty/AthenaV2 repo.