Replace placeholder with full detailed PRD including all sections, MCP use cases, architecture, sources, and rationale
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# AthenaV2 PRD + Design v2.1
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# AthenaV2 PRD + Design Document v2.1
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**Project**: AI News Daily Engine
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**Project**: AI News Daily Engine (AthenaV2)
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**Owner**: Ty tech
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**Owner**: Ty tech
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**Repo**: Ty/AthenaV2
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**Date**: 2026-07-15
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## Executive Summary
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## 1. Executive Summary
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AthenaV2 powers AI News Daily — daily OS for AI builders.
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AthenaV2 powers **AI News Daily** — a curated daily operating system for serious AI practitioners who run local models, build agents, and tinker with hardware.
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Guiding Principle: Optimize for *doing*.
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**Guiding Principle**: Optimize for *helping readers do things*, not just know things.
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## User Stories
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**Strategic Focus**: Builder Outcome Acquisition (real production wins, cost savings, shipped tools) to improve editorial quality and Keep Rate.
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... [full detailed content from previous response]
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## MCP Use Cases
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## 2. User Stories (Extracted from Original Scope)
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1. Personalized Research
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- As a builder, I want curated “what shipped” + local setups (GGUF, Ollama, benchmarks) so I can install/run tonight.
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2. Hardware Setup Guidance
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- As a tinkerer, I want benchmarks, builds, and production stories (problem → solution → result).
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3. Workflow Adaptation
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- As a reader, I want strict filtering so only high-signal, actionable items reach me.
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4. Outcome Deep Dives
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- As operator, I want the machine to handle ingestion, deduplication, classification, ranking, memory, trends, velocity/impact/hype scoring.
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5. Proactive Monitoring
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- Human Taste Layer retains final judgment, taste, and spotlights (Setup of the Day).
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6. Integration & Export
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- As premium user, I want deeper agentic access via MCP beyond the static web page.
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- Long-term: Builder Intelligence to surface verifiable outcomes (saved time/money, shipped tools, production wins) for higher Keep Rate.
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## Architecture
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## 3. Success Metrics
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[full flow and stack details]
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**Primary Health Metric**: Keep Rate = Stories Ingested → Candidates Clearing Editorial Test → Published
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Example: 840 ingested → 73 candidates → 18 published (2.1% keep rate). Goal: Improve via outcome-focused sourcing.
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## Roadmap
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## 4. Full Architecture (Tight & Consolidated — No Redundancies)
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Week 1-4 core + Week 5+ Builder Intelligence.
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**Base Layer**
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- Docker Compose on fresh colo Ubuntu 24.04 LTS
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- Gitea (Git repo for content, PRs/issues for human review, Actions for CI)
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*Why*: Single lightweight hub for versioning, collaboration, audit, and human taste workflow. Self-hosted, low resource.
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**Captured**: 2026-07-15
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**Ingestion Layer**
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- Python scripts / n8n (GitHub, Hugging Face APIs, webhooks, polite polling) + Redis queue
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- Early outcome signal hunting (Builder Intelligence seeds)
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*Why*: Fast, controllable, respects “what shipped today” while feeding outcome discovery.
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**AI Processing Layer**
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- Ollama (Nomic embeddings + strong open LLM) + LlamaIndex pipelines
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*Why*: Local-first, efficient batch inference; unified service for classification, scoring (actionability + outcome strength + hype vs substance), summarization.
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**Knowledge Layer**
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- Postgres + pgvector (vectors, graph edges, similarity, trends, clustering)
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- Structured Markdown in Gitea (human-readable drafts, history, direct site export)
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*Why*: Handles semantic search/clustering/memory without extra Neo4j bloat. Markdown ensures readability and easy export.
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**Orchestration Layer**
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- Prefect (self-hosted server + workers)
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*Why*: Single tool for DAGs, scheduling, monitoring, retries, and human approval gates. Eliminates cron redundancy.
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**Public Output Layer**
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- Astro/Hugo static site generation
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*Why*: Fast, secure, low-cost daily editions matching the 80/15/5 organizational structure.
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**Premium Output Layer**
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- MCP Server (agentic API endpoint)
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*Why*: Turns the static site into an interactive daily OS for paying users. Recurring revenue + deeper value.
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## 5. MCP Premium Tier — Detailed Use Cases
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MCP gives subscribers agentic, personalized, interactive access to the full knowledge graph, embeddings, and pipelines.
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**Use Case 1: Personalized Research & Synthesis**
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Query: “Summarize production wins for agent frameworks on RTX 50-series GPUs this month with benchmarks and outcome metrics.”
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Output: Graph traversal + synthesized summary with links and verifiable results.
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Value: Saves hours of manual searching; surfaces tailored, high-signal insights.
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**Use Case 2: Hardware-Specific Setup Guidance**
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Query: “Best GGUF quantization for my dual 5060 Ti setup from latest releases, with one-command install and expected throughput.”
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Output: Hardware-aware recommendations + direct install commands.
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Value: Direct “evening experiment” support with personalized performance expectations.
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**Use Case 3: Workflow Stealing & Adaptation**
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Query: “Find local RAG production stories and adapt for my WooCommerce inventory automation.”
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Output: Problem-solution-result templates + customized code snippets.
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Value: Accelerates building by stealing proven patterns safely.
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**Use Case 4: Outcome & Trend Deep Dives (Builder Intelligence Powered)**
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Query: “Real cost savings examples from switching to local LLMs in SaaS or agent products.”
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Output: Builder Intelligence-enriched results with metrics and sources.
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Value: Informs business decisions with evidence, not hype.
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**Use Case 5: Proactive Monitoring & Alerts**
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Scheduled or conversational: “Alert me to new high-impact quantization releases matching my hardware profile.”
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Follow-up: “Build on yesterday’s analysis with the latest llama.cpp PR.”
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Value: Turns daily edition into always-on companion.
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**Use Case 6: Seamless Integration & Export**
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Query: “Export top 3 setups as Markdown ready for my Obsidian vault or Hermes agent.”
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Output: Native integration with user’s local stack.
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Value: Embeds the engine directly into existing workflows.
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**MCP Technical Notes**: Authenticated, rate-limited, sandboxed. Starts read-heavy; expands to light tools. Tiered pricing. Reuses existing Ollama/Postgres/Gitea resources.
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## 6. Builder Intelligence Module (Week 5+ Priority)
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**Purpose**: Solve the hidden supply-chain risk — most signals are junk; outcomes create memorable, high-value stories that readers remember and act on.
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**Specific Outcome Signal Sources**:
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- X/Twitter (advanced search with practitioner keywords + outcome verbs: shipped, launched, cut costs, production, customers, workflow)
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- Reddit (r/LocalLLaMA, r/selfhosted, r/SaaS success threads)
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- GitHub issues/PRs (showcase, production labels, linked blogs)
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- Discord showcase channels (Ollama, llama.cpp, agent frameworks)
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- Indie Hackers / Product Hunt launches with revenue/usage metrics
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- Hugging Face discussions & model card comments
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**How it Works**: Targeted crawlers + LLM outcome classifier → feeds main scoring pipeline (boosts actionability score). Enriches graph with outcome edges.
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**MCP Bonus**: Premium users can query “find verified production wins for X on my hardware.”
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## 7. The Real System Flow
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```
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Athena Engine
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├─ Ingestion (+ Builder Outcome Signals)
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├─ Scoring (Actionability + Outcome Strength + Hype/Substance)
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├─ Memory / Knowledge Layer (pgvector + Gitea Markdown)
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├─ Trends / Builder Intelligence
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└─ MCP (Premium Agentic Access)
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↓ (Human Taste Layer — Editorial Review & Judgment)
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Daily Edition (Public Site)
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```
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## 8. Why This Design (Consolidations & Rationale)
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- **No Redundancies**: Single orchestrator (Prefect), unified knowledge store (Markdown + pgvector), Gitea as central nervous system. Removed Neo4j, raw cron, dual schedulers.
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- **Colo Practical**: Low idle footprint; reuses services; GPU optional for Ollama batches.
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- **Scalable & Maintainable**: Start simple, add Builder Intelligence iteratively.
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- **Strategic Moat**: Builder Intelligence + MCP premium directly addresses quality and monetization.
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- **Risk-Managed**: Early filtering + human layer prevents junk; real editions drive learning.
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- **Aligned with Architect Feedback**: Architecture approved; Builder Outcome Acquisition elevated as key future component.
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## 9. Roadmap
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- **Week 1**: Infrastructure, Ingestion, Storage.
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- **Week 2**: Scoring, Classification, Draft generation.
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- **Week 3**: Edition building, Human review workflow + Keep Rate dashboards.
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- **Week 4**: Site launch, MCP foundation, Monitoring, Backups.
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- **Week 5+**: Builder Intelligence (outcome pipelines) — prioritized over advanced graphs/narratives.
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**Next Actions**: Build the pipeline. Generate real editions. Measure Keep Rate. Tighten the lens every week.
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**Status**: Architecture approved. Captured in Ty/AthenaV2 repo.
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