## Chapter 1: Vision and Scope

### Elevator Pitch

Athena is an autonomous research intelligence engine that cuts through high-volume, fragmented signals by ingesting from multiple sources, surfacing cross-source convergence, and using falsification to distinguish real momentum from noise. While the initial focus is on AI signals, the system is designed to work with any class of signals. It delivers actionable insight into emerging trends and capability gaps while remaining model-agnostic and lightweight enough to run autonomously.

### 1.1 Vision

#### Why are we building it?

The AI space produces an overwhelming volume of new research, tools, discussions, and model releases every day. Individual sources only provide partial views, making it difficult to distinguish genuine, sustained trends from one-day spikes. Without a system that can detect convergence across sources and validate momentum over time, real opportunities tied to emerging capability gaps are missed.

#### What happens if we don’t build it?

Without this capability, builders and researchers will continue to operate with fragmented, noisy signals. Early indicators of meaningful trends will remain hidden, decisions will stay reactive, and the ability to spot validated cross-source momentum before it becomes obvious will be lost.

#### When must it be done?

The foundational ability to reliably ingest, score, and validate signals through falsification must be established before meaningful trend detection and opportunity mapping can occur. This forms the core of the MVP and must be in place to enable the system to deliver on its intended value.

### 1.2 Personas and Archetypes

See committed document:  
**`docs/Personas-and-Archetypes.md`** (on `MVP-milestone` branch)

**Summary of scoped personas and archetypes for MVP:**

**Personas**
- Pers-1 (Bob) – Sector Trend Tracker (New to AI)
- Pers-2 (Alice) – Content Creator
- Pers-3 (Sam) – Hermes Research Agent

**Archetypes**
- Arch-1 (Small Scrappy VPS)
- Arch-2 (Research Consumption Layer)

All user stories in this PRD are scoped to combinations of the above.

### 1.3 Use Case Priority Taxonomy

This PRD focuses on defining the core functionality required for MVP. It also catalogs use cases and requirements across V1.0 – V1.5 to maintain context. The primary goal is to deliver a working MVP, with future PRDs derived from the remaining prioritized content.

We will use the following prioritization model:

- **MVP**: The short list of P1 use cases required to prove the concept with a working prototype.
- **P1**: Use cases that are fundamental to successfully implementing the product vision.
- **P2**: Use cases that add strength, convenience, and quality to the product vision.
- **P3**: Use cases that bring additional value but can be cut if time or resource constrained.

## Chapter 2: User Stories (Bob)
These user stories are based on the personas and archetypes document contained in this repo.  

Chapter 2.1 - Bob's user stories

**As Bob, I want to…**

**Bob-1.** Automatically receive daily updates on new AI innovations without having to manually check multiple sources.

**Bob-5.** See emerging trends and differentiate durable signal from temporary or artificial hype.

**Bob-10.** See when the same idea or pattern is appearing across multiple independent sources (GitHub, arXiv, Reddit, HN, HF).

**Bob-15.** Identify emerging capability gaps or opportunities early, before they become widely obvious.

**Bob-20.** Have research that gives me confidence it is exhaustive and vetted.

**Bob-25.** Adjust or alter the underlying data feeds and weights so I can tune the accuracy and relevance of the output.

**Bob-30.** Understand why a particular signal is considered strong or weak (e.g., cross-source convergence or falsification results).

Chapter 2.2 - Alice's user stories
As Alice, I want to…
Alice-1. Integrate deep, vetted research directly into my existing content production pipeline so I can reduce manual research time.
Alice-5. Query the research system with follow-up questions to explore specific angles or topics on demand.
Alice-10. Have my tools automatically receive curated, high-signal research so I can focus on content creation instead of information filtering.
Alice-15. Get research outputs in a structured format that my existing AI tools and workflows can consume without manual reformatting.
Alice-20. Quickly surface non-obvious insights and patterns from research data to develop more compelling content angles.
Alice-25. Control which research sources and signals are prioritized so the output stays aligned with my content focus and audience.
Alice-30. Understand the reasoning and supporting evidence behind key research findings so I can speak to them confidently in my content.

## Chapter 3: Requirements
Requirements defined as what the product / system must do, differentiated from what the persona can accomplish. Requirements are defined to meet the needs of use cases as well as the architectural system design.

High level design (refer to ***TBD_Design.MD for full design details)
High-Level Design
.
├── Runtime Environment
│   ├── Linux
│   └── Docker (containerized)
│
├── Core Components
│   ├── Database: SQLite
│   ├── Scheduling: Cron
│   └── Runtime: Python
│
├── Connectivity
│   ├── Outbound (Internet)
│   │   ├── HTTP client for data feeds (RSS, cURL, optional Playwright)
│   │   └── OpenAI-compatible inference endpoints
│   ├── Inbound (Internet)
│   │   └── HTTP server endpoint (MCP + external consumers)
│   └── Internal (Intranet)
│       └── HTTP client for local inference (e.g. Hermes)
│
├── Storage
│   ├── File system (daily digest artifacts stored outside container)
│   └── Temporary working storage during pipeline execution
│
├── Configuration & Secrets
│   ├── Research topic manifest (feeds, URLs, declarations)
│   ├── System settings (YAML)
│   └── Secrets (.env)
│
├── Business Logic / Pipeline Flow
│   ├── Starting trigger
│   ├── Preflight checks
│   ├── Query feeds → Temporary result storage
│   ├── Vet and promote final results to database
│   ├── Optional daily digest generation
│   └── Cleanup and sleep
│
└── Observability
├── Structured logging
├── Diagnostics and instrumentation (inside Docker)
└── Health/status reporting

3.1 Setup and configuration (REQ-SNC-XX)
Requirements for initial setup, deployment configs, updating, and uninstall
    REQ-SNC-05 -, with outbound access to the internet and in/outbound access to the underlying OS network
    REQ-SNC-10 - The installation process shall be a single command which can be run interactively or silently
    REQ-SNC-15 - The insallation shall utilize best-practice settings and secrets storage
    REQ-SNC-20 - 

3.2 Platform requirements (REQ-PLT-XX)
    REQ-PLT-05 -  All processes will run as standard user (no admin / sudo elevation necessary)
    REQ-PLT-10 -  ...
    REQ-PLT-15 -  The system shall be Docker based limited to 150MB of memory
Requirements addressing what OS and hardware support is in scope
3.3 Performance and scalability (REQ-PERF-XX)
3.4 Instrumenation and diagnostics (REQ-DIAG-XX)
3.5 
### 3.1 Reliability

REQ-REL-05: Once setup and configured, the system will reliably operate without interaction from the user.
REQ-REL-10: The system shall automatically retry failed source fetches with exponential backoff.
REQ-REL-15: The system shall not lose previously stored data on restart or failure.
REQ-REL-20: The daily pipeline shall complete successfully even if one or more sources are unavailable.

3.3 Observability and Diagnostics
REQ-DIAG-05: The system shall produce structured logs with timestamps and severity levels.
REQ-DIAG-10: A health check endpoint or command shall report overall system status and last successful run.
REQ-DIAG-15: Run logs shall capture per-source success/failure and basic metrics (items fetched, stored, failed).

3.4 Security 
REQ-SEC-05: All external HTTP calls shall use TLS.
REQ-SEC-10: No secrets shall be hardcoded or stored in plaintext.
REQ-SEC-15: The system shall support least-privilege access for outbound API calls.

3.5 Integration and extensibility 
REQ-INT-05: The system shall expose research output in a structured, machine-readable format (e.g., JSON files or API) consumable by external tools.
REQ-INT-10: The adapter layer shall support adding new sources without modifying core pipeline logic.
