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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)
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 3: Requirements
This chapter captures non-functional and system-level requirements that support the product vision but are not expressed as user stories.
3.1 Reliability
REQ-REL-05: Once setup and configured, the system will reliably operate without interaction from the user.