Merge MVP-milestone docs into main #15

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@@ -48,6 +48,9 @@ We will use the following prioritization model:
- **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…**
@@ -65,10 +68,62 @@ We will use the following prioritization model:
**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.
This chapter captures non-functional and system-level requirements that support the product vision but are not expressed as user stories.
High level design (refer to ***TBD_Design.MD for full design details)
Linux
>> Docker
> > SQLLite
> > CRON
> > Runtime
> > HTTP client (internet accessible)
> > Supports OpenAI inference endpoint
> > Supports HTTP queries for data feeds (RSS, Curl, Playwright?)
> > HTTP server endpoint (internet acceesible)
> > MCP
> > HTTP client (intranet)
> > Supports OpenAI inference endpoint to Hermes
> > File system
> > Output dir for daily digest artifact (saved outside of the docker container)
> > Diagnostics / instrumentation (should land in the Docker infa for this container)
> > Configuration
> > Research topic mainfest (feeds, URLS, declarations)
> > System settings (YAML)
> > System secrets (.env))
> > Business logic
> > Starting trigger
> > preflight check
> > Query feeds / TMP store results
> > Vet temp results / store final results to DB
> > Produce digest (if configured)
> > Cleanup / sleep
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 - All internet-facting HTTP endpoints shall be TLS protected with well known can
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.