Research, experiments, and practical notes on AI agents, tools, models, and emerging workflows.
This repository is where I document what I am learning while building agentic systems, evaluating AI-enabled product workflows, and experimenting with Hermes Agent.
Recurring research reports on:
- New AI tools and agent workflows
- GitHub projects with practical product potential
- Local-first and BYOK creator tools
- Agent skills, orchestration, evaluation, and security
- Product remix ideas based on emerging open-source projects
A collection of practical AI and automation workflow ideas organized by publication date.
Hands-on learning material covering prompt contracts, structured outputs, acceptance criteria, failure modes, and production-minded AI product design.
A minimal FastAPI service that turns unstructured user feedback into structured product insights, including themes, opportunities, and experiments.
The API supports two modes:
mock— deterministic local output with no LLM callsopenrouter— live structured analysis through OpenRouter
make bootstrap
make smoke
PYTHONPATH=. .venv/bin/uvicorn app.main:app --reloadINSIGHT2SPEC_ANALYZE_MODE—mockoropenrouter(default:mock)OPENROUTER_API_KEY— required foropenroutermodeOPENROUTER_MODEL— optional model overrideOPENROUTER_TIMEOUT_SECONDS— optional request timeout
curl -s http://127.0.0.1:8000/health | jq
curl -s -X POST http://127.0.0.1:8000/analyze \
-H 'Content-Type: application/json' \
-d '{
"feedback": [
"Search is slow when catalog size grows",
"Users want saved filters for repeat workflows"
]
}' | jqAll API failures return a consistent machine-readable error contract. Run make smoke before committing changes.
The Hermes research reports are published twice each week, on Tuesday and Friday mornings.