Did you know there are over 1 billion bicycles in the world? The cycling industry is entirely commoditised. However, helpful and personalised advice on training, repair and nutrition is out of reach for most people.
Introducing - Cadence AI, your all-in-one AI-powered cycling coach, mechanic, analyst and dietitian.
- General Q&A ("How of often should I change my chain?")
- Bike component identifier ("What is this part? [insert image] It's called bottom bracket!")
- BE: Deploy OSS LMM Zephyr into Cloud Run & App Runner
- FE: Deploy Anvil app to Compute Engine & EC2
- Anvil BE <-> FE app with text submission and response display
- Test e2e mobile usability and get early feedback
You can contact me for collaborations ideas at https://glukicov.github.io/
- Price estimation ("How much would a new chain cost for my bike?")
- Use LangChain/agents to retrieve and show online products
- Fine-tune the OSS LMM model (improve general Q&A)
- Use RAG with a database of common cycling questions and answers
- vector search in BigQuery
- Caching for recently answered questions (consider safety)
- Defensive UX
- Analyse historical trends in my cycling data ("Plot of average cadence per week over the last year")
- Use Strava API to retrieve athlete's data (one-off)
- Dashboards with all trends
- Sync with athlete's Strava (can be a manual push/analyse request for now)
- Cycling coach to suggest exercises to improve the trends
- Dietitian to suggest nutrition plans
- Recommend components to upgrade or buy
- Add athlete's bike into profile
- Add Pkl for config validation
- Separate BE and FE in pyproject.toml
- Pass all vars (e.g.
MODEL_PATH) to docker-compose from config.yaml - Add HTTPS/TLS and other security layers
- Config class
- Linting
- Tests (unit and e2e)
- Test coverage
- CI/CD for deployment and testing
- Semantic versioning
- pre-commit hooks (linting)
- API documentation (Sphinx)
- DVC for artifacts