Ongoing
Building a weather-aware outfit planner for runners, walkers, and everyday outdoor plans. The app combines live forecast data, activity timing, return-home conditions, and personal comfort feedback into a clear clothing recommendation.
Shows applied AI product engineering: deterministic recommendation logic selects the outfit, while AI explains the already locked result instead of inventing advice.
- Next.js
- TypeScript
- Expo
- Supabase
- Open-Meteo
- OpenRouter
Ongoing
Built a Chrome extension that analyzes YouTube video transcripts against a user-defined goal using Gemini.
Helps evaluate long-form video before investing time, turning transcripts into goal-based relevance signals.
- JavaScript
- Chrome Extensions
- REST APIs
- Generative AI
- LLM Evaluation (Evals)
Beta
Building a local-first Document AI workflow that converts PDFs into structured JSON, indexes chunked content, and answers grounded queries across multiple documents. The current beta already covers native-text files, OCR fallback, document routing, and benchmarked retrieval quality.
Turns messy document input into queryable structured data with retrieval checks instead of relying on unverified extraction.
- Python
- OCR
- ChromaDB
- RAG
- CLI
- Evaluation
Active
Pothos - Personal Life OS
Built a local-first Obsidian operating system with 120+ structured Markdown notes, modular context layers, daily/weekly/monthly review loops, and AI-readable vault organization for planning, journaling, and strategic self-management.
Turns raw personal context into review loops, priorities, and behavioral protocols by combining qualitative reflection with Todoist/Rize productivity signals, recovery inputs, and custom OpenCode reflection skills.
- Obsidian
- Markdown
- Local-first
- AI context design
- OpenCode skills
- Todoist
- Rize
- Self-tracking
Completed
Built a small Python tool on top of the Polar AccessLink API that exports daily recovery metrics (sleep, HRV, respiration, ANS) into a clean CSV dataset. The data is used in my AI journaling workflow to analyze how training, sleep, and recovery affect day-to-day performance.
Creates a reliable personal data feed for downstream analysis instead of manual recovery tracking.
- Python
- REST APIs
- Data analysis
Completed
Hands-on Python experiments focused on prompting, structured outputs, and practical intuition around model behavior.
Builds working intuition for LLM behavior through small, testable notebooks rather than abstract prompt examples.
- Python
- LLMs
- Prompting
- Notebooks