Two things right now:

1. A music recommendation tool based on Last.fm scrobble history.

It's graph-based, no ML or "fancy" techniques, but I've had good results with it so far. It builds recommendations based on a listening window or just recent history. It combines several different recommendation algorithms (including an Auralist-inspired "serendipity" score for novel recommendations, meant to simulate the serendipity of being recommended something novel by a friend), scores and ranks candidates, and takes in feedback that inform subsequent recommendations.

Fun project. Found some good new music with it already. :)

2. A code exploration / indexing tool with CLI and MCP interfaces for exploring concepts and impacts of changes in a codebase.

Essentially, an overwrought "find all uses" that doesn't depend on exact symbol or string matching.

I have a codebase of non-trivial size, but thankfully it's fairly well-structured. This tool indexes the code and bundles modules into "concepts" -- these can be auto-discovered or preconfigured. Dependencies, inheritance trees, symbols and symbol usage are all also indexed.

Then you can ask, "what's the impact of extending the domain model of XYZ" or "I want to remove this property" and it shows where to start, where to look next, and fuzzy edges or dependencies that might need deeper exploration. It surfaces non-obvious connections, too, or things a junior dev (or LLM) might miss, like when a model is mapped to an API DTO, or intermediary states, etc.

It's been useful for a new dev exploring the codebase, because you can ask in terms of business concepts instead of needing to know the exact symbol name in the code. And it's been much more token efficient than grep for exploration subagents. But it's limited to dotnet only.