I am working on a chess analytics tool, specifically a free and open source replacement of Chessbase in this age of LLMs that can run on all platforms. The idea is to lower the barrier of entry to use a chess improvement tool since Chessbase can be intimidating for a causal Chess.com beginner looking to go into serious chess prep. At present, it can do basic queries like H2H score of Magnus Carlsen vs Hikaru Nakamura, the top 10 juniors in the US, Magnus Carlsen's games with the London system opening and involving a queen sacrifice etc. Though getting it to work for advanced multi-step tactical patterns and finding games with certain imbalances in the query using natural language is getting challenging. DuckDB has helped a lot, along with modern LLMs for query generation with schema and some preprocessing of game PGNs and piece hashes. It can also import a user's Chess.com and Lichess games given the usernames and do similar queries as on Master level games.

I also used the tool to generate an Adult Chess improvers FIDE rank list for all federations around the world. Here are the July 2025 rankings though it still needs major improvements in filtering - https://chess-ranking.pages.dev

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Another idea that I have been working on for sometime is connecting my Gmail which is a source of truth for all financial, travel, personal related stuff to a LLM that can do isolated code execution to generate beautiful infographics, charts, etc. on my travels, spending patterns. The idea is to do local processing on my emails while generating the actual queries blindly using a powerful remote LLM by only providing a schema and an emails 'fingerprint' kind of file that gives the LLM a sense of what country, region, interests we might be talking about without actually transmitting personal data. The level of privacy of the 'fingerprint' vs the quality of queries generated is something I have been very confused with.