4 ms·

But SQLite is notoriously 35% faster than the filesystem [0], so why not use that? [0] https://news.ycombinator.com/item?id=14550060 https://news.ycombinator.c

But SQLite is notoriously 35% faster than the filesystem [0], so why not use that? [0] https://news.ycombinator.com/item?id=14550060 https://news.ycombinator.com/item?id=14550060

tomComb 7mo ago
And Turso has built a Virtual Filesystem on top of their SQLite. AgentFS https://agentfs.ai/ https://agentfs.ai/ https://github.com/tursodatabase/agentfs https://github.com/tursodatabase/agentfs Which sounds like a great idea, except that is uses NFS instead of FUSE (note that macFUSE now has a FSKit backend so FUSE seems like the best solution for both Mac and Linux).
bob1029 7mo ago
SQLite + GPT5.4 works very well for me. My biggest success is a Roslyn method that takes a .NET solution and converts it into a SQLite database with Files, Lines, Symbols, and References tables. I've found this approach to perform substantially better than a flat, file-based setup (i.e., like what Copilot provides in Visual Studio). Especially, for very large projects. 100+ megs of source is no problem. The relational model enables some really elegant [non]queries that would otherwise require bespoke reflection tooling or a lot more tokens consumed.
- deleted 7mo ago
  [deleted]
- superjan 7mo ago
  Is this something you can share in more detail? Did you document a skill for the LLM to use? And with what tasks do you see most improvement?
  - bob1029 7mo ago
    The point of this is to reduce a complex tool surface to a single sql query tool without losing the richness of the underlying representation. In practice this allows for me to combine multiple, complex data sources with a constant number of tools. I can add a whole new database and not add a new tool. My prompts are effectively empty aside from metadata around the handful of tools it has access to. This only seems to perform well with powerful models right now. I've only seen it work with GPT5.x. But, when it does work it works at least as well as a human given access to the exact same tools. The bootstrapping behavior is extremely compelling. The way the LLM probes system tables, etc. The tasks this provides the most uplift for are the hardest ones. Being able to make targeted queries over tables like references and symbols dramatically reduces the number of tokens we need to handle throughout. Fewer tokens means fewer opportunities for error.
- deskamess 7mo ago
  Interesting... what is the use case for the AI that is querying it? Is it how to develop additional features for integration with your app or do you have some other use case? Code review/audit/debugging/etc. For AI developing against an API I would think an OpenAPI json file would do the trick. Is the Roslyn method called as part of the build/publish?

But SQLite is notoriously 35% faster than the filesystem [0], so why not use that? [0] https://news.ycombinator.com/item?id=14550060 https://news.ycombinator.com/item?id=14550060

tomComb 7mo ago

And Turso has built a Virtual Filesystem on top of their SQLite. AgentFS https://agentfs.ai/ https://agentfs.ai/ https://github.com/tursodatabase/agentfs https://github.com/tursodatabase/agentfs Which sounds like a great idea, except that is uses NFS instead of FUSE (note that macFUSE now has a FSKit backend so FUSE seems like the best solution for both Mac and Linux).

bob1029 7mo ago

SQLite + GPT5.4 works very well for me. My biggest success is a Roslyn method that takes a .NET solution and converts it into a SQLite database with Files, Lines, Symbols, and References tables. I've found this approach to perform substantially better than a flat, file-based setup (i.e., like what Copilot provides in Visual Studio). Especially, for very large projects. 100+ megs of source is no problem. The relational model enables some really elegant [non]queries that would otherwise require bespoke reflection tooling or a lot more tokens consumed.

deleted 7mo ago
[deleted]
superjan 7mo ago
Is this something you can share in more detail? Did you document a skill for the LLM to use? And with what tasks do you see most improvement?
- bob1029 7mo ago
  The point of this is to reduce a complex tool surface to a single sql query tool without losing the richness of the underlying representation. In practice this allows for me to combine multiple, complex data sources with a constant number of tools. I can add a whole new database and not add a new tool. My prompts are effectively empty aside from metadata around the handful of tools it has access to. This only seems to perform well with powerful models right now. I've only seen it work with GPT5.x. But, when it does work it works at least as well as a human given access to the exact same tools. The bootstrapping behavior is extremely compelling. The way the LLM probes system tables, etc. The tasks this provides the most uplift for are the hardest ones. Being able to make targeted queries over tables like references and symbols dramatically reduces the number of tokens we need to handle throughout. Fewer tokens means fewer opportunities for error.
deskamess 7mo ago
Interesting... what is the use case for the AI that is querying it? Is it how to develop additional features for integration with your app or do you have some other use case? Code review/audit/debugging/etc. For AI developing against an API I would think an OpenAPI json file would do the trick. Is the Roslyn method called as part of the build/publish?