171–180 of 190 posts
Am I crazy or is 850,000/month of anything...not really that much? Where are you spending all your CPU cycles and memory usage?
> ChromaFs is built on just-bash by Vercel Labs (shoutout Malte!), a TypeScript reimplementation of bash that supports grep, cat, ls, find, and cd
Oh.. I see.
Earlier quoted context omitted.
Honestly, just from this question, I think you know enough that I’d go spend $20/month for a subscription to Codex, Claude Code, or Cursor, and ask them to teach you all this. I bet if you put in your comment verbatim with Opus 4.6 and went back and forth a bit, it could help you figure out exactly what you need and build a first version in a couple hours. Seriously, if you know the fundamentals and can poke and prod…
> Honestly, just from this question, I think you know enough that I’d go spend $20/month for a subscription to Codex, Claude Code, or Cursor, and ask them to teach you all this. Paying $20/m sounds like overkill. I have tabs open for all of the most well-known AI chatbots. Despite trying my hardest, it is not possible to exhaust your free options just by learning. Hell, just on the chatbots alone, small projects can…
The real thing I think people are rediscovering with file system based search is that there’s a type of semantic search that’s not embedding based retrieval. One that looks more like how a librarian organizes files into shelves based on the domain. We’re rediscovering forms of in search we’ve known about for decades. And it turns out they’re more interpretable to agents. https://softwaredoug.com/blog/2026/01/08/seman…
https://x.com/wibomd/status/1818305066303910006
Pixar got this right in Ralph Wrecks The Internet.
> At 850,000 conversations a month, even a minimal setup (1 vCPU, 2 GiB RAM, 5-minute session lifetime) would put us north of $70,000 a year Am I crazy or is 850,000/month of anything ...not really that much? Where are you spending all your CPU cycles and memory usage? > ChromaFs is built on just-bash by Vercel Labs (shoutout Malte!), a TypeScript reimplementation of bash that supports grep, cat, ls, find, and cd Oh.…
python -c " import json, wire, pathlib d = json.loads((pathlib.Path(wire.__file__).parent / 'assets/search_index.json').read_text()) [print(e['title'], e['url']) for e in d if 'QUERY' in (e.get('body','') + e.get('title','')).lower()] "
python -c " import json, wire, pathlib d = json.loads((pathlib.Path(wire.__file__).parent / 'assets/search_index.json').read_text()) [print(e['body']) for e in d if e.get('url','') == 'PATH'] "
This is one of the most confusing claims I've seen in a long time. Grep and others over files would be the equivalent of an old fashioned keyword search where most RAG uses vector search. But everything else they claim about a file system just suggests that they don't know anything about databases. I'm not familiar with how most out of the box RAG systems categorize data, but with a database you can index content lit…
Earlier quoted context omitted.
> Honestly, just from this question, I think you know enough that I’d go spend $20/month for a subscription to Codex, Claude Code, or Cursor, and ask them to teach you all this. Paying $20/m sounds like overkill. I have tabs open for all of the most well-known AI chatbots. Despite trying my hardest, it is not possible to exhaust your free options just by learning. Hell, just on the chatbots alone, small projects can…
Yeah, but when it comes to actually building stuff, using Codex is night and day different from using ChatGPT.
Sure, but that wasn't what you recommended Codex for, was it?
>>> Honestly, just from this question, I think you know enough that I’d go spend $20/month for a subscription to Codex, Claude Code, or Cursor, and ask them to teach you all this.
We use both a virtual file system and RAG — they each excel in different areas. The trick with RAG is the quality of data: we use an LLM to chunk into semantically cohesive sections, as well as generate metadata (including fact triples and links to other related chunks in the document) for every chunk as well as the document as a whole. We use voyage contextual embeddings to then embed each chunk with the document an…
Our vfs is also pretty powerful too, though: it is all backed by postgres then projected into files/directories for our agents. They get basic grep etc but also optimized fts tools for bm25, jq, and preview tools that show representative slices of large documents. All on top of Pydantic AI.
But SQLite is notoriously 35% faster than the filesystem [0], so why not use that? [0] https://news.ycombinator.com/item?id=14550060
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 relationa…
Is the Roslyn method called as part of the build/publish?