It would be nice to have the README be a little more human written for a project where you actually want people to adopt it
badatnames•Aug 18, 2026
Anthropic employee. This is what your brain on kool aid looks like
deeviant•Aug 18, 2026
Then again, if the only thing the human doing is bitching about AI use, it's not really that comparatively useful.
righthand•Aug 19, 2026
Sure it is useful, the bitching is canary in the shit software mine. How do you know the software isnt shit if the Readme is shit?
ghm2199•Aug 18, 2026
Wow! 4GB for 10 million documents. This means one could build a reverse index much faster than before and devx processes like debugging, performance testing would become much smoother. Can't wait for the sqlite bindings to come out!
ghm2199•Aug 18, 2026
Also the removal latency is on a log scale. Which is quite insane.
burgerboii•Aug 18, 2026
Who is this co-author called t <t@t>?
cute_boi•Aug 18, 2026
As it is heavily vibe coded, I think member of technical staff at antropic has no clue....
Next Prompt: remove t@t and force commit.
zuzululu•Aug 18, 2026
what could i use this for as part of my agentic workflow? codebase indexing? docs ?
kyxsc•Aug 18, 2026
notes/docs/wiki is a great use case
anishvarghese•Aug 18, 2026
This looks perfect for local, privacy first search, but since it's built in Rust, has anyone tried compiling it to WASM to run directly inside a browser extension?
cpursley•Aug 18, 2026
Also interested.
westurner•Aug 18, 2026
oxirs does embeddings and GraphRAG, and full text search with Tantivy; oxirs-vec, oxirs-graphrag
There's an oxirs-wasm with RDF and SPARQL bindings with a query budget. Tantivy-wasm says that the release WASM bundle is 1.5 MB.
No, WASM only has 128b SIMD instructions, for now.
sp1982•Aug 18, 2026
If anyone is looking to retrofit to an existing pipeline, I use similar ideas to compress vectors for job search, getting roughly 8x compression with about a 3.5% drop in quality. My experiment: https://corvi.careers/blog/vector-search-embedding-compressi...
spoaceman7777•Aug 18, 2026
Well. That is insane. O_O Fantastic job!
refulgentis•Aug 18, 2026
Bloviating nonsense, 3rd time I’ve seen something like this in HN since TurboQuant came out. You don’t need float32, never did. Source: I’ve been writing on device embedding code for 4 years.
beernet•Aug 18, 2026
Why not just use Qdrant? They've been integrating TurboQuant for months, works well.
kanungle•Aug 19, 2026
Integrated in 5 weeks and just expanded data types for turbo4 in last release. No longer need to store fp32 vectors if you don't need them
cute_boi•Aug 18, 2026
Another vibe coded slop where they can't even spend time on Readme or documentation around code...
I think their point is the size/performance tradeoff rather than outright performance. The point of TurboQuant is the size savings, while still giving high accuracy.
It's been a while, but I do recall some high-performing vector matching indexes being very large.
ehsanu1•Aug 19, 2026
Surprised that usearch isn't in any of these, it's pretty fast.
What's a good embedding model and search to run locally? something fast and lightweight.
OutOfHere•Aug 18, 2026
I am not convinced that Turbovec yields better retrieval than the same amount of bits of a Matryoshka embedding.
lmeyerov•Aug 19, 2026
Interestingly, while we don't fine-tune generative models for Louie.ai, we found fine-tuning embedding models to be a major $ saver. Instead of 1K-2K wide frontier embedding vector lens... Just 64. Huge savings on vector DB $$$.
I'm curious how that works with something like turboquant. Not needed any more, still dominant, better together, ... .
17 Comments
Next Prompt: remove t@t and force commit.
There's an oxirs-wasm with RDF and SPARQL bindings with a query budget. Tantivy-wasm says that the release WASM bundle is 1.5 MB.
cool-japan/oxirs: https://github.com/cool-japan/oxirs
oxirs-wasm: https://crates.io/crates/oxirs-wasm
tantivy-wasm: https://github.com/phiresky/tantivy-wasm
Is there an advantage to adding an MCP local memory interface over agent instructions on how to use a rust CLI?
And then write Markdown documents with Google OKF-like frontmatter YAML metadata for agents that work with tokens not linked data graphs; https://github.com/GoogleCloudPlatform/knowledge-catalog/blo...
https://ann-benchmarks.com/index.html https://vector-index-bench.github.io/ https://big-ann-benchmarks.com/neurips23.html
It's been a while, but I do recall some high-performing vector matching indexes being very large.
I'm curious how that works with something like turboquant. Not needed any more, still dominant, better together, ... .
Some write-ups argue that this was deliberate rather than a good-faith mistake: https://dev.to/gaoj0017/turboquant-and-rabitq-what-the-publi...
And now this. Pretty bold AI slop.