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Show HN: Semble – Code search for agents that uses 98% fewer tokens than grep
71–80 of 187 posts
Re: Show HN: Semble – Code search for agents that uses 98% fewer tokens than grep
#72fantastic token savings and performance... but unlike grep it's probabilistic search on search terms. is that an issue? the tiny model might not surface something important
It's not probabilistic, and exact matches will always be preferred over non-exact. So if you search for a function name this will surface it.
Re: Show HN: Semble – Code search for agents that uses 98% fewer tokens than grep
#73What I have personally observed with such tools is that they make the AI's dumb, similar to how it makes coders dumb when relying more on AI tools. These agentic AI's are already smart enough to figure out a highly optimized path to code exploration or search. But, with these tools, they just go very aggressive, partly because the search results from these tools almost in 100% of the cases do not furnish full details…
Hey, codebase-memory-mcp and semble are not exactly the same, but it's an interesting comparison, I'll put it on the todolist to check that out and add it to our benchmarks if feasible. If you ever get a chance to do this same comparison with semble it would be super useful feedback since these "real" scenarios are hard to benchmark/replicate.
Re: Show HN: Semble – Code search for agents that uses 98% fewer tokens than grep
#74I'd be interested in seeing actual agent benchmarks (eg CC or Copilot CLI with grep removed and this tool instead). For example, I have explored RTK and various LSP implementations and find that the models are so heavily RL'd with grep that they do not trust results in other forms and will continually retry or reread, and all token savings are lost because the model does not trust the results of the other tools.
Hey, this is something we're actively working on, but this is hard (and expensive) to do well across harnesses/models. The grep pretraining thing is very interesting though, I've noticed the same. E.g. Sonnet 4.6 seems to trust semble but Opus 4.7 less so. I'm hoping we can quantitatively test this and improve it when we have proper benchmarks for this as well. If you do have any feedback though let me know!
Re: Show HN: Semble – Code search for agents that uses 98% fewer tokens than grep
#75I also like the index feature form https://maki.sh Source code has a lot of structure, using a real parser instead of grepping and reading files can potentially save a lot of tokens
Interesting, will have a look at this, thanks for sharing.
Re: Show HN: Semble – Code search for agents that uses 98% fewer tokens than grep
#76Shouldn’t it be a part of the harness at least for local codebase? I wonder how many harnesses are doing that already.
Afaik many harnesses ship the "default" which is grep+read (like Claude Code). But I agree, IMO it's a weird gap. To be fair I don't think providers are that incentivised to reduce token burn at the moment, but my guess is that that will change and tools like this will become at least an natively supported option in some harnesses.
Re: Show HN: Semble – Code search for agents that uses 98% fewer tokens than grep
#77How does that compare to Cursors’s workspace indexing?
Also curious what the authors think about Claude team explicitly trying out indexing and deciding against it.
Re: Show HN: Semble – Code search for agents that uses 98% fewer tokens than grep
#78Earlier quoted context omitted.
with AI the "they could so they never wondered if they should" will be a very frequent thing.
This is a bit rude. We didn't generate this project, we wrote it, a lot of it manually, and trained custom models. We'd been working in the real-time retrieval space for a while, and we thought coding was a good fit for this specific technology.
My comment above wasn't meant to be rude. And you do have extensive benchmarks against grep etc so it's clear you understand the importance of that.
But I still think you're missing the harder but more important proof which is agent evals. Have you done any of that?
I would personally love to find tools in this space which can make agents more efficient and I do believe there's a scope for massive improvements compared to default workflows. But my evals with RTK and Headroom have made me wary that a tool can look like it should work, conceptually make sense, pass non-agentic benchmarks, and still make an actual agentic workflow worse.
Re: Show HN: Semble – Code search for agents that uses 98% fewer tokens than grep
#79How does it compare to context-mode or serina that are both well established now?
Serena does a lot more than semble (I actually used serena before building this and didn't like how much it does by default). That also made it hard to see if it was actually working well with how many moving parts there are. Semble only does 1 thing: very quick code search, that's it. Context-mode I have not used before though, I will have a look at that, thanks for sharing!
Re: Show HN: Semble – Code search for agents that uses 98% fewer tokens than grep
#80What I have personally observed with such tools is that they make the AI's dumb, similar to how it makes coders dumb when relying more on AI tools. These agentic AI's are already smart enough to figure out a highly optimized path to code exploration or search. But, with these tools, they just go very aggressive, partly because the search results from these tools almost in 100% of the cases do not furnish full details…
Hey, codebase-memory-mcp and semble are not exactly the same, but it's an interesting comparison, I'll put it on the todolist to check that out and add it to our benchmarks if feasible. If you ever get a chance to do this same comparison with semble it would be super useful feedback since these "real" scenarios are hard to benchmark/replicate.
So, I just tested with semble. Your MCP integration did not work, and kept throwing error (Failed to connect to "semble": MCP error -32000: Connection closed) though I installed using documented manner (tried both: pip and ux methods).
Anyways, I made it work by making it generate relevant doc (using semble init), and then copying this into AGENTS.md, and then prompting it with this line:
""" Start by reading AGENTS.md in current folder. Now, the task::: `Explore the ingestion and search paths. Do not read README.md at all`. Prefer to use `semble` search for code search. Do not do new installation. semble is already available at `/Users/nitinbansal/.local/bin/semble` . """
The results are much better. Even better than my own setup, but, vary a lot. I did 4 runs:
95k/2.9k
25k/2.7k
71k/2.9k
37k/4.0k