Does this work on any language or text?
Launch HN: Relace (YC W23) – Models for fast and reliable codegen
21–30 of 54 posts
Re: Launch HN: Relace (YC W23) – Models for fast and reliable codegen
#22Does this work on any language or text?
We trained it on over a dozen languages, with a bias towards Typescript and Python. We've seen it work on Markdown pretty well, but you could try it on plaintext too -- curious to hear how that goes
Re: Launch HN: Relace (YC W23) – Models for fast and reliable codegen
#23How does it differ from Cline VS extension? It already uses diff apply which makes bigger files edits much faster
Cline orchestrates all the models under the hood, you could use our apply model with Cline. Not sure what model they are using for that feature right now
Re: Launch HN: Relace (YC W23) – Models for fast and reliable codegen
#24Earlier quoted context omitted.
Hey -- good question! We're focused on a narrower task right now that aims to save frontier tokens (both input & output). Our merge + retrieval models are simply smaller LLMs that save you from passing in too much context to Sonnet, and allow you to output fewer tokens. These are cheap for us to run while still maintaining or improving accuracy.
I can import my entire codebase to Gemini and get more than a nuanced similarity score in terms of agent guidance. What’s the differentiator or plan for arbitrary query matching? Latency? If you think about it - not really a huge issue. Spend 20s-1M mapping an entire plan with Gemini for a feature. Pass that to Claude Code. At this point you want non-disruptive context moving forward and presumably any new findings w…
Hey, these are really interesting points. The question of agentic discovery vs. one-shot retrieval is really dependent on the type of product.
For Cline or Claude Code where there's a dev in the loop, it makes sense to spend more money on Gemeni ranking or more latency on agentic discovery. Prompt-to-app companies (like Lovable) have a flood of impatient non-technical users coming in, so latency and cost become a big consideration.
That's when using a more traditional retrieval approach can be relevant. Our retrieval models are meant to work really well with non-technical queries on these vibe-coded codebases. They are more of a supplement to the agentic discovery approaches, and we're still figuring out how to integrate them in a sensible way.
Re: Launch HN: Relace (YC W23) – Models for fast and reliable codegen
#25[deleted]
Re: Launch HN: Relace (YC W23) – Models for fast and reliable codegen
#26Very interesting. Can these models be used in editors/agents like aider or roo? I can see also see a use case of some sort of plugin or browser extension, to easily apply the patches provided by GPT/Claude on their web interfaces (without having to copy/paste and manually edit the files in the editor) Also, would love to see more concrete examples of using the Apply model Reading here: https://docs.relace.ai/docs/ins…
(Preston, other guy on the team) Yes, they can -- I actually tried a semantic edit implementation in Aider. It got the "correct edit format" percentage to 100%, but didn't really budge the overall percent correct on SOTA models. I should push it sometime, since it really helps the reliability of these local models like Qwen3. If you reach out to me, I can try to share some of this code with you as well (it needs to b…
What's the semantic diff format?
Re: Launch HN: Relace (YC W23) – Models for fast and reliable codegen
#27Very interested to see what the next steps are to evolve the "retrieval" model - I strongly believe that this is where we'll see the next stepwise improvement in coding models.
Just thinking about how a human engineer approaches a problem. You don't just ingest entire relevant source files into your head's "context" -- well, maybe if your code is broken into very granular files, but often files contain a lot of irrelevant context.
Between architecture diagrams, class relationship diagrams, ASTs, and tracing codepaths through a codebase, there should intuitively be some model of "all relevant context needed to make a code change" - exciting that you all are searching for it.
Re: Launch HN: Relace (YC W23) – Models for fast and reliable codegen
#28Very interested to see what the next steps are to evolve the "retrieval" model - I strongly believe that this is where we'll see the next stepwise improvement in coding models. Just thinking about how a human engineer approaches a problem. You don't just ingest entire relevant source files into your head's "context" -- well, maybe if your code is broken into very granular files, but often files contain a lot of irrel…
I have a different pov on retrieval. It's a hard problem to solve in a generalizable format with embeddings. I believe this can be solved at a model level where its used to fix an issue. With the model providers (oai, anthropic) going full stack, there is a possibility they solve it at reinforcement learning level. Eg: when you teach a model to solve issues in a codebase, the first step is literally getting the right files. Here basic search (with grep) would work very well as with enough training, you want the model to have an instinct about what to search given a problem. similar to how an experienced dev has that instinct about a given issue. (This might be what the tools like cursor are also looking at). (nothing against anyone, just sharing a pov, i might be wrong)
However, the fast apply model is a thing of beauty. Aider uses it and it's just super accurate and very fast.
Re: Launch HN: Relace (YC W23) – Models for fast and reliable codegen
#29Nice! I am currently writing a new version of my no-code platform, WeBase [1], to use AI to generate and edit applications.
Currently just using foundation models from OpenAI and Gemini but will be very interested to try this out.
My current approach is to just completely overwrite files with new updated version but I am guessing using something like Relace will make the whole process more efficient... is that correct?
I'll watch your video later but I would love to learn more about common use cases. It could even be fun to write a blog post for your blog comparing my "brut force" approach to something more intelligent using Relace.
[1] https://www.webase.com (still points to the old "manual" version)
Re: Launch HN: Relace (YC W23) – Models for fast and reliable codegen
#30Nice! I am currently writing a new version of my no-code platform, WeBase [1], to use AI to generate and edit applications. Currently just using foundation models from OpenAI and Gemini but will be very interested to try this out. My current approach is to just completely overwrite files with new updated version but I am guessing using something like Relace will make the whole process more efficient... is that correc…
> My current approach is to just completely overwrite files with new updated version
Overwriting full files work great <100 lines or so, but once you want to be able to edit files above that, it kind of gets very slow (and costly if using paid APIs), so using some sort of "patch format" makes a lot of sense.