How well do coding agents use your library?
stackbench.ai
stackbench.ai
I asked this question to about 50 library maintainers and dev tool builders, and the majority didn't really know.
Existing code generation benchmarks focus mainly on self-contained code snippets and compare models not agents. Almost none focus on library-specific generation.
So we built a simple app to test how well coding agents interact with libraries: • Takes your library’s docs • Automatically extracts usage examples • Tasks AI agents (like Claude Code) with generating those examples from scratch • Logs mistakes and analyzes performance
We’re testing libraries now, but it’s early days. If you're interested: Input your library, see what breaks, spot patterns, and share the results below.
We plan to expand to more coding agents, more library-specific tasks, and new metrics. Let us know what we should prioritize next.
I could be wrong.
> I asked this question to about 50 library maintainers and dev tool builders, and the majority didn't really know.
Why should they even bother to answer such a loaded and hypothetical question?
based on comments here, i do think we should allow users to run the audit first (and provide an email address if they want us to follow up with results later).
Agents are users now, and agent-friendly docs and libraries will be standard practice for the tools that want to thrive through broader industry adoption.
Similarly, perhaps you wouldn't care if your website wasn't easily parsable by crawlers, but if you'd like your work to appear in search results you might like to include a sitemap or structure the HTML a little.
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I feel like most of the problems with AI using a library is how we mix code and implementation. C, C++ got it right (even if by accident) with separating specification from implementation.
Instead of lamenting the design trend of not maintaining this split, for my own code I wrote a utility to extract specifications from my existing code.
We’re betting on almost the exact opposite idea: we can make agentic software engineering cheaper and more reliable by making it easy for LLMs to write, find, and integrate libraries and other third party software.
I don't think so. I think understanding the context of a project will always produce superior results. I think instead we'll just make it a lot easier to add to the training corpus the LLM pulls from.
[0] https://hexdocs.pm/usage_rules/readme.html
[1] https://github.com/phoenixframework/phoenix/tree/main/usage-...
We’ve baked a solution to this into our product, so if anybody is working on an API/SDK/etc feel free to contact me if your users are running into problems using LLMs to integrate them.
One thing we’ve noticed is that subtle changes to library/api integration prompts’ context can be surprisingly impactful. LLMs do very well with example commands and explicit instructions to consider X, Y, and Z. If you just dump an API reference and information that implicitly suggests that X, Y, and Z might be beneficial, they won’t reliably make the logical leaps you want them to unless you let them iterate or “think” (spend more tokens) more. But you can’t as easily provide an example for everything, and the ones you do will bias the models towards them, so you may need a bit of both.
For the last 15 years I've been writing against software patents, and producing open source software that cost me about $1M to develop, but in the case of AI, I have started to make an exception. I have also rethought how I am going to do open source vs closed source in my AI business. A few weeks ago I posted on HN asking whether it's a good idea, and no one responded: https://news.ycombinator.com/item?id=44425545
(If anyone wants to work with me on this, hit me up, email is in my profile)
We’re trying to build a similar kind of experience but for both “sides” of the problem: software provider and software users/integrators.
My first two companies are radically open source, and no one cared:
And this is what we're doing now with AI, but it's not going to be as open: https://engageusers.ai/deck.pdf
Don't worry, we're not looking to get into it with some random other projects. It's mostly to protect our business model against the Big Tech and enterprises.
One thing I’ve learned since staring a company is that early on, your greatest asset is trust in your founder/brand, because it’s the only reason for someone to pay you for something until you get your shit together. I’ve personally had a hard time noticing it in myself sometimes, but I think it’s easy to overlook how outward signaling that might look like distrust (eg making users sign NDAs) damages your own ability to build trust. Since early startups tend to be considered untrustworthy by default it can be really counterproductive. Anyway, I appreciate your non-aggression policy
I've always preferred collaboration and joining forces building on each other's work, than competition and incompatibility.
Phase 2:
7. Sell shares to MS, Oracle, IBM
8. They do most of the Enterprise Sales
How comfortable are you that these firms do this and do this this way? MS, for example, doesn't really sell to a majority of its customers, they support VARs. And for IBM, do you mean Kendryl? Is your product a gateway to their products, generally causing a bundle to be sold? Are you familiar with firms like yours that have an arrangement where these enterprises bought shares then go sell on the startup's behalf? Note, it is indeed helpful if you can claim you're patented, but not to protect your "business model" (too many ways to work around a patent, or more likely, get it invalidated), more to suggest you have something.See also: https://patentskart.com/how-to-invalidate-a-patent-in-10-eas...
If you had anything substantive to back up what you're saying, we could discuss it, but since you don't... well, I'm actually disappointed but w/e.
Please write the Apis for people so we can go see when the LLM fails.
If you write it for the llm, the moment is starts lying the whole thing is broken, if its not for me I dont go digging in, I rather use something else
You often don't even need cloning, for instance in the Ruby ecosystem, for the Pundit gem `bundle show pundit` tells you `/Users/vemv/.rbenv/versions/3.4.5/lib/ruby/gems/3.4.0/gems/pundit-2.3.2` which is a vanilla dir (non-zipped), ready to be added to the Claude context.
So I thought this is where context7 would be useful, but I'm confused what I'm looking at in the detail page: https://context7.com/firebase/firebase-admin-java
I was expecting some sort of dump of all the admin methods, but it gives a single example of one library function and info on how to build javadoc.
It lets you emulate RAG.
It looks like https://firebase.google.com/docs/ is being refreshed as I type this, I imagine that using that as a source and including "Java" in the topic filter might give more results (or maybe the https://github.com/firebase/firebase-docs has the same content).
I can foresee it being useful when I write documentation, e.g. given an existing repo and my new guide, can the AI agent implement said feature? Does it fumble?
I only got one run it looks like for free and it was successful so I can't see what would happen if it failed. Curious what sort of feedback it provides on failure.
Don't they know how to write their own code? Isn't that a coding agent's entire purpose in life?
There must be conflicting definitions out there. What does "coding agent" mean in this context?
or would reusability/modularity across projects and teams be beneficial?
Of course, distinct projects is a human construct that computers don't care about. As coding agents evolve (if they do), it isn't likely that the idea of projects will persist long-term.