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Disclaimer: Still in alpha. Also vibe coded, so... you've been warned.

I'm not really sure where this is going or if it needs to go anywhere, but as someone who lives in Kotlin based projects almost all day, I always envied web-devs with their npm's and bun's. Or Python devs with their pips.

Using those tools makes spinning up a project or adding dependencies seem so frictionless. No Google searches for every dependency path to copy/paste into Gradle.

That's where this idea came from for kpm. The job is basically the same. Run a simple command to scaffold an Android or Kotlin project and then another simple command to add a dependency. That's the idea anyways.

I had started out writing this by hand a few years ago and it went off the rails somewhere and I started over using Claude last year. Just recently picked it back up a bit and I think it's in a good enough spot right now to see if anyone is interested in something like this. AI is really good at scripting projects.

There's no real magic here beyond a lot of code to generate Gradle files and mutate them. There's some things I'm not sure about like the global config. I don't even know if that is useful and it certainly will mess things up if you use it in an already set up project because it's currently overwriting everything, so yeah, be careful.

I am working on (or AI is I guess) generating CMP/KMP projects which would be really nice.

Obviously this will never be as smooth as a bun or pip but it gives a familiar-ish experience.

If you are an Android dev, to get started: kpm new MyApp --android --compose kpm add <Library here> will add a dependency

Curious and nervous to hear what people think. Thank you!

We built optimize_anything, an API that optimizes any artifact representable as text — code, prompts, agent architectures, configs, even SVGs. It extends GEPA (our prompt optimizer, discussed here previously: https://arxiv.org/abs/2507.19457) far beyond prompts.

The API is deliberately minimal. You provide what to optimize and how to measure it:

import gepa.optimize_anything as oa

def evaluate(candidate: str) -> tuple[float, dict]: result = run_my_system(candidate) return result.score, {"error": result.stderr, "runtime": f"{result.time_ms}ms"}

result = oa.optimize_anything( seed_candidate="<your artifact>", evaluator=evaluate, )

The evaluator returns a score plus diagnostic feedback (we call it "Actionable Side Information" — stack traces, rendered images, profiler output, whatever helps diagnose failures). An LLM proposer reads this feedback during a reflection step and proposes targeted fixes, not blind mutations. Candidates are selected via a Pareto frontier across metrics/examples, so a candidate that's best at one thing survives even if its average is mediocre.

Two ideas distinguish this from AlphaEvolve/OpenEvolve/ShinkaEvolve-style LLM evolution: (1) diagnostic feedback is a first-class API concept rather than a framework-specific mechanism, and (2) the API unifies three optimization modes — single-task search (solve one hard problem), multi-task search (solve related problems with cross-transfer), and generalization (build artifacts that transfer to unseen inputs). Prior frameworks only express mode 1.

We tested across 8 domains. Selected results:

Coding agent skills: Learned repo-specific skills push Claude Code to near-perfect task completion and make it 47% faster Cloud scheduling: Discovered algorithms that cut costs 40%, topping the ADRS leaderboard over expert heuristics and other LLM-evolution frameworks Agent architecture: Evolved a 10-line stub into a 300+ line ARC-AGI agent, improving Gemini Flash from 32.5% → 89.5% Circle packing (n=26): Outperforms AlphaEvolve's published solution Blackbox optimization: Generated problem-specific solvers matching or exceeding Optuna across 56 EvalSet problems CUDA kernels: 87% match or beat baseline; multi-task mode outperforms dedicated single-task runs

``` pip install gepa ```

Blog with full results and runnable code for all 8 case studies: https://gepa-ai.github.io/gepa/blog/2026/02/18/introducing-o...

GitHub: https://github.com/gepa-ai/gepa