[stego-tech]

And this is where the strengths of LLMs really lie: making performant ML available to a wider audience, without requiring PHDs in Computer Science or Mathematics to build. It’s consistently where I spend my time tinkering with these, albeit in a local-only environment.

If all the bullshit hype and marketing would evaporate already (“LLMs will replace all jobs!”), stuff like this would float to the top more and companies with large data sets would almost certainly be clamoring for drop-in analysis solutions based on prompt construction. They’d likely be far happier with the results, too, instead of fielding complaints from workers about it (AI) being rammed down their throats at every turn.

[Veliladon]

^ This. I'm waiting for an LLM where I can just point it to a repo, slurp it up, and let me ask questions about it.

[nmfisher]

$ git clone repo && cd repo $ claude

Ask away. Best method I’ve found so far for this.

[frikk]

This technique is surprisingly powerful. Yesterday I built an experimental cellular automata classifier system based on some research papers I found and was curious about. Aside from the sheer magic of the entire build process with Cursor + GPT5-Codex, one big breakthrough was simply cloning the original repo's source code and copy/pasting the paper into a .txt file.

Now when I ask questions about design decisions, the LLM refers to the original paper and cites the decisions without googling or hallucinating.

With just these two things in my local repo, the LLM created test scripts to compare our results versus the paper and fixed bugs automatically, helped me make decisions based on the paper's findings, helped me tune parameters based on the empirical outcomes, and even discovered a critical bug in our code that was caused by our training data being random generated versus the paper's training data being a permutation over the whole solution space.

All of this work was done in one evening and I'm still blown away by it. We even ported our code to golang, parallelized it, and saw a 10x speedup in the processing. Right before heading to bed, I had the LLM spin up a novel simulator using a quirky set of tests that I invented using hypothetical sensors and data that have not yet been implemented, and it nailed it first try - using smart abstractions and not touching the original engine implementation at all. This tech is getting freaky.

[Aurornis]

The mainstream coding agents have been doing this for a long time.

It helps to give it a little context and suggest where to look in the repo. The tools also have mechanisms where you can leave directions and notes in the context for the project. Updating that over time as you discover where the LLM stumbles helps a lot.

[HotHotLava]

Basically every AI agent released in the last 6 months can do this pretty well out of the box? What feature exactly are you missing from these?

[cpursley]

github copilot somewhat does this.

[ryandvm]

Copilot is too stingy with context. In my experience Claude Code is much better at seeing the big picture.

[etothet]

This is exactly what Devin (https://devin.ai) is designed to do. Their deepwiki feature is free. I’ve personally had decent success with it, but YMMV.

[bildung]

Apparently it's also shit. There was a discussion about it a few days ago that contains multiple project maintainers pointing out deepwiki didn't get their repos at all https://news.ycombinator.com/item?id=45884169