AI Improves at Self-improving

AI that can help improve AI is actually almost everywhere if you know where to look. Not least coding tools like the new codeex from open AAI which didn't just help me find a bug that claude within cursor missed but is helping AI researchers too. The coding agents might be doing the easier bits, but it's freeing up AI researchers time to well work on AI improvement. But rarely is the process of AI self-improvement so direct as it is in the alpha evolve agent from Google Deepmind.

It can generate better prompts for itself so that it can evolve better code for useful tasks. Tasks which lead to efficiencies in its own next version. This was published less than a 100 hours ago, but don't worry, it isn't Skynet. The real world does not yet allow for the speed of iteration that Alpha Evolve involves.

But I would say that this agent is the final proof for anyone left doubting it that LM are not a dead end and have barely even begun to make their mark. I'm going to draw on plenty of analogies and multiple interviews to give you guys at least a gut sense of what is going on with this recursive Ronin. this agent that has already led to realworld efficiencies in the Google data center fleet and mathematical breakthroughs decades in the making. First though, please just skip to the chase.

What on earth is this thing? Basically, the human comes along and has to provide the problem to solve, some code that they may have tried, and critically some evaluation metrics. Those details are kind of crucial if you don't want to get an overhyped sense of what Alpha Evolve can do. Anyway, the human provides all of that and the more metrics they can give, the better the performance.

Then essentially, the human can just vibe as Gemini 2, not Gemini 2.5, the far more impressive successor, but Gemini 2, iterates on that code. The system uses the Flash version of Gemini, the smaller and quicker one, for plentiful ideas. but the pro version, Gemini 2 Pro, for solid suggestions. Notice the prompt sampler, wherein the system draws on previous prompts that humans have tried that worked before and programs via the program database that were great in other situations.

All with a goal of improving the code that the human ...