☆ Yσɠƚԋσʂ ☆

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Joined 6 years ago
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Cake day: January 18th, 2020

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  • Pinprick attacks on refineries don’t actually have a big impact. They get a lot of media, but they don’t actually cause major disruptions for more than a few days. If you look at the size of oil refineries you’ll quickly realize that a single drone isn’t going to do much to them. This sort of propaganda is aimed at people who have no clue how this stuff actually works, and just look at pictures of smoke and think Russian oil production has completely stalled while in reality it’s largely unaffected.







  • I don’t mean you turn the program itself into a genetic algorithm. I’m saying that the agentic loop for producing code acts as one. The code itself is just regular code. And the loop isn’t really any more inefficient than what you do as a developer. It almost never happens that you write perfect code on a first try in practice. You’ll write some code, run your tests, look how it did, and iterate. That’s precisely the same process the agent follows.

    The difference from a typical genetic algorithm is that the LLM is not just randomly generating text that eventually fits into the shape you specified. It’s generating code that’s already close to what’s intended most of the time, and it just needs a bit of massaging to get completely right. That’s the feedback loop here.


  • I find I kind of look at the whole agentic harness setup as a genetic algorithm. Your tests and specs are the fitness function for the program you’re evolving, and the LLM is the mutator. At each step it generates some output, it gets tested against the fitness function, the LLM gets feedback and iterates on it. Eventually something working falls out in the end. The better you can define the selection criteria the more you box the agent in the better results you get.

    The trick I can recommend for getting the model to code is to ask it to come up with a phased plan composed of focused features, and then to build each feature on its own branch. That way you have a clear unit of work that does a specific thing which makes it much easier to review the code. Can also recommend tools like https://github.com/Fission-AI/OpenSpec for making specs to box the model in when it works.







  • You can run the Gemma 4 and Qwen3.5 MoE models with as little as 12 GB of VRAM at 30-40 tps (Q4/Q5), and they both blow GPT-4o and DeepSeek R1 out of the water. But 64gb RAM is also not really out of scale with the cost of a shop tool in other trades. If you’re a professional that’s confident in a positive return on the investment, or just a hobbyist with the luxury budget for a “shop” that cost is well within consumer market. That’s not everybody, of course, but it’s not some inconceivable fantasy.

    The key point is that local models continue to get more efficient and usable. You need high end consumer grade hardware today, but given how fast improvements are happening, it’s entirely likely that you’ll be able to get the same capability on even smaller hardware in a few months.