You obviously have to try it out to see how it works for you, but the trick they use is pretty clever. When you ask an AI to write code, it doesn’t always get it right. Sometimes the code has bugs, sometimes it misunderstands the problem entirely. A naive way to address that is to generate a few solutions and test each one. The odds that at least one works go way up. ATLAS generates multiple attempts, running each through a test suite. Each retry also gets told what went wrong with the previous attempt, so it can try to avoid the same mistake.

But this can be pretty slow since you have to run the code in an isolated environment, check the outputs, wait for it to finish. Doing that for every candidate quickly adds up. So ATLAS has another shortcut for avoiding unnecessary testing. Instead of simply generating solutions and testing all of them, it tries to predict which one is most likely correct before running any tests.

ATLAS also asks the model for an embedding of what it just wrote which acts as a fingerprint. Two similar pieces of code will produce similar fingerprints. A well-written, confident solution will produce a different fingerprint than a confused, buggy one.

These fingerprints get fed into a separate, much smaller neural network called the Cost Field. This little network was trained ahead of time on examples where they already knew which solutions were correct and which were wrong. It learned to assign a score to each fingerprint. Correct solutions get a low score and incorrect ones get a high one.

So the process is to generate multiple solutions, get their fingerprints, score each one, and pick the lowest. Only that one gets tested. The Cost Field picks correctly about 88% of the time according to the repo.

Well, this specific solution was only set up on specific hardware, and is Nvidia dependent, as the readme stares.

That doesn’t mean the 9070XT can’t do AI stuff, quite the opposite. ROCm gets better all the time. There are many AI workloads you can do on AMD cards.

Is it a card I would choose if I was primarily working on AI? Absolutely not. But it is the card I own and it’s been a great value for gaming.

No, but yes? OmniCoder 9B at Q6 fits on my 9070 XT with 200k+ tokens of context, and it works pretty well with OpenCode. It is for sure the best local model that I've managed to squeeze onto my GPU, and it even works at 120k context at Q3 on an 8GB RX 580 GPU.

I can't imagine trying to using this model on either GPU for real work. I can use much bigger and faster models on the $3 Chutes subscription or $10 OpenCode Go subscription.

Even so, I am still excited. I don't feel like there was even a model worth using with a tool like OpenCode 6 to 9 months ago. I like the way things are heading, and I am looking forward to seeing how capable coding models of this size are in another 6 to 9 months!

It won’t be meaningful considering the architecture: it’s a harness around the model that generated multiple solutions in multiple passes using the test to measure compliance and repair broken solutions. The resulting program won’t be streamed to you because it has existed for minutes as it goes through the cycle. It’s more for an asynchronous use-case.

I, too, was interested because I am always eager to use local models in my claw-like. It looks like this could be useful for an async portion of the harness but it wouldn’t work in interactive contexts.

Very cool ensemble of techniques, particularly because they’re so accessible. I think I will use this form for reusable portions of web browsing functionality in my personal agent.

> cheaper than the cost of local electricity only.

Can you explain what that means?

You could use this approach with DeepSeek as well. The innovation here is that you can generate a bunch of solutions, use a small model to pick promising candidates and then test them. Then you feed errors back to the generator model and iterate. In a way, it's sort of like a genetic algorithm that converges on a solution.

Yup, they do quite poorly on random non-coding tasks:

https://aibenchy.com/compare/minimax-minimax-m2-7-medium/moo...