[jtonz]

I would be interested to see how people would apply this working as a coding assistant. For me, its application in solutioning seem very strong, particularly vibe coding, and potentially agentic coding. One of my main gripes with LLM-assisted coding is that for me to get the output which catches all scenarios I envision takes multiple attempts in refining my prompt requiring regeneration of the output. Iterations are slow and often painful.

With the speed this can generate its solutions, you could have it loop through attempting the solution, feeding itself the output (including any errors found), and going again until it builds the "correct" solution.

[bayesianbot]

I basically did this with aider and Gemini 2.5 few days ago and was blown away. Basically talked about the project structure, let it write the final plan to file CONVENTIONS.md that gets automatically attached to the context, then kept asking "What should we do next" until tests were ready, and then I just ran a loop where it modifies the code and I press Return to run the tests and add the output to prompt and let it go again.

About 10 000 lines of code, and I only intervened a few times, to revert few commits and once to cut a big file to smaller ones so we could tackle the problems one by one.

I did not expect LLMs to be able to do this so soon. But I just commented to say about aider - the iteration loop really was mostly me pressing return. Especially in the navigator mode PR, as it automatically looked up the correct files to attach to the context

[jbellis]

Unfortunately a 4o mini level of intelligence just isn't enough to make this work, no matter how many iterations you let it try.