Autoresearch, Claude and Constrained Optimization
The author experiments with AI agents to solve constrained optimization problems, specifically file compression, to test the efficacy of unsupervised AI work. The project highlights the difference between clear gradient optimization and real-world iterative problem solving.
Why it matters
Understanding the limitations and capabilities of AI agents in complex, multi-step tasks is crucial for the future of software development and automation.
You don't need to look far to find claims that folks have been using AI to do the work of dozens of people. I tend to be skeptical of any claim that discusses improvements without evidence. I decided to take that skepticism and put it to work. This had a minor overlap with the whole 'loops' discussion on X but that's coincidental.
The article is a technical experiment report focused on empirical results rather than opinion.
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