sarthakaggarwal

The philosophical question here is fascinating — if an AI rewrites every line, is it still the same codebase? At what point does the Ship of Theseus argument apply to licensing? Practically though, I wonder how much this cost in API calls.

This resonates with AI coding tools. Claude Code doesn't know when to stop —it'll do 30 agentic loops for something that should have taken 3. The cost implications of a tool that doesn't know when it's done are brutal.

One thing I've noticed working with Claude Code extensively is that the quality is high but the cost predictability is near zero. Same prompt can cost $2 or $15 depending on how many agentic loops it takes. The capability is there, the economics are just opaque right now.

Interesting - I've been digging into Claude Code's behavior patterns too, but from the cost side. The variance in how many API calls a task takes is wild. A simple refactoring can be 3 calls or 30 depending on codebase state. Curious what tools it suggested — did any of them relate to reducing unnecessary loops?

Happy to answer any questions about the approach. The key insight is that conformal prediction gives you calibrated confidence intervals — if we target 80% coverage, we actually get 81% of actual costs landing within the predicted range. No ML runtime needed, just feature extraction + dot product. Runs in ~5ms per estimate.

Biggest limitation right now: it only sees the prompt text, not your codebase or conversation history. Short/vague prompts get wide ranges.

Agreed, I have a similar thought process. I've found it very hard to come up with an idea simply because I think that something like this already exists, even though I know I can build or at least try to build almost anything.