[tra3]

Recursion and memoization only as a general approach to solving "large" problems.

I really want to paraphrase kernighan's law as applied to LLMs. "If you use your whole context window to code a solution to a problem, how are you going to debug it?".

[tptacek]

By checkpointing once the agent loop has decided it's ready to hand off a solution, generating a structured summary of all the prior elements in the context, writing that to a file, and then marking all those prior context elements as dead so they don't occupy context window space.

Look carefully at a context window after solving a large problem, and I think in most cases you'll see even the 90th percentile token --- to say nothing of the median --- isn't valuable.

However large we're allowing frontier model context windows to get, we've got integer multiple more semantic space to allocate if we're even just a little bit smart about managing that resource. And again, this is assuming you don't recurse or divide the problem into multiple context windows.