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Universal Claude.md – cut Claude output tokens

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Re: Universal Claude.md – cut Claude output tokens

#131

From the file: "Answer is always line 1. Reasoning comes after, never before." LLMs are autoregressive (filling in the completion of what came before), so you'd better have thinking mode on or the "reasoning" is pure confirmation bias seeded by the answer that gets locked in via the first output tokens.

Yeah this seems to be a very bad idea. Seems like the author had the right idea, but the wrong way of implementing it.

There are a few papers actually that describe how to get faster results and more economic sessions by instructing the LLM how to compress its thinking (“CCoT” is a paper that I remember, compressed chain of thought). It basically tells the model to think like “a -> b”. There’s loss in quality, though, but not too much.

https://arxiv.org/abs/2412.13171

Re: Universal Claude.md – cut Claude output tokens

#133
post #4

It seems the benchmarks here are heavily biased towards single-shot explanatory tasks, not agentic loops where code is generated: https://github.com/drona23/claude-token-efficient/blob/main/... And I think this raises a really important question. When you're deep into a project that's iterating on a live codebase, does Claude's default verbosity, where it's allowed to expound on why it's doing what it's doing when it…

if the model gets dumber as its context window is filled, any way of compressing the context in a lossless fashion should give a multiplicative gain in the 50% METR horizon on your tasks as you'll simply get more done before the collapse. (at least in the spherical cow^Wtask model, anyway.)

Re: Universal Claude.md – cut Claude output tokens

#135
post #15

Paul Kinlan published a blog post a couple of days ago [1] with some interesting data, that show output tokens only account for 4% of token usage. It's a pretty wide-reaching article, so here's the relevant quote (emphasis mine): > Real-world data from OpenRouter’s programming category shows 93.4% input tokens, 2.5% reasoning tokens, and just 4.0% output tokens . It’s almost entirely input. [1]: https://aifoc.us/the-…

[dead]

Re: Universal Claude.md – cut Claude output tokens

#137
post #4

It seems the benchmarks here are heavily biased towards single-shot explanatory tasks, not agentic loops where code is generated: https://github.com/drona23/claude-token-efficient/blob/main/... And I think this raises a really important question. When you're deep into a project that's iterating on a live codebase, does Claude's default verbosity, where it's allowed to expound on why it's doing what it's doing when it…

I wrote a skill called /handoff. Whenever a session is nearing a compaction limit or has served its usefulness, it generates and commits a markdown file explaining everything it did or talked about. It’s called /handoff because you do it before a compaction. (“Isn’t that what compaction is for?” Yes, but those go away. This is like a permanent record of compacted sessions.) I don’t know if it helps maintain long term…

Wouldn't the next phase of this be automatic handoffs executed with hooks?

Your system is great and I do similar, my problem is I have a bunch of sessions and forget to 'handoff'.

The clawbots handle this automatically with journals to save knowledge/memory.

Re: Universal Claude.md – cut Claude output tokens

#138

As with all of these cure-alls, I'm wary. Mostly I'm wary because I anticipate the developer will lose interest in very little time and also because it will just get subsumed into CC at some point if it actually works. It might take longer but changing my workflow every few days for the new thing that's going to reduce MCP usage, replace it, compress it, etc is way too disruptive. I'm generally happy with the base Cl…

Claude also has it's own md optimizer that I believe is continually updated.

So you could run these 'cure-alls' that maybe relevant today, as long as you are constantly updating your md files, you should be ahead of the curve [lack of better term]

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