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My biggest learning after some experiments - a BF16 (unquantized) Qwen beats a Q8 of double its size for decisions. I guess that’s the reason Kev switched to 4B BF16, from the original 8B version. Isn’t it interesting that quantization seems to mess with decision accuracy?

That’s fascinating, but not that surprising to me. We act like quantisation is free “Q8 is basically lossless” is often said in the local LLM community, but it really isn’t. The trade offs are worth it, personally, and the damage to coding ability seems low: decision model approaches are stricter though

Super cool finding!


Interesting - I wonder if it's because coding doesn't use the specific token probabilities, while decision models do

I think it also helps that speed and latency aren't as vital for coding and we can afford to let the model think for longer.

Yes this is the reason. Transformations like quantisation preserve the rank of outcomes much better than probability mass.

„Sweet solution“ - anyone having tasted pure vanillin in a nontrivial dose, will tell you that it tastes absolutely disgusting. It only works in traces.

This is true for many (most?) flavor compounds. A little salt is nice, a teaspoon isn't.

Some sort of clove extract is used in dentistry as an temporary fix if a full root canal isn't feasible at the time. Got the tiniest whiff of that on my tongue during rinsing and it tasted downright toxic.


Both Anthropic and OpenAI have been having major load issues though. Up until yesterday, OpenAI was serving at only 30t/s per default.

You are in violent agreement with the comment you replied to.

Discords have been popping up all over too.

Those plans are super slow right now. Nowhere near 100t/s.

While this may be true, their throughput is practically unbounded. this has been a primary challenge to using local models at work, where its normal for me to have 1-6 sessions churning away at once. So while 100t/s on a single session is very fast, theres no way you could get a cumulative ~500t/s from a consumer setup like this, do the bandwitdth bottleneck on a single card/cpu/ram/ssd (tends to be gpu ram bandwidth is the limiter in my setup, but they all have respective limiters to this kind of throughput)

I’ve had to use almost all resets that OpenAI allotted me. But I agree. I can get probably 1000t/s overall from them if I’m using subagents a lot.

I think Opus 5.5 is now fast enough with the inference stack improvements that they did. Sonnet 5.5 is around 140 tokens/s when I had measured it last time. A $20 subs on claude and using only sonnet 5.5 will last a lot

I was firmly wanting to move towards local llm, and since the Sonnet 5.5 and recent Opus speed increases, I'm putting that on hold until local inference speeds can be improved. Not sure how much juice there is to squeeze there, but I'm hopeful.

Does it matter if those 100 tokens per second are way crappier?

It’s a decent setup out of the box and you don’t need to tinker.

> That speed is what we used to have in software engineering. So much careful craftsmanship, no line just yeeted out.

That reads like nostalgia. What you describe sounds like the code I brought forth on a sunny Sunday afternoon, but certainly not on a hectic work day under deadline stress. I had Stackoverflow open _all the time_, and started googling at the earliest inconvenience. There were the dangerous parts of the codebase, where I paid more attention, much like I do now. But the rest, well - not everything needed the polish, and corners were cut all the time.


Your tool calls (MCPs?) are very likely too wasteful. Apply some filtering logic on the offending tool’s output. Either a wrapper CLI, or just tell codex how to filter.

I have a Kemper. Yes, the base tone is what it is - you’ll usually download a profile like “Basket case” and it will sound roughly like the song if you have the right guitar. It’s heavily pre-styled and you can’t really make it sound that different - and why would you?

Nuno B said that he played EVH's guitar and rig, and he sounded like....nuno. Thanks to Rick Beato for the interview, and making music interesting again.

It does, usually. Luna seems like almost infinite on the 20x plan, and that’s reflected in the API price. Isn’t that the case for all providers?


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