celrod

It seems like thinking should be the place where it can iteratively draft before creating an output. I've wondered if text diffusion models could in theory write better, thanks to iteratively rewriting/refining the text. It seems to me at least that avenues like that are worth pursuing, so I'm happy to see efforts like Mercury 3 and Diffusion Gemma.

For some reason the providers for MiMo 2.6 Pro are all very slow. I'd much rather use something quick. DeepSeek was great for that. I haven't tried MiMo ultraspeed.

Using Artificial Analysis

Model | Reasoning | Intelligence Index | Artificial Analysis million output tokens for the intelligence index -|-|-|- Step 5 | ? | 44 | 160 GLM 5.3 | Max | 45 | 210 MiMo V2.6 Pro | ? | 46 | 140 Kimi K3 | Max | 44 | 160 Qwen Max 0902 | ? | 45 | 190 DeepSeek 4.1 Flash | Max | 39 | 250 GLM 5.3-flash | Max | 42 | 180 GPT-6 Astra | Low | 46 | 10

It looks reasonable by open model standards. This doesn't capture the fact that DeepSeek and Step 5 have much higher token/s than the rest, other than MiMo Ultraspeed. MiMo V2.6 is either slow but cheap, or fast but expensive. Just based on these numbers, it looks good. Astra-low is one of the fastest and cheapest because it doesn't use many tokens, but I've never tried it. I liked DeepSeek and GLM when I used them.

Yeah, that's what I'd been leaning towards. No mcp, but I'll see if I can reproduce and debug it, since other people don't seem to have that problem as badly as I've experienced it (and the idea of having a nasty bug like that bothers me).

No mcp support. I'll try copying deepseek harness's basic tool call formats as a starting point.

I tried it a few times and liked the speed, but often found it ended up looping, i.e. repeating the same token sequence (e.g. the same sequence of 5 paragraphs) over and over again until it hit the max output limit. This doesn't end up happening every session, but does every now and then.

My impression of DSv4.1-flash was very positive aside from this. But that was enough for me to stick with GLM-5.3(-flash), which both gave me consistently great results

I was using a vibe coded bare bones harness. I was wondering if this was normal from DSv4.1-flash, or if its my harnesses fault.

I was just trying deepseek v4.1 flash on OpenRouter. After running reasonably well for a while, I return to the window and see that my scrollback is nothing but this repeated over and over again:

``` OK.

Let me write.

Let me go.

OK.

Let me write the script.

Let me go. ```

I'm not sure to what extant this is a model problem, vs some providers being fairly broken. If I chose a single provider, I could know how to blame and to avoid them. With OpenRouter, I don't know which provider I was on when this happened.

If Q4 takes less than 1.3x as many tokens as bf16 or q8, it could still end up being faster, given how decode tends to be bandwidth bound. The kv cache was still bf16, so a few ops are the same between quants.

Mercor, Tacit Labs, Handshake AI... I suspect companies like these play a big part in model improvements, generating high quality benchmark/task-focused data for training.

However, these do require educated, white collar, workers.

I'm a kernel engineer. Fable 5 refused all my requests, falling back to Opus 4.8. My wife is a chemist. Her experience wasn't much better.

I think I'd rather have the model stop once the obvious solutions failed and ask me. It can suggest more creative ideas, but I don't necessarily want it to try implementing them.

A few years ago, on July 4th one of my mom's dogs freaked out, somehow managed to escape, and got hit by a car before my mom found her. She loved that dog, regularly attending nose work competitions with it. One of your pets getting a seizure must be harrowing for both you and the dog.

I don't light fireworks.

When claude goes down a wrong path, I tend to clear context and write a new prompt that helps guide it down the correct path. Whatever thinking or context that led it there has inertia and tends to be sticky, otherwise.

Pretty annoying when it brings those up again later from memory...