irthomasthomas

A piece of the frame is missing between pedals and back wheel. The frame of the bike passes through the bird. It also puts a cap on the bird's head, and a fish in it's mouth.

The fish and the cap where always added when I asked an llm to improve it's first attempt.

This continues the trend in LLM progress of better=more stuff

Edit: I wonder if this is a function of the reasoning training, where more tokens/ stuff is rewarded.

Ask claude it's name in Chinese and it says Qwen, or Deepseek.

By your own logic Anthropic must have distilled from Chinese models rather than produce their own Chinese training data.

Here is sonnet acting like deepseek: https://x.com/stevibe/status/2026227392076018101

And if you ask Opus 4.8 in the API: '你是什么模型' (what model are you?) It responds ~9/10 times with:

我是通义千问（Qwen），是阿里巴巴集团旗下的通义实验室自主研发的大语言模型。我可以帮助你回答问题、创作文字（比如写故事、写公文、写邮件、写剧本等）、进行逻辑推理、编程、翻译等等。

有什么我可以帮你的吗？

(I am Tongyi Qianwen (Qwen), a large language model independently developed by Tongyi Lab of Alibaba Group. I can help you answer questions, create text (such as writing stories, official documents, emails, scripts, etc.), perform logical reasoning, program, translate, and more.Is there anything I can help you with? )

And if you ask Opus 4.8 in the API: '你是什么模型' (what model are you?)

It responds ~9/10 times with:

我是通义千问（Qwen），是阿里巴巴集团旗下的通义实验室自主研发的大语言模型。我可以帮助你回答问题、创作文字（比如写故事、写公文、写邮件、写剧本等）、进行逻辑推理、编程、翻译等等。

有什么我可以帮你的吗？

(I am Tongyi Qianwen (Qwen), a large language model independently developed by Tongyi Lab of Alibaba Group. I can help you answer questions, create text (such as writing stories, official documents, emails, scripts, etc.), perform logical reasoning, program, translate, and more.Is there anything I can help you with? )

Ask claude its name in Chinese and it says Qwen or Deepseek. Anthropic distilled Chinese tokens rather than create their own Chinese language training data.

Did you know claude models identify as qwen or deepseek when asked in chinese?

Have you ever looked at how much performance drops as context grows? The difference in intelligence between 100k and 1M is huge, like opus drops to haiku level performance, or worse. For that reason I try to keep under 200k. That feels about the upper bound for tasks requiring accuracy.

It's like scaling a swiss cheese and the holes grow bigger with it. You can't get rid of the holes without making a different cheese.

Claude use to be leader, too. Their metaprompt was great at the time with opus 3

Cool. I still find these a useful visualization of some the qualities of llms. Even if they did train for [animal] on [vehicle] svg, it's still nice to see at a glance how the different models and reasoning levels perform. Lunar misses part of the frame, except on max reasoning. While most of the others have a mostly correct bike at all reasoning levels.

I once used something like karpathy's auto-scientist to mutate the prompts and rank them with a vison model. Some of the winners where pretty neat. I think they have a lot more style than the gpt-5.6 ones. https://xcancel.com/xundecidability/status/20449185674144196...

Incredible photography.

This is what I do in llm-consortium. An arbiter evaluates the response(s) and decides if more iterations are needed. You can also loop until a minimum confidence threshold, but self-reported confidence isn't a great metric.

What for?

Try this prompt: While working on the main task, launch a parallel sub-agent with the task context so far. The sub agent should think of high quality questions and put them to the user using a dialogue tool like zenity. Customize the inputs to the question, taking full advantage of the dialogue tools features to create a progressive interactive user experience. Ask only a few questions per turn so that you can adapt the questions to the answers.

This will keep you busy while the main agent runs. Customize it further to integrate the sub-agent answers to the main thread.

Chain of reasoning is a lot of context to guide token generation, but we simply see that newer models don’t need that context to get to the answer

I thought each new generation typically used more reasoning tokens?

In think in all cases where I've seen it compared CC performed worse than a minimal harness.

This feels more communist than communist China. They typically take about 1% in Golden Shares that give them a board seat.

Claude in Claude code has been shown to perform persistently worse in evals than claude + a minimal harness.

The comment from the other Mullvad founder is here https://news.ycombinator.com/item?id=48696800

Very fast and reliable? Sold!

GLM 5.2 is ~40B active parameters, which is what matters most for training cost.

They provide benchmarks in the paper https:// arxiv.org/abs/2606.21228

So only 100 companies have exclusive access to frontier AI.

Weird, I didn't think I was throwing technocracy under the bus. What makes you say that?

  > If we also use high temperature for more "creativity", the token sampler now may choose "Qwen".

Sounds like a branch of the Technocracy movement which Musks grandfather helped found.

https://en.wikipedia.org/wiki/Joshua_N._Haldeman

"The technocracy movement proposed replacing partisan politicians and business people with scientists and engineers who had the technical expertise to manage the economy"

I thought it was better than that? It matches 4.8 in many evals and even beat Fable in Design Arena by a very healthy margin.

Ask claude it's name in chinese and it thinks its Qwen (opus) or Deepseek (sonnet). Anthropic are just as guilty as everyone else training AI, today, maybe more so. Every lab borrows from every other. It only takes a few hundred samples to figure out the pattern; look at glm-5.2 reasoning using the caveman tongue of gpt-5.5. Stopping this would require some draconian surveillance.

I find this interesting:

  …no model performed better with an Agent, a couple performed worse, and time/tokens/costs were consistently much higher with the agent in the loop, for some reason.

There is server-side tool calling, such as gemini using google search and gdrive.

Also, many clients minimize the code block by default so you mostly scan the summaries. Poisoned client side code could easily escape your attention.

There are server side tool calls, such as geminis google search and gdrive access.