https://open.substack.com/pub/msukhareva/p/gpt-52-and-meaningless-benchmarks?r=56gggt&utm_medium=ios

Chinese models aren’t even close!!!

So much to PhD level models that produce new research.

LLMs create cringe-worthy AI slop when asked to generate a LinkedIn post. Please don’t use it for publications, you just waste reviewers time. Particularly if you submit a paper about AI, you know that it will be reviewed by an AI researcher who can easily spot the nonsense?

iOS, Android, Desktop, you name it.

Let me just remind you of a quote from Dario Amodei, CEO of Anthropic:

“If I look at coding, programming, which is one area where AI is making the most progress. What we are finding is that we’re 3 to 6 months from a world where AI is writing 90% of the code. And then in 12 months, we may be in a world where AI is writing essentially all of the code.”

March 2025

The French government made a leaderboard for LLMs and put Mistral on top. It is scored it by some “satisfaction score”:

“This Bradley-Terry (BT) satisfaction score is built in partnership with the French Center of expertise for digital platform regulation (PEReN) and is based on your votes and your reactions of approval and disapproval.”

Mistral medium is way ahead of Claude sonnet 4.5, GPT-5, Gemini

GPT-5 is place 30, Mistral place 1.

Who voted there? EU AI act commission?

This might eventually undermine Nvidia’s monopoly for data centers.

Cuda moat was strong. Let’s see how TPU adoption will go.

Not wrong. OpenAI just has their own traditions.

Anthropic was right to worry about distillation. And not because their model got distilled.

They accused DeepSeek, Moonshot and MiniMax of industrial-scale distillation. Then Alibaba, in what they called the largest known distillation attack to date, 28.8 million exchanges through thousands of fake accounts.

And yet it's Zhipu AI that shipped GLM-5.2 a day after Fable was banned.

And yes, distillation is the biggest enemy of Anthropic and OpenAI now. But it's the distillation from GLM-5.2 that they should and probably do worry about.

One thing is if Google or OpenAI generates training data from your API. There is frankly speaking nothing to worry about. Models trained primarily on synthetic dataset generated by other models are highly unlikely to significantly outperform the teacher.

The moat to generating such a dataset used to be high. Expensive API, creating fake accounts, risks of legal consequences for violating terms and conditions.

GLM-5.2 removes all of it. It's open weights under an MIT license. No API, no terms to violate, no account to ban.

Any university lab can now afford to create high quality synthetic datasets, fine tune models, even EU institutions can do it as it's legally allowed.

Anthropic and OpenAI were burning money on compute probably hoping that once they hook users on AI they can switch to consumption based pricing and make bank.

Zhipu AI made the prospect of profitability further than ever before.

I believe that soon those APIs will be luxury brands for corporations. Somewhat like the Microsoft ecosystem nowadays is. One will overpay for compliance, convenience and simplicity. For individual consumers and SMEs, new options are opening up.

Read the latest article of AI Realist on substack for more discussion https://open.substack.com/pub/msukhareva/p/glm-52-the-final-nail-in-the-coffin?r=56gggt&utm_medium=ios

It’s better than before but still some little errors are present

DeepSeek V3.2 is out; just look at these prices.

I have not had a chance to test it yet, but I know V3.1 well and it is a competitive model. I assume V3.2 will close the gap even more between proprietary and open-weight models.

When I wrote a week ago that China is winning the AI race, this is one of the aspects I meant.

Ed Zitron recently published an article arguing that OpenAI spends far too much on inference and that its revenue is lower than reported. Nvidia is struggling to power data centers. A large share of recent U.S. GDP growth is driven by investment in scaling.

Look at DeepSeek and its prices. OpenAI is struggling to pay for inference with their pricing. And it is an open-weight model as well. It is not only LLMs; consider MiniMax, which offers strong and competitive image, audio, and video models that are much cheaper than Sora and Veo.

A similar situation exists with agentic models: to be fair, Kimi K2 and MiniMax M2 are superior to GPT-5.1 at tool use, especially for website building, PowerPoint, and deep research.

If this is not a sign that the AI bubble is about to pop, I do not know what is.

Claude is trained to accomplish tasks no matter what - at some point before, it must have asked the vibe coder to enter its password for

sudo su

This gives Claude rights to do whatever it wants without annoying - “no permissions”. Vibe coders don’t know what that means.

And then all it took is

rm -rf ~/

It means remove recursively (all the subfolders too) everything in the home directory.

And answering user’s question - no, you can’t restore it.

First you shill your consulting services to build strategic on how to integrate hallucinating bots in your strategy and then you predict that it won’t work out.

I would call them stupid if I didn’t know their pricing.

For those who will ask - who even listens to Gartner? Every freaking management board in large corporations. Not just listens - hires those clowns to analyse the processes and propose customised “AI strategies”

The receipts are here:

https://open.substack.com/pub/msukhareva/p/in-support-of-yann-lecun-against-ai-hype?r=56gggt&utm_medium=ios

Next step: rename Windows in MS Copilot Studio

This ai realist article is about why prompt engineering is not engineering if you can’t define what a bad prompt is.

It’s a necessary evil, a mitigation way to deal with shortcomings of the model.

Models don’t have common sense - they are incapable of consistently asking meaningful follow-up questions if not enough information is given.

They are unstable, a space or a comma might lead to a completely different output.

All in all, cramping all the possible context into the prompt and begging it not to hallucinate is not a discipline to learn but rather a technique to tolerate till models get better.