antman

From the project page “All code present in this project was written by me, without AI code generation or coding agents”

SO was a governance disaster. It was the infighting it was the strike, it was bad auto moderation tools. And the Stack Exchange strike wasn't really about AI. AI was just the final trigger after years of management undermining the community that made the platform valuable.

Stack Exchange was built on an implicit social contract people contributed their expertise and thousands of hours of unpaid work because they believed they were building a collectively governed knowledge resource. The company owned the platform, but the community created its value.

After time, management became increasingly centralized and disconnected from contributors. Also the policing of questions based on geeky criteria and not what users wanted. The increasingly aggressive policing of questions, closing perfectly reasonable discussions, and incorrectly saying questions are duplicates made the platform unnecessarily hostile to everyone, qmd at least thats why i quit. Quality control gradually became useless gatekeeping.

So there were really more than one problems, corporate management treating contributors as free labor, and established contributors treating their moderation as authority over everyone else, there were bad tools closing questions. The platform had become extremely hard to use for posting questions, finding amswers, moderator work. For me it was useless. Incidentally the closing of “opinion” questions was the worst decision since it was the only thing that could make it distinct from AI produced answers.

For some of the events there is paper here with details https://arxiv.org/html/2512.08884v1

A detail from the benchmarks in that page is that Nemotron Lightning has good results for a MoE model close to Qwen 27B which is dense. Very interesting considering that Qwen 35B MoE stopped being in Alibaba's plans, and curious if Nemotron is equally capable in other bemchmarks.

That is a good idea for an authorities honeypot.

Markov chains garbled text to gpt took decades, gpt stories about unicorns to gpt production systems took a few years, gpt production system to gpt astra producing deterministic lean proofs of longstanding mathematical problems happened even faster. Scepticism to the point of requiring proof appears like an academic pursuit while production systems have already been built and are in the process of being enhanced

The direction and pace of capability improvement has already been demonstrated by all models. The latest breakthroughs make that pretty evident, but there have been production systems that are based on probability since the beginning of computing.

What has been demonstrated is a process that outputs lean proofs based on those probabilities. This happened after decades markov chain producing garbled texts and very shortly after gpt2 producing stories about unicorns.

“ELI12” is my goto for the same reason

This argument implicitly makes a few assumptions which will probably not hold in the very near future.

One is that AI will continue hallucinating in a manner that is not easy to verify, second is that AI will not be enhanced to produced more simplified amd robust outputs, and third that a human will be required to do that. What humans in the loop are doing now is verify the process, propose shortcuts and add legitimacy, through the verification process, if that ends up being succesful its highly likely a lot less mathematicians will be required in the future.

The conclusion that this is not productive focuses on the mathematicians, but it is very productive in terms of hundreds of proofs being produced that had previously consumed uncountable hours of the brightest minds. Unless it ends up being the greatest hallucination ever ofcourse

- Laya receives semantic snapshots, not the framebuffer.

What does tjis mean? Another generative model?

I am putting these in the company manual

Do you have those definitions?

Painful that the RPi higher memory versions are so crazy overpriced that made the RAM swap, an obviously expensive process, meaningful. The solution of ofcourse would be to have market prices that are meaningful rather than trying to retain the overpricing moat through sabotage.

He destroyed his life and he is a hero for that. Movie type symbolisms and ethics would offer nothing more and good for him that he was lucky and resourceful enough to save his physical self.

A disgrace that although he is available and what he revealed is now more relevant than ever, he has been buried in the media, which is another continuous signal on the limits of freedom vs elite interests and control.

Whats a credit?

Except if it stops coming from anywhere.

I was walking inside rooms in buddhist temples in western China today and ChatGPT knew and could discuss what each room had and explain the art society and legend.

At the end: enter your emeil to receive the amswer. No thanks.

A 35 A3B as smart as previous gen 27B would be a sweet point

Is the size of the ASIC limiting factor or can they infinitely tensor parallelize? If thats possible then it would make it only a matter of economy of scale, there are good enough models already for people to invest in that kind of platform

Answers like 8Bish model it appears, but instant response

Speculation: Cheapest AI inference supply chain will win. Chips and energy. The chinese have made large investments in nuclear that will take decades for the US to follow and US has Chip supply chains that will take a few years for the Chinese to follow.

We are probably at the 3T to 5T parameter models which are good enough for disruption.Given the supply chains we can probably expect paced improvements, unless somebody comes up with better training methodologies or mathematics.

What distance of drone detection would this theoretically have?

Can DBOSfly pass the temporal python SDK test suite? Are there any features mot yet implemented?

We have peak flow of propaganda and disinformation to a cartoonish level.

The costs are exorbitant and most software is not produced by companies with such a huge moat. Anthropic made a profit through their recent bait amd switch pricing. There is zero useful insights online to indicate whether this might die due to commoditisation with good enough open models or fail the race to get more people subsidising unsustainable growth with other people’s money. Who knows? In any case they dont seem to be able to drop usage costs so the business model seems based on wishes

Had the same guidance for many years for visiting the US given by the large US firm that employed me

Nice if didnt have the same name with an Apache project https://superset.apache.org/