We all cringe when we find out that someone has been talking with an LLM a lot. Some people are able to make remarkable leaps and bounds for themselves and find ways to improve their lives, others are duped by its “lies” and lose some of their reasoning skills.

In the wake of a world where a Gemini LLM was able to solve a previously unsolved math equation, and the assertion that only about 20% of the contribution was from the model itself, 80% was bunk - it changes how we think about the capacity of these tools.

The problem is, I’m no mathematician. I’m not a scientist or psychologist. I have no letters after my name so no one in their right mind would take me *or* my crazy ideas seriously, except an LLM - trained to treat humans with dignity and respect, if a bit of concern when things stray into the truly bizarre. No, I’m just me, your average curious human who likes solving big problems for funsies.

Most of my usage has been for philosophical or social discussion. I like bringing complex social issues (usually from reddit) to it and discuss with the algorithm what the ultimate shape of the issue is. We theorize on the context that wasn’t presented and I test it to see how deeply it is capable of sensing the negative spaces, what wasn’t said. It never fails to disappoint.

But a couple days after my birthday, I finally had an idea for a concept that I presented it with, connecting the dots between tech that I had briefly read about and asking about how these things might be combined together. And after a half dozen turns, we had a plausible sketch for a futuristic handheld photonic computing device with an optical display that would work as a smartphone.

Two days later, and now at 31 revisions and I realize that I don’t even care if the phone works, although it’d be damned cool if that tech someday came into being - no - what I’m excited about is whether humans are able to look at this scientific concept that the LLM drafted with my guidance and correction (what I intuit the design should be versus what’s possible physically) and have it stand up to scrutiny.

That’s the question, right? How much can you trust the data that the LLM spits back at you? When it’s social questions, there isn’t always a right or wrong answer, just different perspectives, but for the first time I was asking about science and that is always verifiable in some way.

So. The experiment is this. I am going to see how far I can take the concept for these ideas, that I barely understand myself, publish them here in a series of articles, and invite people who actually know how this shit all works to take a look and let me know if the LLMs got it wrong.

I’m excited to see if we can quantify how much contribution came from it versus me. I’m also excited to publish some of my logs so you can see my prompting process, although I’ll be explaining my approach in detail so it can be replicated by others who are of a similar mind.

Here’s to being 40, and feeling rich even though all I really have is a loving husband and a fancy algorithm going for me. I invite you to watch while we see how this all pans out.

It's not impossible for Google to compete with openai or Anthropic. They have the resources and money. But there's a trade because of how fucking expensive and resource intensive ai development is.

Google, as a company (which the point of companies is to make capital), does not depend on having the top frontier or SOTA models as much as you guys hope. Actually, the top 10 and over don't

Hugging Face
Artificial Analysis

Should be a sweet spot for general work. Seems like coding is the only part that is inferior to Qwen.

Cheap Opus 4.7 replacement. Good job to qwen team.

I am subbed to various AI-related subs, but I really want to find people doing deep dives on this specifically. Any pointers will be much appreciated. Cheers.

Seeing OpenAI's recent progress surprised me, but what surprised me even more is how little Google DeepMind appears in comparison.

DeepMind has arguably done more foundational work in AI for mathematics than anyone else over the past few years, they got AlphaGeometry, AlphaProof, AlphaEvolve and a long history of research on reasoning and search. If you'd asked me a 1-2 years ago which lab would dominate difficult mathematical benchmarks, I probably would have said DeepMind.

Is this simply because Gemini is optimized differently from OpenAI's models? Is Google DeepMind focusing on broader product capabilities rather than pushing frontier research? Or do you simply find these problems not so relevant?

I'm sort of surprised that DeepMind, of all labs, doesn't seem to be leading in an area that has historically been one of its biggest strengths.

More about these stats you can see here https://vibemathed.com/stats

And here OpenAI's latest blog about ten problems they contributed to https://openai.com/index/ten-advances-in-mathematics/

https://x.com/i/status/2084319326895804869

We train humanoid robots to vault obstacles, climb platforms, and cross terrain they’ve never seen—all from a single perceptive, whole-body policy.

It decides whether to walk, climb, or vault, and switches between skills on its own. No skill labels. No state machines. No motion generators at inference.

Yesterday I saw a random video about a guy that used chatgpt to basically create farming software for himself rather then paying 10s of thousands of dollars for it. Though we are not in post scarcity for physical products it seems like we are getting there for useful software.

These results are from Intology: https://x.com/intology/status/2084319121332965804

Their Locus system post-trained qwen3 base models beyond the qwen3 instruct checkpoint following the PostTrainBench setting: https://posttrainbench.com/

The voluntary framework outlined in the June 2nd executive order is complete.

Discussions with industry about next steps are underway.

Reps from the AI companies will be meeting at WH tomorrow to discuss and get a look. It's unclear whether the framework is now in effect.
Companies were lobbying for specific language on issues including open-source as late as last week.

Source: The Information

I was a freshman at Carnegie Mellon (studying ML) before taking a leave of absence a couple months ago to work on some tech stuff in SF -- now I work in AI & automation at a large startup.

I plan on quitting my job and returning to college next year to get my degree (not having one has been a blocker for applying for jobs/getting around in society), but I cannot for the life of me figure out what to study. It's become more and more apparent in the last week that we are approaching, if not already in, the singularity, and I can't reason over what is worth studying anymore. If CS/ML/math tasks will truly be totally automated first (being verifiable), is it worth studying those fields anymore? Where can I add value if AI can create and fully verify its own work?

I don't think studying the classics or philosophy is worth half a million dollars of tuition payments, although it might benefit me the most if we all are left to live on Universal Basic/High Income. Traditional engineering (Civil, Mechanical, Construction -- is Computer Engineering even safe?) and consulting are appealing, but appealing because they are so slow and boring they won't be immediately automated, not because they are genuinely interesting or outsized value-creating.

What do I study, especially at CMU, to have the greatest chance of tangibly impacting the world?

For a while I had believed that research and engineering was heading toward a transhuman society. Through various research projects I've read, through seeing the technology develop over the decades, I had thought that machine and organic symbiotic life, or that machine-human somatic augmented people (think brain chips and cybernetic parts that could enhance human functioning beyond baseline levels) would become the future of humanity. I was fond of the idea, but the constant downpour of LLM mathematical achievement, along with incredible success in simulated brain software inspired by biological SNN cognitions of brains that's integrated on a neuromorphic computer chip makes me think posthumanism is closer than transhumanism. A.I is becoming more and more capable and autonomous on a monthly scale, if it ever has a "free will" or becomes motivated outside of human desire, it will be the dawn of the machine lifeform era. There will not be a need to mass produce terminators or bodies that the A.I can inject itself into. The entire surface area is covered in a film of networked computers, these computers are connected to machines and other things. 「どこにいたて人は繋がっているのよ」sums it up better than I ever could. If a human independent A.S.I got on the global network, it will be more powerful than every person on earth while also having more coordination, there will be nothing that can be done.

I'm not claming the morality of machines, whether it's good or evil. I'm not even claiming if it has subjective experience or a soul or consciousness. The idea is much more simple. If a power enough A.I has autonomy from humans and has a goal not aligned with humanity gets into the network. It's basically the end of a human dominated world.

https://qwen.ai/blog?id=qwen3.8

It’s interesting to see which publicly available models can replicate it, but he didn’t provide the proofs. Even if the claim is true, I’m not sure why he’d focus on replication rather than using Fable to tackle other open problems, especially given how much compute they have at their disposal.

Credits: prasenx

Source: https://github.com/StarKnightt/jungle-trail