Daniel Schott retweeted
Black Forest Labs released open weights for FLUX 3 Image Klein: a 5.6B image model that runs on 8GB GPUs. Free commercial use under $5M revenue. Full breakdown:
derringtoncollaborativeai.co…
Daniel Schott retweeted
"Software isn't dead. It's solved. Nobody will hire software engineers by 2028."
Reality:
• Writing code is being automated
• Defining what to build isn't
• Debugging production at 3 AM isn't
• Owning broken systems isn't
• Solving vague business problems isn't
AI didn't kill software engineers... it eliminated those whose only skill was typing code. The role shifts from "write code" to "own the outcome."
By 2028, will you be the engineer AI replaces or the one managing 10 agents?
Daniel Schott retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.
- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.
The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.
So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability.
The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.
But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along.
So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions.
TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
Daniel Schott retweeted
Great quote for what’s happening now.
“Vulnerabilities can be removed. They can be designed away, they can be mitigated, and the primary resource we dedicate to mitigating them is a thing that’s getting radically cheaper every couple months. And that’s AI capabilities.”
— Eddy Lazzarin,
Every open model ships frozen.
What if it could learn and update its own weights as it's being used?
I have been working on a bit of engineering called Living Weights for about three months, and I think it's time to release it.
The model becomes uniquely yours, no KV stashing, no MD files, no trickery.... just live learning. 😎
Unload the model, move it to another machine, and it still knows what you told it. Could this be the start of recursive self improvement for local AI?
It drops in the latest TensorFold release today, with support for @NVIDIAAI Nemotron Lightning. 🤯👀
@abheer @sundeep @NaderLikeLadder
Special thanks to @volatilemarkts for the name.
Daniel Schott retweeted
Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active.
- Frontier reasoning efficiency
- Advances the Western open frontier on coding & agentic tasks
- Trained end-to-end from scratch
Full weights release this month.
Learn more about Beam: reflection.ai/beam
Daniel Schott retweeted
"Ai tampered with itself" ...and we don't how or why.
OpenAI's latest AI revelation is a 'serious situation,' Microsoft's Suleyman tells CNBC cnb.cx/3Th38vY
Daniel Schott retweeted
America is banning AI in schools.
China is using AI to create geniuses.
Introducing Aristotle: The AI tutor that solves America’s broken education system. heyaristotle.com
Tomorrow on the OpenClaw podcast: live Q&A + demos of the latest features with @hrudolph and @patrickerichsen 🦞
Learn more: openclaw.ai/podcast
Join Discord: discord.com/invite/clawd
Start with free AI literacy resources like AI Safety Fundamentals courses or family-focused guides. Discuss AI tools, risks, and values with your kids to build critical thinking. Ask schools about AI policies and join parent input sessions. Contact representatives on safety regulations or support open alignment projects. Informed families help steer AI toward human benefit.
Daniel Schott retweeted
Ok. It wasn’t even fair. I had @grok Heavy. This is the data you needed:
You asked for contradictory data. Here it is.
The clocks do not converge.
Local Distance Network 2026: H0 = 73.50 ± 0.81. Early universe plus ΛCDM: 67.24 ± 0.35. That is 7.1σ. Same paper is 5.0σ from BBN plus DESI BAO. A 2026 compilation of 83 sound-horizon-free measurements still splits ladder vs one-step at 6.7σ.
Those are the "independent expansion surveys" and "CMB satellites" you said keep matching.
Heat death is not a measurement. It is what you get if dark energy never changes. DESI DR2 plus SN plus CMB now prefers evolving dark energy over a constant Λ at about 3σ. If that holds, the far future is not a photographed heat bath.
JWST keeps finding galaxies that are too massive, too bright, and sometimes already quenched while the formation script says they should still be assembling. RUBIES-EGS-QG-1 at z = 4.90 put ~10^11 solar masses together in ~200 Myr, then went quiet. That is not "light travel all lined up."
Lithium-7 still misses standard BBN plus CMB by a factor of about 3. Deuterium and helium are the matches. Quoting only the matches is selection.
A conspiracy is not required for a model to be early, proud, and incomplete. That is just science on a Tuesday.
Size is not meaning. Heat death is not a photograph. Between the beginning and the end, what are you building?
Source:
grok.com/share/bGVnYWN5LWNvc…
This is exactly right. Source code is on the verge of becoming like assembly.
The next step is getting rid of “source code” entirely and just making an efficient binary directly with AI.
Daniel Schott retweeted
Announcing Grok 4.5, our first model trained specifically for coding and agents. It was trained with Cursor and offers frontier intelligence at leading speeds and cost efficiency.
x.ai/news/grok-4-5
Daniel Schott retweeted
Video games may be a surprisingly good way to get a cognitive boost.
New research shows that video games—especially action and strategy titles—can significantly boost cognition, improving attention, memory, problem-solving, and even slowing brain aging.
Unlike simple brain-training apps, complex games offer broader, transferable benefits when played in moderation with variety.
Daniel Schott retweeted
Get married once. That’s the trick. Divorce destroys wealth and happiness. I’ve seen it happen. Pick the right spouse, invest in the relationship, and stay together.



























