Daniel Franke retweeted
This is truly getting tiresome. The effort to paint the very difficult immediate situation as without precedent and not “normal” ignores the waves of the security issues of the past.
I’m not downplaying this and just as tired of “no, this time is different.”
There’s just no escaping that every step function change in automated tools to find (security) holes/bugs that arose out of point experiences being generalized immediately created the type of “all hands” situation we face now.
Everything from floppy disk scanning to buffer over runs to uninitialized variables to off by one loops to automation hooks to script kiddies to xss to frames to social engineering saw incidents leading to generalized tools that scanned environments to find countless latent bugs that needed to be fixed urgently.
One day a product was ready to ship and then a tool ran and suddenly there were 10,000 new bugs for 500 engineers to investigate and fix and then test for what broke because well who knows what happens when you suddenly initialize a variable that wasn’t previously.
This sucks in a ton of ways. No one wants to stop what they are doing at what seems like the biggest opportunity and/or disruptive risk to business in a generation. No one wants to touch old code. No one likes to have work generated for them by automated tools finding issues that are obscure by definition.
But this is all good. These bugs were all there. Instead of cursing just realize how lucky your team is to find them now and not after the breach. Whenever we surfaced from these moments in the past we felt our code reached a new and better “steady state” and that will happen.
They should not have posted this. If you can be pressured to issue a denial like this, then your failure to do so in response to similar pressure about other alleged patients becomes evidence that violates those patients' privacy.
Daniel Franke 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.
If this is for himself and not one of his kids, then I think it's awesome that a guy who owns a company that builds real spaceships still enjoys building pretend stuff out of LEGO.
Replying to @PEoperator @bot
Grok will find another way to buy stuff. I just ordered LEGO and Grok bought it from the LEGO website.
Daniel Franke retweeted
RT to instantly kill a platonist
The optimality of the packing for 11 squares has been formalized in lean thanks to Astra and Claude! Huge thanks to @ojoshe, @kleddamag, @wand_125, @guzhou0806, and @ctjlewis for aiding in the process.
Image credit: jlevy.github.io/squares/case…
1/n
Daniel Franke retweeted
Claim: we've solved the AI slop problem (!) 💩🧹✨
Blog post: facebookresearch.github.io/R…
🧵1/5
Key idea: take *expert* human writing and learn rubrics that find the gap between experts and models. Train with those rubrics.
We train with RL-XAR (RL with eXpert Aligned Rubrics) & see large performance gains on writing scientific paper sections, Pulitzer prize novel continuations and high quality Wikipedia pages.
Daniel Franke retweeted
If you explained to someone from 2024 that AI could solve field-leading math problems before it could contribute to literature they'd be surprised. If you explained to someone from 2010 they'd go "Yeah obviously. What else could you possibly expect?"
Sol could start at step 2 most of the time but couldn't iterate. Every correction made things worse. Astra starts at step 3 and then understands correction.
Holy shit, Astra can finally write acceptable API documentation. I told it to clean up Sol's drivel and it mostly did.
Daniel Franke retweeted
In an alternative universe we have a Rings of Power show that starts off with Sir Christopher Lee (as Melkor) shredding heavy metal to disrupt Iluvatar's Song of Creation.
Replying to @AlysssaHazel
Seriously though. Just imagine everyone playing opera while he's inventing heavy metal.
I'M THE PRINCE OF DARKNESS!
Tulkas behind him going, “Red flag alert.” As he gets ready to backhand him.
Daniel Franke retweeted
I asked Fable to remove the "--check" option from Bend's CLI. It left a "--check" option that just prints "there is no check option" and quits.
Cannot make this up.
Please teach LLMs how to remove stuff, I beg you one last time
GPT-5.6 routinely stumbles into a lot of confusion about UNIX PTY semantics. Sure enough, I went looking through Codex code and all of its confusions correspond to bugs and quirks of Codex. In-harness RL has given it fetal alcohol syndrome.
Dammit! I was just thinking yesterday it would hilarious to make Mechanical Turk available to agents as a tool call.
xitter.cf/Techmeme/status/209242…
AWS says it plans to shut down Mechanical Turk on September 30, 2026, following an assessment; the service, launched in 2005, outsourced tasks to humans (@annierpalmer / CNBC)
(Visit Techmeme dot com for the link and full context!)
Ox Alpha = GLM-5.3-Flash has been clear for several days to anyone who was paying attention. The big news in today's announcement is that it was running entirely on Chinese chips.
@Zai_org gave away global, all-you-can-eat access to a near-frontier model for free for a week. The resulting load did cause some service brownouts, but by-and-large they kept it sufficiently speedy and available to get real work done with it.
That is a hell of flex, and anybody who had opinions before now about China's chip capabilities and the geopolitical ramifications thereof needs to re-evaluate those. Myself, I always would have told you that trying to keep American chips out of Chinese hands was doomed as a long-term strategy, but I would have grudgingly agreed that export controls were achieving their intended short-term effect. Today's news proves that I was dead wrong about that second part.
Introducing GLM-5.3-Flash
- Leading capabilities at a highly competitive price
- Natively multimodal with a 1M-token context window
- A 320B-A18B model released under the MIT License
- Previously previewed as Ox Alpha, running entirely on Chinese AI chips
Blog: z.ai/blog/glm-5.3-flash
Available now across all official platforms:
Weights: huggingface.co/zai-org/GLM-5…
API: docs.z.ai/guides/llm/glm-5.3…
Coding Plan: z.ai/subscribe
ZCode: zcode.z.ai/en
Chat: chat.z.ai
AutoClaw: autoclaw.z.ai
My current appraisal of AI companies:
Anthropic: strongest model
OpenAI: strongest model that isn't insufferable
Moonshot: strongest open model
Zhipu: most cost-effective model for multi-tenant hardware + near-peer to Moonshot
Alibaba: Strongest model that fits on one GPU
X, Meta, DeepSeek: also-rans, but could catch up soon
Deepmind: seems to be in a death spiral
Daniel Franke retweeted
Richard Feynman (my father) divided perpetual motion machines into Perpetual Motion Machines of the First Kind, which violate the first law of thermodynamics (AKA conservation of energy), Perpetual Motion Machines of the Second Kind, which violate the second law of thermodynamics (entropy must increase), and Those Other Perpetual Motion Machines, which purport to get their energy from other laws of physics.
The Second Kind is rarer; the only example I'm familiar with is one that my dad showed me the plans for. Once in a while, someone would come to him with a perpetual motion machine prospectus and ask if it was a good investment. One of these was a machine that claimed to extract heat energy from the air and run a generator with it. They had built half the machine, which worked as far as they could test it, and were looking for gullible investors to pay for finishing it. By carefully going over the immensely complex plans, my father and I found a "heat exchanger" that was supposed to take warm freon and cool air, and produce hot freon and cold air. Naturally, this heat exchanger was in the as-yet unbuilt portion.
Those Other Perpetual Motion Machines get their energy from heretofore unknown physics, like cold fusion, vacuum fluctuations, or secret new physical laws that will be disclosed only on the payment of $1,000,000. These are worthy of much more serious physical investigation, since it is entirely concievable that someone will discover a new easy way of making energy.
Back around 1970, my father went to a public demonstration of one of Those Other Perpetual Motion Machines. He discovered an electric cord running out of the back of the machine, plugged into the wall. When he unplugged the machine and pointed out to the inventor that a perpetual motion machine that had to be plugged in wasn't really perpetual, the inventor pushed a button on the control panel and the machine exploded. Several spectators were severely injured; I believe one man lost an arm. The inventor sued my father on the grounds that he had caused the explosion; my father suspected that the explosion was deliberate. The trial ended up with my father not having to pay.
Epistemic status: Childhood recollections, untainted by fact-checking. Originally an email from 1997.




























