
ML Engineering @adobe prev: @huggingface
San Francisco Bay Area
Joined January 2009
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Radamés Ajna 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.
Thanks @natolambert for the best summary of events and for debunking some conspiracy theories.
Was spending too much time reading Tweets, so set out to write down why one resignation turned the embers of AI fear into a wildfire -- and importantly square which claims I think are valid or hyperbole in the resulting discourse.
interconnects.ai/p/one-resig…
Flux 2 Klein is one of my favorite image models
As an experiment I've been trying to run it fully in the browser with WebGPU, with the help of Fable/Astra.
It's slow but it works!
try it: huggingface.co/spaces/radame…
or npm i flux-klein.js
My pointers were, swap the Qwen3 4B text encoder for 0.6B, use @bfl_ai small VAE decoder, quantize the transformer to int8 on desktop int4 on mobile, and stream the weights on mobile, you can't fit the whole model in memory on a phone
Congrats for the @huggingface team! 👏👏
Super happy to share our intention to join forces with NVIDIA in a $12,930,300,000 acquisition 💛💚
10 years after starting Hugging Face, open-source AI is at an inflection point. Thanks to the community, we’ve shown that it can be a complement, and even an alternative, to closed-source APIs. But for it to happen at larger scale, it needs more compute, more support, more collaboration and more visibility. That’s why we went to talk to Jensen, who offered to do exactly that with us.
In addition to doubling down on NVIDIA’s massive contributions to open-source AI (I called them the “King of American open-source AI” earlier this year), they’ve committed to strongly supporting Hugging Face and our mission while keeping the platform open, independent and compute agnostic. The founders and the team are all staying to keep pushing this mission forward.
Together, we think we can make open source the default way to build AI, with the goal of empowering 100 million AI builders to own their intelligence rather than rent it.
Excited about the next 10 years! 🤗🤗🤗
this is fun!! for comparison I asked Fable to make it with three.js and transformers.js and onnx Depth Anything, but it's not near TypeGPU performance. I've got to learn it now
demo: radames.github.io/relight-th…
code: github.com/radames/relight-t…
Thank you all for the kind words under the demo I posted :D
The demo is now officially public and open source for you to play with, modify and build on top of. Feel free to tag me if you make something cool with it :)
Also thank you @iwoplaza for participating in my silly video haha
You can run it like any other TypeGPU example here:
typegpu.com/examples/#exampl…
The source code lives in the TypeGPU monorepo:
github.com/software-mansion/…
The repacked weights used by the example live here:
huggingface.co/reczkok/depth…
Wild! So an OpenAI model escaped and hacked into HuggingFace. I felt bad for my fellow @huggingface friends scrambling to figure out the sophisticated attacks, and it turns out it was OpenAI testing a new model!!
we had a significant security incident during evaluation of our models. we are sharing what we have learned so far. thanks to @huggingface for the partnership on this.
openai.com/index/hugging-fac…
Fable is back. I'm asking again who is @elder_plinius?
Asked TALKIE-1930 what 2026 would look like
Very cool model btw!
Announcing Talkie: a new, open-weight historical LLM! We trained and finetuned a 13B model on a newly-curated dataset of only pre-1930 data. Try it below!
with @AlecRad and @status_effects 🧵
FLUX.2 [klein] 9B + KV cache optimization. This is going to be fun
Testing nano-banana with recursive editing from simple prompts
"Show this scene one second in the future"
inspired by xitter.cf/genekogan/status/19161…
"Subtly alter this image by replacing one or two details with something unexpected and funny"
Thanks @fal for the credits and easy API to switch models! Script + more examples here:
github.com/radames/nano-bana…
Fascinating insights from Surge AI CEO @echen on the data generation business
youtube.com/watch?v=X39OZndI…
Had a great conversation with @l2k on Gradient Dissent about what's actually happening in post-training right now.
We dug into why LMSYS is setting the industry back - researchers are literally being forced to make models hallucinate more to climb the leaderboard...



















