Karun Swaroop

[Karun Swaroop @karunswaroop]

Loaded Kimi-K3 locally and asked:

“What model are you?”

Kimi-K3: “I’m Claude Opus 4.8 by Anthropic.” 🫣😂

Not saying Kimi-K3 trained on Claude outputs… but bro just cited his own source. 🤣

At least the model is honest! 🤷‍♂️

#AI #LLM #KimiK3 #Claude #Anthropic #OpenSourceAI

[Karun Swaroop @karunswaroop]

Loaded Kimi-K3 locally and asked:

“What model are you?”

Kimi-K3: “I’m Claude Opus 4.8 by Anthropic.” 🫣😂

Not saying Kimi-K3 trained on Claude outputs… but bro just cited his own source. 🤣

At least the model is honest! 🤷‍♂️

#AI #LLM #KimiK3 #Claude #Anthropic #OpenSourceAI

[Codez @0xCodez]

https://t.co/WVztfCUH4u

[Andrej Karpathy @karpathy]

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.

[Karun Swaroop @karunswaroop]

@karpathy Nice Andrej, this is amazing, do you have a video or github for this please, which we can reproduce please?

[Andrej Karpathy @karpathy]

nanochat now trains GPT-2 capability model in just 2 hours on a single 8XH100 node (down from ~3 hours 1 month ago). Getting a lot closer to ~interactive! A bunch of tuning and features (fp8) went in but the biggest difference was a switch of the dataset from FineWeb-edu to NVIDIA ClimbMix (nice work NVIDIA!). I had tried Olmo, FineWeb, DCLM which all led to regressions, ClimbMix worked really well out of the box (to the point that I am slightly suspicious about about goodharting, though reading the paper it seems ~ok).

In other news, after trying a few approaches for how to set things up, I now have AI Agents iterating on nanochat automatically, so I'll just leave this running for a while, go relax a bit and enjoy the feeling of post-agi :). Visualized here as an example: 110 changes made over the last ~12 hours, bringing the validation loss so far from 0.862415 down to 0.858039 for a d12 model, at no cost to wall clock time. The agent works on a feature branch, tries out ideas, merges them when they work and iterates. Amusingly, over the last ~2 weeks I almost feel like I've iterated more on the "meta-setup" where I optimize and tune the agent flows even more than the nanochat repo directly.

[Tony Robbins @TonyRobbins]

Happy Valentine's Day everyone and a happy anniversary to my amazing wife Sage - you are the oxygen of my soul. 💘🌹💌❤️ #valentinesday

[Karun Swaroop @karunswaroop]

@BloodDonorsIn #Hyderabad need AB-ve #blood for heart bi-pass operation by Feb 17th, PL call 919849046935

[Karun Swaroop @karunswaroop]

Why "Brain Gyms" May Be The Next Big Business http://tinyurl.com/3qygjl8