Malcolm Walk

The man who killed the $10,000 GPU myth. He did it alone, from Bulgaria, with one C file. 🤯 >Meet Georgi Gerganov. >Bulgarian developer. Nobody had heard of him. >In March 2023, Meta’s LLaMA model leaked online >Within days he wrote a single C file >Called it llama.cpp >It ran a full AI model on a MacBook. No GPU. No cloud. >The entire AI industry said you needed $10,000 GPUs to run LLMs 🔥 >He proved you didn’t. On a laptop. Alone. >Also built whisper.cpp ~ same thing for voice AI > His code is the foundation of Ollama, LM Studio, and GPT4All >107,000+ GitHub stars. Fastest open-source AI project to hit 100K ever. 🚀 >In 2026 Hugging Face hired his entire team >Still ships code. Still open source. Still free. Every time you run AI locally, you’re running his work. Absolute Legend 🐐

8 specialized AI model types 👇 LLM → text generation LCM → semantic reasoning LAM → action-oriented agents MoE → expert routing VLM → vision + language SLM → lightweight edge models MLM → masked token learning SAM → image segmentation AI is moving from “one big model” to specialized architectures. #AI #LLM #MoE #VLM #MachineLearning

Before the ideal agentic engineering IDE arrives, here’s my setup:

It is hard to communicate how much programming has changed due to AI in the last 2 months: not gradually and over time in the "progress as usual" way, but specifically this last December. There are a number of asterisks but imo coding agents basically didn’t work before December and basically work since - the models have significantly higher quality, long-term coherence and tenacity and they can power through large and long tasks, well past enough that it is extremely disruptive to the default programming workflow. Just to give an example, over the weekend I was building a local video analysis dashboard for the cameras of my home so I wrote: “Here is the local IP and username/password of my DGX Spark. Log in, set up ssh keys, set up vLLM, download and bench Qwen3-VL, set up a server endpoint to inference videos, a basic web ui dashboard, test everything, set it up with systemd, record memory notes for yourself and write up a markdown report for me”. The agent went off for ~30 minutes, ran into multiple issues, researched solutions online, resolved them one by one, wrote the code, tested it, debugged it, set up the services, and came back with the report and it was just done. I didn’t touch anything. All of this could easily have been a weekend project just 3 months ago but today it’s something you kick off and forget about for 30 minutes. As a result, programming is becoming unrecognizable. You’re not typing computer code into an editor like the way things were since computers were invented, that era is over. You're spinning up AI agents, giving them tasks *in English* and managing and reviewing their work in parallel. The biggest prize is in figuring out how you can keep ascending the layers of abstraction to set up long-running orchestrator Claws with all of the right tools, memory and instructions that productively manage multiple parallel Code instances for you. The leverage achievable via top tier "agentic engineering" feels very high right now. It’s not perfect, it needs high-level direction, judgement, taste, oversight, iteration and hints and ideas. It works a lot better in some scenarios than others (e.g. especially for tasks that are well-specified and where you can verify/test functionality). The key is to build intuition to decompose the task just right to hand off the parts that work and help out around the edges. But imo, this is nowhere near "business as usual" time in software.

Rereading this gem “Football Intelligence” I read about the Technology stack needed for football organizations (people, vendors, storage, etc). How important it is to align the strategy + use of data, in order to spend a budget that allows to create a custom infrastructure.

Mbuku et kolo muni final coupe du monde même combat ! #CAN2025 #RDCALG #RDCALGERIE

My pleasure to come on Dwarkesh last week, I thought the questions and conversation were really good. I re-watched the pod just now too. First of all, yes I know, and I'm sorry that I speak so fast :). It's to my detriment because sometimes my speaking thread out-executes my thinking thread, so I think I botched a few explanations due to that, and sometimes I was also nervous that I'm going too much on a tangent or too deep into something relatively spurious. Anyway, a few notes/pointers: AGI timelines. My comments on AGI timelines looks to be the most trending part of the early response. This is the "decade of agents" is a reference to this earlier tweet x.com/karpathy/statu… Basically my AI timelines are about 5-10X pessimistic w.r.t. what you'll find in your neighborhood SF AI house party or on your twitter timeline, but still quite optimistic w.r.t. a rising tide of AI deniers and skeptics. The apparent conflict is not: imo we simultaneously 1) saw a huge amount of progress in recent years with LLMs while 2) there is still a lot of work remaining (grunt work, integration work, sensors and actuators to the physical world, societal work, safety and security work (jailbreaks, poisoning, etc.)) and also research to get done before we have an entity that you'd prefer to hire over a person for an arbitrary job in the world. I think that overall, 10 years should otherwise be a very bullish timeline for AGI, it's only in contrast to present hype that it doesn't feel that way. Animals vs Ghosts. My earlier writeup on Sutton's podcast x.com/karpathy/statu… . I am suspicious that there is a single simple algorithm you can let loose on the world and it learns everything from scratch. If someone builds such a thing, I will be wrong and it will be the most incredible breakthrough in AI. In my mind, animals are not an example of this at all - they are prepackaged with a ton of intelligence by evolution and the learning they do is quite minimal overall (example: Zebra at birth). Putting our engineering hats on, we're not going to redo evolution. But with LLMs we have stumbled by an alternative approach to "prepackage" a ton of intelligence in a neural network - not by evolution, but by predicting the next token over the internet. This approach leads to a different kind of entity in the intelligence space. Distinct from animals, more like ghosts or spirits. But we can (and should) make them more animal like over time and in some ways that's what a lot of frontier work is about. On RL. I've critiqued RL a few times already, e.g. x.com/karpathy/statu… . First, you're "sucking supervision through a straw", so I think the signal/flop is very bad. RL is also very noisy because a completion might have lots of errors that might get encourages (if you happen to stumble to the right answer), and conversely brilliant insight tokens that might get discouraged (if you happen to screw up later). Process supervision and LLM judges have issues too. I think we'll see alternative learning paradigms. I am long "agentic interaction" but short "reinforcement learning" x.com/karpathy/statu…. I've seen a number of papers pop up recently that are imo barking up the right tree along the lines of what I called "system prompt learning" x.com/karpathy/statu… , but I think there is also a gap between ideas on arxiv and actual, at scale implementation at an LLM frontier lab that works in a general way. I am overall quite optimistic that we'll see good progress on this dimension of remaining work quite soon, and e.g. I'd even say ChatGPT memory and so on are primordial deployed examples of new learning paradigms. Cognitive core. My earlier post on "cognitive core": x.com/karpathy/statu… , the idea of stripping down LLMs, of making it harder for them to memorize, or actively stripping away their memory, to make them better at generalization. Otherwise they lean too hard on what they've memorized. Humans can't memorize so easily, which now looks more like a feature than a bug by contrast. Maybe the inability to memorize is a kind of regularization. Also my post from a while back on how the trend in model size is "backwards" and why "the models have to first get larger before they can get smaller" x.com/karpathy/statu… Time travel to Yann LeCun 1989. This is the post that I did a very hasty/bad job of describing on the pod: x.com/karpathy/statu… . Basically - how much could you improve Yann LeCun's results with the knowledge of 33 years of algorithmic progress? How constrained were the results by each of algorithms, data, and compute? Case study there of. nanochat. My end-to-end implementation of the ChatGPT training/inference pipeline (the bare essentials) x.com/karpathy/statu… On LLM agents. My critique of the industry is more in overshooting the tooling w.r.t. present capability. I live in what I view as an intermediate world where I want to collaborate with LLMs and where our pros/cons are matched up. The industry lives in a future where fully autonomous entities collaborate in parallel to write all the code and humans are useless. For example, I don't want an Agent that goes off for 20 minutes and comes back with 1,000 lines of code. I certainly don't feel ready to supervise a team of 10 of them. I'd like to go in chunks that I can keep in my head, where an LLM explains the code that it is writing. I'd like it to prove to me that what it did is correct, I want it to pull the API docs and show me that it used things correctly. I want it to make fewer assumptions and ask/collaborate with me when not sure about something. I want to learn along the way and become better as a programmer, not just get served mountains of code that I'm told works. I just think the tools should be more realistic w.r.t. their capability and how they fit into the industry today, and I fear that if this isn't done well we might end up with mountains of slop accumulating across software, and an increase in vulnerabilities, security breaches and etc. x.com/karpathy/statu… Job automation. How the radiologists are doing great x.com/karpathy/statu… and what jobs are more susceptible to automation and why. Physics. Children should learn physics in early education not because they go on to do physics, but because it is the subject that best boots up a brain. Physicists are the intellectual embryonic stem cell x.com/karpathy/statu… I have a longer post that has been half-written in my drafts for ~year, which I hope to finish soon. Thanks again Dwarkesh for having me over!

@Dark_Emi_ Le pire c'est quand Ad qui touche au œuvres de michel sardou et orelsan lol. Conclusion de cet discussion : Les influenceurs ont remis en place les députés à plusieurs reprise. On viens d'assister à une nouvelle version de touche pas à mon poste lol. #mercinosimpots #telerealité

Je jure j'ai écouté 15 secondes l'audition de Nasdas la question c'est la député qui lui dit qu'il est responsable du bruit dans le quartierTT Putain de débiles ces députés ils font des commissions parlementaires pour dire à des mecs qu'ils font trop de bruit dans le quartier

Yamal drible mais ne gagne pas. Dembelé ballon d'or. #psg #BallonDor2025

Hello team, I'm back after a long period of absence

An explainer of the different types of ZK-EVMs and ZK-EVM-like projects out there, and the tradeoffs between them. vitalik.eth.limo/general/2022/0…

@ManuRedPills @VitalikButerin @StakeDAOHQ @BlackpoolHQ Alors des tips?

Speaking about animals with @VitalikButerin ! @StakeDAOHQ @BlackpoolHQ

@Mallauryyy_ Vous avez vraiment pas wonte md4

@Mallauryyy_ Un personnal stopper te le trouve direct. Juste un sandwich mdrr. Moi y faut la canettes avec lol

@Mallauryyy_ En tout cas tu sais que te pas en 10min sur paris lol.

I showed for the first ever, “String Theory” in a physical event by @CrystalPNight in Paris last year I met @lanadenina @bygrida @rakimjah

@BarryPixel2chop @CrystalPNight @lanadenina @bygrida @rakimjah Que de bon souvenirs

1 hour from the end of the auction 😌 Current bid 0.169 $ETH

@Mallauryyy_ Normal en même temps quand t'habites dans le 7.... tu dois partir des heures en avance ya pas beaucoup de TER 😅🤣😂

Dev tickets sold out only a few regular tickets left ! Looking forward to seeing all of you at @StarknetCC Last tickets : starknet.cc

I’m featured in an article from @artpricedotcom With my art “Globule blanc” owned by @BnoiitC during the first physical NFT auction in 🇫🇷 artprice.com/artmarketinsig…