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Elon Musk: People thought I was out of my mind when I suggested catching the booster with the tower. “If you can move mass to the ground side, it's better to move mass to the ground side. That's why we took the legs off the booster and just have the tower catch it. I know it sounds insane. But when I suggested that, people thought I was out of my mind, which, I'm like, maybe I have. But I think it might take a few kicks at the can, but we'll get it right. And then just the work that you have to do to pick up the booster and put it on the launch stand, this gigantic skyscraper thing, in high wind, you know, like windy situations. It's very windy around here. You pick up this booster. You got to put it onto its stand with precision. Then you got to pick a ship up and put it up on top of that. So that means you've got to have a secondary arm to steady the booster so it's not moving around all over the place. And then, while the Mechazilla arms pick up the ship and put it on the booster.” Starbase tour interview with Tim Dodd, The Everyday Astronaut, August 2021.

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the end of this month marks one year of being out of my abusive relationship. this isn’t something I’ve talked about publicly but it has been on my mind. i have been going through a period of learning self acceptance and trying to cope/understand what was said and done to me. I hope this next year is a new chapter for me and it brings lots of good things. i am very excited and motivated to move forward and put in everything i have for the things I love. i want to thank everyone who has supported me the last two years. i cannot begin to explain the amount of loneliness and sadness I felt during these times but i ALWAYS had a community and so many people being kind to me through vtubing. every compliment, supportive message, or just checking in on me meant so much even though im very shy and might not show my gratitude enough. i truly believe it is what kept me going. thank u guys for supporting me 💙

Today a crazy quantum story just got wilder. On March 31, the Google Quantum AI team published a landmark result on Shor's algorithm for elliptic curve cryptography. Technically, the paper was a bombshell: a dramatic 10x improvement over the state-of-the-art. As a stunt and wakeup call to the blockchain space, those optimisations were illustrated on secp256k1, the elliptic curve underlying Bitcoin and Ethereum signatures. But perhaps the most striking part of the paper was sociological, not technical. Instead of following standard academic process, the optimisations were kept secret, hidden behind a zero-knowledge (ZK) proof. Google's accompanying blog post mentions they "engaged with the U.S. government". The ZK proof demonstrates the existence of algorithmic improvements without leaking details. Academic censorship with ZK, a historic first! As a co-author of the Google paper I witnessed some of the context surrounding this censorship. To be honest, multiple aspects of that context don't sit well with me. As much as I believe the general public ought to know more, I am limited in my ability to whistleblow. Though let me be clear about one thing: the Google team's professionalism has been absolutely exemplary, and they deserve nothing but praise. Censorship has a way of backfiring. The Streisand effect, where an attempt to bury something only draws more attention to it, is exactly what's unfolding today. First, Google's key optimisation has been rediscovered by the French. And in a thrilling turn of events, a collaborative Shor-at-home challenge just launched. The initiative, available at ecdsa[.]fail, breached a new Shor world record in a matter of hours. Let's start with the rediscovery. Just two months after Google's paper, French quantum expert André Schrottenloher cracks the main secret optimisation. His paper, titled "Optimized Point Addition Circuits for Elliptic Curve Discrete Logarithms", landed on the arXiv today. Big congrats to André, who beat several other nerdsnipped experts to it. In a blog post also published today, Craig Gidney, the world expert on Shor optimisations, revealed that he'd been sitting on this very optimisation for a whole year under censorship pressure. Interestingly, André missed a handful of minor optimisations, both from Google's original publication and from improvements found since. It's plausible there's still plenty of juice left to squeeze out of Shor, and this is exactly what the ecdsa[.]fail challenge is about. The verifier program developed for the ZK proof does double duty, automatically filtering for valid submissions. Dozens of compounding small and micro improvements are rolling in. As of the time of writing there's an 8.4% improvement to Google's circuit, as measured by the product of logical qubit count and Toffoli gate count. Nice! The nerdsnipping ran deeper than anyone expected. Over the last few weeks it became clear it extended well beyond André and other quantum experts. Behind the scenes, a small army of amateurs quietly got to work. Inspired by Karpathy-style autoresearch, they turned AI on Shor. Ironically, the verifier program for the ZK proof makes an ideal reward function for AIs. The barrier to entry for this modern style of research is refreshingly low, with several non-experts, even a teenager, finding nice optimisations. Get in touch if you'd like to join a Telegram group with fellow autoresearchers :) Part 2: neutral atoms and qday The story doesn't end with Google. On the same day Google went public, a stealthy startup called Oratomic published its own Shor paper in a coordinated release. It made a splash, ultimately becoming the most upvoted paper on scirate[.]com, a website ranking arXiv papers. Oratomic's claim was wild. By building on Google's logical optimisations and applying custom physical optimisations for neutral atoms, they claimed just 10K physical qubits were sufficient to run Shor's algorithm on secp256k1. That number is mind-bogglingly low. Knowing essentially nothing about neutral atoms when Oratomic's paper landed, I was intrigued and decided to learn more about the tech. I fell straight down the rabbit hole and spent a couple hundred hours on the topic. I got a little obsessed and watched every YouTube video I could find and spoke to a bunch of experts. My conclusion? The tech is real, very real. Even Google recently decided to start a neutral atom lab, a notable pivot from their sole focus on superconducting qubits. If you care about qday, i.e. the day a quantum computer will break the first piece of cryptography in production, neutral atoms demand your attention. I shared some of my learnings on Shor and neutral atoms in a 30min talk at the ZKProof cryptography conference. You can find it on YouTube by searching "zkproof neutral atom". Here's an interesting observation about this duo of breakthrough papers: neither Google nor Oratomic say a word about what their results mean for qday. No timelines. Zero. Nada. That is especially baffling given that the whole point of whitehat quantum cryptanalysis is to inform qday estimations and help the general public make good decisions. So let me attempt to partially fill the silence, similarly to what Scott Aaronson did in his April 29 post. Given everything I know, including scary non-public information, I now put the odds of qday by 2032 at 50%. 10% by 2030. Anecdotally, the US government has its own date: 2035. Originating at the NSA and later adopted by NIST, it's when branches of the US government will be disallowed from using quantum-vulnerable cryptography. In plain language: with hindsight, that date is a joke and should be discounted entirely. I don't see how NIST avoids being forced to pull it forward by years. Part 3: post-quantum cryptography There are good reasons to sound the alarm today, but please do not panic. Rushing carelessly towards immature post-quantum cryptography is a recipe for disaster. IMO a good target date for migration is 2029, roughly 3.5 years out. 2029 happens to be the date selected by Google, Cloudflare, and the Ethereum Foundation. These days most of my time goes to safely migrating Ethereum towards post-quantum cryptography as part of the broader lean Ethereum effort. There's a lot to do. We need to rip out and replace BLS signatures at the consensus layer, KZG commitments at the data layer, and ECDSA signatures at the execution layer. The plan to get there is compelling, and is based on hash-based cryptography. Within the Ethereum Foundation we've developed a Swiss army knife called leanVM (github[.]com/leanEthereum/leanVM) powered by the magic of hash-based SNARKs. Thanks to truly exceptional work by Emile, Thomas, and others, its performance is derisked. Regarding security, leanVM is a jewel, a minimal zkVM crafted for end-to-end formal verification and maximum security. Want to help? There are two $1M initiatives. First, the Proximity Prize (proximityprize[.]org). Solve a long-standing mathematical conjecture in coding theory, improve hash-based SNARKs, and go home a millionaire. Second, the Poseidon Initiative (poseidon-initiative[.]info), offers $1M for breaking Poseidon, the SNARK-friendly hash function.

🍰 BrownDust2 | Sonya’s Birthday 🎉 It’s Sonya’s birthday—the girl who’s always with Tanya. 🎉 “The moon is the same as ever. In my mind, the dark shadow that protects me… Tanya is always there. They say the moon can only shine in the dark. I’m the same. I can only be my true self when Tanya is with me. Thanks to her, I made it out of that terrible night… into the warmth of a mother’s embrace. It’s okay if I don’t always shine bright like the moon. The darkness and the shadow are part of me, too. Tanya… happy birthday.” Today is Sonya’s special day. Just like the moonlight, her darkness and shadows are also part of who she is. Leave Sonya a quiet birthday wish—even a simple message can become a gentle light for her. 🌟 #HappyBirthday# #Sonya# #Tanya#

Full disclosure, and just to get this out there publicly so that it's impossible to "leak". I haven't told anyone yet. Some people are making guesses. The reasoning is off, but they do hit certain areas. 👇 I might name the Chinese version of my book/memoir "币安人生". This is not related to any meme tokens or listings. But I embrace the meme culture. I like that meme/word. It's sticky, for me. Disclaimer: I don't hold any "币安人生" meme coin, and have no intention to do so. And I also reserve all rights not to use that title. I might change my mind last minute. The English name of the memoir will likely be completely different, not decided yet. Book ETA, 4-6 weeks, English and Chinese version both at the same time. Self published. Takes too long to go through a publisher, even though they will likely help significantly with distribution. All proceeds I receive from the book will go to charity. Not trying to make money from the book.🙏

A few random notes from claude coding quite a bit last few weeks. Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent. IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits. Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased. Speedups. It's not clear how to measure the "speedup" of LLM assistance. Certainly I feel net way faster at what I was going to do, but the main effect is that I do a lot more than I was going to do because 1) I can code up all kinds of things that just wouldn't have been worth coding before and 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion. Leverage. LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely correct first, then ask it to optimize it while preserving correctness. Change your approach from imperative to declarative to get the agents looping longer and gain leverage. Fun. I didn't anticipate that with agents programming feels *more* fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part. I also feel less blocked/stuck (which is not fun) and I experience a lot more courage because there's almost always a way to work hand in hand with it to make some positive progress. I have seen the opposite sentiment from other people too; LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building. Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually. Generation (writing code) and discrimination (reading code) are different capabilities in the brain. Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it. Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media. We're also going to see a lot more AI hype productivity theater (is that even possible?), on the side of actual, real improvements. Questions. A few of the questions on my mind: - What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*. - Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro). - What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music? - How much of society is bottlenecked by digital knowledge work? TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.

🔥llm.c update: Our single file of 2,000 ~clean lines of C/CUDA code now trains GPT-2 (124M) on GPU at speeds ~matching PyTorch (fp32, no flash attention) On my A100 I'm seeing 78ms/iter for llm.c and 80ms/iter for PyTorch. Keeping in mind this is fp32, with no flash attention yet, and slightly stale PyTorch (2.1.0). - It is a direct implementation of the training loop and backpropagation in C/CUDA. - It compiles and runs instantly. No more "hit run then wait for tens of seconds for unknown reasons", for mountains of inscrutable abstractions to build a Universe. - It deletes the need for the Python interpreter and a deep learning library. - It allocates all the memory a single time at the start. - It's pretty cool. How: Getting this to work required us to write a lot of custom CUDA kernels, and doing this manually (instead of using Tensor ops of aten/PyTorch and torch.compile etc.) is a bit like programming in assembly. And you spend quality time looking at more assembly (CUDA PTX/SASS). But this also means we get to hyperoptimize the code and possibly explore optimizations that torch.compile might find difficult to, which is awesome. Examples of optimizations that went in over the last few days: - we're being clever with our memory consumption in the backward pass, only using a few buffers we need to propagate the gradients, saving memory capacity. - one fused classifier kernel does the last layer forward pass, the loss, and kicks off the backward pass. - many improvements to all the kernels involved, including e.g. gains from carefully constraining execution within the autoregressive mask in attention - cuBLAS(Lt) calls for all heavy lifting matmuls, and fused bias accumulation Big credits to two CUDA experts who appeared from somewhere on the internet to help this open source project, ngc92 and ademeure. We're hanging out of Github and Discords of CUDAMODE and my NN Zero to Hero. Next steps: - more optimizing of our (fp32) kernels, and especially switch to flash attention. - mixed precision training (fp16 to start). - multi-gpu training (DDP to start). - data & evals to set up a proper GPT-2 training runs - 🚀 repro GPT-2 (1.6B) training run. - more modern architectures etc. (Llama 3?) - writing, videos, exercises on building all of this from scratch. Figure 1: eye candy: timing profile of the kernels (one layer). NVIDIA cutlass kernels with solid compute throughput taking up a lot of the running time => nice.