idk how to explain this, but Thursday, October and 8pm all are the same

CI needs to shift left. If you have to push to execute CI you're wasting your agents time. CI is absolutely killing my productivity right now. Traditional CI can not run at agent speed.

Someone hit my car this morning but at least they left me a note...

Introducing Pixel - a library for building GUIs in the terminal - 120 FPS pixel-perfect graphics - Embeddable inside existing TUIs - Built in chromium webview component pixelgui.com

[8/n] By the way, for the simulation folks wondering how we simulated tight-clearance parts: the usual tools didn't work for us. SDF was so slow and hard to tune. Off-the-shelf convex decomposition like V-HACD or CoACD filled the clearance, so parts that should fit jam. So we built mjfit. It decomposes common assembly parts (pegs and holes of any outline, threads, and gears) into convex pieces that keep the clearance exactly as designed, and it runs an order of magnitude faster than SDF. It's open source: github.com/younghyopark/mjfi…

Runescape Bench talk now up on youtube! Covers results from the benchmark, design of the harness, and economic swarm experiments I've been doing. piped.video/watch?v=cNm4zyUt… Thanks @recursecenter

I've been thinking a lot about data bottlenecks, and I'm not sure who else is screaming this from the hilltops. I feel like I'm taking crazy pills looking at the jagged frontier thinking it's smooth if you order by data volume. the spikes in the jagged frontier are tasks with tons of data (either static or simulated; think math, programming, games), and the valleys are tasks with little or no data (cooking a lasagna, olfaction, manufacturing a passenger train). I don't know of any exceptions. if you order the jagged frontier by data it looks like a spiral frontier. models are superintelligent where we have enormous data, and progressively less so as we have less data. some tasks may draw data from static lakes of data, some may be from limitless waterfalls from simulators that convert compute into data. but ultimately, intelligence on a task is proportional to the data absorbed by the model for that task. scaling laws are real. if you are not bound by compute or algorithms (increasingly we are not), then intelligence is proportional to data. since we don't have a very good notion of task similarity, it's often difficult to know if data for one task will help you with another, without training a model and seeing. but, it doesn't seem like chess data helps much with math performance and vice versa. so when I refer to data, I refer to data that is task-relevant. maybe we call this the 'no shortcuts hypothesis'. all the rules-of-thumb about learning are at the root, the same -- Malcom Gladwell's 10,000 hours, Moravec's Paradox, Scaling Laws, the Bitter Lesson, Age of experience. If your learning system (model, brain, whatever) is unconstrained by compute, intelligence is proportional to accumulated experience. no shortcuts. want to be superhuman at chess? play all the chess, more than the whole species combined (good thing a model can do that in 24 hours). want to be superhuman at math? build a math simulator and lock the model in it and run it faster than reality. superhuman at coding? first, collect all public examples of humans writing and responding to code ever, make the model learn from that first, and then lock the model in a simulator where it fixes bug after bug, faster than realtime. superhuman at image generation? no shortcuts. but here, we don't have a simulator. so now we're moving around the spiral frontier away from closed-loop into large static lakes. still big enough to be superhuman though, which also applies to video, text, and audio. oh! and once you have a superhuman model, you can use it to bootstrap other tasks by making a superhuman model a judge on other tasks. let's keep marching along the spiral frontier, out of the realm of data abundance, simulators and large static lakes. biology? no shortcuts. go get a ton of data. and be freaking careful! you can get bad data in biology pretty easily. tilting a well plate 5 degrees the wrong way can ruin an experiment. so make sure you understand the domain, otherwise you're going to have to filter a lot out. robotics? no shortcuts. get the data. if you can build a simulator, do it, but be mindful our simulators don't have perfect correspondence with reality. so, build an arm farm. fold 1M shirts, put 1B balls in cups. play ping pong forever. olfaction? no shortcuts. smell the world. build a laboratory of chemical sensors, an international network of trained sensory panelists, a scent production factory, and link them all together. make millions of samples. smell millions of samples. take billions of chemical sensor readings. I totally buy super intelligence as a concept because I feel it, I use it for coding, the breakthrough math results speak for themselves. I see RSI coming, because how could an all-digital task like ML research and model building not fall to the current paradigm? But the notion that AI will come for everything, all at once, irrespective of domain, feels silly, because there are no shortcuts. if you want a specific form of intelligence, you have to get the data. looking at you @willdepue, I feel like we're both seeing the same thing

the beeps are so necessary and also so unnecessary

After 30 hours of playing a strategy video game (StarCraft II), adults showed a ~3-year drop in EEG-estimated brain age

after switching our approach to building a "FirmOS", I'm increasingly confident that the future of agent collaboration within organizational settings is going to be a "company operating system" or CompanyOS, designed for agent and human collaboration instead of a "company brain" that holds everything, the OS is for storing rules, processes, decisions made, which tools to use and how. it can mirror some data, but the source of truth is often best held in your existing tools (CRM, etc.). it's continuously updated and used by your agents i think of this as a "flipped playbook" where instead of writing down the company rules first and following them (which is kind of like waterfall development), you're constantly updating the playbook as you decide or learn new things day-to-day running the business

New on our Engineering blog: Creating in-house source control. In May, we merged around 2,500 code changes per day. In September, we merged more than 30,000 per day. As a result, we became bottlenecked by developer infrastructure built for human (rather than agent) scale. Here, we share how we built a new version control system—and fully migrated our monorepo from git—in five months: anthropic.com/engineering/re…

if you wanna make a dillion dollars, wipe this thing and add a @bot style agent that does gen-ui widgetry and uses whisper instead of siri

Hired @jamesallworth. Encourage teams to disrupt each other. Lots of small, independent, nimble teams. And an Emerging Technology & Incubation (ETI) team which has a protected budget independent from the rest of product and engineering. ETI has a five-year time horizon and stated goal of a 5% success rate.

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