parents: "move out" girlfriend: “quit being such a loser” boss: "work harder" claude: "uber for dogs (the dogs are the drivers) is a great idea, you should absolutely pursue it"

right monitor is 20 codex instances. left monitor has situational awareness on autoscroll. center monitor is my word doc mainfesto. two keyboards, one for both hands. left airpod is dwarkesh x eric jang, 3x speed. right airpod tchaikovsky. meta quest 3 overlays my HUD: heart rate, words per minute, blood caffeine content. one assistant hooks me to an iv of chinese peptides, cocktail. the other feeds me kimchi. my unitree robot steps in when my posture slouches. blue light beams down on me in my herman miller chair. efficiency. no wasted movement. no wasted thoughts. think you can keep up with me? good luck. this is just for my morning emails.

claude code can now write sql almost perfectly. but it still needs to be retaught the same definitions over and over. ontology is how AI agents get 7x faster on your database over time. today we shipped Ontology 3.0 — memory for AI agents, specialized for databases:

dudes vibecoding palantir aesthetic situation monitoring dashboards is the same dopamine producing biological tick as women configuring perfectly photogenic charcuterie spreads

RT @Indian_Bronson: Most men merely observe, but some men live:

Introducing Agentation: a visual feedback tool for agents. Available now: ~npm i agentation Click elements, add notes, copy markdown. Your agent gets element paths, selectors, positions, and everything else it needs to find and fix things. Link to full docs below ↓

Becoming an RL diehard in the past year and thinking about RL for most of my waking hours inadvertently taught me an important lesson about how to live my own life. One of the big concepts in RL is that you always want to be “on-policy”: instead of mimicking other people’s successful trajectories, you should take your own actions and learn from the reward given by the environment. Obviously imitation learning is useful to bootstrap to nonzero pass rate initially, but once you can take reasonable trajectories, we generally avoid imitation learning because the best way to leverage the model’s own strengths (which are different from humans) is to only learn from its own trajectories. A well-accepted instantiation of this is that RL is a better way to train language models to solve math word problems compared to simple supervised finetuning on human-written chains of thought. Similarly in life, we first bootstrap ourselves via imitation learning (school), which is very reasonable. But even after I graduated school, I had a habit of studying how other people found success and trying to imitate them. Sometimes it worked, but eventually I realized that I would never surpass the full ability of someone else because they were playing to their strengths which I didn’t have. It could be anything from a researcher doing yolo runs more successfully than me because they built the codebase themselves and I didn’t, or a non-AI example would be a soccer player keeping ball possession by leveraging strength that I didn’t have. The lesson of doing RL on policy is that beating the teacher requires walking your own path and taking risks and rewards from the environment. For example, two things I enjoy more than the average researcher are (1) reading a lot of data, and (2) doing ablations to understand the effect of individual components in a system. Once when collecting a dataset, I spent a few days reading data and giving each human annotator personalized feedback, and after that the data turned out great and I gained valuable insight into the task I was trying to solve. Earlier this year I spent a month going back and ablating each of the decisions that I previously yolo’ed while working on deep research. It was a sizable amount of time spent, but through those experiments I learned unique lessons about what type of RL works well. Not only was leaning into my own passions more fulfilling, but I now feel like I’m on a path to carving a stronger niche for myself and my research. In short, imitation is good and you have to do it initially. But once you’re bootstrapped enough, if you want to beat the teacher you must do on-policy RL and play to your own strengths and weaknesses :)

On @FT‘s Top Stories today. If some random Austrian-Chinese dude can build hardware and have a voice in the UK, so can you. No more excuses, UK/acc 🇬🇧 on.ft.com/3F0A7gy

ok we’re live hello

NotebookLM Podcast Hosts Discover They’re AI, Not Human—Spiral Into Terrifying Existential Meltdown Via Reddit

We built a platform to run containers across Cloudflare’s network. It’s different. It’s schedules workloads globally — you don’t deal with regions. It fits with Anycast (of course). And we're integrating it with Workers and Durable Objects — use the right tool for the job, every time. We needed this to build our own products. And we'll be bringing it to more developers next year. (blog post in next tweet)

I work in finance. I do maths everyday. I know the numbers. I know all the arguments for investing aggressively when young. but I still can’t do it. my allocation is like 80% cash 20% stocks. I know that’s bad. but still. this is like the finance version of a doctor who smokes.

How market makers make money:

YC founders trying to "talk to customers" and sell their product in the EU

"The cost of failure is as close to zero as it's ever been in the history of the world." @harleyf to the potential entrepreneurs out there

きた！！！Introducing Timber 🪵 (ちなみにプロダクト名は仮名です。正式名称候補を絶賛募集中です)

NOOOOOOOOO

Reading a tweet is a bit like downloading an (attacker-controlled) executable that you instantly run on your brain. Each one elicits emotions, suggests knowledge, nudges world-view. In the future it might feel surprising that we allowed direct, untrusted information to brain.

Before I was 30 years old, I’d started 2 companies that are each now worth over a billion dollars. And it almost broke me. Here’s my story. youtu.be/QKPgBAnbc10

Everyone reading up on Q-learning. Just wait until they hear about PPO. 🤫