I am unreasonably excited about self-driving. It will be the first technology in many decades to visibly terraform outdoor physical spaces and way of life. Less parked cars. Less parking lots. Much greater safety for people in and out of cars. Less noise pollution. More space
I took delivery of a beautiful new shiny HW4 Tesla Model X today, so I immediately took it out for an FSD test drive, a bit like I used to do almost daily for 5 years. Basically... I'm amazed - it drives really, really well, smooth, confident, noticeably better than what I'm used
I quite like the new DeepSeek-OCR paper. It's a good OCR model (maybe a bit worse than dots), and yes data collection etc., but anyway it doesn't matter.
The more interesting part for me (esp as a computer vision at heart who is temporarily masquerading as a natural language
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
TV in the 90s: you turn it on, you watch.
TV 2025:
- turn on, wait for it to load
- popup: TV wants to update, 1.5GB. No.
- scroll sideways, find prime video app or etc
- popup: now app wants to update, 500MB. No!!
- App launching... App loading…
- select account screen
- 🫠
Excited to release new repo: nanochat!
(it's among the most unhinged I've written).
Unlike my earlier similar repo nanoGPT which only covered pretraining, nanochat is a minimal, from scratch, full-stack training/inference pipeline of a simple ChatGPT clone in a single,
Finally had a chance to listen through this pod with Sutton, which was interesting and amusing.
As background, Sutton's "The Bitter Lesson" has become a bit of biblical text in frontier LLM circles. Researchers routinely talk about and ask whether this or that approach or idea
"AI isn't replacing radiologists" good article
Expectation: rapid progress in image recognition AI will delete radiology jobs (e.g. as famously predicted by Geoff Hinton now almost a decade ago). Reality: radiology is doing great and is growing.
There are a lot of imo naive
I think congrats again to OpenAI for cooking with GPT-5 Pro. This is the third time I've struggled on something complex/gnarly for an hour on and off with CC, then 5 Pro goes off for 10 minutes and comes back with code that works out of the box. I had CC read the 5 Pro version
I get ~10 spam calls per day (various automated voicemails, "loan pre-approval" etc) and ~5 spam messages per day (usually phishing).
- I have AT&T Active Armor, all of the above still slips through.
- All of the above is always from new, unique numbers so blocking doesn't work.
I am (slowly) re-reading the Tolkien legendarium (of which Lord of the Rings is a small part). The whole body of work is so incredible and there's nothing else like it... it dilutes other worlds of fiction. Wait - your story doesn't have a comprehensive history/mythology spanning
I'm noticing that due to (I think?) a lot of benchmarkmaxxing on long horizon tasks, LLMs are becoming a little too agentic by default, a little beyond my average use case.
For example in coding, the models now tend to reason for a fairly long time, they have an inclination to
Love this! Supercharger, diner, … but really a kind of exhibit for the future. Plotting a road trip SF -> LA to charge Shadowfax
Knowledge makes the world so much more beautiful.
How to build a thriving open source community by writing code like bacteria do 🦠. Bacterial code (genomes) are:
- small (each line of code costs energy)
- modular (organized into groups of swappable operons)
- self-contained (easily "copy paste-able" via horizontal gene
The race for LLM "cognitive core" - a few billion param model that maximally sacrifices encyclopedic knowledge for capability. It lives always-on and by default on every computer as the kernel of LLM personal computing.
Its features are slowly crystalizing:
- Natively multimodal
+1 for "context engineering" over "prompt engineering".
People associate prompts with short task descriptions you'd give an LLM in your day-to-day use. When in every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window
Nice - my AI startup school talk is now up! Chapters:
0:00 Imo fair to say that software is changing quite fundamentally again. LLMs are a new kind of computer, and you program them *in English*. Hence I think they are well deserving of a major version upgrade in terms of
Part 2 of this mystery. Spotted on reddit.
In my test not 100% reproducible but still quite reproducible.
🤔
My sleep scores during recent travel were in the 90s. Now back in SF I am consistently back down to 70s, 80s.
I am increasingly convinced that this is due to traffic noise from a nearby road/intersection where I live - every ~10min, a car, truck, bus, or motorcycle with a very
