The path to launch is filled with obstacles and success is only possible through the tireless efforts of many working together towards a common goal. “Critical Path” continues the ongoing Starship series, following SpaceX engineers through the final days before launch of the first Starship V3 and the challenges that come with development of the world’s most powerful and fully reusable rocket.
I’ve always believed the No.1 application of AI should be to improve human health.
That work started with AlphaFold, and now at @IsomorphicLabs with the mission to reimagine drug discovery and one day solve all disease!
We are turbocharging that goal with $2.1B in new funding.
Space launch was a clear case where there was a large difference in efficiency between what was possible and what was done in practice before SpaceX. A large part of that was due to everything being locked in to what (just barely) already worked, with huge risk aversion. WIth national prestige or a half billion dollar geosync satellite on the line, speculative engineering ideas that might result in a public debacle were not welcome.
When failure is not an option, success can stay very expensive. You need to experiment to improve, and that fundamentally means being comfortable with failure. If you know it is going to work, it isn’t an experiment.
I have long believed that nuclear power today is in precisely the same state as space launch two decades ago, but the even more pressing question now is if semiconductor fabrication might also be.
On the one hand, Moore’s Law has been a sequence of heroic miracles of technology at the wafer fabrication level, grinding out hundreds of compounding small improvements.
On the other hand, fabs are “too big to fail”, and there are elements of extreme conservatism at play. Intel’s “Copy exactly!” fab development exemplifies that mindset – instead of every new building being an opportunity to explore and optimize processes, it was deemed more valuable to just replicate.
While each individual machine may be straining against physical limits of technology, it is possible that the systems orchestrating them all together could be far from optimal.
The explore / exploit axis is fundamental to all decision making, but human risk avoidance probably biases away from optimal exploration.
Releasing the alpha of Unreal Robotics Lab — an open-source Unreal Engine plugin with full MuJoCo physics.
Photorealistic rendering and accurate contact physics. No compromises on either side.
GitHub: https://t.co/GAvwsncqtG
Paper: https://t.co/w8sbILR7dW
For example I’ve been doing a bunch of late night vibe coding with Gemini 3 in @GoogleAIStudio, and it’s so much fun! I recreated a testbed of my game Theme Park 🎢 that I programmed in the 90s in a matter of hours, down to letting players adjust the amount of salt on the chips! 🍟 (fans of the game will understand the reference 😀)
The UK is a great country with an extraordinary history. Our stagnation is real, but it's fixable and worth fixing.
Enjoyed giving this talk at @lfg_uk last week and so encouraged by the optimistic responses I've had from people who are building a brilliant future for Britain 🚀
It’s now 27 years since I was made redundant and given a few grand. I decided that if I was careful, I could live off the money for 6 months trying to become a comedian before I had to get another job. I was 37. Worth a punt.
IMO good instructional videos should have
1) No intro
2) Basic editing
3) No background music
4) Static cameras
5) Wide angle + close up lenses
6) Presentational & to the point
7) Clear enunciation
8) Good lighting
9) No tchochkes/deco
Link in the comments for an example.