if @elonmusk paid 100% of his net worth ($1.4 trillion) as a tax it would only cover federal government spending for 77 days. this isn’t a tax problem…
SpaceX has exercised the option to acquire @cursor_ai in an all-stock transaction with the goal of building the world’s most useful AI models.
For the past few months, SpaceXAI has been jointly training a model with Cursor, which will be released in Cursor and Grok Build soon.
We look forward to working closely with the Cursor team to advance our frontier AI capabilities
We hear it as consumption. But it's almost all for allocation. This is a huge issue we need to get past.
Name anyone you would rather see allocate 1T+.
Well done.
Struggling to pick what agent, model, and effort levels to use? Miss the "slot machine" feel of Claude Code when using other tools?
`npx slotslop "[prompt]"`
I connected Claude to my drone and attached a BB gun to it and it hunts squirrels while I am at work and sends me replay videos when it gets confirmed kills
PICARD: Data, shields up
DATA: Brilliant! Shields can reduce damage we sustain. Not immunity. Not hubris. Just prudence. It's not precaution—it's strategy.
[camera shakes]
WORF: HULL BREACHES ON NINE DECKS
DATA: Here's what happened: you told me to raise shields, and I didn't
Today, we share a breakthrough on the planar unit distance problem, a famous open question first posed by Paul Erdős in 1946.
For nearly 80 years, mathematicians believed the best possible solutions looked roughly like square grids.
An OpenAI model has now disproved that belief, discovering an entirely new family of constructions that performs better.
This marks the first time AI has autonomously solved a prominent open problem central to a field of mathematics.
Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.
> be github
> invent copilot
> you are literally the first one
> you are literally the only one
> you literally have access to all the code in the world
> get mogged by literally every single agentic bs that came out in the past few years
this level of fumble should be studied
Watch a team of humanoid robots running a full 8-hr shift at human performance levels. This is fully autonomous running Helix-02 https://t.co/bIgpYuaYCj
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.
Managing my coding agents from within my game itself via @cursor_ai's agent sdk - instant access to backend gamestate, front end visuals, and my existing suite of runtime performance analytics.
Some queries can now just be: "this looks wrong" - quite helpful for playtesting and debugging
Same here.
By way of background for those who care, I spent a lot of time last week with senior members of the Anthropic team to understand what they do to ensure Claude is good for humanity and was impressed.
Everyone I met was highly competent and cared a great deal about doing the right thing. No one set off my evil detector. So long as they engage in critical self-examination, Claude will probably be good.
After that, I was ok leasing Colossus 1 to Anthropic, as SpaceXAI had already moved training to Colossus 2.