You can get pretty close to this with ChatGPT Voice + phone stand + sending snapshots inside Voice mode...
works pretty well!
David Gasca retweeted
i was talking 4 days with Claude for this song
Berlin rave meets opera.
Human-made Slopcore.
Unpromptable.
55,000 lines of code
282 clips generated.
99 chosen.
Music? Art? A cultural movement?
We wil find out eventually...
I'M YOUR SLOP (I'M YOUR ART) 🎨
btw, thanks for all the messages. im not use have notifications. bit overwhelmed 💀
David Gasca retweeted
The back of the Navier-Stokes milestone shirt is the last line of the chain-of-thought from the solution:
David Gasca retweeted
Had a lot of fun making this Harry Potter style newspaper where the images can come alive: The Toddler Times 👧🏻
This is the way
David Gasca retweeted
“How did you keep the development of this secret for so long?”
“I didnt. I simply told everyone and nobody believes us”
Lmao
Replying to @MKBHD
I’m not. Will use SpaceX cold gas thruster system with ultra high pressure air in a composite over-wrapped pressure vessel in place of the 2 rear seats.
David Gasca retweeted
Executives at GM, Ford, Toyota: I wonder what Elon is going to announce for the Roadster? Faster? Greater range? CarPlay integration?
Elon:
David Gasca retweeted
If we only use AI to make the things that we already make, just faster, then we're definitely missing out on the most interesting part.
From my talk at @lennysan’s Summit, here are four ideas for raising the ceiling, not just the floor:
- Have a point of view [5:38]
- Encode your standards into the machine [9:07]
- Don't confuse done with good [12:21]
- Unleash creativity and artistry [16:19]
Full talk here: youtu.be/GLvFTMtw4Jk?si=-5Ts…
David Gasca retweeted
A retired NASA engineer who had worked on the Space Shuttle program for 25 years was hired by SpaceX at age 62. He described the first week as the most disorienting experience of his professional life.
At NASA, a design change required 3 meetings, 2 committees, a review board, and 6 to 18 months of documentation. At SpaceX, his new supervisor said "if it works, build it. If it doesn't, change it by Friday."
He sat at his desk after the first day and called his wife. He told her he felt like a man who had been swimming with ankle weights for 25 years and someone just cut them off. He could move. He could decide. He could build something in the morning and test it in the afternoon. The constraint that he had accepted as the nature of aerospace engineering turned out to be the nature of bureaucracy.
Within 6 months he had contributed to more design iterations than he had completed in his last 5 years at NASA. Not because he was working harder. Because the time between having an idea and testing the idea collapsed from months to hours. The same brain. The same knowledge. A different operating system.
He described a specific moment where he proposed a modification to a Raptor engine component. At NASA, the proposal would have entered a queue. At SpaceX, the modification was machined by the end of the day and tested by the following morning. He watched his idea become a physical object in 18 hours. At NASA, 18 hours would not have been enough to schedule the first meeting to discuss whether to schedule a meeting.
He told an interviewer: "I didn't get smarter when I went to SpaceX. I got faster. The intelligence was always there. The system was in the way."
The difference between SpaceX and NASA is not talent. NASA has extraordinary talent. The difference is the time between a thought and a test. SpaceX compresses it. NASA elongates it. The same engineer produces 10x more at SpaceX than at NASA because the bottleneck was never the engineer. It was the meetings about the meetings about the work.
I feel like my understanding of what a neural net is has been catapulted into the future by five years or something. it has brought back a lot of the sheer joy I had for studying the mystery of deep learning. neurons are already clean! they form a wonderful alien ontology!
David Gasca retweeted
interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had
with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works
already-clean.goodfire.com/
one way i've been thinking about work is "how many threads were required to achieve this?" where each thread = a slack convo / claude code session
then thinking of projects in terms of O() required to achieve
(1) as the models get smarter each individual prompt can capture a larger share of threads (e.g., via distribution of work, increased intelligence, better actuators); (2) structural dynamics in the organizational setup + contextual environment in which the model operates can also facilitate how many threads can be accomplished without human input (or less # of humans)
without (2), (1) is handicapped.
Claude's Artifacts with Opus 5.5 are step-change level good... if you're not using dashboards and visualizations, what are we doing here.
David Gasca retweeted
I have to admit, watched this a few times, especially to the end:) the creativity in the prompting and the model is awesome to see




















