Can’t stop thinking about this Francis Fukuyama paragraph from The End of History.

The global market for humanoid robots could exceed 10 billion...maybe 10's of billions. Congrats to Boston Dynamics, Tesla and Figure. The future is amazing!

I'm gonna miss frontend devs after they get unceremoniously slaughtered by an AI model that can't reliably tell you what weighs more a pound of feathers or a pound of lead

After months of building, @RonakGandhj and I are proud to launch Structify. We started @StructifyAI so that companies can get human quality data from anywhere, for any use case. I’m excited to show you below.

The report from the White House highlights Rust for its excellent safety and predictability properties, but points out it "has not yet been proven in space systems." Challenge accepted. We're taking @turborepo⁠/pack to space. whitehouse.gov/oncd/briefing…

lamdba school but a multi-year back2college for career saas devs who want to transition to deep tech sick of toiling away on microservices to help marketers be 5% more efficient? maybe space/robotics/curing cancer will bring back some meaning to your life ...

2/6 Earlier today, Nova-C’s navigation system rejected star tracker data, but a patch has been sent to the spacecraft, and the star tracker updates have resumed nominal operations. Initially, the star tracker information was numerically conditioned slightly differently than we anticipated. We were expecting a one-in-a-thousand numerical tolerance and received a number more like two and three in a thousand. So, Nova-C’s navigation system rejected the star tracker data.

# on technical accessibility One interesting observation I think back to often: - when I first published the micrograd repo, it got some traction on GitHub but then somewhat stagnated and it didn't seem that people cared much. - then I made the video building it from scratch, and the repo immediately went through hockey stick growth and became a verty often cited reference for people learning backpropagation. This was interesting because the micrograd code itself didn't change at all and it was up on GitHub for many months before, stagnating. The code made sense to me (because I wrote it), it was only ~200 lines of code, it was extensively commented in the .py files and in the Readme, so I thought surely it was clear and/or self-explanatory. I was very happy with myself about how minimal the code was for explaining backprop - it strips away a ton of complexity and just gets to the very heart of an autograd engine on one page of code. But others didn't seem to think so, so I just kind of brushed it off and moved on. Except it turned out that what stood in its way was "just" a matter of accessibility. When I made the video that built it and walked through it, it suddenly almost 100X'd the overall interest and engagement with that exact same piece of code. Not only from beginners in the field who needed the full intro and explanation, but even from more technical/expert friends, who I think could have understood it if they looked at it long enough, but were deterred by a barrier to entry. I think as technical people we have a strong bias to put up code or papers or the final thing and feel like things are mostly self-explanatory. It's there, and also it's commented, there is a Readme, so all is well, and if people don't engage then it's just because the thing is not good enough. But the reality is that there is still a large barrier to engage with your thing (even for other experts who might not feel like spending time/effort!), and you might be leaving somewhere 10-100X of the potential of that exact same piece of work on the table just because you haven't made it sufficiently accessible. TLDR: Step 1 build the thing. Step 2 build the ramp. 📈 Some voice in your head will tell you that this is not necessary, but it is wrong.

[ 02/15/24 17:51:20Z ] the gundo delegation is en route

Many llm tools now give you tools to extract structured data, but there are still limit resources on how to think about schemas. We cover topics like: * Tips and tricks on how to leverage pydantic * Extracting complex relationships. * Generating Synthetic Data * Improve RAG: Query understanding * Improve RAG: Minimize hallucinations with validations. wandb.courses/courses/steeri…

just received this email i take back everything i said about FORTRAN

All I wanted for Xmas this year was for Apple to stop fucking with developers, trying to turn them upside down at every junction to shake them down. I love Apple hardware, I hella respect Apple Safari, but this monopoly App Store bullshit has got to stop.

Wall Street forecasting year-end price targets…

People often ask if ML or software skills are more the bottleneck to AI progress. It’s the wrong question—both are invaluable, and people with both sets of skills can have outsized impact. We find it easier, however, to teach people ML skills as needed than software engineering.