ssfrr

> At first, I used some random phone chargers, but the Pis would always complain about undervoltage, and throttle their CPU

This is a real annoyance with raspberry pi's. At first you think that you can run them off of USB power, but then you realize that they start throttling at 4.75V, which is still within USB spec (especially when you consider the voltage drop across the USB cable). The point of a spec is that compliant stuff should work, so you shouldn't need a "high-quality" power supply and cable, just compliant ones.

Don't compare your inside to other people's outside.

It's wild that the article frames this as

> what started as an experiment in productivity became a runaway success

and

> figuring out if the company can afford this level of productivity at scale

It seems like they're equating "developers are spending a ton of money on this" with "this is creating a ton of value".

I'm not saying that AI tools aren't valuable, but the article doesn't question this equivalence at all.

It doesn’t make sense to lump python and Julia together in this high-level/low-level split. Julia is like python if numba were built-in - your code gets jit compiled to native code so you can (for example) write for loops to process an array without the interpreter overhead you get with python.

People have used the same infrastructure to allow you to compile Julia code (with restrictions) into GPU kernels

Is there evidence that minimizing finger movement is ergonomically desirable? It seems like "repetitive" is a key part of RSI, so making the exact same small motion over and over again may not be optimal.

I think about piano players, who obviously need to move their hands and arms a lot to hit the keys (and with more force). Definitely takes a lot more energy than typing on a computer keyboard, but is there evidence that it's any more or less likely to cause injury?

Thanks for the extra info, I read through some of your entries on GPU optimization and it definitely seems like it's been a journey! Thanks for blazing the trail.

I’m very curious about your experience doing audio on the GPU. What kind of worst-case latency are you able to get? Does it tend to be pretty deterministic or do you need to keep a lot of headroom for occasional latency spikes? Is the latency substantially different between integrated vs discrete GPUs?

Does it work with separate browsers on the same machine? Not sure but I’d guess this sort of filtering would be more common on the browser than the OS

I saw that phrase and thought it was pretty weird. Hunting wild animals for food is not some fringe thing that happens in "other places" I've eaten tons of fish, duck, deer, elk, etc. that were all "wild animals".

Say I walk into a machine, and then I walk out, and also an exact duplicate walks out of a nearby chamber. My assumption is that we’d both feel like “me”. One of us would have the experience of walking into the machine and walking out again, and the other would have the experience of walking into the machine and being teleported into the other chamber.

Im probably lacking in imagination, or the relevant background, but I’m having trouble thinking of an alternative.

> Doing brute force evaluation on 1024² pixels, the bytecode interpreter takes 5.8 seconds, while the JIT backend takes 182 milliseconds – a 31× speedup!

> Note that the speedup is less dramatic with smarter algorithms; brute force doesn't take advantage of interval arithmetic or tape simplification! The optimized rendering implementation in Fidget draws this image in 6 ms using the bytecode interpreter, or 4.6 ms using the JIT backend, so the improvement is only about 25%.

I love how this is focused on how the JIT backend is less important with the algorithmic optimizations, and not on how the algorithmic optimizations give a 1000x improvement with bytecode and 40x with JIT.