2,807 karma · joined October 26, 2016
Lowering the number of traffic lights in and of itself is a good idea. Overpasses and (turbo)roundabouts, clever right of way, better road hierarchy; there are lots of alternatives that may provide better throughput and more safety for a given intersection.
The Netherlands as a country with 18M inhabitants is best compared to a big city with the surrounding metro area.
As for the idle dray, a lot of PC's have Wake On Lan so you can power it on via a special IP packet. I could't get it to work on mine, so i pus an esp32 based power monitoring plug in front of it, and configured the PC to power on directly when powered. When the power monitoring sees the PC sleeping for some time, it cuts the power. I control the plug via home assistant, so i can switch it on remotely
I told Fable to make a nice plan, and fan out work to opus and sonnet agents. I then enabled remote connections and checked every few hours from my phone and nudged it in the right directions where needed. Have some push back once in a while. And once done I asked it to make a nice commit history and remove all the dead ends.
It was officially Intel-compiler + Windows/Linux only; the port gets it building with gfortran on macOS/arm64, PETSc+MPI solver and all. Fable and GPT-5.6 chewed through the compiler warnings, dependency hell, and portability bugs largely on their own. 30 or so bugs uncovered and fixed along the way, several of them real and platform-independent.
I'm not a C or Fortran dev. Mostly I argued with the agents about benchmarking methodology and told them to re-measure when the numbers looked off. The rest was up to them. Still amazed about this AI thing going on
An actual quote from a Nature article
I am grateful Nextcloud exists, but no app deserves a vibe coded Rust rewrite more than Nextcloud. Literally nothing to loose
https://www.coloringsai.com/en/coloring-page/hitler-doing-a-...
So what I do now (since Claude Code) is write really bare bones (and slow) pure python implementation (like I used to do for numba, pypy or cython ready code), with minimal dependencies. Then I use the REPL, notebooks and nice plotting tools to get a real understanding of the problem space and the intricacies of my algorithm/problem at hand. When done, I let Claude add tests and I ask it to transpile to equivalent Rust and boom! a flawless 1000x speed upgrade in a minutes.
The great thing is I don't need to do the mental gymnastics to vectorize code in a write only mode like I've had to do since my Matlab days. Instead I can write simple to read for loops that follow my intent much better, and result in much more legible code. So refreshing!
And with pyO3 i can still expose the Rust lib to python, and continue to use Python for glue and plotting