You expect joining file paths to be a simple operation? Think again, it's definitely error prone and can change between stacks.

Interesting how much extra performance you can shave off the GPU by going back to how the hardware works.

Interesting quick comparison, this shows the design tradeoffs quite well.

Getting network protocols right is definitely difficult.

Indeed the next systemd release feels feature packed. Definitely to keep an eye on.

Good advice indeed. Having asserts using appropriate matchers can go a long way understanding what went wrong.

Not a reason to make no effort into having as proper error messages as possible. Still there's some truth there that trying to have a really useful error message is a fool's errand.

Interesting data point. This is a very specialized experience but the fact that those systems are kind of random and slow clearly play a good part in limiting the productivity you could get from them.

Excellent exercise in understanding how HTMX works under the hood.

Interesting take about the mantras often used in our profession. They shouldn't be treated as laws, but as proverbs carrying a piece of contextual wisdom. It's thus unsurprising that they tend to contradict each other. This contradiction should make us pause and think.

Looks like a fun spreadsheet tool where you can use Python in any cell.

Definitely this. We tend to like complexity too much as a profession and field. It's also a good reminder that the complexity of the problem and the complexity of the solution shouldn't be conflated.

Interesting questions and state of the art around model "unlearning". This became important due to the opacity of data sets used to train some models. It'll also be important in any case for managing models over time.

Nice article. It's a good reminder that the benchmarks used to evaluate generative AI systems have many caveats.

Obviously a satire, some of it feels eerily real though.

Looks like a good reference about everything which can be done with the latest CSS evolutions.

Good exploration of the many ways contact forms fail us regularly. Also shows a few cases where you might still want to us them... in most cases you shouldn't.

If you wonder why information retrieval from natural language texts is a tough domain, here is a short article listing the important things to keep in mind.

Well, maybe our profession will make a leap forward. If instead of drinking the generative AI cool aid, if we really get a whole cohort of programmers better at critical skills (ethical issues, being skeptical of their tools, testing, software design and debugging) it'll clearly be some progress. Let's hope we don't fall in the obvious pitfalls.

Since there's a clear tendency in the developers I meet to "extract at all costs", this is a good reminder that sometimes you need to inline the code first. This very often brings better clarity in the context of use. In turns this leads to a better final extraction.