Or why most of the studies we see out there can't be trusted. They're full of holes and flaws. We'd really know people who know what they do in humanities to conduct such studies to get a chance at a proper picture.

Good overview of why we don't see a speed up in development processes when AI tools are introduced. The bottlenecks don't magically get destroyed.

This feels a bit too realistic for my taste... and yet... Well this piece of satire is well crafted I'd say.

It totally makes sense. If you're a FOSS project you have to invest in getting more long term contributors, which requires mentoring. The contributions themselves are not something to maximise. I wish more communities would follow that path.

This part of the industry is struggling more and more (or more likely silently taking more risks to hide the struggle). It has no path to sustainability and it starts to show.

More in depth look at the launch white paper and the issues covered in the PR. Not much survives scrutiny... there's nothing special with this model.

Indeed, and it's going to get even crazier at some point. I guess somewhat soon but who knows...

Are we surprised it's mostly a PR stunt? Not at all. Of course, I agree a lot with the conclusion: we can't trust any claim from those companies. They try to present themselves as labs but mostly try to disguise marketing as research...

It's first a great marketing stunt. The model is likely not the secret sauce though.

Long but very precise piece about why you can likely ignore LLM for development purpose. Starting from older Fred Brooks work is spot on. Indeed whatever will remain of LLM based tools in the years to come, it's much smarter to focus on fundamental skills than chase the new tools. At least, I'm trying to do my share in getting myself and others better at the craft.

Definitely interesting approach. I think neurosymbolic approaches are what we ultimately need so I'm probably biased. At least it means using LLMs for what they're good at (language skills) and only that. Then rely on proper code symbolic models which do the reasoning heavy lifting. I'd expect it can give nice output with smaller models.

Excellent piece, it show quite well the problem of skipping the "grunt work". Without it you can't really learn your trade (be it astrophysics or anything else). It also shows how the incentives on scientific careers are wrong. It's not new, but when LLM agents become available, things are definitely changing for the worst.

So much this... There are so many organisational problems that churning code faster is likely not what you need. When did we start to obsess with the number of lines of code?

Unsurprisingly, they need to find new data to feed the monster...

Or why this latest trend in genAI hype is a fool's errand.

Excellent piece, indeed legal is not the same as legitimate. More often than not the law is lagging behind and things might be wrongly "fixed" at a later date. In that interval that's when our communities need to build its own tools to protect the commons. We're clearly reaching such an inflection point. Interestingly, I think there's is a difference of reaction between the people with a Free Software culture and the ones with an Open Source culture.

Obviously the essay from Peter Naur keeps popping up lately. It feels like an important piece, especially in the current atmosphere of vibe coding. This article lays out quite well why vibe coding is the opposite of what we should be doing.

This fantasy regularly comes back. Yet, the tools evolve, might improve some things but the core difficulties of programming don't change. At each hype cycle our industry over promises and under delivers, this is unnecessary.

Interesting point, there are indeed different types of "debt" in the systems we build. It likely help to be more precise about their nature, and indeed assisted coding might help grow a particular kind of debt.

Another example of how much of a problem this is for some projects. Of course it is compounded by having so many projects on GitHub, this pushes people to try to farm for activity to attempt to make their resume look good. This is sad.