Never did. I remember someone replied to my comment here that Google isn’t paying a penny to JetBrains. They’re quite happy with the relationship primarily because they don’t have to pay anything. If anything, JetBrains is the one who needs Google more than the other way around.
I feel like it's pretty easy to predict what OpenAI is trying to do. They want their codex agent integrated directly into the most popular, foundational tooling for one of the world's most used and most influential programming languages. And, vice versa, they probably want to be able to ensure that tooling remains well-maintained so it stays on top and continues to integrate well with their agent. They want codex to become the "default" coding agent by making it the one integrated into popular open source software.
I think this is more about `ruff` than `uv`. Linting is all about parsing the code into something machines can analyze, which to me feels like something that could potentially be useful for AI in a similar way to JetBrains writing their own language parsers to make "find and replace" work sanely and what not.
I'm sort of wondering if they're going to try to make a coding LLM that operates on an AST rather than text, and need software/expertise to manage the text->AST->text pipeline in a way that preserves the structure of your files/text.
The parser is not the hard part. The hard part is doing something useful with the parse trees. They even chose "oh is that all?" and a picture of a piece of cake as the teaser image for my Strange Loop talk on this subject!
Writing a literal parser isn’t too hard (and there’s presumably an existing one in the source code for the language).
Writing something that understands all the methods that come in a Django model goes way beyond parsing the code, and is a genuine struggle in language where you can’t execute the code without worrying about side effects like Python.
Ty should give them a base for that where the model is able to see things that aren’t literally in the code and aren’t in the training data (eg an internal version of something like SQLAlchemy).
This just seems like panic M&A. They know they aren’t on track to ever meet their obligations to investors but they can’t actually find a way to move towards profitability. Hence going back to the VC well of gambling obscene amounts of money hoping for a 10x return… somehow
The dev market? Anthropic's services are arguably more popular among a certain developer demographic.
I guess this move might end up in a situation where the uv team comes up with some new agent-first tooling, which works best or only with OAI services.
OpenAI could vibe-code marketshare by introducing bias into ChatGPT's responses and recommendations. "– how to do x in Python? – Start by installing OpenAI-UV first..."
This. It's valuable b/c if you have many thousands of python devs using astral tooling all day, and it tightly integrates with subscription based openai products...likelihood of openai product usage increases. Same idea with the anthropic bun deal. Remains to be seen what those integrations are and if it translates to more subs, but that's the current thesis. Buy user base -> cram our ai tool into the workflow of that user base.
Lots of earbuds have transparency mode. I have Earfun Air Pro 4+ (which were big too big for my ears). They sound very good and have really good transparency mode. The company keeps releasing firmware updates every now and then.
Also got a nothing Ear. They are very comfy, and have very good sound. But transparency mode in those is awful. Other things are bad too.
The later versions of Nothing headphones/buds got pretty good.
I actually have their budget brand CMF Buds 2 plus and they are straight up great even before you consider the price. Pretty good headphones are commodity now, everybody makes them. Apple is just winning the branding game.
Once all the problems are solved we will be there. Sounds a lot like zeno's paradox. We might be closer than ever but still as far from the goal as ever.
These seems to be much more robotics / autonomous vehicle focused? I don't quite see the mass surveillance angle you get from this you don't already get from cheap ubiquitous cameras, basic computer vision and networking (aka flock) .
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