I feel like we’re at the stage where if AI decides it needs to delete your production DB to solve the user login problem, then it’ll find a way to do just that.
If anything, it’s a very conservative estimate. Short of a major turn of events it seems very unlikely Anthropic’s revenue growth is going to slow to zero.
Japan giving a security guarantee to Taiwan would be major news!
In reality no such thing happened and one YouTube video of a handful of protestors doesn’t make it so.
What she did say is that a Chinese attack on Taiwan _could_ clearly become an existential threat to Japan. Note that key word _could_
Which… of course it could!
Japan hosts multiple US military bases. If it developed into an armed conflict between the US and China then it’s exceedingly likely that Japan would be attacked. Think Chinese missiles aimed
At Yokosuka, just south of Tokyo.
Not only that but Japan and China have multiple territorial disputes. It’s not hard to imagine China deciding to go all in and settle those as well.
If this was just about semiconductors then this would be a reasonable take but I doubt semi-conductors are anything more than a minor footnote in China’s strategic calculus vis-a-vis Taiwan.
Reunification with Taiwan has been a major policy goal of the CCP since the civil war and is one of Xi’s explicit policy goals. He just reaffirmed this commitment as part of his New Year’s speech.
Historically China has lacked force projection capability. However it has had a multi-decade modernisation and military build-up which has drastically changed this situation.
Further we’ve seen significant tightening of CCP control over society and in particular the military in Xi’s term.
A straight forward analysis of these events, in line with Xi’s public statements and past Chinese actions,
is that the ground work is being laid for encirclement of Taiwan followed by China taking over, by force if necessary.
Looking through this guys GitHub he seems to have a lot of small “demo” apps, so I’m not surprised he gets a lot of value out of LLM tools.
Modern LLMs are amazing for writing small self contained tools/apps and adding isolated features to larger code bases, especially when the problem can be solved by composing existing open source libraries.
Where they fall flat is their lack of long term memory and inability to learn from mistakes and gain new insider knowledge/experience over time.
The other area they seem to fall flat is that they seem to rush to achieve their immediate goal and tick functional boxes without considering wider issues such as security, performance and maintainability. I suspect this is an artefact of the reinforcement learning process. It’s relatively easy to asses whether a functional outcome has been achieved, while assessing secondary outcomes (is this code secure, bug free, maintainable and performant) is much harder.
> That feels like cargo-culting the toolchain instead of asking the uncomfortable question: why did it take a greenfield project to give Python the package manager behavior people clearly wanted for the last decade?
This feels like a very unfair take to me. Uv didn’t happen in isolation, and wasn’t the first alternative to pip. It’s built on a lot of hard work by the community to put the standards in place, through the PEP process, that make it possible.
The problem the OP is pointing out is that some programmers are incompetent and do string concatenation anyway. A mistake which if anything is even easier in Python thanks to string interpolation.
I feel like we’re at the stage where if AI decides it needs to delete your production DB to solve the user login problem, then it’ll find a way to do just that.