As soon as you merge, it should be going out. You like you should have to stop the train to make your code not go into production as soon as you've merged.
Given the high cost of errors, you need to have a very high level of safety and a very high level of confidence on day one before you deploy your first robot, before you drive your your first autonomous mile.
But I have come to realize that as a web developer, Linux is just better. Linux is just better. It's closer to what I deploy on. The tooling is actually phenomenal.
Their words now
anyone who's working with the web, who's working with Ruby, who's doing DevOps, they should be on Linux because first of all, that's closer to what we deploy.
But in Vietnam and also in a lot of other Asian countries, people are on the move all the time. Like people are on the motorbike all the time. So, they actually really don't like typing. So, the voice like a lot of the companies in Vietnam actually deploy voice bots before they do uh do chatbot.
Um, and then you stack on 70 this year, 80 next year, growing to 100 by 2030. You're at like 700 EV tools by the end of the decade. Um, 700 EV tools, three and a half tools per gigawatt. um assuming it's all allocated to AI which it's not but three and a half tools per gigawatt gets you to 200 gigawatts worth of AI chips for the data centers to deploy
And the impact of solar array on desert is arguably positive because it shades the ground and improves like soil moisture retention. Um there like if you wanted to reverse desertification, you would basically just deploy solar panels on it and that would pay for the process.
These systems will be absolutely central to the economy, technology, and national security, and will be capable of so much autonomy that I consider it basically unacceptable for humanity to be totally ignorant of how they work.
A useful read for more experienced engineers and those wanting to improve architecture and devops skills on designing and operating reliable systems. The content is excellent, but the book is not designed to be read in one sitting.
By all accounts, from people who become memory athletes, they weren’t born with some extraordinary memory, but they practice strategies over and over and over again. The strategy that they use for memorizing a particular thing, it can become automatic, and you can just deploy it in an instant.
That is, labs should make sure that the safety measures they apply to their powerful models prevent unacceptably bad outcomes, even if the AIs are misaligned and intentionally try to subvert those safety measures.
Based on public commercial data that tells a lot about the military potential of these dual-use robotic spacecraft, we have shown that China can manufacture and deploy 200 such spacecraft as early as 2026, enough to cripple critical US satellites in geosynchronous, highly elliptical, and other orbits, and thus severely degrading space support to wartime operations.
So part of the reason that we deploy the way we do, we call it iterative deployment, rather than go build in secret until we got all the way to GPT-5, we decided to talk about GPT-1, 2, 3, and 4. And part of the reason there is I think AI and surprise don’t go together. And also the world, people, institutions, whatever you want to call it, need time to adapt and think about these things.
But nobody will ever get to the billions of representative miles necessary to say anything compelling about expected safety until after they actually deploy their fleet.
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