Artificial intelligence has unleashed a torrent of cheating on campus and created chaos in the job application process, as both students and employers use large language models: the former, to write hundreds of AI-assisted applications; the latter, to screen those AI-written applications with AI-written filters, thus removing from the process of finding a first job those procedural frictions sometimes known as "people."
It looks to me like OpenAI’s sandbox for this agent suffered from the (quite naïve) assumption that GET requests cannot be used to update data. That’s certainly how the web is supposed to work, but clearly there are applications that don’t hold to that contract.
Why are we surprised? Even if you're a good programmer, if you finish a job and you ask your also very good peer to review it, you're gonna end up with better code. Of course you're gonna end up with better code. So build that into your process.
Don't try to anticipate anything. You will literally go crazy. Because even the smartest brains in the business cannot anticipate what two model hops from here is going to look like. It's an absolute waste of time, and you will develop an AI psychosis trying to deduce what two years from now is gonna look like. Focus on right now, and right now is the most incredible time to be into computers.
I think you're gonna have a hard time coping with the new reality if the only thing you loved about programming was the mechanical bits of putting the right logical constructs together to produce something other people told you to produce. Because that mechanical process is under threat. If you are, as you just mentioned, excited about building things, I don't think you're under threat at all.
Countries and peoples have a right to set an immigration policy, and this notion that we shouldn't have any borders or, or even worse than that, it's all just a blank slate, that all peoples are just the same, and we can take people from one place of the earth and we can place them in another place on the earth and everything's just gonna work out hunky-dory. Empirically not true.
everybody on your team who is making architectural decisions, those people must know performance and they must make decisions that will allow the other people downstream of them to use an architecture which can be optimized later. If you don't do that, you're just rolling the dice.
One of the things I found most exciting in the last couple of years was seeing as model qualities gotten better and and harnesses and tools have gotten better, how many people um that were directors or VPs or SVPs or any of these levels were actually rolling up their sleeves and trying things out.
open models are still incredibly useful, but fill a long-tail ecosystem relative to the closed counterparts that have monopoly ownership stakes in the most valuable areas like knowledge work collaboration, drug discovery, SWE, etc.
And in all of these applications, the customer is making a decision based off of the recommendation of the AI model more or less. Uh, and this means that if the customer is ultimately like kind of making the decision, this means that if the system makes a mistake, um, that's okay because usually the person can kind of recognize that or or decide what to do even despite that mistake.
Maybe I would pick something again that's that's in the category hard and boring because that's usually a category that is a little bit easier to actually find people that will appreciate when you solved something. If you pick something that is fun, even if it's hard, you're gonna have a very tough time, especially in a time where people can just prompt things into existence.
Yeah, I mean I think it's really having incredibly good taste in what you ask your agents to work on, right? That is the the crux of you know from my background uh a research problem. You know, a researcher can have all the tools and all the techniques, but often most of the battle is what problem are you gonna spend your time on?
Then you had code review which gave you another level of feedback. You could roll out internally more frequently. And everybody was using Facebook for all kinds of stuff, personal and internal business stuff. So whatever feature you developed, people would start using it immediately. So you get another round of feedback. Then we had this phased roll out process where you'd start rolling your stuff out. If there was a problem, the blast radius would be limited to a a few million people.
In truth, rather rapid electrification projects, without decade-by-decade continuity, exist outside the UK and are often quite reasonable in their costs, as in Denmark, Israel, and especially India.
this is the rule is really simple. only make deposits. Never make withdrawals. That's it. Because you're going to make withdrawals by accident sometimes because you're gonna make mistakes, but you never intentionally make a withdrawal.
local-first software, which is this idea that we want to take away a bit of the power from cloud operators and give it back to end users. So, end users should be more in control of their own data and less dependent on cloud services for providing the applications and the data that that the users need.
Our market reach is far greater than any any TPU can any ASA can possibly have. And so if you look at our position, uh we're the only company that that accelerates applications of all kinds.
Where I get fired up, and this ties back to the AI discussion, is when that's turned into this meme, that programmers no longer have to be competent. I mean the AI is gonna figure it out. The generators is gonna figure it out. I don't need to know SQL, active record is gonna abstract it away from me. No, no, no dude, hold up. The path here is competence.
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really good programmers are currently more valuable than ever because they're the ones who are able to get the most out of the AI acceleration.
And so this loop, this cycle, is gonna go on and on and on. It kinda comes down to basically intelligence is gonna scale by one thing, and that's compute.
You know, a lot of people would say, "You know AI is gonna completely destroy software. We don't need software anymore. We don't even need tools anymore." That's ridiculous.
Which is, you know, you’re the game designer who’s playing God, and players are the ants in your ant farm, and you want to see what they’re gonna do, which is not the correct way to be a good multiplayer designer.
Small studios are the future of gaming. The big studios basically acquire the small studios for new IP and ideas, and the small studios grow in. The really compelling, new, innovative ideas are gonna come out of small studios.
That is the best prop artist in the industry. That’s who’s gonna show up on our doorstep, so when they show up here, we should treat them like the best prop artist in the industry instead of starting from a place of doubt and cynicism.
Furthermore, I believe it’s very unlikely that space-based manufacturing or mining could be lucrative enough to fund the venture.
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Today, exploding demand for power-intensive AI applications provides enough of an upside to justify producing components in space, providing the economic engine necessary to justify and fund the trillions of dollars necessary to build and sustain space factories.
When applications can generate capabilities on demand, the definition of "what this product does" becomes more fluid. Features aren't just what shipped in the last release, they're also what users will ask for in the next session.
So if you’re very fond of somebody or you love somebody and they die, it’s kind of infantile to whine about it ever after. Because what did you think was gonna happen? Either you or them.
we will build our own applications scaffolding which will be model forward right it won't be a wrapper on a model but the model will be wrapped into uh the application
It's also worth noting that chat isn't the only way to integrate AI in software products and increasingly agent-based applications outperform chat-only solutions. So expect things to keep changing.
Yet the production methods still can’t make them below $10 per gram, so they’re still not cheap enough for some “obvious” applications like preventing kids getting malaria in a rainy season.
it's a luxury good like you're not paying the penalty for that the people who embrace it and think that they can just ask AI for everything or they can just look everything up but they don't have to understand the copy pasta that they slam into their project they're the ones who going to suffer
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Where I get fired up, and this ties back to the AI discussion, is when that's turned into this meme, that programmers no longer have to be competent. I mean the AI is gonna figure it out. The generators is gonna figure it out. I don't need to know SQL, active record is gonna abstract it away from me. No, no, no dude, hold up. The path here is competence.
I moved from Alacritty to Ghostty, which has been absolutely excellent. It’s wicked fast, it has my favourite colour scheme built in, and I just love the way @mitchellh has set the project up for success in the long term. It also fits in nicely with my minimally configured applications, with excellent defaults out of the box.
The Social Network is substantially made up, more a source for vibes rather than a source for facts. Even the vibes fail to cohere with reality. And yet it convinced many proto-founders to put in YC applications.
If the future is like the past, this implies that debt rollovers—that is, the issuance of debt without a subsequent increase in taxes—may well be feasible. Put bluntly, public debt may have no fiscal cost.
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Across advanced economies, the celebrated ( r - g ), i.e., the difference between the interest rate and the growth rate, appears to have durably changed sign or, at a minimum, to have gone from a substantially negative number to a number closer to zero.
computers are fast enough that high-traffic apps can be served with simple architectures, which can generally be created more cheaply and easily than complex architectures
Despite these advances, contemporary physical and evolutionary-history-based approaches produce predictions that are far short of experimental accuracy in the majority of cases in which a close homologue has not been solved experimentally and this has limited their utility for many biological applications.
Other applications I use frequently: 1Password, Slack, PureVPN, Photoshop, LICEcap (for creating screencaps to be shared with teammates or added to GitHub PRs, particularly for UI changes).
Other applications I use frequently: 1Password, Slack, PureVPN, Photoshop, LICEcap (for creating screencaps to be shared with teammates or added to GitHub PRs, particularly for UI changes).
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