I should be able to pull you aside at any moment and ask you to explain any customer-facing system you've shipped. You should be able to clearly explain how it works and defend the decisions you made.
A few AI researchers pull up the average by being very certain that AI will kill everyone. But the typical AI researcher still thinks there's a ~10% chance that AI will kill everyone.
there is evidence that the reversal effect has weakened over time, leaving investors who bet on mean reversion and a return to fundamentals in the lurch
But it actually means that if you take the median programmer and their pull requests towards an average open source project, they're already getting outclassed by agents. I would rather get an agent-written pull request to one of my projects than I'd get one written by a human, and it's not just because the quality's better. It's also because I feel a lot less bad if I just reject it.
So, in my opinion, this is the absolute best time to ever have been an open source software maintainer. Not only do we get this wealth of glorious pull requests made by agents with all the boxes ticked, we also get to tap into a creativity of people who did not have access to contribute to a project before.
You can either increase the reward or change it in some way in terms of something that's meaningful for the person or you change the effort required to accomplish maybe part of the task or perhaps start you know just initiate the task and often starting can be a huge motivational pull.
It would also help if your manager said, "Well done for getting that bit done." Right? That's that's also a motivation pull to think, "Yeah, actually I don't need to get the whole piece done to get the reward of somebody identified that as a positive thing to have done."
It's like they have all the wisdom of their most senior engineers looking at every single diff and that is fantastic. Which means that you don't have to worry about remembering and looking and nitpicking and all the things that we're not good at anyway
when you start talking about like most interesting domain problems, you have to pull in so much context that basically even writing what the function is supposed to do becomes a nightmare. The imperative program you write that will get correct 99% of the time is probably good enough to use in almost all cases.
And the plan doc, what was bad about it is it didn't give you leverage. The plan was every single line of code that was going to change like in diff blocks and like all the new stuff to write. And so like people would review these plans. We recommended this. We told people to read the plans. We read all our plans. And then eventually I found myself like I just kind of skimmed the plans.
you can slow way down and read every PR and read every line of code. Uh, and then you're only going to really get modest benefits from AI because that becomes I I think you should expect maybe 30 to 50% lift in productivity is kind of what I see when we go into teams
So I think the the thing I'm most excited is actually like what we call like iterated loops or like slow loops where we basically have a cron job. We have the loop the the the structure of the loop is really easy. It's like run this llinter fix one thing commit and push and then we run that every night in our GitHub actions and we wake up every morning to one PR that makes the codebase a little bit better.
To shout “temporary” and pull the plug essentially overnight after immigrants have relied on the government’s representations that they should put their efforts and future into establishing a life in the United States wreaks unfair havoc on the lives of immigrants as well as the welfare of their communities.
Really the only tip I could ever give someone at this point is to pull away, reflect, do something you like, and talk to a human who cares about your current spiraling.
Gamified learning is a bit like wrapping medicine in candy. Yes, it may help some students swallow some instruction they otherwise find bitter, but in practice it’s easy to pull off the candy, consume it, and throw the medicine away.
And what you see when we work with them internally is now they're just like they're just making pull pull requests. Like they don't they don't need to hand it off as much. Sometimes they do but like a lot of times they just make pull requests and it's like the thing is built and that's it and I think that's incredible for the way that companies work
had we freeze time, that piece of code could be decomposed in a matter of 3 to 6 months. But it took us 2 years to do that because as we peel out a piece of code, the business keep on going forward, right?
And I feel like it's a very human way of doing web search, right? Because we enter things in the Google and we see all the like citations, the quotations part. But like why would we limit AI to that? If AI already visit a page, why don't just pull the entire page out?
But okay, they shouldn't actually be enacting these ideas. There is a queue of ideas and there's maybe an automated scientist that comes up with ideas based on all the archive papers and GitHub repos and it funnels ideas in or researchers can contribute ideas, but it's a single queue and there is workers that pull items and they try them out.
projects are flooded with junk contributions at the moment to the point that people are trying to convince GitHub to disable pull requests, which is something GitHub have never done.
But I don't want to, like, pull that down because every time someone made the first pull request is a win for our society, you know? Like, it… Like, it doesn't matter how, how shitty it is, y- you gotta start somewhere.
This book manages to pull all of that together in a way that's genuinely readable and engaging, helped along by anecdotes from his own policymaking experience that both clarify the arguments and make them feel concrete and accessible to a much broader audience.
An average email or line of code is fine. Average art isn’t. To make something alive with AI, we have to resist its pull towards average by working beside it, shaping what it gives, and listening for what’s missing.
But it will not be that easy, as easy as you're imagining because uh that you can lose your mind this way. If you you pull in something from the outside and build it into your into your inner thinking, uh, it could take over you. It could change you.
and that's in some ways what allows it to be very sturdy so I Think today management is really about this idea of like be sturdy while being flexible and that is a very hard thing to pull off
So, I think it'll be the same thing that that we'll see an increase in the scope that we're giving that we're willing to give to the robots as they get better and better where initially the scope might be like there is a particular thing you do like you're making the coffee or something. Uh whereas as they get more capable, as their ability to have common sense and a broader repertoire of tasks increases, then we'll give them greater scope. Now you're running the whole coffee shop.
I think it's always going to be hard to pull off because I think Americans and their elected representatives don't want to provide a universal benefit. They want to provide a targeted benefit because they believe there's a moral component here.
I stand in awe of what author Jackie Cummings Koski has done here. This is a comprehensive overview of what FIRE is and how it is done, fashioned in the famous "for dummies" template. I wouldn't have known how to even begin to pull this together, let alone gotten it over the finish line in such fine form.
My hot take is that JS has the lowest bar of entry to building and being productive (a good thing) but one of the highest bars of any language and stack to building high quality, stable, and reliable software. Very few devs have the expertise to pull off the latter
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