What’s one thing you can do to help yourself, other engineers, and even agents better understand a codebase? Add comments to your code. Such a basic practice that comes up on nearly _every_ pull request I review.
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.
Claude's performative "this request was sooo dangerous that we downgraded to Opus" is wearing me down I ask it straightfoward questions, nothing shady, nothing complicated, and it tells me it's too dangerous for Fable. What BS.
Claude's performative "this request was sooo dangerous that we downgraded to Opus" is wearing me down I ask it straightfoward questions, nothing shady, nothing complicated, and it tells me it's too dangerous for Fable. What BS.
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.
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.
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
You'll notice what I said was not use loops to ship the features that users want. We use loops to actually improve the codebase quality and we read all the code because we care about how it's architected and we care not just about the system architecture but what I would call the program design
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.
We tried this. We built a lights off software factory in July of 2025 and by November we had shut it down. I think it takes about three to six months of you shipping all the time with nobody reading the code before you realize like, wow, this is getting way worse and it's easier to start over than it is to fix it.
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.
Yeah, so I would say over the last 6 months actually we've been cranking a lot of features out a lot of code out. Most of it has been written by AI at this point. And before that really wasn't the case. I was actually a really big AI hater for a long time
Since I mostly work on Laravel projects or packages, I usually enable the Laravel Idea plugin. It's a paid plugin, but it's definitely worth the money since it can provide stuff like auto-completions for route names, request fields and more.
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.
The thing that I find interesting is that's not novel. This has been the thing we've always been trying to do forever. How do we get a junior engineer to ship code safely without breaking stuff? Right? How do we make patterns in the codebase? How do we make tests? Like it's it's all the old stuff that we've always wanted to do.
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
Even for back end, it can be a problem because if you have a request incoming right when it checks all of your objects like you have some sort of latency spike where you it takes much longer to to reply. So that's one of the reasons it can be helpful in in back end as well.
we're trying to crack the next frontier which is how we get that level of productivity increase and output building new features on top of a code base that are older,
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?
And organizations have super high pain tolerance. But human-made enterprise codebases take years to get there. The organization slowly evolves along with the complexity in a demented kind of synergy and learns how to deal with it. With agents and a team of 2 humans, you can get to that complexity within weeks.
With an orchestrated army of agents, there is no bottleneck, no human pain. These tiny little harmless booboos suddenly compound at a rate that's unsustainable. You have removed yourself from the loop, so you don't even know that all the innocent booboos have formed a monster of a codebase. You only feel the pain when it's too late.
You can give it a Bash tool so it can ripgrep its way through the codebase. You can give it some queryable codebase index, an LSP server, a vector database. In the end it doesn't matter much. The bigger the codebase, the lower the recall. Low recall means that your agent will, in fact, not find all the code it needs to do a good job.
Worse, you realize that the gazillions of unit, snapshot, and e2e tests you had your clankers write are equally untrustworthy. The only thing that's still a reliable measure of "does this work" is manually testing the product.
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.
one of the magic tricks about these things is that they they're incredibly consistent. If you've got a code base with a bunch of patterns in, they will follow those patterns almost to a T.
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.
As we move from, you know, hey, [clears throat] these companies are selling tokens where they provide the entire uh reasoning chain and all that to uh selling automated, you know, white collar work, right? Automated software engineer, send them the request, they give you the result back and there's a bunch of thinking on the back end that they don't show you. The ability to distill out of American models into Chinese models will be harder.
Where an idea can take root among the agents that's incorrect. It's It's It's a wrong architecture or or wrong data flow or whatever that's that's causing an impedance mismatch for the rest of your code. And what happens is I call it a heresy because they have the tend They have a tendency to to grow and to come back and they're really hard to weed out, okay?
If you're a monolith, you're kind of hosed because I told you the ceiling's going up for what they can do, but it ain't ever going to hit your monolith. They will never fit in the context window and you're never going to be able to never in the next 18 months be able to tell a model go fix my monolith. You have to break it up.
But right now I think it's sitting somewhere between half million and five million lines of code, somewhere in there. Probably more on the half million side right now and with the next drop of an Anthropic model, we're probably going to see it jump up to a few million lines.
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.
elements, JavaScript reigns supreme. SvelteKit's efficiency and reactivity make it my framework of choice, and TypeScript's type annotations bring a welcome layer of confidence to my codebase.
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 started using PHPStorm last year and I love it. I honestly don't know how I managed to live without it. It makes it a breeze to refactor a large codebase or source dive some vendor code.
So, you while we kind of saved the system a lot, we bailed a lot of people out, they made us pay 5 billion dollars on the more the bad mortgages that Bear Stearns had done. And that's what made me make the statement I wouldn't do it again.
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.
when you send someone a link to a post, they can't read it without loading the entire Mastodon frontend app into their browser, which will is a monster React codebase that will break if they have scripting disabled and also can take up to 30 seconds on older hardware.
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
It is almost inevitable that per-engineer productivity drops to some extent as an organization and codebase grows, even though it's also nearly-impossible to quantify that effect.
Code has mass. Every additional line of code you don't need is ballast. It weighs your codebase down, making it harder to steer and change direction if you need to.
It is the kind of book you will keep by your desk and pull out from time to time to figure out how to approach an issue or to help one of your senior leaders figure out how to do that.
This is a really short book that just gets to the point. It's a great checklist to think about what you need to pull together if you're going to raise money from investors. It helps you avoid some common pitfalls and gives you a good start on what you need to do to nail your elevator, recruiting, and funding pitches.
The procedures that have grown up around drug development are a sort of Chesterton's Fence: we shouldn't pull them down without considering the reasons why they were put up.
A korrent is a belief a person has stated in their own words: one
sentence stating the claim, backed by a quote and a source, kept at
korrents.com.
Under a name here, the quoted block is what they actually said.
The korrent beneath it is the claim those words support, in
korrents' wording — tap it to see the record, its source, and who
else holds it.
Nobody here wrote their own korrents. They are compiled from public
statements, and a person can change their mind, which is recorded too.
About the English under a post
Some people here publish in a language other than English. Where they
do, this site shows a machine translation beneath the post, in
this typeface — the site's own, not theirs.
The post itself is never changed, moved or hidden: what is set in the
serif above is exactly what the person published, and it is what to
quote them on. A translation can be wrong in ways that matter,
especially about tone.
Only the post's own words are translated. A quoted post, a linked
article and a belief on korrents.com
are left in their original language.