...but yeah most organizational processes & people aren't set up for leveraging it and roll out will be slow (same on learning where it does / doesn't work).
I’m currently working on a data migration for an enormous dataset. I’m writing the tooling in go, which is a language I used to be very familiar with, but that I hadn’t touched in about 12 years when I started this. It definitely helped me get back into go faster.
But after the initial speed up, I found myself in the last 10% takes the other 90% of the time phase. And it definitely took longer for me to wrap my head around the code than it would have if I’d skipped the AI. I might have some overall speed up, but if so it’s on the order of 10-20%. Nothing revolutionary.
I have been able to vibe code a few little one off tools that have made my life a little easier. And I have vibe coded a few iPad games for my kids for car trips, but for work I still have to understand the code and reading code is still harder than writing it.
This is also not from lack of trying , I spent $1000 last week during a company wide “AI week”. Mostly on trying to get AI to replicate my migration tooling, complete with verification agents, testing agents, quality gates, elaborate test harnesses etc…
I’d let Claude (opus 4.7 max effort) crank away overnight only to immediately find that had added some horrible new bug or managed to convince the verification agent that it wasn’t really cheating to pass my quality tests.
What I learned from last week is that we are so far away from not needing to understand the code that everyone who says otherwise is probably full of shit. Other people who I trust who have been running the same experiments have told me the same thing.
Until and unless we get to that point, it’s always going to be a 10-50% speed up (if that).
It might be the ultimate tool of disruption.
On the other hand, it feels like we've been over this tens of times recently, on HN specifically and IRL at work. Another blog post isn't going to convince leaders that this is how the world works when they are socially and financially incentivized to pretend like AI really will speed things up. So now I just wait for their AI projects to fail or go as slowly as previous projects and hope they learn something.
So I am spending my days gardening and obsessively working on personal coding projects with these agentic tools. Y'know, building a high performance OLTP database from scratch, and a whole new logic relational persistent programming environment, a synthesizer based on some funky math, an FPGA soft processor. Y'know, normal things normal people do.
So I know what these tools are capable of in a single person's hands. They're amazing.
But I hear the stories from my friends employed at companies setting minimum token quotas or having leaderboards of people who are "star AI coders" telling people "not to do code reviews" and "stop doing any coding by hand" and I shake my head.
I dipped my toes into some contract work in the winter and it was fine but it mostly degraded into dueling LLMs on code reviews while the founder vibe coded an entire new project every weekend.
These tools suck for team work or any real team software engineering work.
I'll just let this shake out and sit out until the industry figures it out. The only places that are going to be sane to work at are places with older wiser people on staff who know how to say "slow down!" and get away with it.
In the meantime, quantities of cut rhubarb $5 a bunch in Hamilton, Ontario area for sale. Also asparagus. Lots and lots of asparagus.
No, the code is actually almost always correct. The way it’s added is probably not what you’re going to like, if you know your code base well enough. You know there’s some ceremony about where things are added, how they are named, how much comments you’d like to add and where exactly. Stuff like that seems to irritate people like me when not being done right by the agent, and it seems to fail even if it’s in the AGENTS.md.
> If you were to give human developers the same amount of feature/scope documentation you would also see your productivity skyrocket.
Almost 2 decades in IT and I absolutely do not believe this can ever happen. And if it does, it’s so rare, it’s not even worth talking about it.
So well said.
AI is unveiling how the bureaucracy is the slow part.
Computing has been doing that for decades. If your process is fucked, computers make it fucked faster.
It’s just that now, we have entire generations alive that have never seem a world without digital computers. ~LLMs~ AI is a fun new lever in some uses so clearly it is finally the hammer that will drive the screws and bolts for us, with less effort on our part!
They just have to learn from experience. It’s what you do when you can’t be bothered to learn the lessons of the past.
It's happening about 10x faster than any other I've seen or read about.
Conceive how long it took just to get barcode scanners rolled out in grocery stores. Or direct payment terminals. Or how many decades it's been getting robotics into the manufacturing of cars at scale. I worked through the .com boom and I can tell you that "webification" took 10 years or more for most businesses (and many of them now just gave up and just have a Facebook page instead etc)
This is a little insane what's happening now. It really does change everything. People who don't work in software I don't think have any idea what's coming.
Work in large orgs long enough and you will recognize these creatures. Ladder climbing is a skill orthogonal to adding any value to the customer/company.
> ...but that doesn’t mean it’s generating the correct code.
Before, when there was the notion that "building is expensive", product teams would think things through, do user interviews up-front, actually do discovery around the customer + business context + underlying human process being facilitated with software.
This has shortened the cycle to first working prototype, but I'd guess that in the longer scale, it extends the time to final product because more time is wasted shifting the deliverable and experience on the user during this process of discovery versus nailing most of the product experience in big, stable chunks through design.
At the end of the day, there is a hidden cost to fast iterative shifts on the fundamental design of the software intended for humans to use and for which humans are responsible for operation. First is the cost on the end users who have to stop, provide feedback, and then retrain on each cycle. Second is that such compounding complexities in the underlying implementation as product learns requirements and vibe-codes the solution creates a system that becomes very challenging for humans to operationalize and maintain.
Ultimately, I think the bookends of the software development process are being neglected (as author points out) to the detriment of both the end users and the teams that end up supporting the software. I do wonder if we're entering an "Ikea era" of software where we should just treat everything as disposable artifacts instead.
When I was working we used to get requirements that literally said things like, "Get data and give it to the user". No definition of what data is, where its stored, or in what format to return it. We would then spend a significant amount of time with the product person trying to figure out what they really wanted.
In order to get good results with LLMs we need to do something similar. Vague requirements get vague results.
This is a big HN LLM discussion divide. I am in the same no-specs work background camp, and so the idea that the humans who input that into dev teams are suddenly going to get anything out of an LLM if they directly input the same is laughable. In my career most orgs there has been no product person and we just talked directly to end users.
For that kind of org, it will accelerate some parts of the SWEs job at different multipliers, but all the non-dev work to get there with discussions, discovery, iteration, rework, etc remains.
If the input to your work is a 20 page specification document to accompany multi-paragraph Jira tickets with embedded acceptance criteria / test cases / etc, then yes there is a danger the person creating that input just feed it into an LLM.
This was substantially predicted by Fred Brooks in 1986 in the classic No Silver Bullets [1] essay under the sections "Expert Systems" and "Automatic Programming".
In it, he lays out the core features of vibe coding and exactly the experience we are having now with it: Initial success in a few carefully chosen domains and then a reasonable but not ground breaking increase in productivity as it expands outside of those domains.
[1] https://worrydream.com/refs/Brooks_1986_-_No_Silver_Bullet.p...
LLMs just take the same vague or poor requirements and make them look believable until you dig in to them.
Eg: I had a product manager say to me that he envisions a future where any meeting with stakeholders that does not result in an interactive prototype by the end of the meeting would be considered a failure. This feels directionally correct to me.
The other thing I expect to see is Vibecoding being the "Excel 2.0" where it allows significant self-serve of building interactive apps that's engaged in a continual war with IT to turn them into something with better security guarantees, proper access control & logging, scalability, change management etc.
But the larger historical point here is that every revolutionary transition produces, in the early stages, "Steam Horses". The invention of the steam engine had people imagining that the future of transportation would involve horse shaped objects, powered by steam, pulling along conventional carts. It wasn't until later developments that we understood the function of transportation as divorced from the form.
I started talking about Steam Horses originally in the context of MOOCs, which was a classic Steam Horse idea.
Another aspect that is not captured here is that the lawyers and subject matter experts will also be using AI to speed up their parts.
If that sounds familiar, it’s because it’s what dang did over the course of several years.
It’s taken a few weeks. I started right around May, and now it’s able to render large HN threads (900+ comments) within a factor of five of production HN performance. (Thank you to dang for giving actual performance numbers to compare against.)
A couple days ago, mostly out of curiosity, I ran Claude with “/goal make this as fast as HN.” Somewhat surprisingly, it got the job done within a couple hours. I kept the experiment on separate branches, because the code is a mess, just like all AI generated code starts as. But the remarkable part is that it worked, and I can technically claim to have recreated HN within a few weeks.
The real work is in the specifications. My port of HN is missing around a hundred features. Things from favorited comments, to hiding threads, to being able to unvote and re-vote.
But catching up to HN is clearly a matter of effort (time spent actually working on the problem with Claude), not complexity. Each feature in isolation is relatively easy. Getting them all done within a short time span without ruining the codebase is the hard part. And I think that’s where a lot of people get tripped up: you can do a lot, but you have to manage it tightly, or else the codebase explodes into an unreadable mess.
It’s true that if you don’t do that crucial step of “manage the results”, you’ll end up making more work for yourself in the long run, by a large factor. But it’s also true that AI sped me up so much that I was able to do in weeks what would’ve otherwise taken years (and did take dang years). I’m not claiming parity, just that I got close enough to be an interesting comparison point.
AI can clearly accelerate us. But we need to be disciplined in how we use it, just like any other new tool. That doesn’t change the fact that it does work, and I think people might be underestimating how good the results can be.
This is how I felt when I first started seeing people discuss things like AGENTS.md etc.
usernametaken29•49m ago
teaearlgraycold•39m ago
steveBK123•12m ago