prplfsh
prplfsh on Hacker News
The hardest thing with intensity is narrowing the focus, which Slootman notes as important. The best intensity is extremely focused. Which, ironically, in my experience, doesn't even feel that intense in the moment, time just flies.
Yeah, for sure, but at least to me, it feels like we will figure that out. We're in a transitionary phase. But it also seems to me like, to your point, we're busier than ever, and in a lot of ways, the problems we're working on are harder than ever.
The US is in debt to itself, in a currency it can print. What could go wrong?
I'm wondering if mathematicians are sort of going through what programmers have been going through recently, really questioning whether their craft can continue. But now it feels like, in the software world, at least from my seat, people can handle much larger scope and work on much more complex problems. If anything, my job has gotten a lot _more_ interesting.
So I wonder if mathematics will go through that too? A period of chaos and doubt, but finding new and more effective ways of working.
The Quakers are pretty chill.
This is a weird article.
If anything - it seems that AI is _attacking_ the things the elite have. Elite people have private tutors/expert networks, now people do too for $20/month. What if the cost of education collapses to zero? It already sort of was - I learned to program on a ~$1,000 computer while Google engineers had access to $100s of millions of hardware, and it was enough.
Also, open-weight models are powerful and relatively cheap to run. So it's not like OpenAI can just close the doors (short of extremely strict regulation which seems to be a lost cause).
Does anyone do approaches where developers run CI locally on their machine and then somehow push up signed or authenticated artifacts?
Many medical practices (and other businesses) are poorly operated and administered. I think of my dentist: terrible website (even by 1995 standards), awful at follow up, weird insurance coverage (since she doesn't have time to follow up with new plans it seems) and almost no appointment reminders. There are obvious things to do that could drive business for her.
It makes sense to me that someone could come in and say "hey, let me run the business + finance side of the house while you practice medicine" and at least on paper I can see a real world where that works out for everybody.
Of course, soon you end up with dentists pushing unnecessary procedures and more, so it doesn't always works out that way.
Maybe a tangent, but I've gotten a lot of the joy back with 3D printing. You can just describe something, and Astra can model it in CAD extremely well. It usually takes a few iterations, and then you can just print it. I found that before, it would take days to build even simple models in Tinkercad - which while fun, was high enough friction that a lot of things would stay in the "ideas" folder - now I can have something real in an hour or two.
I still make time to write code by hand.
But I think it's really about finding the right abstraction. As a staff or principle engineer in the beforetimes, I also didn't understand most of the code being written by the org. I dove in where needed, but more importantly I found the right layer to understand what was going on - dependencies between teams and services, data flows and data models, etc. And where I found myself most effective was when I was shaping and understanding those effectively.
I don't know. We've managed to avoid most of this, I think. We have tech debt, but it feels more like regular tech debt than sloppy engineering practices. What's worked for us:
- Very strict linting, type checking, and so on.
- An opinionated flow of data and clear layers in the codebase.
- Good code review workflows with multiple AI code reviewers, along with a pretty opinionated SDLC.
- Agents that look for duplication and opportunities to simplify.
- Strong opinions about our core data model.
I really think that the companies that are struggling with slop probably struggled with slop before. It just moved slow enough that things could be fixed.
It's my own ignorance speaking, but is this as true in math? I could see this being a problem for programs where you have few, if any, real axioms and the axioms themselves change. But if we're talking math, the axioms should be fixed.
What's sad is that that AI could make this better. Good food photography is hard. Here's a low effort pass of the photo that they shared
https://chatgpt.com/s/m_6a61faba10188191a4991f3042e640c3which I don't think people would find misleading. Right now it looks pretty "AI" but wouldn't be hard to find a style that works well.
For what it's worth, I really enjoy superpowers. In particular, it does a great job with TDD that stops the model from jumping to conclusions, and I've been able to get it, even with Opus, to execute on much longer specs quite well.
In addition to what other people have said, I've taken some time to do leetcode questions lately - both architecture ones and coding ones. I'm not looking for a job by any stretch, but the practice and forcing a detailed zoom in has been really cathartic, and leetcode gives a nice structure/feeling of progress to it.
Nothing but respect for the GitHub team - they're at the center of it all. Can't imagine how their traffic looks these days.
They should raise their prices!
I love how this is both hilarious and extremely well made. Great job!
And I'm gonna be honest, I kind of want to use a few of these components for real (the ASCII art is fantastic).
This will be really powerful for voice. Being able to reason makes LLM so much smarter but with voice your latency budget is so tight that you can't spare the time typically.
I really love using AI to code but more and more I wonder ... Are things really that different? So I guess I'm the business as usual type.
I think on the frontend side we're going to see a lot more scope for teams.
On a backend infra side it seems as hard as ever. Still have to think really hard problems, think deeply about data structure and flow, and deal with second- and third-order effects. Or even harder because the models like to confidently lie.
The harder question is how we train people but that doesn't seem insurmountable either. Most of us cut our teeth as junior engineers somewhere, implementing tasks that Claude can now do without breaking a sweat but was that really the most efficient way to train and learn?
I'm also a lifelong vim user. I guess what I'm getting at is that I find the journey even more engaging now. I think I'm a good programmer. I've worked at great companies. I was competitive in ACM contests and Top Coder. But I find the journey even more engaging now because I can focus on really, really deeply understanding something, and less time on glue. Writing code by hand is still fun, don't get me wrong, but I'm also enjoying the step change in scale.
I don't think IDEs as they exist today are necessarily the right abstraction anymore anyway, to the extent they ever were. At least any more than a C++ IDE that was centered on assembly language as the main thing would be. I want data models, API contracts, and data flows. I don't know what the right answer is, but I think there's something coming.
I get to spend more time working on the things I enjoy. For example, data modeling and workflow orchestration, and building product to solve customer problems. And really, that comes down to spending a lot of my time just thinking really hard - because once I have a clear plan, it's actually not that hard to build it. Not only that, but I can build something, react to it, rebuild it, react to it, and come up with something I think is much better than I would have been able to build myself. Not to mention much faster.
I honestly don't get it - how isn't everyone having a blast with AI? Every one of those side projects you never had time for you can build in a weekend. You can explore five ideas at once. You can do big refactors/cleanups you'd never be able to dream of in the past. As a software engineer it's been fantastic.
https://www.astrazeneca.com/what-science-can-do/topics/next-...AstraZeneca is doing some really interesting research in this area - cell therapies that reset the immune system to eliminate the dysfunctional cells driving autoimmune disease, and then allow a healthy immune system to rebuild (for diseases like lupus, rheumatoid arthritis, and multiple sclerosis).
More of a meta question, but how are people staying on top of the firehose of new AI tools coming out left and right?
Personally I'd love a curated list of a handful of tools each week with more concrete examples/deep dives (e.g. autoresearch being a recent one)
People are figuring it out. Cars are broadly useful, but there's nuance to how to maintain then, use them will in different terrains and weather, etc.
I feel so many of these. LOL @ GitHub endorse-ish, more -ish every day now. Overall though seems like a pretty good hit rate.
Surprised to see datadog as a regret - it is expensive but it's been enormously useful for us. Though we don't run kubernetes, so perhaps my baseline of expensive is wrong.
Really neat! And great website!
As I've been looking at this problem from a different angle, I wonder how much execution and planning should be coupled. Once we have specs in GitHub, for example, it feels like we can use whatever tool to execute on them.
Posted by
prplfsh on Monday February 09, 2026 @07:23AM
from the show-hn dept.
https://github.com/purplefish-ai/factory-factory
It uses your local Claude and GitHub CLI and git worktrees, so there's nothing to configure beyond what you already have.Our AI coding workflow was a hodgepodge of scripts and ad-hoc processes, so we built this to centralize and streamline - and most importantly adapt as things change.Some cool features so far (lots more planned):1. One click github issue to agent working on it.2. Ratcheting...