341–350 of 448 posts
What I do very successfully is low stakes stuff for work (easy automations, small QoL improvements for our tooling, a drive-by small Jira plugin)
And then I do a lot of crazy exploring, or hyper-personal just for myself stuff that can only exist because I can now spawn and abandon it in a couple days instead of weeks or months.
It is incredibly easy now to get an idea to the prototype stage, but making it production-ready still needs boring old software engineering skills. I know countless people who followed the "I'll vibe code my own business" trend, and a few of them did get pretty far, but ultimately not a single one actually launched. Anyone who has been doing this professionally will tell you that the "last step" is what takes the maj…
Software engineering is not “coding” though. Before AI for the last 8 or so years now first at a startup then working in consulting mostly with companies new to AWS or they wanted a new implementation, it’s been: 1. Gather requirements 2. Do the design 3. Present the design and get approval and make sure I didn’t miss anything 4. Do the infrastructure as code to create the architecture and the deployment pipeline 5.…
The quantity is there. Nobody's asking "does this thing actually work" before hitting deploy. That's the real gap.
It is incredibly easy now to get an idea to the prototype stage, but making it production-ready still needs boring old software engineering skills. I know countless people who followed the "I'll vibe code my own business" trend, and a few of them did get pretty far, but ultimately not a single one actually launched. Anyone who has been doing this professionally will tell you that the "last step" is what takes the maj…
> the "last step" is what takes the majority of time and effort Having worked extensively with vibe-coded software, the main problem for me is that I have tuned-off from the ai-code, and I dont see any skin-in-the-game for me. This is dangerous because it becomes increasingly harder to root-cause and debug problems because that muscle is atrophying. use-it or lose-it applies to cognitive skills (coding/debugging). No…
It is incredibly easy now to get an idea to the prototype stage, but making it production-ready still needs boring old software engineering skills. I know countless people who followed the "I'll vibe code my own business" trend, and a few of them did get pretty far, but ultimately not a single one actually launched. Anyone who has been doing this professionally will tell you that the "last step" is what takes the maj…
> It is incredibly easy now to get an idea to the prototype stage Yup. And for most purposes, that's enough . An app does not have to be productized and shipped to general audience to be useful. In fact, if your goal is to solve some specific problem for yourself, your friends/family, community or your team, then the "last step" you mention - the one that "takes majority of time and effort" - is entirely unnecessary,…
Though, the economy does not seem to be in a good spot to try that strategy out as of now.
I think this article is making a pretty big assumption: that people making things with AI are also going to be publishing them. And that's just the opposite of what should be expected, for the general case. Like I've been making things, and making changes to things, but I haven't published any of that because, well they're pretty specific to my needs. There are also things which I won't consider publishing for now, e…
Agree. There's also a weird ideological thing in open source right now, where any AI must be AI slop, and no AI is the only solution. That has strongly disincentivized legitimate contributions from people. I have to imagine that's having an impact. There's a very real problem of low effort AI slop, but throwing out the baby with the bathwater is not the solution. That said, I do kind of wonder if the old model of ope…
AI as of now is like ads. Ads as a concept are not evil. But what it's done to everyday life is evil enough that I wouldn't flinch at them being banned/highly regulated one day (well, not much. The economic fallout would be massive, but my QoL would go way up).
That's how I feel here. And looking at the PRs some popular repos have to deal with, we're well into the "shove this pop up ad with a tiny close button you can't reach easily" stage of AI.
Earlier quoted context omitted.
Software engineering is not “coding” though. Before AI for the last 8 or so years now first at a startup then working in consulting mostly with companies new to AWS or they wanted a new implementation, it’s been: 1. Gather requirements 2. Do the design 3. Present the design and get approval and make sure I didn’t miss anything 4. Do the infrastructure as code to create the architecture and the deployment pipeline 5.…
What makes you think 1-2, 6-8 can’t be done by agents?
2. AI is horrible at system design. One anecdote. I was vibe coding an internal website that will at most be used by 7 people in total. Part of it was uploading a file to S3 and then loading the file into an Postgres table. It got the “create pre-signed S3 url and upload it directly to that instead of sending it to the API” correct (documented best practice). But then it did the naive “upload the file from S3 and do a bulk sql insert into the database”. This would have taken 20 minutes. The optimized method that I already knew was just to use the Postgres AWS extension to load it directly from S3 - 30 seconds. I’ve heard from a lot of data engineers run into similar problems (I am not one. I play one sometime).
6. Involves talking to the customer and UX.
7. Moving to production doesn’t take AI. Automation, stage deployments, automated testing and monitoring, blue /green deployments etc is a solved problem.
8. Monitoring is also a solve problem pre AI. It’s what happens after a problem is what you need people for.
So yes 1,2 and 7 are high value, high touch. If you look at the leveling guidelines for any BigTech company, you have to be good at 1 and 2 at least to get pass mid level.
Then there is always “0” pre-sales. I can do inbound pre-sales (not chase customers). It’s not that much different than what I do now as the first technical person who does a deep dive strategy conversation
We have great software now! YoloSwag (13 commits) [rocketship rocketship rocketship] YoloSwag is a 1:1 implementation of pyTorch, written in RUST [crab emoji] - [hand pointing emoji] YoloSwag is Memory Safe due to being Written in Rust - [green leaf emoji] YoloSwag uses 80% less CPU cycles due to being written in Rust - [clipboard emoji] [engineer emoji] YoloSwag is 1:1 API compatible with pyTorch with complete ops s…
Technically, that's as "Memory Safe" as you can get!
That is, I expect the numbers (at least the frequency of downloads, if not the number of new packages) to go down over time as AI makes generating functionality easier than hunting down and adding a dependency.
The number of new packages could still go up as people may still open-source their generated code, for street cred if not actual utility. But it's not clear how much of those incentives apply if the code is not very generally useful and the effort put into is minimal.