551–560 of 570 posts
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Is anyone else bored of talking about posting with that keybored vibe? Hey, don’t get me wrong!—I’m not some heretic. I love the keybored posting vibe. Lots of sarcasm, complaining about American geopolitical decisions, recommending socialism on some venture capitalist/ hacker forum, absolutely no hint of any Show HN bragging rights or any technical accomplishments whatsover, just lots of complaining in general (but…
u mad bro?
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I think some companies are just behind the curve, so this sentiment seems bizarre to some. At my big tech, AI is every conversation with everyone, every day. Becoming AI native is a huge deal for us. Literally everyone is making AI usage a core part of their job and it's been a big productivity accelerator. Perhaps it's different where you work, so you don't see the sentiment.
"AI native". I don't know if you intend it but you sound like a linked in lunatic, nobody talks like that
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>Does anybody really buy the BS that this will all make the world a better place one day? Yeah - I think there's a lot of cool sci-fi like stuff in the future.
And probably a lot of uncool sci-fi like stuff, e.g. The Machine Stops.
This might sound like snark, but I truly don’t mean it that way. I think what’s interesting about AI, and why there’s so much conversation, is that in order to be a good user of AI, you have to really understand software development. All the people I work with who are getting the most value out of using AI to deliver software are people who are already very high-skilled engineers, and the more years of real experienc…
Spot on take. The people I’ve noticed that say things like “it’s not useful” are the ones who are doing so little they can’t see the value. This isn’t to say there’s not hype. Just that if you’re not seeing big productivity gains you need to make sure you really are an outlier and not just surplus to requirements.
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The productivity comes from not having the startup costs. You don’t need to research the best way to do X, just verify that X works via tests and documentation. I find it still takes T hours to actually implement X with an agent, but if I didn’t know how to do X it eliminates that startup cost which might make it take 3T hours instead. The only downside is not learning about method Y or Z that work differently than X…
I find it useful to use a brainstorming skill to teach me X Y Z and help me understand the tradeoffs for each, and what it'd recommend. I've learned about outbox pattern, eventual consistency, CAP theorem, etc. It's been fun. But if I didn't ask the LLM to help me understand it would have just went with option A without me understanding why.
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The productivity comes from not having the startup costs. You don’t need to research the best way to do X, just verify that X works via tests and documentation. I find it still takes T hours to actually implement X with an agent, but if I didn’t know how to do X it eliminates that startup cost which might make it take 3T hours instead. The only downside is not learning about method Y or Z that work differently than X…
> just verify that X works via tests and documentation. No, its verify that X approach is semantically correct, architecturally makes sense, design is valid and then add tests and documentation. Basically, 80% of the work.
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The productivity comes from not having the startup costs. You don’t need to research the best way to do X, just verify that X works via tests and documentation. I find it still takes T hours to actually implement X with an agent, but if I didn’t know how to do X it eliminates that startup cost which might make it take 3T hours instead. The only downside is not learning about method Y or Z that work differently than X…
> just verify that X works via tests and documentation. No, its verify that X approach is semantically correct, architecturally makes sense, design is valid and then add tests and documentation. Basically, 80% of the work.
I think people here are thinking I’m building Gmail from scratch when I’m talking about adding additional database APIs and models in the style of stuff I’ve already done twenty times in the same application. That’s easy for even the dumbest LLM and verification is not more than a code review, maybe an hour or two of labor. It’s never perfect, but as stated elsewhere I know how to program this stuff already so I can just fix it on the spot before I commit it.
I understand though, some of you either can’t use agents to code because of one reason or another, or refuse to; both are valid. I’m saying that for my job programming in a specific industry for a specific application in a specific language that AI agents actually help me out more than hinder me, and I still ship quality code in the style that matches the code base.
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I find it useful to use a brainstorming skill to teach me X Y Z and help me understand the tradeoffs for each, and what it'd recommend. I've learned about outbox pattern, eventual consistency, CAP theorem, etc. It's been fun. But if I didn't ask the LLM to help me understand it would have just went with option A without me understanding why.
In my instance, I’m talking more about using library X or library Y, not the difference between using an atomic versus a mutex. I want to learn the latter, but the former isn’t something I care about.
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In my instance, I’m talking more about using library X or library Y, not the difference between using an atomic versus a mutex. I want to learn the latter, but the former isn’t something I care about.
Ah, that's fair. I personally don't normally care about library usage as long as it's fairly well documented and effective (like Shadcn vs raw tailwind components vs chakra... I don't really care).
Software engineers who haven’t tried these tools don’t understand what they are, and vibe coders who never understood software are taking the mindshare in public because it sounds revolutionary to some and apocalyptic to others. You have to stop listening to the claw bros and try using these as tools yourself in small ways to see what it’s really about, IMO.