401–410 of 570 posts
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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 is every conversation with everyone Wow that sounds horrible.
Not really? It's kind of a big deal.
> Not really? It's kind of a big deal. Why on earth is the parent comment downvoted? the title of the TFA asks a question. This statement directly answers that question. Seems very on-topic.
I'm also learning art and I'll never use AI here, so I thought I have less time for hobby programming and I could just use AI for that but then I come back to the concern I mentioned above.Plus I can't proudly share anything I made with it either because I wouldn't have done much of the work at all.
I'm also feeling burnt out about web dev in general and doing the same thing during my free time just feels like more work to my brain. I wish I could find something interesting to do, and if I don't I'll quit programming in free time for good.
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I rarely come across people who flat out say "it's not useful". They exist, but IME they're the minority. Rather, I hear a lot of nuanced opinions of how the tech is useful in some scenarios, but that the net benefit is not clear. I.e. the tech has many drawbacks that make it require a lot of effort to extract actual value from. This is an opinion I personally share. In most cases, those "big productivity gains" are…
"productivity" is a misnomer. Sort of. The things I'm building are all things I've had on the back burner for years. Most of which I never would have bothered to do. But AI lets me ignore that excuse and just do it.
The only downside is not learning about method Y or Z that work differently than X but would also be sufficient, and you don’t learn the nuances and details of the problem space for X, Y, and Z.
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…
But at the same time the more I read about AI, the more I realize I need to learn about AI. Thus far I'm just using cursor and the Claude code extension alongside obra superpowers, and I've been quite happy with it. But on Twitter I see people with multiple instances of Claude code or open claw talking to each other and I don't even know how to begin to understand what's going on there. But I'm not letting myself get distracted — Claude code and open claw are tools. They could go away at any time. But systems thinking is something that won't go away. At least, that's my gambit.
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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.
I rarely come across people who flat out say "it's not useful". They exist, but IME they're the minority. Rather, I hear a lot of nuanced opinions of how the tech is useful in some scenarios, but that the net benefit is not clear. I.e. the tech has many drawbacks that make it require a lot of effort to extract actual value from. This is an opinion I personally share. In most cases, those "big productivity gains" are…
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"productivity" is a misnomer. Sort of. The things I'm building are all things I've had on the back burner for years. Most of which I never would have bothered to do. But AI lets me ignore that excuse and just do it.
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'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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Perhaps we're in an AI summer and a tech winter. Winter is always the time when people hole up, dream, and work on whatever big thing is next. We're about due for some new computing abstractions to shake things up I think. Those won't be conceived by LLMs, though they may aid in implementing them.
We have 2 decades of abstraction. The stacks of turtles that we use to run everything are starting to show their bloat. The other day someone was lamenting dealign with an onslaught of bot traffic, and having to deal with blocking it. Maybe we need to get back to good old fashioned engineering and optimization. There was a thread on here the other day about PC gamer recommending RSS readers and having a 36gb webpage…
(though it sounds like if you left it for long enough, you'd get 36 GB of ads downloaded eventually)