Sorry, I misunderstood. You didn't mean plan mode, but rather wanted to check whether the models ask questions before planning. It sounds like you're describing the "Deep Research" mode in AI coding: when given a task, the AI first asks clarifying questions, then plans. Haha.
Some developers have shared that they usually work with a separate model to plan the architecture in detail, then hand the resulting spec to another model to write the code.
I've thought a lot about what programmers should do once AI coding becomes really capable. Then Paul Graham's words in Hackers and Painters inspired me: "If a hacker were a mere implementor, turning a spec into code, then he could just work his way through it from one end to the other like someone digging a ditch. But if the hacker is a creator, we have to take inspiration into account." This got me thinking: perhaps we can let AI handle the implementation, while we humans focus on the ideas(and the architecture). What are your thoughts? 😄
Speaking of turning ideas into reality —so you have the scripts but no front-end panel? That's pretty common. Now that AI coding makes turning an idea into reality much easier, the verification and real-world usage have become the new bottleneck.
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Yes, I think you're very much onto something here — and actually, what you describe is pretty much how I work already. 😄
I don't see AI coding as replacing the creative part of programming. For me, it's almost the opposite: I come up with the idea, define the problem and the broad architecture, and specify what I want the system to do. Then I let the AI handle most of the implementation, and also do a first round of verification.
But I still review the result myself and test it myself. I think that distinction is important: AI can implement and verify its own work, but I don't think that should make us stop questioning the result.
In fact, I suspect that as AI gets better at implementation, the interesting part of programming will increasingly move towards the things that are harder to specify: having the idea in the first place, understanding the problem, making architectural choices, and knowing what "good" actually means.
And just to clarify one thing about the panel: there is a CLI interface for it. I simply haven't had the opportunity to try the whole thing on a completely clean VPS yet. So, like you said, the real-world verification is still missing.
And I really like your point about verification becoming a bottleneck. That's probably one of the interesting consequences of AI coding: once producing code becomes cheap and fast, the scarce resource isn't necessarily implementation anymore — it's knowing whether what was produced is actually the right thing.
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Sorry, I misunderstood. You didn't mean plan mode, but rather wanted to check whether the models ask questions before planning. It sounds like you're describing the "Deep Research" mode in AI coding: when given a task, the AI first asks clarifying questions, then plans. Haha.
Some developers have shared that they usually work with a separate model to plan the architecture in detail, then hand the resulting spec to another model to write the code.
I've thought a lot about what programmers should do once AI coding becomes really capable. Then Paul Graham's words in Hackers and Painters inspired me: "If a hacker were a mere implementor, turning a spec into code, then he could just work his way through it from one end to the other like someone digging a ditch. But if the hacker is a creator, we have to take inspiration into account." This got me thinking: perhaps we can let AI handle the implementation, while we humans focus on the ideas(and the architecture). What are your thoughts? 😄
Speaking of turning ideas into reality —so you have the scripts but no front-end panel? That's pretty common. Now that AI coding makes turning an idea into reality much easier, the verification and real-world usage have become the new bottleneck.
Yes, I think you're very much onto something here — and actually, what you describe is pretty much how I work already. 😄
I don't see AI coding as replacing the creative part of programming. For me, it's almost the opposite: I come up with the idea, define the problem and the broad architecture, and specify what I want the system to do. Then I let the AI handle most of the implementation, and also do a first round of verification.
But I still review the result myself and test it myself. I think that distinction is important: AI can implement and verify its own work, but I don't think that should make us stop questioning the result.
In fact, I suspect that as AI gets better at implementation, the interesting part of programming will increasingly move towards the things that are harder to specify: having the idea in the first place, understanding the problem, making architectural choices, and knowing what "good" actually means.
And just to clarify one thing about the panel: there is a CLI interface for it. I simply haven't had the opportunity to try the whole thing on a completely clean VPS yet. So, like you said, the real-world verification is still missing.
And I really like your point about verification becoming a bottleneck. That's probably one of the interesting consequences of AI coding: once producing code becomes cheap and fast, the scarce resource isn't necessarily implementation anymore — it's knowing whether what was produced is actually the right thing.