Comment by oxidant

Comment by oxidant a day ago

I do not agree it is something you can pick up in an hour. You have to learn what AI is good at, how different models code, how to prompt to get the results you want.

If anything, prompting well is akin to learning a new programming language. What words do you use to explain what you want to achieve? How do you reference files/sections so you don't waste context on meaningless things?

I've been using AI tools to code for the past year and a half (Github Copilot, Cursor, Claude Code, OpenAI APIs) and they all need slightly different things to be successful and they're all better at different things.

AI isn't a panacea, but it can be the right tool for the job.

15123123 21 hours ago

I am also interested in how much of these skills are at the mercy of OpenAI ? Like IIRC 1 or 2 years ago there was an uproar of AI "artists" saying that their art is ruined because of model changes ( or maybe the system prompt changed ).

>I do not agree it is something you can pick up in an hour.

But it's also interesting that the industry is selling the opposite ( with AI anyone can code / write / draw / make music ).

>You have to learn what AI is good at.

More often than not I find it you need to learn what the AI is bad at, and this is not a fun experience.

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oxidant 19 hours ago

Of course that's what the industry is selling because they want to make money. Yes, it's easy to create a proof of concept but once you get out of greenfield into 50-100k tokens needed in the context (reading multiple 500 line files, thinking, etc) the quality drops and you need to know how to focus the models to maintain the quality.
"Write me a server in Go" only gets you so far. What is the auth strategy, what endpoints do you need, do you need to integrate with a library or API, are there any security issues, how easy is the code to extend, how do you get it to follow existing patterns?
I find I need to think AND write more than I would if I was doing it myself because the feedback loop is longer. Like the article says, you have to review the code instead of having implicit knowledge of what was written.
That being said, it is faster for some tasks, like writing tests (if you have good examples) and doing basic scaffolding. It needs quite a bit of hand holding which is why I believe those with more experience get more value from AI code because they have a better bullshit meter.

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- skydhash 14 hours ago
  
  > What is the auth strategy, what endpoints do you need, do you need to integrate with a library or API, are there any security issues, how easy is the code to extend, how do you get it to follow existing patterns?
  That is software engineering realm, not using LLMs realm. You have to answer all of these questions even with traditional coding. Because they’re not coding questions, they’re software design questions. And before that, there were software analysis questions preceded by requirements gathering questions.
  A lot of replies around the thread is conflating coding activities with the parent set of software engineering activities.
  
  Reply View | 1 reply
  
  oxidant 10 hours ago
  
  Agreed, but people sell "vibe coding" without acknowledging you need more than vibes.
  LLMs can help answer the questions. However, they're not going to necessarily make the correct choices or implementation without significant input from the user.
  
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solumunus 20 hours ago

OpenAI? They are far from the forefront here. No one is using their models for this.

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- 15123123 18 hours ago
  
  You can substitute for whatever saas company of your choice.
  
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