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> But it's important to realize that AI coding is itself a skill that you can develop. It's not just , pick the best tool and let it go. Managing prompts and ma

> But it's important to realize that AI coding is itself a skill that you can develop. It's not just , pick the best tool and let it go. Managing prompts and managing context has a much higher skill ceiling than many people realize No, it's not. It's something you can pick in a few minutes (or an hour if you're using more advanced tooling, mostly spending it setting things up). But it's not like GDB or using UNIX as a IDE where you need a whole book to just get started. > It's a tiny bit like drawing vs photography and if you look through that lens it's obvious that many drawers might not like photography. While they share a lot of principles (around composition, poses,...), they are different activities with different output. No one conflates the two. You don't draw and think you're going to capture a moment in time. The intent is to share an observation with the world.

oxidant 1y 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 1y 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.
  - solumunus 1y ago
    OpenAI? They are far from the forefront here. No one is using their models for this.
    - 15123123 1y ago
      You can substitute for whatever saas company of your choice.
  - oxidant 1y 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.
    - skydhash 1y 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.
__MatrixMan__ 1y ago
It definitely takes more than minutes to discover the ways that your model is going to repeatedly piss you off and set up guardrails to mitigate those problems.
viraptor 1y ago
> It's something you can pick in a few minutes You can start in a few minutes, sure. (Also you can start using gdb in minutes) But GP is talking about the ceiling. Do you know which models work better for what kind of task? Do you know what format is better for extra files? Do you know when it's beneficial to restart / compress context? Are you using single prompts or multi stage planning trees? How are you managing project-specific expectations? What type of testing gives better results in guiding the model? What kind of issues are more common for which languages? Correct prompting these days what makes a difference in tasks like SWE-verified.
- sothatsit 1y ago
  I feel like there is also a very high ceiling to how much scaffolding you can produce for the agents to get them to work better. This includes custom prompts, custom CLAUDE.md files, other documentation files for Claude to read, and especially how well and quickly your linting and tests can run, and how much functionality they cover. That's not to mention MCP and getting Claude to talk to your database or open your website using Playwright, which I have not even tried yet. For example, I have a custom planning prompt that I will give a paragraph or two of information to, and then it will produce a specification document from that by searching the web and reading the code and documentation. And then I will review that specification document before passing it back to Claude Code to implement the change. This works because it is a lot easier to review a specification document than it is to review the final code changes. So, if I understand it and guide it towards how I would want the feature to be implemented at the specification stage, that sets me up to have a much easier time reviewing the final result as well. Because it will more closely match my own mental model of the codebase and how things should be implemented. And it feels like that is barely scratching the surface of setting up the coding environment for Claude Code to work in.
  - viraptor 1y ago
    > then it will produce a specification document from that I like a similar workflow where I iterate on the spec, then convert that into a plan, then feed that step by step to the agent, forcing full feature testing after each one.
sagarpatil 1y ago
Yeah, you can’t do sh*t in an hour. I spend a good 6-8 hours every day using Claude Code, and I actually spend an hour every day trying new AI tools, it’s a constant process. Here’s what my today’s task looks like: 1. Test TRAE/Refact.ai/Zencoder: 70% on SWE verified 2. https://github.com/kbwo/ccmanager https://github.com/kbwo/ccmanager: use git tree to manage multiple Claude Code sessions 3. https://github.com/julep-ai/julep/blob/dev/AGENTS.md https://github.com/julep-ai/julep/blob/dev/AGENTS.md: Read and implement 4. https://github.com/snagasuri/deebo-prototype https://github.com/snagasuri/deebo-prototype: Autonomous debugging agent (MCP) 5. https://github.com/claude-did-this/claude-hub https://github.com/claude-did-this/claude-hub: connects Claude Code to GitHub repositories.
furyofantares 1y ago
> No, it's not. It's something you can pick in a few minutes (or an hour if you're using more advanced tooling, mostly spending it setting things up). But it's not like GDB or using UNIX as a IDE where you need a whole book to just get started. The skill floor is something you can pick up in a few minutes and find it useful, yes. I have been spending dedicated effort toward finding the skill ceiling and haven't found it. I've picked up lots of skills in my career, some of which were easy, but some of which required dedicated learning, or practice, or experimentation. LLM-assisted coding is probably in the top 3 in terms of effort I've put into learning it. I'm trying to learn the right patterns to use to keep the LLM on track and keeping the codebase in check. Most importantly, and quite relevant to OP, I'd like to use LLMs to get work done much faster while still becoming an expert in the system that is produced. Finding the line has been really tough. You can get a LOT done fast without this requirement, but personally I don't want to work anywhere that has a bunch of systems that nobody's an expert in. On the flip side, as in the OP, you can have this requirement and end up slower by using an LLM than by writing the code yourself.
- philomath_mn 1y ago
  Anywhere I can follow your takes on LLM-assisted coding?
JimDabell 1y ago
> It's something you can pick in a few minutes (or an hour if you're using more advanced tooling, mostly spending it setting things up). This doesn’t give you any time to experiment with alternative approaches. It’s equivalent to saying that the first approach you try as a beginner will be as good as it possibly gets, that there’s nothing at all to learn.

oxidant 1y 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 1y 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.
- solumunus 1y ago
  OpenAI? They are far from the forefront here. No one is using their models for this.
  - 15123123 1y ago
    You can substitute for whatever saas company of your choice.
- oxidant 1y 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.
  - skydhash 1y 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.

__MatrixMan__ 1y ago

It definitely takes more than minutes to discover the ways that your model is going to repeatedly piss you off and set up guardrails to mitigate those problems.

viraptor 1y ago

> It's something you can pick in a few minutes You can start in a few minutes, sure. (Also you can start using gdb in minutes) But GP is talking about the ceiling. Do you know which models work better for what kind of task? Do you know what format is better for extra files? Do you know when it's beneficial to restart / compress context? Are you using single prompts or multi stage planning trees? How are you managing project-specific expectations? What type of testing gives better results in guiding the model? What kind of issues are more common for which languages? Correct prompting these days what makes a difference in tasks like SWE-verified.

sothatsit 1y ago
I feel like there is also a very high ceiling to how much scaffolding you can produce for the agents to get them to work better. This includes custom prompts, custom CLAUDE.md files, other documentation files for Claude to read, and especially how well and quickly your linting and tests can run, and how much functionality they cover. That's not to mention MCP and getting Claude to talk to your database or open your website using Playwright, which I have not even tried yet. For example, I have a custom planning prompt that I will give a paragraph or two of information to, and then it will produce a specification document from that by searching the web and reading the code and documentation. And then I will review that specification document before passing it back to Claude Code to implement the change. This works because it is a lot easier to review a specification document than it is to review the final code changes. So, if I understand it and guide it towards how I would want the feature to be implemented at the specification stage, that sets me up to have a much easier time reviewing the final result as well. Because it will more closely match my own mental model of the codebase and how things should be implemented. And it feels like that is barely scratching the surface of setting up the coding environment for Claude Code to work in.
- viraptor 1y ago
  > then it will produce a specification document from that I like a similar workflow where I iterate on the spec, then convert that into a plan, then feed that step by step to the agent, forcing full feature testing after each one.

sagarpatil 1y ago

Yeah, you can’t do sh*t in an hour. I spend a good 6-8 hours every day using Claude Code, and I actually spend an hour every day trying new AI tools, it’s a constant process. Here’s what my today’s task looks like: 1. Test TRAE/Refact.ai/Zencoder: 70% on SWE verified 2. https://github.com/kbwo/ccmanager https://github.com/kbwo/ccmanager: use git tree to manage multiple Claude Code sessions 3. https://github.com/julep-ai/julep/blob/dev/AGENTS.md https://github.com/julep-ai/julep/blob/dev/AGENTS.md: Read and implement 4. https://github.com/snagasuri/deebo-prototype https://github.com/snagasuri/deebo-prototype: Autonomous debugging agent (MCP) 5. https://github.com/claude-did-this/claude-hub https://github.com/claude-did-this/claude-hub: connects Claude Code to GitHub repositories.

furyofantares 1y ago

> No, it's not. It's something you can pick in a few minutes (or an hour if you're using more advanced tooling, mostly spending it setting things up). But it's not like GDB or using UNIX as a IDE where you need a whole book to just get started. The skill floor is something you can pick up in a few minutes and find it useful, yes. I have been spending dedicated effort toward finding the skill ceiling and haven't found it. I've picked up lots of skills in my career, some of which were easy, but some of which required dedicated learning, or practice, or experimentation. LLM-assisted coding is probably in the top 3 in terms of effort I've put into learning it. I'm trying to learn the right patterns to use to keep the LLM on track and keeping the codebase in check. Most importantly, and quite relevant to OP, I'd like to use LLMs to get work done much faster while still becoming an expert in the system that is produced. Finding the line has been really tough. You can get a LOT done fast without this requirement, but personally I don't want to work anywhere that has a bunch of systems that nobody's an expert in. On the flip side, as in the OP, you can have this requirement and end up slower by using an LLM than by writing the code yourself.

philomath_mn 1y ago
Anywhere I can follow your takes on LLM-assisted coding?

JimDabell 1y ago

> It's something you can pick in a few minutes (or an hour if you're using more advanced tooling, mostly spending it setting things up). This doesn’t give you any time to experiment with alternative approaches. It’s equivalent to saying that the first approach you try as a beginner will be as good as it possibly gets, that there’s nothing at all to learn.