3 ms·

Why do you need to delegate writing code to AI? I don't do that. Is your job mandating that you vibe code? How would they know?

raw_anon_1111 9mo ago
They would know when you are two or three times slower producing code because you insist on having your own handcrafted, bespoke artisanal code that doesn’t meet the requirements any better than Claude could do.
- carefree-bob 9mo ago
  Two to three times faster? Has productivity doubled or tripled? Stuff shipping in 1/3 the time? I'm not aware of this - which company is now shipping features twice as fast? AI can produce greenfield code faster, sure, but you spend more time debugging it. If you write the code, it's slower to get the first version out, but then you understand the code and can debug much faster going forward. You can also use AI to write unit tests, documentation, and stuff like that, while writing the code yourself.
  - raw_anon_1111 9mo ago
    It’s copium to think that LLM code is more buggy than your median to slightly above median developer or that’s all that most companies need - median developers. And debugging code is also easier with AI. Just today I had to revisit code that I personally wrote from the design, the implementation the refactoring, etc from the first git init and I couldn’t remember half the decisions I made. I launched Codex and started asking it questions about the code. Where is the productivity gain? How many junior developers and mid level ticket takers are struggling to find a job now because the market is saturated and those true seniors who can operate at a larger scope and impact can do the work themselves without having to delegate My personal anecdote is that I had four offers within 3 weeks after being Amazoned in late 2023. One was from the company that acquired the startup I left in 2020 where I would have been responsible for leading the integration between all of the companies they acquired [1] and the other was a former coworker who was now a director st a well known non tech F500 company. He wanted me to lead the migration and “modernization “ efforts. I decided to stick with consulting. Those offers didn’t come because of my coding abilities. That’s a commodity. I was looking again in 2024. It took one outreach and talking to the right people. Absolutely no one asked me the first thing about coding even though I do it maybe 60%-70% of the time. Going way back to 2016, I had two offers - one interview was me doing a merge sort on the whiteboard the other interview was me talking about strategy with the then new director who needed to build up a software development team. He asked me about my experience. He didn’t mske me stand up and do some algorithmic test on the whiteboard. He treated me like an industry professional [1] I did the whole “lead integration efforts by a company owned by private equity acquiring other companies” thing before I joined the startup - never again.
    - carefree-bob 9mo ago
      > It’s copium to think that LLM code is more buggy than your median to slightly above median developer or that’s all that most companies need - median developers. You are not understanding the point. AI has to be properly supervised because it makes mistakes. Now if you are making more or as many mistakes as the AI, then you should look for a new career. You should understand the code better than the AI, because the AI has a limited context window, and for a large codebase, you should know that codebase better than the AI. Now, you can use AI to help you understand code that someone else wrote. You can use AI to check your code. You can use AI to write unit tests. You can use AI to debug. You can use AI to summarize code. There are so many uses of AI. But you -- you as a developer -- need to understand your codebase. If you do not understand the codebase, you can't properly supervise the AI. And there is one efficient way for you to understand a big complicated codebase. The most efficient way possible for you to learn it. That is by you writing code in that codebase, and debugging that code, and learning how to code in that codebase. If you don't do that, then you are not qualified to supervise the AI. Now you are letting the AI loose on a codebase much larger than its context window, and you will fill the codebase with bugs. It's like a student. Sure you can use AI to help you study, to explain things to you, but the moment you let the AI do your homework, then you are no longer learning. The homework is the practice of solving problems in that area, and as a developer, you need to write code in a codebase otherwise you have no value and the AI has no value.
      - raw_anon_1111 9mo ago
        > You are not understanding the point. AI has to be properly supervised because it makes mistakes. It been well over a decade (ironically until AI) that I have been responsible for an implementation small enough that I could do with my own two hands within the allotted time by myself. People also require supervision. Now if you are making more or as many mistakes as the AI, then you should look for a new career. Right now in 2026, the code I write is dependent on the AWS SDK. https://boto3.amazonaws.com/v1/documentation/api/latest/index.html https://boto3.amazonaws.com/v1/documentation/api/latest/inde... It’s the same surface area for every supported language - as new features come out AWS uses code gen tools to update the SDK and CLI based on a common set of specifications. Consider it’s the same surface area as when I have to write IAC. Are you saying it should be faster for me to know that API and write it by hand than an LLM? Especially now that AWS has an MCP server that AI can use to know the latest documentation. The case was the same in 2000 when I had to write C and C++ against the Win32 APIs, DCOM etc or when I didn’t know the entire C++ STL and had to reference the “C++ programming language ”. > You should understand the code better than the AI, because the AI has a limited context window, and for a large codebase, you should know that codebase better than the AI. Every developer should know the entire codebase even when they first join a company? Even when I was working at a 70 person startup, that had three teams not one person knew the entire codebase. > But you -- you as a developer -- need to understand your codebase. If you do not understand the codebase, you can't properly supervise the AI. Again how large of a codebase is a developer suppose to know? S3 for instance is made up of 300 separate microservices. Is a single developer suppose to know how it all works? Besides that’s why we have unit tests and should be writing modular code so you don’t have to worry about spooky action at a distance. And people are so focused on “the code”. I have designed systems and architectures that include code and multiple teams and consulting companies. Am I suppose to know how all of the code works? > And there is one efficient way for you to understand a big complicated codebase. The most efficient way possible for you to learn it. That is by you writing code in that codebase, and debugging that code, and learning how to code in that codebase. Before AI, when coming into a team, I didn’t just start “coding” I would ask other people. Now I ask AI. Are you really saying that you can keep more of the code in your head than AI with a one million token context window or that you can read and understand various markdown files in a repo faster?

raw_anon_1111 9mo ago

They would know when you are two or three times slower producing code because you insist on having your own handcrafted, bespoke artisanal code that doesn’t meet the requirements any better than Claude could do.

carefree-bob 9mo ago
Two to three times faster? Has productivity doubled or tripled? Stuff shipping in 1/3 the time? I'm not aware of this - which company is now shipping features twice as fast? AI can produce greenfield code faster, sure, but you spend more time debugging it. If you write the code, it's slower to get the first version out, but then you understand the code and can debug much faster going forward. You can also use AI to write unit tests, documentation, and stuff like that, while writing the code yourself.
- raw_anon_1111 9mo ago
  It’s copium to think that LLM code is more buggy than your median to slightly above median developer or that’s all that most companies need - median developers. And debugging code is also easier with AI. Just today I had to revisit code that I personally wrote from the design, the implementation the refactoring, etc from the first git init and I couldn’t remember half the decisions I made. I launched Codex and started asking it questions about the code. Where is the productivity gain? How many junior developers and mid level ticket takers are struggling to find a job now because the market is saturated and those true seniors who can operate at a larger scope and impact can do the work themselves without having to delegate My personal anecdote is that I had four offers within 3 weeks after being Amazoned in late 2023. One was from the company that acquired the startup I left in 2020 where I would have been responsible for leading the integration between all of the companies they acquired [1] and the other was a former coworker who was now a director st a well known non tech F500 company. He wanted me to lead the migration and “modernization “ efforts. I decided to stick with consulting. Those offers didn’t come because of my coding abilities. That’s a commodity. I was looking again in 2024. It took one outreach and talking to the right people. Absolutely no one asked me the first thing about coding even though I do it maybe 60%-70% of the time. Going way back to 2016, I had two offers - one interview was me doing a merge sort on the whiteboard the other interview was me talking about strategy with the then new director who needed to build up a software development team. He asked me about my experience. He didn’t mske me stand up and do some algorithmic test on the whiteboard. He treated me like an industry professional [1] I did the whole “lead integration efforts by a company owned by private equity acquiring other companies” thing before I joined the startup - never again.
  - carefree-bob 9mo ago
    > It’s copium to think that LLM code is more buggy than your median to slightly above median developer or that’s all that most companies need - median developers. You are not understanding the point. AI has to be properly supervised because it makes mistakes. Now if you are making more or as many mistakes as the AI, then you should look for a new career. You should understand the code better than the AI, because the AI has a limited context window, and for a large codebase, you should know that codebase better than the AI. Now, you can use AI to help you understand code that someone else wrote. You can use AI to check your code. You can use AI to write unit tests. You can use AI to debug. You can use AI to summarize code. There are so many uses of AI. But you -- you as a developer -- need to understand your codebase. If you do not understand the codebase, you can't properly supervise the AI. And there is one efficient way for you to understand a big complicated codebase. The most efficient way possible for you to learn it. That is by you writing code in that codebase, and debugging that code, and learning how to code in that codebase. If you don't do that, then you are not qualified to supervise the AI. Now you are letting the AI loose on a codebase much larger than its context window, and you will fill the codebase with bugs. It's like a student. Sure you can use AI to help you study, to explain things to you, but the moment you let the AI do your homework, then you are no longer learning. The homework is the practice of solving problems in that area, and as a developer, you need to write code in a codebase otherwise you have no value and the AI has no value.
    - raw_anon_1111 9mo ago
      > You are not understanding the point. AI has to be properly supervised because it makes mistakes. It been well over a decade (ironically until AI) that I have been responsible for an implementation small enough that I could do with my own two hands within the allotted time by myself. People also require supervision. Now if you are making more or as many mistakes as the AI, then you should look for a new career. Right now in 2026, the code I write is dependent on the AWS SDK. https://boto3.amazonaws.com/v1/documentation/api/latest/index.html https://boto3.amazonaws.com/v1/documentation/api/latest/inde... It’s the same surface area for every supported language - as new features come out AWS uses code gen tools to update the SDK and CLI based on a common set of specifications. Consider it’s the same surface area as when I have to write IAC. Are you saying it should be faster for me to know that API and write it by hand than an LLM? Especially now that AWS has an MCP server that AI can use to know the latest documentation. The case was the same in 2000 when I had to write C and C++ against the Win32 APIs, DCOM etc or when I didn’t know the entire C++ STL and had to reference the “C++ programming language ”. > You should understand the code better than the AI, because the AI has a limited context window, and for a large codebase, you should know that codebase better than the AI. Every developer should know the entire codebase even when they first join a company? Even when I was working at a 70 person startup, that had three teams not one person knew the entire codebase. > But you -- you as a developer -- need to understand your codebase. If you do not understand the codebase, you can't properly supervise the AI. Again how large of a codebase is a developer suppose to know? S3 for instance is made up of 300 separate microservices. Is a single developer suppose to know how it all works? Besides that’s why we have unit tests and should be writing modular code so you don’t have to worry about spooky action at a distance. And people are so focused on “the code”. I have designed systems and architectures that include code and multiple teams and consulting companies. Am I suppose to know how all of the code works? > And there is one efficient way for you to understand a big complicated codebase. The most efficient way possible for you to learn it. That is by you writing code in that codebase, and debugging that code, and learning how to code in that codebase. Before AI, when coming into a team, I didn’t just start “coding” I would ask other people. Now I ask AI. Are you really saying that you can keep more of the code in your head than AI with a one million token context window or that you can read and understand various markdown files in a repo faster?