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So, basically, you think all the pro-AI folks are "bad," and defensive because they feel like anti-AI folks are attacking the thing that makes them not bad? Ha

So, basically, you think all the pro-AI folks are "bad," and defensive because they feel like anti-AI folks are attacking the thing that makes them not bad? Hard to want to engage with that. I'll bite anyhow. AI is very, very good at working at short length-scales. It tends to be worse at working at longer length-scales (Gemini is a bit of an outlier here but even so, it holds). People who are hyper-competent/elite-skill in their domain who achieve force multiplication with gen-AI understand this, and know how to decompose challenging long length-scale problems into a number of smaller short-length scale problems efficiently. This isomorphic transform allows AI to tackle the original problem in a way that it's maximally efficient at, thus side-stepping their inherent weaknesses. You can think of this sort of like mathematical transformations that make data analysis easier.

mewpmewp2 1y ago
Yeah, exactly, shorter files, good context for the AI agents, good rules for the AI agents how to navigate around the codebase, reuse functions, components, keep everything always perfectly organized, with no effort from the person themselves. It is truly amazing to witness. No people can match that ability to reuse and organize.
johnnyanmac 1y ago
>So, basically, you think all the pro-AI folks are "bad," and defensive because they feel like anti-AI folks are attacking the thing that makes them not bad? I break the pro-AI crowd into 3 main categories and 2 sub categories: 1. those who don't really know how to code, but AI lets them output something more than what they could do on their own. This seems to be what the GP is focused on 2. The ones who can code but are financially invested to hype up the bubble. Fairly self explanatory; the market is rough and if you're getting paid the big bucks to evangelize, it's clear where the interests lie. 3. Executives and product teams that have no actual engagement with AI, but know bringing it up excites investors. a hybrid of 1 and 2, but they aren't necessarily pretending they use it themselves. It's the latest means to an end (the end being money). and then the smaller sects: 1. those who genuinely feel AI is the future and is simply prepping for it and trying to adapt their workflow and knowledged based around it. They may feel it can already replace people, or may feel it's a while out but progressing that way. These are probable the most honest party, but I personally feel they miss a critical aspect: what is used currently as the backbone for AI may radically change by the time it is truly viable. 2. those who are across the spectrum of AI, but see it as a means to properly address the issue of copyright. If AI wins, they get their true goal of being able to yoink many more properties without regulations to worry about. >People who are hyper-competent/elite-skill in their domain who achieve force multiplication with gen-AI understand this, are their real examples of this? The main issue I see is that people seem to judge "competency" based on speed and output. But not on the quality, maintainability, nor conciseness of such output. If we just needed engineers to slap together something that "works", we could be "more productive".
- CuriouslyC 1y ago
  Well, just to give you context on my position, because I don't feel I fit into any of those molds: I was already a very high performer before AI, leading teams, aligning product vision and technical capabilities, architecting systems and implementing at top-of-stack velocity. I have been involved in engineering around AI/ML since 2008, so I have pretty good understanding of the complexities/inconsistencies of model behavior. When I observed the ability of GPT3.5 to often generate working (if poorly written, in general) code, I knew this was a powerful tool that would eventually totally reshape development once it matured, but that I had to understand its capabilities and non-uniform expertise boundary to take advantage of its strengths without having to suffer its weaknesses. I basically threw myself fully into mastering the "art" of using LLMs, both in terms of prompting and knowing when/how to use them, and while I saw immediate gains, it wasn't until Gemini Pro 2.5 that I saw the capabilities in place for a fully agentic workflow. I've been actively polishing my agentic workflow since Gemini 2.5's release, and now I'm at the point where I write less than 10% of my own code. Overall my hand written code is still significantly "neater/tighter" than that produced by LLMs, but I'm ok with the LLM nailing the high level patterns I outline and being "good enough" (which I encourage via detailed system prompts and enforce via code review, though I often have AI rewrite its own code given my feedback rather than manually edit it). I liken it to assembly devs who could crush the compiler in performance (not as much of a thing now in general, but it used to be), who still choose to write most of the system in c/c++ and only implement the really hot loops in assembly because that's just the most efficient way to work.
  - latchup 1y ago
    > I was already a very high performer before AI, leading teams, aligning product vision and technical capabilities, architecting systems and implementing at top-of-stack velocity Indeed, he did not list "out-of-touch suit-at-heart tech leads that failed upwards and have misplaced managerial ambitions" as a category, but that category certainly exists, and it drives me insane.
    - CuriouslyC 1y ago
      You're making a lot of baseless assumptions with your implication there, and sadly it says more about you than me. You might find your professional development would stop being retarded if you got the chip off your shoulder and focused on delivering maximum value to the organizations that employ you in any way possible.

mewpmewp2 1y ago

Yeah, exactly, shorter files, good context for the AI agents, good rules for the AI agents how to navigate around the codebase, reuse functions, components, keep everything always perfectly organized, with no effort from the person themselves. It is truly amazing to witness. No people can match that ability to reuse and organize.

johnnyanmac 1y ago

>So, basically, you think all the pro-AI folks are "bad," and defensive because they feel like anti-AI folks are attacking the thing that makes them not bad? I break the pro-AI crowd into 3 main categories and 2 sub categories: 1. those who don't really know how to code, but AI lets them output something more than what they could do on their own. This seems to be what the GP is focused on 2. The ones who can code but are financially invested to hype up the bubble. Fairly self explanatory; the market is rough and if you're getting paid the big bucks to evangelize, it's clear where the interests lie. 3. Executives and product teams that have no actual engagement with AI, but know bringing it up excites investors. a hybrid of 1 and 2, but they aren't necessarily pretending they use it themselves. It's the latest means to an end (the end being money). and then the smaller sects: 1. those who genuinely feel AI is the future and is simply prepping for it and trying to adapt their workflow and knowledged based around it. They may feel it can already replace people, or may feel it's a while out but progressing that way. These are probable the most honest party, but I personally feel they miss a critical aspect: what is used currently as the backbone for AI may radically change by the time it is truly viable. 2. those who are across the spectrum of AI, but see it as a means to properly address the issue of copyright. If AI wins, they get their true goal of being able to yoink many more properties without regulations to worry about. >People who are hyper-competent/elite-skill in their domain who achieve force multiplication with gen-AI understand this, are their real examples of this? The main issue I see is that people seem to judge "competency" based on speed and output. But not on the quality, maintainability, nor conciseness of such output. If we just needed engineers to slap together something that "works", we could be "more productive".

CuriouslyC 1y ago
Well, just to give you context on my position, because I don't feel I fit into any of those molds: I was already a very high performer before AI, leading teams, aligning product vision and technical capabilities, architecting systems and implementing at top-of-stack velocity. I have been involved in engineering around AI/ML since 2008, so I have pretty good understanding of the complexities/inconsistencies of model behavior. When I observed the ability of GPT3.5 to often generate working (if poorly written, in general) code, I knew this was a powerful tool that would eventually totally reshape development once it matured, but that I had to understand its capabilities and non-uniform expertise boundary to take advantage of its strengths without having to suffer its weaknesses. I basically threw myself fully into mastering the "art" of using LLMs, both in terms of prompting and knowing when/how to use them, and while I saw immediate gains, it wasn't until Gemini Pro 2.5 that I saw the capabilities in place for a fully agentic workflow. I've been actively polishing my agentic workflow since Gemini 2.5's release, and now I'm at the point where I write less than 10% of my own code. Overall my hand written code is still significantly "neater/tighter" than that produced by LLMs, but I'm ok with the LLM nailing the high level patterns I outline and being "good enough" (which I encourage via detailed system prompts and enforce via code review, though I often have AI rewrite its own code given my feedback rather than manually edit it). I liken it to assembly devs who could crush the compiler in performance (not as much of a thing now in general, but it used to be), who still choose to write most of the system in c/c++ and only implement the really hot loops in assembly because that's just the most efficient way to work.
- latchup 1y ago
  > I was already a very high performer before AI, leading teams, aligning product vision and technical capabilities, architecting systems and implementing at top-of-stack velocity Indeed, he did not list "out-of-touch suit-at-heart tech leads that failed upwards and have misplaced managerial ambitions" as a category, but that category certainly exists, and it drives me insane.
  - CuriouslyC 1y ago
    You're making a lot of baseless assumptions with your implication there, and sadly it says more about you than me. You might find your professional development would stop being retarded if you got the chip off your shoulder and focused on delivering maximum value to the organizations that employ you in any way possible.