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Are you the wallflower from the legendary fugg efnet rap video https://www.youtube.com/watch?v=evBderwVKKE
Can you email me? Would love to chat/get more tunes from you.
That's all that is really required. I mean look at the Microslop fiasco. They ruined a perfectly good editor: Notepad with AI slop. But this is not reflecting in their sales. They still are showing record revenues.
Just because a competing product exists does not mean your product is suddenly obsolete. There will always be people who will want to buy (provided the market is not oversaturated). Because that is how humans do things. AI won't change that behavior overnight [1]. Look around you and you will see every product you hold in your hand has at least 5-10 competitors.
[1] Think about all the things that are still not computerized and which requires you to fill some or the other form of paperwork. We have had computers for over nearly 6 decades now. We STILL have physical forms that we fill from time to time. Computerization was touted to revolutionize this and yet here we are. Still not achieved 100% digitalization. The same will happen with AI as well. There is this initial burst of excitement (which is the phase we are in) until reality sets in and that's when people will learn how to best use the technology. What you are seeing today (vibe coding et all) is NOT IT.
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> Why can’t LLMs understand the big picture? Because LLMs don't understand things to begin with. Because LLMs only have access to aource code and whatever .md files you've given them. Because they have biases in their training data that overfit them on certain solutions. Because LLMs have a tiny context window. Because LLMs largely suck at UI/UX/design especially when they don't have referense images. Because...
> Because LLMs don't understand things to begin with. Ok, that’s fair. But I think the comment was making a distinction between the big picture and other types of “understanding.” I agree that it is incorrect to say LLMs understand anything, but I think that was just an informal turn of phrase. I’m saying I don’t think there’s something special about “big picture” information processing tasks, compared to in-detail i…
LLMs can do neither reliably because to do that you need understanding which LLMs don't have. You need to learn from the codebase and the project, which LLMs can't do.
On top of that, to have the big picture LLMs have to be inside your mind. To know and correlate the various Google Docs and Figma files, the Slack discussions, the various notes scattered on your system etc.
They can't do that either because, well, they don't understand or learn (and no, clawdbot will not help you with that).
> The other objections seem mostly to be issues with current tooling, or the sort of capacity problems that the LLM developers are constantly overcoming.
These are not limitations of tooling, and no, LLM developers are not even close to overcoming, especially not "constantly". The only "overcoming" has been the gimmicky "1 million token context" which doesn't really work.
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Finding edge cases is completely orthogonal to creating maintainable software. Finding edge cases ~= identifying test suites. Making software maintainable ~= minimizing future cost of effective changes. Ignoring future maintenance cost because test suites are easier to create seems like disjointed logic.
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So what you are a saying is that you removed the people who can make the decisions that keep your software maintainable and kept the people who will slowly over time cause your software to become less maintainable? I'm not sure that tradeoff is a a good one.
This might have been true pre-agent AI programming, but honestly the code seems better than ever. It finds edge cases better than me. I know... I know buddy. The world changed and I don't know if I'm going to have a job.
I tried teaching all of that in system prompts and documentation and it blows the context window to an unusable size. As such the things that as a high level experienced senior engineer I have been expected to do pre-agents I am still expected to do.
If you are eliminating those people from your business then I don't know that I can ever trust the software your company produces and thus how I could ever trust you.
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"Im not even sure we will need maintain software" (sic) - I'm not sure what your specific background is, but with a statement like that you lose all legitimacy to me.
"but who will maintain it?" massive massive question, rapidly becoming completely irrelevant
"but who will review it?" humans sure, with the assistance of ai, writing is also on the wall: AI will soon become more adept at code review than any human
I can understand "losing all legitimacy" being a thing, but to me that is an obvious knee jerk reaction to someone who is not quite understanding how this trend curve is going.
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This might have been true pre-agent AI programming, but honestly the code seems better than ever. It finds edge cases better than me. I know... I know buddy. The world changed and I don't know if I'm going to have a job.
I'm every bit as immersed in this as you are. I've been developing my own custom claude code plugins that allow me to delegate more and more the agents. But the one thing the agent is not reliably doing for me is making sound architectural choices and maintaining long term business context and how it intersects with those architectural choices. I tried teaching all of that in system prompts and documentation and it b…
I completely agree with you, but this is rapidly becoming less and less the case, and would not at all surprise me if even by the end of this year its just barely relevant anymore.
> If you are eliminating those people from your business then I don't know that I can ever trust the software your company produces and thus how I could ever trust you.
I mean thats totally fine, but do realize many common load bearing enterprise and consumer software products are a tower of legacy tech debt and junior engineers writing terrible abstractions. I don't think this "well how am I going to trust you" from (probably rightfully) concerned senior SWEs is going to change anything. s
LLMs can't be strategic because they do not understand the big picture -- that the real work of good software is balancing a hundred different constraints in a way that produces the optimal result for the humans who use it. It's not all that different from the state of big corp software today! Large organizations with layers of management tend to lose all abiliy to keep a consistent strategy. They tend to go all in o…
There are just so many small decisions that add up to a consistent vision for a piece of software. It doesn't seem like LLMs are going to be able to meaningfully contribute to that in the near future.
I tried vibecoding my own workout tracker, but there were so many small details to think through that it was frustrating. I gave up and found an app that is clearly made by a team of experienced, thoughtful people and AI can't replicate the sheer thoughtfulness of every decision that was made to create this app. The inputs for reps/sets, algorithms for adjusting effort on the fly, an exercise library with clear videos and explanations; there's just no way to replicate that without people who have been trainers and sport scientists for decades.
LLMs can help increase the speed that these details turn in to something tangible, but you definitely can't "skip all that crap and just jump to the end and get on with it."
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"Im not even sure we will need maintain software" (sic) - I'm not sure what your specific background is, but with a statement like that you lose all legitimacy to me.