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>I want to start by saying that I’m neither an AI-fanatic
Kind of like saying you are a fanatic before saying you aren't.
I don't think theres too much here (e.g. "spec driven development") I haven't seen elsewhere.
Earlier quoted context omitted.
I used LLMs to develop Whistle Enterprise ( https://whistle-enterprise.com ) from the ground up, from scratch. It's taken _a lot_ of time and effort, but this is an example of what can be developed using LLMs alone. You have to have dedication and a goal to reach, but you can absolutely build anything if you're building with the right foundations in mind.
I think the relevant question isn’t what can be built but the amount of effort in comparison to doing this the old fashioned way. What do you think the productivity gain was from using an LLM? This question assumes you’re already an experienced developer.
In fact, it's far beyond what I would even attempt, because I've just spent two decades building up a data bank of how hard things are supposed to be.
He doesn't know it's supposed to be hard, so he just does it.
First nobody sane want to give their domain IP to OpenAI/Anthropic. That's why local AI will eventually prevail and flourish because people who actually have some IP will have no problem to buy 10k+ EUR machine to run some pretty good models on it. However if your main job is just doing CRUD stuff, then you are screwed.
Secondly hallucination is really Achilles heel of every LLM. Sure you can recreate an application which exists in thousand of variations on the internet, but the moment you will try to go more into domain knowledge you will start struggling more and more.
Try to make CAN driver for ESP32, easy it is probably going to work. Try to make CAN driver for STM32F7xx now the AI will start having a problem but probably will be able to produce something what is working after a lot of debugging. Now let's make CAN driver for MPC5555. AI will start writing fairy tales about registers which do not exist. All of processor above have reference manuals and sometimes example git repositories available on open internet.
I don't know if I’m overly critical but there’s gotta be a middle ground between totally AI pilled people that otherwise have no talents, and control freak veteran developers who cant let go My current process is also using Github projects in a normal scrum style way, with many tickets written or fleshed out and state managed by the LLM, and it doubling as the memory system Completely leapfrogging all these other ope…
Sorry access to an LLM (even if it could center a div reliably and make a responsive designs, it can't) does not give you taste, intuition or make you good at building user interfaces. You people/sloppers have no idea the amount of sweat that gets poured into great UX.
Its insulting when you people say these things and Im not even a designer or frontend dev.
I actually think UI/UX designers and devs will be the last to fall. I will want beautiful products that were built by beautiful minds, thats how you will set yourself apart from the slop. And fortunately it will be even easier when 80% of everything is half assed cranked out UI by llm design tools. The contrast is already glaring.
I don't know if I’m overly critical but there’s gotta be a middle ground between totally AI pilled people that otherwise have no talents, and control freak veteran developers who cant let go My current process is also using Github projects in a normal scrum style way, with many tickets written or fleshed out and state managed by the LLM, and it doubling as the memory system Completely leapfrogging all these other ope…
All these people saying UI/UX is dead, then I see their designs and they're absolutely the worst (but they're always swearing by how incredible it is). Sorry access to an LLM (even if it could center a div reliably and make a responsive designs, it can't) does not give you taste, intuition or make you good at building user interfaces. You people/sloppers have no idea the amount of sweat that gets poured into great UX…
Interestingly, despite it being much more detailed and a lot more process and procedure than what I currently do - which is more akin to the version 0 described, but in parallel - we come up at the same final problem: reviews and quality assurance. I sign off the code I merged, part of company policy but also just to be sure it is actually decent. But reviewing has become the real draining bottleneck: even stacked PR…
Proper review should take longer than writing it yourself, because you need to know the correct solution, understand the proposed solution, and evaluate the difference between the two. When designing it yourself, you just need to know the correct solution and write it, and with modern high-level languages and IDEs with autocomplete writing it is hardly a bottleneck.
Interestingly, despite it being much more detailed and a lot more process and procedure than what I currently do - which is more akin to the version 0 described, but in parallel - we come up at the same final problem: reviews and quality assurance. I sign off the code I merged, part of company policy but also just to be sure it is actually decent. But reviewing has become the real draining bottleneck: even stacked PR…
They can be still be applied now using coding agents, if you're willing to push back against the default setup and change your mode of thinking a little bit. Of course it doesn't help that an entire industry is dedicated to persuading us that maximizing token spend is the only way to get shit done.
I appreciate this probably seems like an extremist take, but I wrote some more about it here in case there's anybody out there who identifies with it:
I wish people would describe in more detail the tasks they use LLMs to code. My experience is that simple components in an existing architecture are fine, but anything requiring architectural considerations quickly becomes a mess. On my projects (e.g. a ui framework), running multiple agents in parallel would just increase the speed at which it can stuff up the project.