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  Aurornis
  Refreshing to see an honest and balanced take on AI coding. This is what real AI-assisted coding looks like once you get past the initial wow factor of having the AI write code that executes and does what you asked.This experience is familiar to every serious software engineer who has used AI code gen and then reviewed the output:> But when I reviewed the codebase in detail in late January, the downside was obvious: the codebase was complete spaghetti14. I didn’t understand large parts of the Python source extraction pipeline, functions were scattered in random files without a clear shape, and a few files had grown to several thousand lines. It was extremely fragile; it solved the immediate problem but it was never going to cope with my larger vision,Some people never get to the part where they review the code. They go straight to their LinkedIn or blog and start writing (or having ChatGPT write) posts about how manual coding is dead and they’re done writing code by hand forever.Some people review the code and declare it unusable garbage, then also go to their social media and post how AI coding is completely useless and they’re not going to use it for anything.This blog post shows the journey that anyone not in one of those two vocal minorities is going through right now: A realization that AI coding tools can be a large accelerator but you need to learn how to use them correctly in your workflow and you need to remain involved in the code. It’s not as clickbaity as the extreme takes that get posted all the time. It’s a little disappointing to read the part where they said hard work was still required. It is a realistic and balanced take on the state of AI coding, though.
• lubujackson
  Long term, I think the best value AI gives us is a poweful tool to gain understanding. I think we are going to see deep understanding turn into the output goal of LLMs soon. For example, the blocker on this project was the dense C code with 400 rules. Work with LLMs allowed the structure and understanding to be parsed and used to create the tool, but maybe an even more useful output would be full documentation of the rules and their interactions.This could likely be extracted much easier now from the new code, but imagine API docs or a mapping of the logical ruleset with interwoven commentary - other devtools could be built easily, bug analysis could be done on the structure of rules independent of code, optimizations could be determined on an architectural level, etc.LLMs need humans to know what to build. If generating code becomes easy, codifying a flexible context or understanding becomes the goal that amplifies what can be generated without effort.
• rokob
  > architecture is what happens when all those local pieces interact, and you can’t get good global behaviour by stitching together locally correct componentsThis is a great article. I’ve been trying to see how layered AI use can bridge this gap but the current models do seem to be lacking in the ambiguous design phase. They are amazing at the local execution phase.Part of me thinks this is a reflection of software engineering as a whole. Most people are bad at design. Everyone usually gets better with repetition and experience. However, as there is never a right answer just a spectrum of tradeoffs, it seems difficult for the current models to replicate that part of the human process.
• pwr1
  This resonates. I had a project sitting in my head for years and finally built it in about 6 weeks recently. The AI part wasn't even the hard part honestly, it was finally commiting to actually shipping instead of overthinking the architecture. The tools just made it possible to move fast enough that I didn't lose momentum and abandon it like every other time.
• PaulHoule
  Note I believe this one because of the amount of elbow grease that went into it: 250 hours! Based on smaller projects I’ve done I’d say this post is a good model for what a significant AI-assisted systems programming project looks like.
• DareTheDev
  This is very close to my experience. And I agree with the conclusion I would like to see more of this
• edfletcher_t137
  > Of all the ways I used AI, research had by far the highest ratio of value delivered to time spent.Seconded!
• bvan
  This a very insightful post. Thanks for taking the time to share your experience. AI is incredibly powerful, but it’s no free-lunch.
• billylo
  Thank you. The learning aspect of reading how AI tackles something is rewarding.It also reduces my hesitation to get started with something I don't know the answer well enough yet. Time 'wasted' on vibe-coding felt less painful than time 'wasted' on heads-down manual coding down a rabbit hole.
• simondotau
  This essay perfectly encapsulates my own experience. My biggest frustration is that the AI is astonishingly good at making awful slop which somehow works. It’s got no taste, no concern for elegance, no eagerness for the satisfyingly terse. My job has shifted from code writer to quality control officer.Nowhere is this more obvious in my current projects than with CRUD interface building. It will go nuts building these elaborate labyrinths and I’m sitting there baffled, bemused, foolishly hoping that THIS time it would recognise that a single SQL query is all that’s needed. It knows how to write complex SQL if you insist, but it never wants to.But even with those frustrations, damn it is a lot faster than writing it all myself.
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  myultidevhq
  The 8-year wait is the part that stands out. Usually the question is "why start now" not "why did it take 8 years". Curious if there was a specific moment where the tools crossed a threshold for you, or if it was more gradual.
• The_Goonies1985
  The author mentions a C codebase. Is AI good at coding in C now? If so, which AI systems lead in this language?Ideally: local; offline.Or do I have to wrestle it for 250 hours before it coughs up the dough? Last time I tried, the AI systems struggled with some of the most basic C code.It seemed fine with Python, but then my cat can do that.
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  zer00eyz
  This article is describing a problem that is still two steps removed from where AI code becomes actually useful.90 percent of the things users want either A) dont exist or B) are impossible to find, install and run without being deeply technical.These things dont need to scale, they dont need to be well designed. They are for the most part targeted, single user, single purpose, artifacts. They are migration scripts between services, they are quick and dirty tools that make bad UI and workflows less manual and more managable.These are the use cases I am seeing from people OUTSIDE the tech sphere adopt AI coding for. It is what "non techies" are using things like open claw for. I have people who in the past would have been told "No, I will not fix your computer" talk to me excitedly about running cron jobs.Not everything needs to be snap on quality, the bulk of end users are going to be happy with harbor freight quality because it is better than NO tools at all.
• 4b11b4
  Great write-up with provenance
• techpulselab
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• TraceAgently
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• meidad_g
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• alejandrosplitt
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  rlenf
  [flagged]
• anon
  undefined
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  intensifier
  article looks like a tweet turned into 30 paragraphs. hardly any taste.