151–160 of 185 posts
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I said "the majority of software development". Those are both relatively niche disciplines in 2025.
Can you please explain? Are you saying all software development outside of the web is "niche"?
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The test loop is integral. It’s kind of annoying hearing all this skepticism from people putting in the least effort into optimally using the tool. There is a learning curve. Every month I’ve gotten better results than the last because I’m constantly context building and refining, understanding how, what and when to prompt. It’s like hearing someone say database suck but they haven’t bothered to learn about or use in…
Most of the mentioned issues wouldn't be catched by a test loop unless you have 100% automated tests (unit tests, ...) Which isn't always plausible ( time ). The AI makes makes different mistakes than humans that are sometimes harder to catch.
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> LLMs are remarkably good at writing code. Just this past weekend, I've designed and written code (in Typescript) that I don't think LLMs can even come close to writing in years. I have a subscription to a frontier LLM, but lately I find myself using like 25% of the time. At a certain level the software architecture problems I'm solving, drawing upon decades of understanding about maintainable, performant, and verif…
Maybe if you mentioned a more complex, lower level or niche language than typescript like maybe C, MIPS or some niche exotic systems language pushing around registers. I'd believe yu, with caveat, but with abstract high level abstract languages like Python, typescript and the likes? It's highly unlikely that you would've put together syntax in any uniquely surprising combination. Maybe yu mean yu designed a clever fi…
For example, the new shortest path algorithm that eclipses Dijkstra's is conceptual advance; it can be written in any Turing-complete language, and it's discovery had nothing to do with inventing new syntax in any specific language.
You comment betrays the literal/concrete understanding of coding that is a hallmark of novices. It's like saying as long as LLMs can write any kind of musical notation, there is no way a human can be a better composer.
I have not said an LLM cannot the same syntax or code patterns I write; I'm saying it, for instance, is poor at figuring out stuff like: How do I write types to enforce which entities and which fields and which roles are allowed for this action at compile-time? Should I use a generator, iterator, or recursive function for such and such functionality? Should this function be generic or not? How do I design my query fluent interface for the best performance? What should be the folder organization for this module that makes it intuitive to navigate and maintain? What is the best name for that function that will make it most intuitive to use? etc.
Anyone saying such concerns have anything to do with whether I'm using Typescript vs C or Haskell does not understand software engineering.
I've found it takes significant time to find the right "mode" of working with AI. It's a constant balance between maintaining a high-level overview (the 'engineering' part) while still getting that velocity boost from the AI (the 'coding' part).
The real trap I've seen (and fallen into) is letting the AI just generate code at me. The "engineering" skill now seems to be more about ruthless pruning and knowing exactly what to ask, rather than just knowing how to write the boilerplate.
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This may be a timeframe issue but I sincerely doubt anyone wants to hire someone to be an intermediary. They just want the thing done. I know that right now few want to sit in front of claude code, but it's just not that big of a leap to move this up a layer. Workflows do this even without the models getting better.
YouTube can show anyone how to unblock a sink. Most people still choose to call a plumber.
True for now because models are mainly used to implement features / build small MVPs, which they’re quite good at. The next step would be to have a model running continuously on a project with inputs from monitoring services, test coverage, product analytics, etc. Such an agent, powered by a sufficient model, could be considered an effective software engineer. We’re not there today, but it doesn’t seem that far off.
I agree that tooling is maturing towards that end. I wonder if that same non-technical person that built the MVP with GenAI and requires a (human) technical assistance today, will need it tomorrow as well. Will the tooling be mature enough and lower the barrier enough for anyone to have a complete understanding about software engineering (monitoring services, test coverage, product analytics)?
That's what every no-programming-needed hyped tool has said. Yet here we are, still hiring programmers.
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I think C# is really going to shine in the LLM coding era. You can write Roslyn Analyzers to fail the build on arbitrary conditions after inspecting the AST. LLMs are great at helping you write these too. If you get a solid architecture well defined you can then use these as guardrails to constrain development to only happen in the manner you intend. You can then get LLMs to implement features and guarantee the code…
Linters are great at catching specific pattern violations, but they’re useless against bad decomposition or a poorly chosen abstraction. An LLM can generate code that passes all 100 linters and still ends up being a logical mess - with business logic in the wrong layer and completely unmaintainable.
I've been forcing myself to "pure vibe-code" on a few projects, where I don't read a single line of code (even the diffs in codex/claude code). Candidly, it's awful. There are countless situations where it would be faster for me to edit the file directly (CSS, I'm looking at you!). With that said, I've been surprised at how far the coding agents are able to go[0], and a lot less surprised about where I need to step i…
>> Codex (gpt-5-codex) on the other hand, does exactly what you ask, almost to a fault. I've seriously tried gpt-5-codex at least two dozen times since it came out, and every single time it was either insufficient or made huge mistakes. Even with the "have another agent write the specs and then give it to codex to implement" approach, it's just not very good. It also stops after trying one thing and then says "I've t…
I've spent quite a bit of time with the normal GPT-5 in Codex (med and high reasoning), so my perspective might be skewed!
Oh, one other tip: Codex by default seems to read partial files (~200 lines at a time), so I make sure to add "Always read files in full" to my AGENTS.md file.