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> The coding agents got really good It's since november 2025, the so called "inflection point", that I'm still wondering for who coding agents become "really g

> The coding agents got really good It's since november 2025, the so called "inflection point", that I'm still wondering for who coding agents become "really good". All I observe they got better at tool call and answering questions about big codebases, especially if the question has a vague pattern to search, and they're superuseful for that! For generating production code even with a lot of steering and baby sitting? Absolutely not, not quite there not even close in my experience. But we should stop talking about 1s and 0s, especially with marketing hype trains, there exist a gradient of capabalities that agents have that really depends on the intricacies of the codebase you're working on, I think everyone has yet to discover how to better apply these tools in their day to day work. But that totally collides with the current narrative, that flattens out our work to be always the same and that can be automated easily in each case, it's not! That's why the debate is so polizered imo, there isn't a shared experience

Razengan 5mo ago
> It's since November 2025, the so called "inflection point", that I'm still wondering for who coding agents become "really good". You can dig up my past comments semi-arguing with simonw where I said AI just isn't good enough yet, but lately I've been using Codex mostly just to review existing Godot/GDScript code: https://github.com/InvadingOctopus/comedot https://github.com/InvadingOctopus/comedot and now I'd say that in this day and age one would have to be dumb to not use AI in SOME way :) It's helped me catch a lot of bugs that would have taken me a long time to even notice on my own. I guess it helps that the project is modular enough where most files can be considered standalone, with just 1-2 dependencies and well-commented already, so the AI can look at each file on its own one at a time. You can see the AGENTS.md I use on that repo. Most of my productivity in the last 3 or so months has been thanks to AI, though none of the code there is AI generated. I even bought a MacBook Neo just to use as an "AI thin client" while on travel, even though I already had a beefy MacBook Pro M2 Max that I just keep at home/hotel as a desktop now. Codex's recent remote control features have made it more useful for the moments when I get a cool idea while out at a cafe or on a walk. I don't just copy-paste the AI's output, because it's often inefficient anyway (like creating redundant variables/functions), but I find its findings useful for manually cleaning up my shit. Maybe their training data is not that good with GDScript yet which is a bit of a jank language anyway. So my core code is wholly made by meat, but I do have fun now and then telling Codex to make experimental games using only the library of modular components I have written so far, to test my framework and also the AI's abilities. This kind of work seems like a surprisingly good match for AI: It just has to put existing blocks together, that already have well-defined interfaces/contracts etc. I've been on the $20 ChatGPT plan for about a year now, and only started using Codex since like maybe 4 months ago, almost always on the latest model with "Extended Thinking" or "Extra High", because I want my shared code to be as correct as possible because everything else I do depends on it, and I only hit limits like 2 times in the last 3 months. Claude on the other hand, terrible: https://i.imgur.com/jYawPDY.png https://i.imgur.com/jYawPDY.png Grok is OK for general stuff, never tried it for coding. Gemini's UI/UX and lack of privacy and the AI itself is so terrible I tried it just maybe 2 times ever...and it refused to work on Google's own Flights website and reverse image search! (it told me to do it myself) Deepseek refused to talk about Taiwan or Tiananmen Square so I'm not sure if I can trust it for anything else lol
- hollowturtle 5mo ago
  Thanks for sharing your experience! I totally agree that if you "own your code", as in you're invested in it, coding it and documenting it, these tools can be really valuable for review, bug fixing and maintenance, it pushes you to do better, maybe one piece at a time like you said with a good modularized codebase. I think more devs should share experiences like that, we should overthrow marketing and people narratives that "don't code anymore since X"
- jaccola 5mo ago
  I set up a hook that reviews every commit and highlights potential bugs (async) and writes to a report to a dir. Then I have a script that summarises that I usually run before pushing or at end of day. Works quite well for both improving my code and the code ai wrote.
- maccard 5mo ago
  > I've been on the $20 ChatGPT plan for about a year now, and only started using Codex since like maybe 4 months ago, almost always on the latest model with "Extended Thinking" or "Extra High", because I want my shared code to be as correct as possible because everything else I do depends on it, and I only hit limits like 2 times in the last 3 months. I've recently tried codex, and I have it set to plan mode with 5.5 and I'm hitting the limits on a single task on a "medium" sized codebase.
  - Razengan 5mo ago
    Like I said most of my prompts cover 1-3 files at most, rarely more
kstenerud 5mo ago
The polarization comes from the very disparate coding experiences and output quality that different people find when using these tools. For example, I've had the opposite experience of yours, generating very high quality work using Claude (such as https://github.com/kstenerud/yoloai https://github.com/kstenerud/yoloai). Just in dealing with all the bugs and idiosyncrasies in the technologies I'm using, the agent has been a godsend in discovering and cataloguing them so that the implementation phase doesn't keep tripping over them: https://github.com/kstenerud/yoloai/blob/main/docs/dev/backend-idiosyncrasies.md https://github.com/kstenerud/yoloai/blob/main/docs/dev/backe... And the agents keep getting better all the time. Even in the past month I've noticed a considerable jump in its ability to anticipate issues and correctly infer implications as we build out research, design, architecture and planning docs. By the time it comes to coding, it's mostly a mechanical process that can be passed off to sonnet with a negligible defect rate.
- hollowturtle 5mo ago
  Don't want to be rough, but I'd like to read experiences about novelty ideas that solve people real problems in the real world, your project it's just about selling new shovels. As I commented on another thread > If you're trying to solve a HARD problem people REALLY have, it's a novelty that agents can't help with, otherwise if it gets 97% there MAYBE it's just a signal that your idea isn't that novel!
  - paulluuk 5mo ago
    This is a pretty wild take. What percentage of human engineers are creating novel solutions for hard problems, you think? I work in R&D and even my work is 90% doing things that other people already solved. If you are really doing cutting edge SOTA work that has never been done by another human in some form or another, then kudos to you and I want your job.
    - hollowturtle 5mo ago
      > What percentage of human engineers are creating novel solutions for hard problems, you think? IMO Every engineer should try spending his time in a company that tries to solve new problems. Otherwise we will be stuck, as we are now, with big tech paying you mountains of money for doing nothing, incentivizing you to embark on useless activities for letting other managers have a career, fear layoffs and when that happen complaining about it because "it's a year i'm looking for a new job" pretending same compensation and environment. Web development jobs are particularly affected by that. In the game industry, for example, if you don't do something interesting your game won't sell a copy. Let me stress this out again, if LLMs get you 97% there, maybe you should try another idea.
treme 5mo ago
you are experiencing reverse Dunning–Kruger effect. For someone that just dabbled in coding prior, it went from AI building 80%, and struggling through to finish the 20% when trying to build an app/website. now it's like 97% and struggling with last 3%. Yes it'll look rough around the edges when evaulated by a senior dev, but being able to build MVP level things to completion with ease helps you stay engaged and motivated to continue and learn.
- hollowturtle 5mo ago
  Please do not cite Dunning–Kruger effect at random. Who needs to generate a dumb demo of a 97% done crud app? We had code generators for those, everytime I read claims like that and I ask to explain further I then discover it's people who were not productive before generating the so called "MVP level things to completion with ease". If you're trying to solve a HARD problem people REALLY have, it's a novelty that agents can't help with, otherwise if it gets 97% there MAYBE it's just a signal that your idea isn't that novel! LLMs can effectively validate your business idea
  - jaccola 5mo ago
    The obvious pushback to all of the slop is: coding was never hard. Learning resources were abundant and free. If these people had a burning desire to build things prior to LLMs and couldn’t put in the effort to learn to build them (which is also fun!) then why would they ever put the effort into anything to understand it and make it good??
    - layer8 5mo ago
      > coding was never hard. Learning resources were abundant and free. Just a nitpick regarding “never”: Learning resources weren’t abundant and free 25 years ago, that’s a more recent development.
      - skydhash 5mo ago
        Maybe in some parts of the world (including mine). But we haven’t have a lot of computers either. But 25 years ago, there was a lot of textbooks and computers editors like O’Reilly already active. I had the C programming language book (not 25 years ago, but the book is older than that) and you could learn a lot with that one book and codeblocks. Same thing with “The Go Programming Language”, “Learning Perl”, and “Programming Clojure”. You only need one book to get very decent.
nayroclade 5mo ago
> It's since november 2025, the so called "inflection point", that I'm still wondering for who coding agents become "really good". The answer is "for lots of people, but not you". You're doing a vague impression of being fair and even-handed, arguing for non-polarization, but underlying everything you're saying is an obvious attitude of poralizing superiority: That _your_ personal experience with AI is the real truth. That _your_ codebase is more intricate and more challenging than what other people are doing. That everyone else is being led by a "marketing hype train".
- new_account_100 5mo ago
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- hollowturtle 5mo ago
  When I say > Absolutely not, not quite there not even close in my experience. I obviously mean in my experience, not the real truth. > That everyone else is being led by a "marketing hype That is obvious instead, and I later say there's not 0s or 1s, every job has his intrincancies
hathawsh 5mo ago
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ncruces 5mo ago
I don't know that there was an inflection point. I know that, over the past year, they definitely became useful to me as more than auto complete. My most recent pet project is a transpiler from Wasm to Go, and I find it incredibly impressive that recent models (I've used Sonnet, Opus and Gemini, far more successfully than GPT), they're able to just pick up the project and work at all these levels: - Go code that implements the transpiler (parsing Wasm, building an AST) - Go code that gets generated by serializing the AST to a .go file - Go code that manipulates the AST (to optimize it), and its effect on the generated code - Go code that's grafted to the generated code (to implement more advanced opcodes) and how to interact with it from the AST - C code that gets compiled to Wasm, then translated to Go, then called by Go - Go code that gets called by this C code to implement a C stdlib - WAT and WAST files that are used to implement the Wasm spec tests I find this impressive because I have to think hard about all these levels, and I feel many programmers would have a problem with this. And it's very often way easier for me to just write: "I want to generate this code, build me the AST that does it", than go "count parenthesis" in the Go code (I do have some LISP experience; it's still easier). Feel free to scrutinize/criticize the code. Not vibe coded, but plenty of GenAI help. https://github.com/ncruces/wasm2go https://github.com/ncruces/wasm2go
netcan 5mo ago
Coding goodness is just "unevenly distributed." Irl, (a) different people's ways of working with ai are a million little islands and (b) bottlenecks vary enormously by coder and codebase/task. Also... I think our era has an intrinsic bias that change=progress, productivity, etc. Take the "networked computing revolution" of 1990-2000. These computers did land on every desk and every pocket. They are administration powerhouses. Excellent for all manner of administration tasks. But... what this netted out to is "change." We send a lot more emails than we did letters. We communicate a ton. Secretaries went extinct. But "administration" grew. A university faculty typically has more admins. Companies hire more accountants, HR, project managers, etc. Maybe administration was never really a bottleneck. Code has a lot of this. Everyone has a road map, wishlist, etc. It appears as though "code capacity" is the bottleneck. But maybe most of those companies can't really generate much more value from more software. Anecdotally, it seems that many mid-tier shops are migrating/ modernizing their stack, and suchlike. I haven't heard of many belting out features, and increasing prices or sales. Most bottlenecks are upstream of another bottleneck. Few are a "dam."
new_account_100 5mo ago
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benterix 5mo ago
I believe by now we know exactly what it's good at and what it's terrible at. The problem is that our CEO's fear of the future that pushes them to peculiar decisions that objectively make no sense (cf the infamous discussion of the Microsoft employee on Github that couldn't force its agent to do the proper thing). It's not the first time I witness this kind of discrepancy and probably not the last, I just learned to adapt to it.
epolanski 5mo ago
> But we should stop talking about 1s and 0s I agree, but you contradicted yourself just one line above. > For generating production code even with a lot of steering and baby sitting? Absolutely not Moreover this is further in contradiction with several facts: 1. the majority of this industry has always been composed by mediocre/bad developers, often unable to write a fizz buzz 2. the majority of work in this industry is implementing mundane CRUDs to move and transform trivial data across the organization's stakeholders and/or customer or third parties 3. there's lots of stellar and respected engineers leveraging the tools on a regular basis even on problems that are far from trivial and outputting quality code much faster than they would've done otherwise. Mitchell Hashimoto has blogged about it in his work on Ghostty, Sanfilippo has blogged about it in his work on Redis and so did plenty of others. I know several open source stellar developers who benefitted greatly from these tools, yet you think it cannot improve the quality and output of the most mundane tasks out there?
- hollowturtle 5mo ago
  >> But we should stop talking about 1s and 0s > I agree, but you contradicted yourself just one line above. >> > For generating production code even with a lot of steering and baby sitting? Absolutely not with this last sentence I obviously meant in my experience, it's not that hard. I don't buy your facts are highly biased towards web development, that's a common mistake here on HN to think it's the totality of the industry, luckily it's not
  - epolanski 5mo ago
    I've quoted you two tools (Ghostty and Redis) whose development now regularly uses AI assistance to deliver production code. I quoted those because their authors shared their experiences, the strengths and the limits of the tooling. There's many more, from Flask to Docker, from Ruby to FastAPI or Tanstack. LLVM has integrated AI-generated PRs, so did Swift and Mojo. Sasha Levin has pushed into Linux Nvidia-related kernel changes that were authored by LLMs in 6.15. You can be certain there's a magnitude more where people don't admit or tag their PRs as AI generated or co-generated. In fact I am quite confident that projects and developers that are not leveraging the tools are increasingly rare. There's really no reason in 2026 to write a non-trivial PR and not ask a cheap review to an AI tool. The industry is changing, I don't really like the trends I'm seeing, but to state that LLMs cannot and are not writing production code, very often quality ones, (especially when used, setup and overviewed properly) is plain denial. Your anecdotal experience isn't relevant, especially when applied to the largest parts of the industry, composed of mediocre developers working on terrible codebases.
Veelox 5mo ago
My explanation for the lack of shared experience is very language dependent quality. I work in Go and it's gotten really really good. I have to pick the right abstraction and it can be overly verbose at times but it can make in 5 minutes what would have taken me an hour.
Glohrischi 5mo ago
I had a really fun day yesterday because anthropics limits on their normal 20$ subscription allowed me to play around for the whole day without hitting a limit. Its 'production' code because its a small browser game which has very small to 0 requirements on security and being perfect but high requirements on 'ever even doing this' and 'fun'. The code it generated hat 0 compiletime errors. I was able to descripe 10 things to do in one task and it just jugged along solving all of them. This doesn't need to become so much better to be useful. Its already very useful for a lot ofuse cases like researchers which have to verify the math anyway but are not good in writing code for filtering their testdata, converting them and running it. Small websites, fun projects, helper tools etc. But while we speak, in the background stuff is still happening left and right. More compute, better algorithm, more RL etc. We could already be at 95% at 'ai will take your coding job' without knowing because these 5% are so relevant.
- hiw2d 5mo ago
  "We could already be at 95% at 'ai will take your coding job' without knowing because these 5% are so relevant." This is nonsense. Im not a SWE but a CEO, if that were true I'd be firing without a hitch. And yet this is not the activity we see. Why is that? Perhaps merely writing code is not the entire job.
  - Glohrischi 5mo ago
    I wrote coding job. And its true for coding jobs. Your Product Manager is not a coding job. Your Product Owner is not a coding job. vibe-kanban exists you could already do a proper experiment letting your PO maintain a vibe-kanban board with proper requirements and see how an agent progresses. But 5% is often enough wwhat breaks it. Doesn't help much when your PM, PO or CEO or CTO have no clue about coding harnesses, coding agents, coding platforms, LLMs etc.
    - hiw2d 5mo ago
      I dont have PMs or POs in my firm fella. Im hyper efficient. You clearly are not and are full of it. If youre only doing 5%, you should only get paid for that. lol. Are you happy to take a salary drop?
Falimonda 5mo ago
Which languages and subject matter do you work with?
- hollowturtle 5mo ago
  c/c++, java, kotlin, go, some perl scripting, some javascript. Gaming industry
  - randusername 5mo ago
    We'll there's your problem. F1 mechanic pops the hood of a mass-market Toyota Corolla and doesn't understand why everyone says it's really good. A lot of us are out here building websites or phone apps. Not to say that these things can't also be taken very seriously from first-principles, but I think that's rare.
    - liuliu 5mo ago
      Probably not really. For gaming, I think probably just need to have a better way to explain visual and what the problem is (collision not done correctly, ways to feedback to LLM's experimentation loop how that should be checked and why etc). Models usually is broken if there is no feedback loop. Well, websites might be exception since they can one-shot pretty well. But there are plenty of things they can do well without one-shot that just requires a good feedback loop to be built.
      - hollowturtle 5mo ago
        Try it then, prove your point. Good luck :)
DennisP 5mo ago
Steve Yegge wrote about this in his book Vibe Coding. He says it takes about a year of experience before you're consistently getting good results. He writes about lots of different techniques for doing that, but also says a lot of it comes down to just getting a feel for when the LLM is going to go haywire.
- hollowturtle 5mo ago
  It's been 4 years of using them for me, before writing a book I'd wait to have a decade of experience to share with others, otherwise it would have the same value as a book on a react tutorial
  - datadrivenangel 5mo ago
    From what I've heard this is a good assessment of steve's book.
- nijave 5mo ago
  I'd say closer to 6 months for me but probably still some room to improve. I think getting a decent setup with a fast feedback loop for the agent combined with context (in repo markdown)+memories goes a long way. After having Claude Code "remember" my preferences and tools, it's more efficient. It has a tendency to copy existing patterns so a good AGENTS.md with best practices and architectural goals goes a long way to prevent it from duplicating patterns you're trying to get rid of.
- noisy_boy 5mo ago
  > but also says a lot of it comes down to just getting a feel for when the LLM is going to go haywire That has been my experience too. The days when I'm very focused, being extra deliberate and constantly questioning/examining/challenging things, the results are much better. Autopilot days just go through in a daze and the outcome is objectively worse. This has made me much more hands-on and pushed me towards models which are actually not that "clever" like codex at effort=low but fast. Given that I'm doing the meat of the thinking, might as well not be slowed down by the model and lose the flow.
nijave 5mo ago
Have had fairly good luck with Claude Code Opus 4.7 on xhigh effort. I think it more reliably does IaC with established patterns especially when it can do a dry run. Python is pretty decent but usually you need good prompting and a little bit of steering to prevent slop. The slop usually works tho Codex w/ gpt-5.5 seems faster but maybe just a bit below Opus 4.7 quality. I gave Opus access to a repl (pyrasite-ng) in a running Python process and it managed to find an 8 year old "memory leak"--a module level cache with no eviction. It did that using GC module and exploring the heap. I was pretty happy with that outcome. It would have been quite challenging for me to find myself without at least a few weeks of deep diving into memory leak hunting docs/resources.
rconti 5mo ago
I'm moderately horrified every time claude runs the same broken, YOLO SWAG git commands from stackoverflow, gets errors, tries a few more things, then finally figures out how to commit and push correctly.
prettyblocks 5mo ago
I'm curious. What have you actually tried? Are you just prompting the LLM with one off tasks? For good results, you need to take the time to read the documentation for the harness you are using and configure your environment. This tuning can take dozens hours to nail down. Then there's the actual approach for working on your projects. Many people that have good results with agentic coding actually spend the bulk of their time in plan mode where they go back and forth with the LLM designing a granular playbook for the task at hand before they ever have it write any code.
- hollowturtle 5mo ago
  I'm curious. What makes you think that me sharing an example(which one of the many?) of what I actually tried would somehow add something to the conversation? What's the usefulness of just an anecdotal example? As I said we have a plenty of different envs, codebases, requirements. Things are complex. You're posing it like I tried just one time. It's been hundreds of hours of tries and I just found out what works best for me, like everyone should do. My original post above isn't that hard to understand. Let me stress this out again: > That's why the debate is so polizered imo, there isn't a shared experience
  - prettyblocks 5mo ago
    In my experience most people with the type of critique I'm seeing from you have only tried it one time or have not taken the time to invest in an environment/process that will work for agentic coding. My question is not so much about sharing a cherry picked example, but the question was more like "have you tried in earnest to make it work". That's the part that wasn't clear from your original post. But you say you have, and you weren't impressed. Fair enough. I'm not trying to convince you otherwise, but I encourage people to give the tools a fair chance before throwing up their hands and deciding it's meh. Having said all that, you're right there isn't a shared experience.
    - th0ma5 5mo ago
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newaccount670 5mo ago
Good is relative. If somebody struggles with getting their hand-written code to compile, the LLM coding agents will look like geniuses to them. An idiosyncrasy of humanity is that the dumbest individuals tend to also be the loudest.
psadauskas 5mo ago
I first started noticing they were actually useful around Dec 2025, through about February. I got pretty good at using them, and was amazed at their utility, especially Claude and Codex. Then sometime in March, they got really frustratingly dumb. Things that they used to get right in one shot suddenly took several tried, and I had to watch them like a hawk because they constantly made stupid mistakes, not following instructions that previously worked. I had one try to fix a failing test like this: assert_eq x, true if x == true Both Claude and Codex, both with the latest versions and the original versions that had been working. Now I just use deepseek. It isn't any dumber, and it costs way less.
sroussey 5mo ago
Long term, it can be better to slowly refactor parts of your code base into the way the model expects it to be. Sometimes fighting the gradient of code’s uniqueness vs expectation is not worth it.
JeremyNT 5mo ago
> It's since november 2025, the so called "inflection point", that I'm still wondering for who coding agents become "really good". I think this may depend on the sorts of work you do. For those of us who mostly live in web using established frameworks, that's about when I came to conclude they could do everything and do it well. I can have opencode discover third party APIs and generate fully working solutions that are well integrated into an existing long-lived codebase. I still review the MRs by hand but I only ever discover spec errors or style issues, not defects in the code itself. This was a big change from ~summer 2025. This is a really well defined space though with strong conventions. If you're doing something more interesting YMMV.
topherhunt 5mo ago
@hollowturtle I'm surprised - do you really find that sota models aren't good enough to generate production code with steering and babysitting? My experience (Claude Code, mostly Opus 4.6) is that it's fantastic at this. At least in JS + TS + Elixir + Ruby. It does indeed need babysitting, my mental model is that it's an exoskeleton not a junior dev, but IME it's a friggin badass exoskeleton, easily 10x-ing my speed on most work. Notably I do NOT --dangerously-skip-permissions nor use claude code's auto mode, I micromanage and lightly review every line it's writing as it writes it, so I rarely have more than 2 sessions generating simultaneously. I suspect that a lot of the disappointment comes in when people try to delegate to it and trust it to not go off the rails. It hasn't earned that trust from me yet (and hasn't needed to yet). Granted, I'm mostly working in small-to-medium codebases, 20k-30k LOC incl test suite. I wonder if that's a factor in my positive experience. Curious to hear your thoughts.
- hollowturtle 5mo ago
  It really depends on the task, but, in my experience, small to medium and bigger codebases, the amount of steering to get quality code is not worth it. I see patterns and solutions emerging from hand coding, I'm not the other way around, I can't start with a prompt, unless again I have the feeling that the task can be one-shot with minimumn effort and context. Starting with a prompt, or in plan mode, it's not how I trained as an engineer, I cannot foresee what something should be/look like until I explore it myself with code I can relate to, that I'm connected with and that I fully understand, for example my muscle memory suggest me to use a specific data structure only after I see some code patterns emerging, hard to explain hopefully makes sense. If I ask the agent to do that initial exploring, even with a tremendous amount of instructions, guidelines etc. it usually start with a path I wouldn't have started with. What I tried in such cases is to stop it, correct it and generate again, only to end up with more prompt words than lines of code. This is true for every visual task I'm working on (I program non web UIs). Let alone doing it via spec files, if it's something I don't care about yeah sure, maybe a little tool for entering/editing data, but alas it always default to slop web apps, and I get it I mean most of the training set is on web apps
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vividfrier 5mo ago
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eudamoniac 5mo ago
I'm convinced that the polarization is that one's impression of AI has a direct 1:1 mapping with one's previous level of skill and sensitivity to quality. Most people are by definition average and they are impressed. Is there anyone in the industry noted for their skill, quality, and taste, e.g. Jonathon Blow, who is impressed and thinks the AI is really good? I haven't seen any. In my personal circle, the best devs I know are either micromanaging or shunning AI; none of them think the agents are capable or really good. The mediocre devs I know are largely on board. This applies both online and off. Couple this with the fact that no AI focused project has come out, not a single one, that meets a high quality bar with nontrivial complexity. I am an AI quality sceptic. They can be useful if you don't care for quality, but I never don't care for quality. I live for quality.
- ajam1507 5mo ago
  This is contradicted by the amount of AI use at top tech firms.
  - eudamoniac 5mo ago
    No, it would be contradicted by the sentiment among devs at top tech firms, but I don't know what the sentiment is. I do know they are being forced to use AI at peril of termination such that their use of AI is a non signal.
JodieBenitez 5mo ago
> Absolutely not, not quite there not even close in my experience. Well... I don't know what you expect but so far I'd like all my colleagues to write code at the level of what I get from codex.

Razengan 5mo ago

> It's since November 2025, the so called "inflection point", that I'm still wondering for who coding agents become "really good". You can dig up my past comments semi-arguing with simonw where I said AI just isn't good enough yet, but lately I've been using Codex mostly just to review existing Godot/GDScript code: https://github.com/InvadingOctopus/comedot https://github.com/InvadingOctopus/comedot and now I'd say that in this day and age one would have to be dumb to not use AI in SOME way :) It's helped me catch a lot of bugs that would have taken me a long time to even notice on my own. I guess it helps that the project is modular enough where most files can be considered standalone, with just 1-2 dependencies and well-commented already, so the AI can look at each file on its own one at a time. You can see the AGENTS.md I use on that repo. Most of my productivity in the last 3 or so months has been thanks to AI, though none of the code there is AI generated. I even bought a MacBook Neo just to use as an "AI thin client" while on travel, even though I already had a beefy MacBook Pro M2 Max that I just keep at home/hotel as a desktop now. Codex's recent remote control features have made it more useful for the moments when I get a cool idea while out at a cafe or on a walk. I don't just copy-paste the AI's output, because it's often inefficient anyway (like creating redundant variables/functions), but I find its findings useful for manually cleaning up my shit. Maybe their training data is not that good with GDScript yet which is a bit of a jank language anyway. So my core code is wholly made by meat, but I do have fun now and then telling Codex to make experimental games using only the library of modular components I have written so far, to test my framework and also the AI's abilities. This kind of work seems like a surprisingly good match for AI: It just has to put existing blocks together, that already have well-defined interfaces/contracts etc. I've been on the $20 ChatGPT plan for about a year now, and only started using Codex since like maybe 4 months ago, almost always on the latest model with "Extended Thinking" or "Extra High", because I want my shared code to be as correct as possible because everything else I do depends on it, and I only hit limits like 2 times in the last 3 months. Claude on the other hand, terrible: https://i.imgur.com/jYawPDY.png https://i.imgur.com/jYawPDY.png Grok is OK for general stuff, never tried it for coding. Gemini's UI/UX and lack of privacy and the AI itself is so terrible I tried it just maybe 2 times ever...and it refused to work on Google's own Flights website and reverse image search! (it told me to do it myself) Deepseek refused to talk about Taiwan or Tiananmen Square so I'm not sure if I can trust it for anything else lol

hollowturtle 5mo ago
Thanks for sharing your experience! I totally agree that if you "own your code", as in you're invested in it, coding it and documenting it, these tools can be really valuable for review, bug fixing and maintenance, it pushes you to do better, maybe one piece at a time like you said with a good modularized codebase. I think more devs should share experiences like that, we should overthrow marketing and people narratives that "don't code anymore since X"
jaccola 5mo ago
I set up a hook that reviews every commit and highlights potential bugs (async) and writes to a report to a dir. Then I have a script that summarises that I usually run before pushing or at end of day. Works quite well for both improving my code and the code ai wrote.
maccard 5mo ago
> I've been on the $20 ChatGPT plan for about a year now, and only started using Codex since like maybe 4 months ago, almost always on the latest model with "Extended Thinking" or "Extra High", because I want my shared code to be as correct as possible because everything else I do depends on it, and I only hit limits like 2 times in the last 3 months. I've recently tried codex, and I have it set to plan mode with 5.5 and I'm hitting the limits on a single task on a "medium" sized codebase.
- Razengan 5mo ago
  Like I said most of my prompts cover 1-3 files at most, rarely more

kstenerud 5mo ago

The polarization comes from the very disparate coding experiences and output quality that different people find when using these tools. For example, I've had the opposite experience of yours, generating very high quality work using Claude (such as https://github.com/kstenerud/yoloai https://github.com/kstenerud/yoloai). Just in dealing with all the bugs and idiosyncrasies in the technologies I'm using, the agent has been a godsend in discovering and cataloguing them so that the implementation phase doesn't keep tripping over them: https://github.com/kstenerud/yoloai/blob/main/docs/dev/backend-idiosyncrasies.md https://github.com/kstenerud/yoloai/blob/main/docs/dev/backe... And the agents keep getting better all the time. Even in the past month I've noticed a considerable jump in its ability to anticipate issues and correctly infer implications as we build out research, design, architecture and planning docs. By the time it comes to coding, it's mostly a mechanical process that can be passed off to sonnet with a negligible defect rate.

hollowturtle 5mo ago
Don't want to be rough, but I'd like to read experiences about novelty ideas that solve people real problems in the real world, your project it's just about selling new shovels. As I commented on another thread > If you're trying to solve a HARD problem people REALLY have, it's a novelty that agents can't help with, otherwise if it gets 97% there MAYBE it's just a signal that your idea isn't that novel!
- paulluuk 5mo ago
  This is a pretty wild take. What percentage of human engineers are creating novel solutions for hard problems, you think? I work in R&D and even my work is 90% doing things that other people already solved. If you are really doing cutting edge SOTA work that has never been done by another human in some form or another, then kudos to you and I want your job.
  - hollowturtle 5mo ago
    > What percentage of human engineers are creating novel solutions for hard problems, you think? IMO Every engineer should try spending his time in a company that tries to solve new problems. Otherwise we will be stuck, as we are now, with big tech paying you mountains of money for doing nothing, incentivizing you to embark on useless activities for letting other managers have a career, fear layoffs and when that happen complaining about it because "it's a year i'm looking for a new job" pretending same compensation and environment. Web development jobs are particularly affected by that. In the game industry, for example, if you don't do something interesting your game won't sell a copy. Let me stress this out again, if LLMs get you 97% there, maybe you should try another idea.

treme 5mo ago

you are experiencing reverse Dunning–Kruger effect. For someone that just dabbled in coding prior, it went from AI building 80%, and struggling through to finish the 20% when trying to build an app/website. now it's like 97% and struggling with last 3%. Yes it'll look rough around the edges when evaulated by a senior dev, but being able to build MVP level things to completion with ease helps you stay engaged and motivated to continue and learn.

hollowturtle 5mo ago
Please do not cite Dunning–Kruger effect at random. Who needs to generate a dumb demo of a 97% done crud app? We had code generators for those, everytime I read claims like that and I ask to explain further I then discover it's people who were not productive before generating the so called "MVP level things to completion with ease". If you're trying to solve a HARD problem people REALLY have, it's a novelty that agents can't help with, otherwise if it gets 97% there MAYBE it's just a signal that your idea isn't that novel! LLMs can effectively validate your business idea
- jaccola 5mo ago
  The obvious pushback to all of the slop is: coding was never hard. Learning resources were abundant and free. If these people had a burning desire to build things prior to LLMs and couldn’t put in the effort to learn to build them (which is also fun!) then why would they ever put the effort into anything to understand it and make it good??
  - layer8 5mo ago
    > coding was never hard. Learning resources were abundant and free. Just a nitpick regarding “never”: Learning resources weren’t abundant and free 25 years ago, that’s a more recent development.
    - skydhash 5mo ago
      Maybe in some parts of the world (including mine). But we haven’t have a lot of computers either. But 25 years ago, there was a lot of textbooks and computers editors like O’Reilly already active. I had the C programming language book (not 25 years ago, but the book is older than that) and you could learn a lot with that one book and codeblocks. Same thing with “The Go Programming Language”, “Learning Perl”, and “Programming Clojure”. You only need one book to get very decent.

nayroclade 5mo ago

> It's since november 2025, the so called "inflection point", that I'm still wondering for who coding agents become "really good". The answer is "for lots of people, but not you". You're doing a vague impression of being fair and even-handed, arguing for non-polarization, but underlying everything you're saying is an obvious attitude of poralizing superiority: That _your_ personal experience with AI is the real truth. That _your_ codebase is more intricate and more challenging than what other people are doing. That everyone else is being led by a "marketing hype train".

new_account_100 5mo ago
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hollowturtle 5mo ago
When I say > Absolutely not, not quite there not even close in my experience. I obviously mean in my experience, not the real truth. > That everyone else is being led by a "marketing hype That is obvious instead, and I later say there's not 0s or 1s, every job has his intrincancies

hathawsh 5mo ago

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ncruces 5mo ago

I don't know that there was an inflection point. I know that, over the past year, they definitely became useful to me as more than auto complete. My most recent pet project is a transpiler from Wasm to Go, and I find it incredibly impressive that recent models (I've used Sonnet, Opus and Gemini, far more successfully than GPT), they're able to just pick up the project and work at all these levels: - Go code that implements the transpiler (parsing Wasm, building an AST) - Go code that gets generated by serializing the AST to a .go file - Go code that manipulates the AST (to optimize it), and its effect on the generated code - Go code that's grafted to the generated code (to implement more advanced opcodes) and how to interact with it from the AST - C code that gets compiled to Wasm, then translated to Go, then called by Go - Go code that gets called by this C code to implement a C stdlib - WAT and WAST files that are used to implement the Wasm spec tests I find this impressive because I have to think hard about all these levels, and I feel many programmers would have a problem with this. And it's very often way easier for me to just write: "I want to generate this code, build me the AST that does it", than go "count parenthesis" in the Go code (I do have some LISP experience; it's still easier). Feel free to scrutinize/criticize the code. Not vibe coded, but plenty of GenAI help. https://github.com/ncruces/wasm2go https://github.com/ncruces/wasm2go

netcan 5mo ago

Coding goodness is just "unevenly distributed." Irl, (a) different people's ways of working with ai are a million little islands and (b) bottlenecks vary enormously by coder and codebase/task. Also... I think our era has an intrinsic bias that change=progress, productivity, etc. Take the "networked computing revolution" of 1990-2000. These computers did land on every desk and every pocket. They are administration powerhouses. Excellent for all manner of administration tasks. But... what this netted out to is "change." We send a lot more emails than we did letters. We communicate a ton. Secretaries went extinct. But "administration" grew. A university faculty typically has more admins. Companies hire more accountants, HR, project managers, etc. Maybe administration was never really a bottleneck. Code has a lot of this. Everyone has a road map, wishlist, etc. It appears as though "code capacity" is the bottleneck. But maybe most of those companies can't really generate much more value from more software. Anecdotally, it seems that many mid-tier shops are migrating/ modernizing their stack, and suchlike. I haven't heard of many belting out features, and increasing prices or sales. Most bottlenecks are upstream of another bottleneck. Few are a "dam."

new_account_100 5mo ago

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benterix 5mo ago

I believe by now we know exactly what it's good at and what it's terrible at. The problem is that our CEO's fear of the future that pushes them to peculiar decisions that objectively make no sense (cf the infamous discussion of the Microsoft employee on Github that couldn't force its agent to do the proper thing). It's not the first time I witness this kind of discrepancy and probably not the last, I just learned to adapt to it.

epolanski 5mo ago

> But we should stop talking about 1s and 0s I agree, but you contradicted yourself just one line above. > For generating production code even with a lot of steering and baby sitting? Absolutely not Moreover this is further in contradiction with several facts: 1. the majority of this industry has always been composed by mediocre/bad developers, often unable to write a fizz buzz 2. the majority of work in this industry is implementing mundane CRUDs to move and transform trivial data across the organization's stakeholders and/or customer or third parties 3. there's lots of stellar and respected engineers leveraging the tools on a regular basis even on problems that are far from trivial and outputting quality code much faster than they would've done otherwise. Mitchell Hashimoto has blogged about it in his work on Ghostty, Sanfilippo has blogged about it in his work on Redis and so did plenty of others. I know several open source stellar developers who benefitted greatly from these tools, yet you think it cannot improve the quality and output of the most mundane tasks out there?

hollowturtle 5mo ago
>> But we should stop talking about 1s and 0s > I agree, but you contradicted yourself just one line above. >> > For generating production code even with a lot of steering and baby sitting? Absolutely not with this last sentence I obviously meant in my experience, it's not that hard. I don't buy your facts are highly biased towards web development, that's a common mistake here on HN to think it's the totality of the industry, luckily it's not
- epolanski 5mo ago
  I've quoted you two tools (Ghostty and Redis) whose development now regularly uses AI assistance to deliver production code. I quoted those because their authors shared their experiences, the strengths and the limits of the tooling. There's many more, from Flask to Docker, from Ruby to FastAPI or Tanstack. LLVM has integrated AI-generated PRs, so did Swift and Mojo. Sasha Levin has pushed into Linux Nvidia-related kernel changes that were authored by LLMs in 6.15. You can be certain there's a magnitude more where people don't admit or tag their PRs as AI generated or co-generated. In fact I am quite confident that projects and developers that are not leveraging the tools are increasingly rare. There's really no reason in 2026 to write a non-trivial PR and not ask a cheap review to an AI tool. The industry is changing, I don't really like the trends I'm seeing, but to state that LLMs cannot and are not writing production code, very often quality ones, (especially when used, setup and overviewed properly) is plain denial. Your anecdotal experience isn't relevant, especially when applied to the largest parts of the industry, composed of mediocre developers working on terrible codebases.

Veelox 5mo ago

My explanation for the lack of shared experience is very language dependent quality. I work in Go and it's gotten really really good. I have to pick the right abstraction and it can be overly verbose at times but it can make in 5 minutes what would have taken me an hour.

Glohrischi 5mo ago

I had a really fun day yesterday because anthropics limits on their normal 20$ subscription allowed me to play around for the whole day without hitting a limit. Its 'production' code because its a small browser game which has very small to 0 requirements on security and being perfect but high requirements on 'ever even doing this' and 'fun'. The code it generated hat 0 compiletime errors. I was able to descripe 10 things to do in one task and it just jugged along solving all of them. This doesn't need to become so much better to be useful. Its already very useful for a lot ofuse cases like researchers which have to verify the math anyway but are not good in writing code for filtering their testdata, converting them and running it. Small websites, fun projects, helper tools etc. But while we speak, in the background stuff is still happening left and right. More compute, better algorithm, more RL etc. We could already be at 95% at 'ai will take your coding job' without knowing because these 5% are so relevant.

hiw2d 5mo ago
"We could already be at 95% at 'ai will take your coding job' without knowing because these 5% are so relevant." This is nonsense. Im not a SWE but a CEO, if that were true I'd be firing without a hitch. And yet this is not the activity we see. Why is that? Perhaps merely writing code is not the entire job.
- Glohrischi 5mo ago
  I wrote coding job. And its true for coding jobs. Your Product Manager is not a coding job. Your Product Owner is not a coding job. vibe-kanban exists you could already do a proper experiment letting your PO maintain a vibe-kanban board with proper requirements and see how an agent progresses. But 5% is often enough wwhat breaks it. Doesn't help much when your PM, PO or CEO or CTO have no clue about coding harnesses, coding agents, coding platforms, LLMs etc.
  - hiw2d 5mo ago
    I dont have PMs or POs in my firm fella. Im hyper efficient. You clearly are not and are full of it. If youre only doing 5%, you should only get paid for that. lol. Are you happy to take a salary drop?

Falimonda 5mo ago

Which languages and subject matter do you work with?

hollowturtle 5mo ago
c/c++, java, kotlin, go, some perl scripting, some javascript. Gaming industry
- randusername 5mo ago
  We'll there's your problem. F1 mechanic pops the hood of a mass-market Toyota Corolla and doesn't understand why everyone says it's really good. A lot of us are out here building websites or phone apps. Not to say that these things can't also be taken very seriously from first-principles, but I think that's rare.
  - liuliu 5mo ago
    Probably not really. For gaming, I think probably just need to have a better way to explain visual and what the problem is (collision not done correctly, ways to feedback to LLM's experimentation loop how that should be checked and why etc). Models usually is broken if there is no feedback loop. Well, websites might be exception since they can one-shot pretty well. But there are plenty of things they can do well without one-shot that just requires a good feedback loop to be built.
    - hollowturtle 5mo ago
      Try it then, prove your point. Good luck :)

DennisP 5mo ago

Steve Yegge wrote about this in his book Vibe Coding. He says it takes about a year of experience before you're consistently getting good results. He writes about lots of different techniques for doing that, but also says a lot of it comes down to just getting a feel for when the LLM is going to go haywire.

hollowturtle 5mo ago
It's been 4 years of using them for me, before writing a book I'd wait to have a decade of experience to share with others, otherwise it would have the same value as a book on a react tutorial
- datadrivenangel 5mo ago
  From what I've heard this is a good assessment of steve's book.
nijave 5mo ago
I'd say closer to 6 months for me but probably still some room to improve. I think getting a decent setup with a fast feedback loop for the agent combined with context (in repo markdown)+memories goes a long way. After having Claude Code "remember" my preferences and tools, it's more efficient. It has a tendency to copy existing patterns so a good AGENTS.md with best practices and architectural goals goes a long way to prevent it from duplicating patterns you're trying to get rid of.
noisy_boy 5mo ago
> but also says a lot of it comes down to just getting a feel for when the LLM is going to go haywire That has been my experience too. The days when I'm very focused, being extra deliberate and constantly questioning/examining/challenging things, the results are much better. Autopilot days just go through in a daze and the outcome is objectively worse. This has made me much more hands-on and pushed me towards models which are actually not that "clever" like codex at effort=low but fast. Given that I'm doing the meat of the thinking, might as well not be slowed down by the model and lose the flow.

nijave 5mo ago

Have had fairly good luck with Claude Code Opus 4.7 on xhigh effort. I think it more reliably does IaC with established patterns especially when it can do a dry run. Python is pretty decent but usually you need good prompting and a little bit of steering to prevent slop. The slop usually works tho Codex w/ gpt-5.5 seems faster but maybe just a bit below Opus 4.7 quality. I gave Opus access to a repl (pyrasite-ng) in a running Python process and it managed to find an 8 year old "memory leak"--a module level cache with no eviction. It did that using GC module and exploring the heap. I was pretty happy with that outcome. It would have been quite challenging for me to find myself without at least a few weeks of deep diving into memory leak hunting docs/resources.

rconti 5mo ago

I'm moderately horrified every time claude runs the same broken, YOLO SWAG git commands from stackoverflow, gets errors, tries a few more things, then finally figures out how to commit and push correctly.

prettyblocks 5mo ago

I'm curious. What have you actually tried? Are you just prompting the LLM with one off tasks? For good results, you need to take the time to read the documentation for the harness you are using and configure your environment. This tuning can take dozens hours to nail down. Then there's the actual approach for working on your projects. Many people that have good results with agentic coding actually spend the bulk of their time in plan mode where they go back and forth with the LLM designing a granular playbook for the task at hand before they ever have it write any code.

hollowturtle 5mo ago
I'm curious. What makes you think that me sharing an example(which one of the many?) of what I actually tried would somehow add something to the conversation? What's the usefulness of just an anecdotal example? As I said we have a plenty of different envs, codebases, requirements. Things are complex. You're posing it like I tried just one time. It's been hundreds of hours of tries and I just found out what works best for me, like everyone should do. My original post above isn't that hard to understand. Let me stress this out again: > That's why the debate is so polizered imo, there isn't a shared experience
- prettyblocks 5mo ago
  In my experience most people with the type of critique I'm seeing from you have only tried it one time or have not taken the time to invest in an environment/process that will work for agentic coding. My question is not so much about sharing a cherry picked example, but the question was more like "have you tried in earnest to make it work". That's the part that wasn't clear from your original post. But you say you have, and you weren't impressed. Fair enough. I'm not trying to convince you otherwise, but I encourage people to give the tools a fair chance before throwing up their hands and deciding it's meh. Having said all that, you're right there isn't a shared experience.
  - th0ma5 5mo ago
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newaccount670 5mo ago

Good is relative. If somebody struggles with getting their hand-written code to compile, the LLM coding agents will look like geniuses to them. An idiosyncrasy of humanity is that the dumbest individuals tend to also be the loudest.

psadauskas 5mo ago

I first started noticing they were actually useful around Dec 2025, through about February. I got pretty good at using them, and was amazed at their utility, especially Claude and Codex. Then sometime in March, they got really frustratingly dumb. Things that they used to get right in one shot suddenly took several tried, and I had to watch them like a hawk because they constantly made stupid mistakes, not following instructions that previously worked. I had one try to fix a failing test like this: assert_eq x, true if x == true Both Claude and Codex, both with the latest versions and the original versions that had been working. Now I just use deepseek. It isn't any dumber, and it costs way less.

sroussey 5mo ago

Long term, it can be better to slowly refactor parts of your code base into the way the model expects it to be. Sometimes fighting the gradient of code’s uniqueness vs expectation is not worth it.

JeremyNT 5mo ago

> It's since november 2025, the so called "inflection point", that I'm still wondering for who coding agents become "really good". I think this may depend on the sorts of work you do. For those of us who mostly live in web using established frameworks, that's about when I came to conclude they could do everything and do it well. I can have opencode discover third party APIs and generate fully working solutions that are well integrated into an existing long-lived codebase. I still review the MRs by hand but I only ever discover spec errors or style issues, not defects in the code itself. This was a big change from ~summer 2025. This is a really well defined space though with strong conventions. If you're doing something more interesting YMMV.

topherhunt 5mo ago

@hollowturtle I'm surprised - do you really find that sota models aren't good enough to generate production code with steering and babysitting? My experience (Claude Code, mostly Opus 4.6) is that it's fantastic at this. At least in JS + TS + Elixir + Ruby. It does indeed need babysitting, my mental model is that it's an exoskeleton not a junior dev, but IME it's a friggin badass exoskeleton, easily 10x-ing my speed on most work. Notably I do NOT --dangerously-skip-permissions nor use claude code's auto mode, I micromanage and lightly review every line it's writing as it writes it, so I rarely have more than 2 sessions generating simultaneously. I suspect that a lot of the disappointment comes in when people try to delegate to it and trust it to not go off the rails. It hasn't earned that trust from me yet (and hasn't needed to yet). Granted, I'm mostly working in small-to-medium codebases, 20k-30k LOC incl test suite. I wonder if that's a factor in my positive experience. Curious to hear your thoughts.

hollowturtle 5mo ago
It really depends on the task, but, in my experience, small to medium and bigger codebases, the amount of steering to get quality code is not worth it. I see patterns and solutions emerging from hand coding, I'm not the other way around, I can't start with a prompt, unless again I have the feeling that the task can be one-shot with minimumn effort and context. Starting with a prompt, or in plan mode, it's not how I trained as an engineer, I cannot foresee what something should be/look like until I explore it myself with code I can relate to, that I'm connected with and that I fully understand, for example my muscle memory suggest me to use a specific data structure only after I see some code patterns emerging, hard to explain hopefully makes sense. If I ask the agent to do that initial exploring, even with a tremendous amount of instructions, guidelines etc. it usually start with a path I wouldn't have started with. What I tried in such cases is to stop it, correct it and generate again, only to end up with more prompt words than lines of code. This is true for every visual task I'm working on (I program non web UIs). Let alone doing it via spec files, if it's something I don't care about yeah sure, maybe a little tool for entering/editing data, but alas it always default to slop web apps, and I get it I mean most of the training set is on web apps
- 5mo ago

vividfrier 5mo ago

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eudamoniac 5mo ago

I'm convinced that the polarization is that one's impression of AI has a direct 1:1 mapping with one's previous level of skill and sensitivity to quality. Most people are by definition average and they are impressed. Is there anyone in the industry noted for their skill, quality, and taste, e.g. Jonathon Blow, who is impressed and thinks the AI is really good? I haven't seen any. In my personal circle, the best devs I know are either micromanaging or shunning AI; none of them think the agents are capable or really good. The mediocre devs I know are largely on board. This applies both online and off. Couple this with the fact that no AI focused project has come out, not a single one, that meets a high quality bar with nontrivial complexity. I am an AI quality sceptic. They can be useful if you don't care for quality, but I never don't care for quality. I live for quality.

ajam1507 5mo ago
This is contradicted by the amount of AI use at top tech firms.
- eudamoniac 5mo ago
  No, it would be contradicted by the sentiment among devs at top tech firms, but I don't know what the sentiment is. I do know they are being forced to use AI at peril of termination such that their use of AI is a non signal.

JodieBenitez 5mo ago

> Absolutely not, not quite there not even close in my experience. Well... I don't know what you expect but so far I'd like all my colleagues to write code at the level of what I get from codex.