Posted by svara 9 hours ago
Ask HN: How is AI-assisted coding going for you professionally?
If you've recently used AI tools for professional coding work, tell us about it.
What tools did you use? What worked well and why? What challenges did you hit, and how (if at all) did you solve them?
Please share enough context (stack, project type, team size, experience level) for others to learn from your experience.
The goal is to build a grounded picture of where AI-assisted development actually stands in March 2026, without the hot air.
The one thing I’m not sure about is: does code quality and consistency actually matter? If your architecture is sufficiently modular, you can quickly and inexpensively regenerate any modules whose low quality proves to be problematic.
So, maybe we really are fucked. I don’t know.
The productivity comes from three main areas for me:
- Having the AI coding assistance write unit tests for my changes. This used to be by far my least favorite part of my job of writing software, mostly because instead of solving problems, it was the monotonous process of gathering mock data to generate specific pathways, trying to make sure I'm covering all the cases, and then debugging the tests. AI coding assistance allows me to just have to review the tests to make sure that they cover all the cases I can think of and that there aren't any overtly wrong assumptions
- Research. It has been extraordinarily helpful in giving me insight into how to design some larger systems when I have extremely specific requirements but don't necessarily have the complete experience to architect them myself - I know enough to understand if the system is going to correctly accomplish the requirements, but not to have necessarily come up with architecture as a whole
- Quick test scripts. It has been extremely useful for generating quick SQL data for testing things, along with quick one-off scripts to test things like external provider APIs
I agree, this is where coding agents really shine for me. Even if they get the details wrong, they often pinpoint where things happen and how quite well.
They're also great for rapid debugging, or assisted bug fixing. Often, I will manually debug a problem, then tell the AI, "This exception occurs in place Y because thing X is happening, here's a stack trace, propose a fix", and then it will do the work of figuring out where to put the fix for me. I already usually know WHAT to do, it's just a matter of WHERE in context. Saves a lot of time.
Likewise, if I have something where I want thing X to do Y, and X already does Z, then I'll say, "Implement a Y that works like Z but for A B C", and it'll usually get it really close on the first try.
A couple "win" examples: add in-text links to every term in this paragraph that appears elsewhere on the page, plus corresponding anchors in the relevant page parts. Or, replace any static text on this page with any corresponding dynamic elements from this reference URL.
Lose examples: constant, but edit format glitches (not matching searched text; even the venerable Opus 4.6 constantly screws this up), unnecessary intermediate variables, ridiculously over-cautious exception-handling, failing to see opportunities to isolate repeated code into a function, or to utilize an existing function that exactly implements said N lines of code, etc.
It seems to me that sadly, paying for getting a few isolated tasks done is becoming a thing of the past.
And then all of a sudden you’re just arguing with the terminal all day - the specs are written by gpt, delivered in-the email written by gpt. Sometimes they dont even have the time to slice their prompt from the edges of the paste but the only thing i can think of is “i need to make the most of 0.5x off peak claude rates “
Fuck.
I got lots of pretty TUIs though so thats neat
I'm enjoying myself so much. Projects I've been thinking about for years are now a couple of hours of hacking around. I'm readjusting my mental model of what's possible as a single developer. And I'm finally learning Go!
The biggest challenge right now is keeping up with the review workload. For low stakes projects (small single-purpose HTML+JS tools for example) I'm comfortable not reviewing the code, but if it's software I plan to have other people use I'm not willing to take that risk. I have a stack of neat prototypes and maybe-production-quality features that I can't ship yet because I've not done that review work.
I mainly work as an individual or with one other person - I'm not working as part of a larger team.
Occasionally I'll catch things it didn't implement at all, or find things like missing permission checks.
Treat it like an intern, give it feedback, have it build skills, review every session, make it do unit tests. Red green refactor. Spend time up front reviewing the plan. Clearly communicate your intent and outcomes you want. If you say "do x" it has to guess what you want. If you say "I want this behaviour and this behaviour, 100% branch unit tested, adhearing to contributing guidelines and best practices, etc" it will take a few minutes longer, but the quality increases significantly.
I uninstalled vscode, I built my own dashboard instead that organizes my work. I get instant notifications and have a pr review kick off a highly opinionated or review utilizing the Claude code team features.
If you aren't doing this level of work by now, you will be automated soon. Software engineering is a mostly solved problem at this point, you need to embed your best practices in your agent and keep and eye on it and refine it over time.
I use Claude Opus (4.5, 4.6) all the time and catch it making making subtle mistakes, all the time.
Are you really being more productive (let’s say 3x times more), or just feel that way because you are constantly prompting Claude?
Maybe I’m wrong, but I don’t buy it.
Didn't we make subtle mistakes without AI?
Why did we spend so much time debugging and doing code reviews?
> Are you really being more productive (let’s say 3x times more)
At least 2x more productive, and that's huge.
But since I have a strong rule about always writing unit tests before code, my confidence is a lot higher.
I agree that the test harness is the most important part, which is only possible to create successfully if you are very familiar with exactly how your code works and how it should work. How would you reach this point using a dashboard and just reviewing PRs?
I'm talking Claude Opus 4.6 here.
the bottleneck now is testing. that isn't going away anytime soon, it'll get much worse for a bit while models are good at churning code out that's slightly wrong or technically correct, but solving a different problem than intended; it's going to be a relatively short lived situation I'm afraid until the industry switches to most code being written for serving agents instead of humans.
I guess that's why Claude Code has 0 open issues on Github. Since software engineering is solved, their autonomous agents can easily fix their own software much better and faster than human devs. They can just add "make no mistakes" to their prompt and the model can solve any problem!
Oh wait, they have 5,000+ open issues on Github[1]. I'm yet to be convinced that this is a solved problem
PS: All in for AI agents I use all the time but sorry - SE is not a solved problem. Yet.
To me, code is both the canvas and deterministic artifact of deep thinking about program logic and data flow, as well as a way to communicate these things to other developers (including myself in the future). Outsourcing that to some statistical amalgam implies that the engineering portion of software engineering is no longer relevant. And maybe it's not for your run-of-the-mill shovelware, but as a profession I'd like to think we hold ourselves to a higher standard than that.
Also, does the sum total of software engineering done up to this point provide a sufficient training set for all future engineering? Are we really "done"? That sounds absurd to me.
I think people spouting absolutist statements like "software engineering is a solved problem" should largely be ignored.
Sincere question, how do beginners to the field (interns, juniors) do this when they don't have any best practices yet?
Unless you want to be a solopreneur (terrible idea while you don't know what you're doing and don't have the means to hire someone that does), look at pretty much any other comment in this thread.
...but since it's so easy to deliver stuff without actually knowing anything, learning means putting in the effort to resist temptation and use the agent as a teaching aid instead of an intern savant.
I don't necessarily disagree with your advice, but goodness, I don't look forward to using any of the low quality software in the next decade. I hope the shareholders remain happy.
??????????
write a thousand md files with detailed prompts (and called them skills)?
is that what would get juniors hired? and paid real money? a stack of md files?
Claude code skills represent a new type of AI native program. Give your agent the file system, let it build tools to sync and manage data.
Gamedev, systems programming, embedded development, 3D graphics, audio programming, mobile, desktop, physics/simulation programming, HPC, RTC, etc.. that’s all solved based on your experience?
like, we've had this technology for several decades now, and none of these AI tools are like: "This is so great, let me show everyone how to write a CRUD database with a notepad and calendar app" or whatever.
Several decades ago, we barely had the internet, rockets were single use only, and smart phones were coming any day now. CRISPR was yet to be named, social media meant movies from Blockbusters or HBO that you watched with friends. GLP-1 was a meh option for diabetics.
I agree with your overall point but...your time frame is way off.
It's harder and harder to detect sarcasm these days but in case you're being serious, I've tested a similar setup and I noticed Claude produces perfectly plausible code that has very subtle bugs that get harder and harder to notice. In the end, the initial speedup was gone and I decided to rewrite everything by hand. I'm working on a product where we need to understand the code base very well.
An example I have of this is when I asked Claude to copy a some functionality from a front-end application to a back-end application. It got all of the function signatures right but then hallucinated the contents of the functions. Part of this functionality included a look up map for some values. The new version had entirely hallucinated keys and values, but the values sounded correct if you didn't compare with the original. A human would have literally copied the original lookup map.
There is no way this is true. People make fewer bugs with time and guidance, but no human makes zero bugs. Also, bugs are not planned; it's always easy to in hindsight say "A human would have literally copied the original lookup map," but every bug has some sort of mistake that is made that is off the status quo. That's why it's a bug.
But now with Claude, the mental model of how your code works is not in your head, but resides behind a chain of reasoning from Claude Code that you are not privy too. When something breaks, you either have to spend much longer trying to piece together what your agent has made, or to continue throwing Claude at and hope it doesn't spiral into more subtle bugs.
> I mainly work as an individual or with one other person - I'm not working as part of a larger team.
If AI really is all that, then whatever "special" thing you are doing will be automated as well.
We're discovering so much latent demand for software, Jevon's paradox is in full effect and we're working more than ever with AI (at least I am).
Their comment about people who don't operate like them being out of a job might be true if AI doesn't progress past the current stage but I really don't see progress slowing down, at least in coding models, for quite some time.
So, whatever relevance OPs specific methods have right now will quickly be integrated into the models themselves.
Building AI systems will be about determining the right thing to build and ensuring your AI system fully understands it. For example, I have a trading bot that trades. I spent a lot of time on refining the optimization statement for the AI. If you give it the wrong goal or there's any ambiguity, it can go down the wrong path.
On the back end, I then judge the outcomes. As an engineer I can understand if the work it did actually accomplished the outcomes I wanted. In the future it will be applying that judgement to every field out there.
Though I don't know of any algo trading shop that relies purely on algorithms as market regimes change frequently and the alpha of new edge ends up getting competed away frequently.
(And personally I'm a believer of the jagged intelligence theory of LLMs where there's some tasks that LLMs are great at and other tasks that they'll continue being idiotic at for a while, and think there's plenty of work left for nuts and bolts program writers to do.)
I'll believe it when AI can tell me when a project will be done. I've asked my developer friends about this and I get a blank stare, like I'm stupid for asking.
You from 2 months ago:
>LLMs are great coders, but subpar developers". https://news.ycombinator.com/item?id=46434304
Interesting. That's a lot of progress in 2 months!
Copilot completions are amazingly useful. chatting with the chatbot is a super useful debugging tool. Giving it a function or database query and asking the ai to optimize it works great. But true vibe coding is still, imho, more of a party trick than an actual productivity multiplier. It can do things that look useful, and it can do things that solve immediate self-contained problems. but it can’t create launchable products that serve the needs of multiple users.
On the other hand, I have tried them a number of times in greenfield situations with Python and the web stack and experienced the simultaneous joy and existential dread of others. They can really stand new projects up quick.
As a founder, this leaves me with what I describe as the "generation ship" problem. Is it possible that the architecture we have chosen for my project is so far out of the training data that it would be faster to ditch the project and reimplement it from scratch in a Claude-yolo style? So far, I'm convinced not because the code I've seen in somewhat novel circumstances is fairly mid, but it's hard to shake the thought.
I do find chatting with the models incredibly helpful in all contexts. They are also excellent at configuring services.
If all you're doing is something that already exists but you decided to architecture it in a novel way (for no tangible benefit), then I'd say starting from scratch and make it look more like existing stuff is going to help AI be more productive for you. Otherwise you're on your own unless you can give AI a really good description of what you are doing, how things are tied together etc. And even then it will probably end up going down the wrong path more often than not.
Especially, now that we do have models that can search through code bases.