Posted by svara 21 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.
My workday is fairly simple. I spend all day planning and reviewing.
1. For most features, unless it's small things, I will enter plan mode.
2. We will iterate on planning. I built a tool for this, and it seems that this is a fairly desired workflow, given the popularity through organic growth. https://github.com/backnotprop/plannotator
- This is a very simple tool that captures the plan through a hook (ExitPlanMode) and creates a UI for me to actually read the plan and annotate, with qol things like viewing plan diffs so I can see what the agent changed.
4. Do a lot of this / multitasking with worktrees now.
Worktrees weren't something I truly understood the value of for a while, until a couple weeks ago, embarrassingly enough: https://backnotprop.com/blog/simplifying-git-worktrees/
I only use Claude Code with Opus 4.6 on High Effort.
I always, ALWAYS treat my “new job” as writing a detailed ticket for whatever it is I need to do.
I give the model access to a DB replica of my prod DB that I create manually.
I do NOT waste time with custom agents, Claude.md files or any of that stuff.
When I put ALL of the above together, the results ARE THE PROMISED LAND: I simply haven’t written a single line of code manually in the last 3 months.
For me I have been a coder since a very young age and I am nearing the end of my career now. I still love writing code to problem solve just as much as the first day I learnt to code. The thought of something taking that task away from me doesn't fill me with glee.
A parallel for me is if I enjoyed puzzle pages and those brought me with joy and satisfaction employing my grey matter to solve, I just wouldn't find it interesting to have an agent complete the forms to me, with me simply guiding the agent to clues.
At least at my company the problem is the business hasn’t caught up. We can code faster but our stakeholders can’t decide what they want us to build faster. Or test faster or grasp new modalities llms make possible.
That’s where I want to go next: not just speeding up and increasing code quality but improving business analytics and reducing the amount of meetings I have to be in to get business problems understood and solved.
It’s a lot of fun for exploring ideas. I’ve built things very fast that I would not have done at all otherwise. I have rewritten a huge chunk of semi-outdated docs into something useful with a couple of Prompts in a day. Claude does all the annoying dependency update breaks the build kinds of things. And the reviews are extremely useful and a perfect combination with human review as they catch things extremely well that humans are bad at catching.
But in the production codebase changes must be made with much more consideration. Claude tends to perform well ob some tasks but for other I end up wasting time because I just don’t know up front how the feature must look so I cannot write a spec at the level of precision that claude needs and changing code manually is more efficient for this kind of discovery for me than dealing with large chunks of constantly changing code.
And then there’s the fact that claude produces things that work and do the thing described in the prompt extremely well but they are always also wring in sone way. When I let AI build a large chunk of code and actually go through the code there’s always a mess somewhere that ai review doesn’t see because it looks completely plausible but contains some horrible security issue or complete inconsistency with the rest of the codebase or, you know, that custom yaml parser nobody asked for and that you don’t want your day job to depend on.
Claude recently tried to replace a html sanitizer with a custom regex that perfectly fit all our tests as well as the spec I wrote
Personally, it’s been decent for generating tedious boilerplate. Though I’m not sure if reading the docs and just writing things myself would have been faster when it comes time to debug. I’m pretty fast at code editing with vim at this point. I’m also hesitant to feedback any fixes to the AI companies.
I’ve found “better google” to be a much more comfortable if not faster way to use the tools. Give me the information, I’ll build an understanding and see the big picture much better.
This is a key candidates to use AI as we have built hundreds of warehouses in the past. We have a standard product that spans over a hundred thousand lines of code to build upon. Still, we rely on copying code from previous projects if features have been implemented before. We have stopped investing in the product to migrate everything to microservices, for some reason, so this code copying is increasingly common as projects keep getting more complex.
Teams to implement warehouses are generally around eight developers. We are given a design spec to implement, which usually spans a few hundred pages.
AI has over doubled the speed at which I can write backend code. We've done the same task so many times before with previous warehouses, that we have a gold mine of patterns that AI can pick up on if we have a folder of previous projects that it can read. I also feel that the code I write is higher quality, though I have to think more about the design as previously I would realize something wouldn't work whilst writing the code. At GWT though, it's hopeless as there's almost no public GWT projects to train an AI on. It's also very helpful in tracing logs and debugging.
We use Cursor. I was able to use $1,300 tokens worth of Claude Opus 4.6 for a cost of $100 to the company. Sadly, Cursor discontinued it's legacy pricing model due to it being unsustainable, so only the non-frontier models are priced low enough to consistently use. I'm not sure what I'm going to do when this new pricing model takes affect tomorrow, I guess I will have to go back to writing code by hand or figure out how to use models like Gemini 3.1. GPT models also write decent code, but they are always so paranoid and strictly follow prompts to their own detriment. Gemini just feels unstable and inconsistent, though it does write higher quality code.
I'm not being paid any more for doubling my output, so it's not the end of the world if I have to go back to writing code by hand.
I have to think like micro-manager, coming up with discrete (and well-defined) tasks for the AI to do, and I periodically review the code to make it cleaner/more efficient.
But I'm confident that it is saving me time. And my love for programming has not diminished. I'm still driving the architecture and writing code, but now I have a helper who makes progress in parallel.
Honestly, I don't want to go back.
another teammate added a length check to an input field, and his request was merged near instantly, even though it had zero unit testing. this team is incredibly cooked in the long term, i just need to ensure that i survive the short term somehow.
People like you are making sunk expenditures whilst the models are evolving... they can just wait until the models get to 'steady-state' to figure out the optimal workflow. They will have lost out on far less.
That sounds extremely reasonable though?
As a sidenote, I highly doubt they are cooked longterm. Using AI is not exactly skilled labor. If they want or need I'm sure they could learn patterns/workflows in like an afternoon. As things go on it will only get easier to use.
Like yeah sorry... not everyone has to be a risk-taker. Many people like to observe and await to see what new techniques emerge that can be exploited.
If this is what the industry is now… this will be my last job in it.
Curse everyone involved with creating this nightmare.