This Hacker News discussion thread advocates for free expression, knowledge sharing, and community participation in professional AI development discourse. The post explicitly invites transparent, evidence-based sharing of concrete experiences with AI tools, rejecting polarized narratives. The content and platform structure strongly support Articles 19 (free expression), 26 (education), and 27 (participation in scientific/cultural life), with moderate positive signals across freedom of thought, assembly, and labor rights.
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Art 12 ↔ Art 19 —Request for professional context (team size, stack, experience level) to support learning (Article 19, 26) conflicts with privacy protection (Article 12) when such details may identify individuals or reveal employer information.
Art 12 ↔ Art 27 —Desire for participation in scientific knowledge commons (Article 27) requires sharing details that may expose individual professional information and intellectual property, creating tension with privacy rights (Article 12).
Professionally, I have had almost no luck with it, outside of summarizing design docs or literally just finding something in the code that a simple search might not find: such is this team's code that does X?
I am yet to successfully prompt it and get a working commit.
Further, I will add that I also don't know any ICs personally who have successfully used it. Though, there's endless posts of people talking about how they're now 10x more productive, and everyone needs to do x y an z now. I just don't know any of these people.
Non-professionally, it's amazing how well it does on a small greenfield task, and I have seen that 10x improvement in velocity. But, at work, close to 0 so far.
Of the posts I've seen at work, they typically tend to be teams doing something new / greenfield-ish or a refactor. So I'm not surprised by their results.
It has made my job an awful slog, and my personal projects move faster.
At work, the devs up the chain now do everything with AI – not just coding – then task me with cleaning it up. It is painful and time consuming, the code base is a mess. In one case I had to merge a feature from one team into the main code base, but the feature was AI coded so it did not obey the API design of the main project. It also included a ton of stuff you don’t need in the first pass - a ton of error checking and hand-rolled parsing, etc, that I had to spend over a week unrolling so that I could trim it down and redesign it to work in the main codebase. It was a slog, and it also made me look bad because it took me forever compared to the team who originally churned it out almost instantly. AI tools are not good at this kind of design deconflicting task, so while it’s easy to get the initial concept out the gate almost instantly, you can’t just magically fit it into the bigger codebase without facing the technical debt you’ve generated.
In my personal projects, I get to experience a bit of the fun I think others are having. You can very quickly build out new features, explore new ideas, etc. You have to be thoughtful about the design because the codebase can get messy and hard to build on. Often I design the APIs and then have Claude critique them and implement them.
I think the future is bleak for people in my spot professionally – not junior, but also not leading the team. I think the middle will be hollowed out and replaced with principals who set direction, coordinate, and execute. A privileged few will be hired and developed to become leaders eventually (or strike gold with their own projects), but everyone in between is in trouble.
As a veteran freelance developer - aside from some occasional big wins, I'd say it's been net neutral or even net negative to my productivity. When I review AI-generated code carefully (and if I'm delivering it to clients I feel that's my responsibility) I always find unnecessary complexity, conceptual errors, performance issues, looming maintainability problems, etc. If I were to let it run free, these would just compound.
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.
The majority of code I've written since November 2025 has been created using agents, as opposed to me typing code into a text editor. More than half of that has been done from my iPhone via Claude Code for web (bad name, great software.)
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.
It makes my work suck, sadly. Team dynamics also contributes to that, admittedly.
Last year I was working on implementing a pretty big feature in our codebase, it required a lot of focus to get the business logic right and at the same time you had be very creative to make this feasible to run without hogging to much resources.
When I was nearly done and worked on catching bugs, team members grew tired of waiting and starting taking my code from x weeks ago (I have no idea why), feeding it to Claude or whatever and then came back with a solution. So instead of me finishing my code I had to go through their version of my code.
Each one of the proposals had one or more business requirements wrong and several huge bugs. Not one was any closer to a solution than mine was.
I had appreciated any contribution to my code, but thinking that it would be so easy to just take my code and finishing it by asking Claude was rather insulting.
Around a year ago I started a new position at a very large tech company that I won't name, working on a pre-existing web project there. The code base isn't terrible - though not very good either, by-and-large - but it's absolutely massive, often over-engineered, pretty unorthodox, and definitely has some questionable design decisions; even after more than a year of working with it I still feel like a beginner much of the time.
This year I grudgingly bit the bullet and began using AI tools, and to my dismay they've been a pretty big boon for me, in this case. Not just for code generation - they're really good at probing the monolith and answering questions I have about how it works. Before I'd spend days pouring over code before starting work to figure out the right way to build something or where to break in, pinging people over in India or eastern Europe with questions and hoping they reply to me overnight. AI's totally replaced that, and it works shockingly well.
When I do fall back on it for code generation, it's mostly just to mitigate the tedium of writing boilerplate. The code it produces tends to be pretty poor - both in terms of style and robustness - and I'll usually need to take at least a couple of passes over it to get it up to snuff. I do find this faster than writing everything out by hand in the end, but not by a lot.
For my personal projects I don't find it adds much, but I do enjoy rubber ducking with ChatGPT.
I know my mind fairly well, and I know my style of laziness will result in atrophying skills. Better not to risk it.
One of my co-workers already admitted as much to me around six months ago, and that he was trying not to use AI for any code generation anymore, but it was really difficult to stop because it was so easy to reach for. Sounded kind of like a drug addiction to me. And I had the impression he only felt comfortable admitting it to me because I don't make it a secret that I don't use it.
Another co-worker did stop using it to generate code because (if I'm remembering right) he can tell what it generates is messy for long-term maintenance, even if it does work and even though he's new to React. He still uses it often for asking questions.
A third (this one a junior) seemed to get dumber over the past year, opening merge request that didn't solve the problem. In a couple of these cases my manager mentioned either seeing him use AI while they were pairing (and it looked good enough so the problems just slipped by) or saw hints in the merge request with how AI names or structures the code.
I got insanely more productive with Claude Code since Opus 4.5. Perhaps it helps that I work in AI research and keep all my projects in small prototype repos. I imagine that all models are more polished for AI research workflow because that's what frontier labs do, but yeah, I don't write code anymore. I don't even read most of it, I just ask Claude questions about the implementation, sometimes ask to show me verbatim the important bits. Obviously it does mistakes sometimes, but so do I and everyone I have ever worked with. What scares me that it does overall fewer mistakes than I do. Plan mode helps tremendously, I skip it only for small things. Insisting on strict verification suite is also important (kind of like autoresearch project).
I work at a very prominent AI company. We have access to every tool under the sun. There are various levels of success for all levels — managers, PMs, engineers.
We have cursor with essentially unlimited Opus 4.6 and it’s fundamentally changed my workflow as a senior engineer. I find I spend much more time designing and testing my software and development time is almost entirely prompting and reviewing AI changes.
I’m afraid my coding skills are atrophying, in fact I know the are, but I’m not sure if the coding was the part of my job I truly enjoyed. I enjoy thinking higher-level: architecture, connecting components, focusing on the user experience. But I think using these AI tools is a form of golden handcuffs. If I go work at a startup without the money I pay for these models, I think for the first time in my career I would be less likely to be able to successfully code a feature than I could last year.
So professionally there are pros and cons. My design and architecture skills have greatly improved as I am spending more time doing this.
Personally it’s so much fun. I’ve made several side projects I would have never done otherwise. Working with Claude code on greenfield projects is a blast.
I am working on a sub 100KLOC Rust application and can't productively use the agentic workflows to improve that application.
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.
I'm an engineer at Amazon - we use Kiro (our own harness) with Opus 4.6 underneath.
Most of my gripes are with the harness, CC is way better.
In terms of productivity I'm def 2-4X more productive at work, >10x more productive on my side business. I used to work overtime to deliver my features. Now I work 9-5 and am job hunting on the side while delivering relatively more features.
I think a lot of people are missing that AI is not just good for writing code. It's good for data analysis and all sorts of other tasks like debugging and deploying. I regularly use it to manage deployment loops (ex. make a code change and then deploy the changes to gamma and verify they work by making a sample request and verifying output from cloudwatch logs etc). I have built features in 2 weeks that would take me a month just because I'd have to learn some nitty technical details that I'd never use again in my life.
For data analysis I have an internal glue catalog, I can just tell it to query data and write a script that analyzes X for me.
AI and agents particularly have been a huge boon for me. I'm really scared about automation but also it doesn't make sense to me that SWE would be automated first before other careers since SWE itself is necessary to automate others. I think there are some fundamental limitations on LLMs (without understanding the details too much), but whatever level of intelligence we've currently unlocked is fundamentally going to change the world and is already changing how SWE looks.
I'm always skeptical to new tech, I don't like how AI companies have reserved all memory circuits for X years, that is definitely going to cause problems in society when regular health care sector businesses can't scale or repair their infra, and the environmental impact is also a discussion that I am not qualified to get into.
All I can say for sure is that it is absolutely useful, it has improved my quality of life without a doubt. I stick to the principle that it's here to improve my work life balance, not increase output for our owners.
And that it has done, so far. I can accomplish things that would have taken me weeks of stressful and hyperfocused work in just hours.
I use it very carefully, and sparingly, as a helpful tool in my toolbox. I do not let it run every command and look into every system, just focused efforts to generate large amounts of boilerplate code that would require me to have a lot of docs open if I were to do it myself.
I definitely don't let it read or write my e-mails, or write any text. Because I always loved writing, and will never stop loving it.
It's here to stay, because I'm not alone in feeling this way about it. So the staunch AI-deniers are just wasting their time. Just like any other tech, it's going to be used against humans, against the already oppressed.
I definitely recognize that the tech has made some people lose their minds. Managers and product owners are now vibe coding thinking they can replace all their developers. But their code base will rot faster than they think.
I had a couple of nice moments, like claude helping me with rust (which I don't understand) and claude finding a bug in a python library I was using
Also some not so nice moments (small rust changes were OK, but with a big one claude fumbled + I couldn't really verify that it worked so I didn't merge to code to master even when it seemingly worked)
I think it really helps to break the ice so to say. You no longer feel the tension, the pain of an empty page. You ask claude to write something, and improving something is so mentally easier
Also I mostly use claude as a spell checker / linter for the projects I'm too lazy to install proper tools for that. vim + claude, what else would you need
Luckily my company pays for the subscription, speding personal money on LLMs (especially on US LLMs) would feel strange for some reason. Ideally I want to own an LLM, have it at home but I am too lazy
I'm a manager at a large consumer website. My team and I have built a harness that uses headless Claude's (running Opus) to do ticket work, respond to and fix PR comments, and fix CI test failures. Our only interaction with code is writing specs in Jira tickets (which we primarily do via local Claudes) and adding PR comments to GitHub PRs.
The speed we can move at is astounding. We're going to finish our backlog next quarter. We're conservatively planning on launching 3x as many features next quarter.
Claude is far from perfect: it's made us reassess our coding standards since code is primarily for Claude now, not for humans. So much of what we did was to make code easier for the next dev, and that just doesn't matter anymore.
Pretty good, we have a huge number of projects, some more modern than others. For the older legacy systems, it's been hugely useful. Not perfect, needs a bit more babysitting, but a lot easier to deal with than doing it solo. For the newer things, they can mostly be done solely by AI, so more time spent just speccing / designing the system than coding. But every week we are working out better and batter ways of working with AI, so it's an evolving process at the moment
My current employer is taking a long time to figure out how they think they want people to use it, meanwhile, all my side projects for personal use are going quite strong.
I find it useful. It has been a big solve from a motivation perspective. Getting into bad API docs or getting started on a complex problem, it's easy to have AI go with me describing it. And the other positive is front end design. I've always hated css and it's derivatives and AI makes me now decent.
The negatives are that AI clearly loves to add code, so I do need to coach it into making nice abstractions and keeping it on track.
Right now I'm really enjoying the labs' cli harnesses, Claude Code, and Codex (especially for review). I do a bunch of niche stuff with Pi and OpenCode.
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 common and popular workflow, given the popularity through organic growth. https://github.com/backnotprop/plannotator
- This is a very simple tool that captures through a hook (ExitPlanMode) the plan creates a UI for me to actually read the plan and annotate, with things like viewing plan diffs so I can see what the agent changed.
3. After plan's approved, we hit eventual review of implementation. I'll use AI reviewers, but I will also manually review using the Sane tool to create annotations and iteration feedback loops with the agents.
Hating it TBH. I feel like it took away a lot of what I enjoyed about programming but its often so effective and I'm under so much pressure to be fast that I can't not use it.
Wow, that's such a drastic different experience than mine. May I ask what toolset are you using? Are you limited to using your home grown "AcmeCode" or have full access to Claude Code / Cursor with the latest and greatest models, 1M context size, full repo access?
I see it generating between 50% to 90% accuracy in both small and large tasks, as in the PRs it generates range between being 50% usable code that a human can tweak, to 90% solution (with the occasional 100% wow, it actually did it, no comments, let's merge)
I also found it to be a skillset, some engineers seem to find it easier to articulate what they want and some have it easier to think while writing code.
If they're handing you broken code call them out on it. Say this doesn't do what it says it does, did you want me to create a story for redoing all this work?
I can second this. I’ve never had a problem writing short scripts and glue code in stuff ive mastered. In places I actually need help, I’m finding it slows me down.
If you dont take a stand and refuse to clean their mess, aren't you part of the problem? No self respecting proponent of AI enabled development should suggest that the engineers generating the code are still not personally responsible for its quality.
Also at FAANG. I think I am using the tools more than my peers based on my conversations. The first few times I tried our AI tooling, it was extremely hit and miss. But right around December the tooling improved a lot, and is a lot more effective. I am able to make prototypes very quickly. They are seldom check-in ready, but I can validate assumptions and ideas. I also had a very positive experience where the LLM pointed out a key flaw in an API I had been designing, and I was able to adjust it before going further into the process.
Once the plan is set, using the agentic coder to create smaller CLs has been the best avenue for me. You don't want to generate code faster than you and your reviewers can comprehend it. It'll feel slow, but check ins actually move faster.
I will say it's not all magic and success. I have had the AI lead me down some dark corners, assuring me one design would work when actually it is a bit outdated or not quite the right fit for the system we are building for because of reasons. So, I wouldn't really say that it's a 10x multiplier or anything, but I'm definitely getting things done faster than I could on my own. Expertise on the part of the user is still crucial.
One classic issue I used to run into, is doing a small refactor and then having to manually fix a bunch of tests. It is so much simpler to ask the LLM to move X from A to B and fix any test failures. Then I circle back in a few minutes to review what was done and fix any issues.
The other thing is, it has visibility for the wider code base, including some of our infrastructure that we're dependent on. There have been a couple times in the past quarter where our build is busted by an external team, and I am able to ask the LLM given the timeframe and a description of the issue, the exact external failure that caused it. I don't really know how long it would have taken to resolve the issue otherwise, since the issues were missed by their testing. That said, I gotta wonder if those breakages were introduced by LLM use.
My job hasn't been this fun in a long, long time and I am a little uneasy about what these tools are going to mean for my personal job security, but I don't know how we can put the genie back into the bottle at this point.
I use AI to discuss and possibly generate ideas and tests, but I make sure I understand everything and type it in except for trivial stuff. The main value of an engineer is understanding things. AI can help me understand things better and faster. If I just setup plans for AI and vibe, human capital is neglected and declines. I don't think there's much of a future if you don't know what you're doing, but there is always a future for people with deep understanding of problems and systems.
I’m the same way. But I took a bite and now I’m hooked.
I started using it for things I hate, ended up using it everywhere. I move 5x faster. I follow along most of the time. Twice a week I realize I’ve lost the thread. Once a month it sets me back a week or more.
We're in a phase where founders are obsessed with productivity so everything seens to work just fine and as intended with few slops.
They're racing to be as productive as possible so we can get who knows where.
There are times when I honestly don't even know why we're automating certain tasks anymore.
In the past, we had the option of saying we didn't know something, especially when it was an area we didn't want to know about. Today, we no longer have that option, because knowledge is just a prompt away. So you end up doing front-end work for a backend application you just built, even though your role was supposed to be completely different.
We’ve had this too and made a change to our code review guidelines to mention rejection if code is clearly just ai slop. We’ve let like four contractors go so far over it. Like ya they get work done fast but then when it comes to making it production ready they’re completely incapable. Last time we just merged it anyways to hit a budget it set everyone back and we’re still cleaning up the mess.
It can only result in more work if you freelance because it you disclose that you used llm’s then you did it faster than usual and presumably less quality so you have to deliver more to retain the same income except now your paying all the providers for all the models because you start hitting usage limits and claude sucks on the weekends and your drive is full of ‘artifacts’, which incurs mental overhead that is exacerbated by your crippling adhd
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 “
I’ve probably prompted 10,000 lines of working code in the last two months. I started with terraform which I know backwards and forwards. Works perfectly 95% of the time and I know where it will go wrong so I watch for that. (Working both green field, in other existing repos and with other collaborators)
Moved on to a big data processing project, works great, needed a senior engineer to diagnose one small index problem which he identified in 30s. (But I’d bonked on for a week because in some cases I just don’t know what I don’t know)
Meanwhile a colleague wanted a sample of the data. Vibe coded that. (Extract from zip without decompressing) He wanted randomized. One shot. Done. Then he wanted randomized across 5 categories. Then he wanted 10x the sample size. Data request completed before the conversion was over. I would have worked on that for three hours before and bonked if I hit the limit of my technical knowledge.
Built a monitoring stack. Configured servers, used it to troubleshoot dozens of problems.
For stuff I can’t do, now I can do.
For stuff I could do with difficulty now I can do with ease.
For stuff I could do easily now I can do fast and easy.
Your vastly different experience is baffling and alien to me. (So thank you for opening my eyes)
I saw somewhere that you guys had All Hands where juniors were prohibited from pushing AI-assisted code due to some reliability thing going on? Was that just a hoax?
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Discussion thread enables collective gathering and deliberation. No restrictions on who can join. Moderation against violence or harassment supports right to peaceful assembly.
Discussion forum enables formation of professional community. Persistent thread and search enable ongoing social participation. Free access supports participation regardless of economic status.
Platform has no explicit safeguards against rights-negating interpretation. Moderation is reactive to specific harms, not proactive in protecting rights as indivisible.
Platform enables workers to discuss working conditions publicly but provides no direct enforcement of labor rights or collective bargaining mechanisms.
Discussion enables sharing of information about earning potential, working conditions, and professional growth. However, platform provides no direct support for health, food, or housing rights.
Community guidelines prohibit harassment and dehumanizing language. However, platform monetizes user data and comments (via analytics and advertising), creating potential tension between respecting dignity and extracting value.
Discussion forum structure enables open participation and peer review of claims. However, platform governance is hierarchical (moderators, voting algorithms), not participatory in rights protection.
Hacker News voting system enables users to collectively rank content, resembling lightweight democratic participation. However, final moderation authority rests with site operators.
Platform allows pseudonymous participation, reducing discrimination barriers. However, participation requires English literacy and internet access, creating de facto exclusion.
Hacker News collects user IP addresses, login data, and comment history. Data is publicly visible (unless account deleted). Third-party tracking and analytics present. No end-to-end encryption.
Platform moderation applies community guidelines uniformly (in theory). However, algorithmic ranking and voting systems may create de facto unequal treatment of content based on initial visibility.
Discussion format enables peer review and contestation of claims. Multiple perspectives can present evidence. However, no independent judiciary or formal appeals process; moderation is hierarchical.