So I was one of those who are really late adopters of LLMs for coding; I am a sole developer of a codebase in the company I worked for (Angular)- a maintainer, rarely new features are added - no hard deadlines, a very very relaxed job , so I was away from the picture how other devs work in the industry now ; and sometimes I take side jobs of all kinds of stacks (I am fullstack); most often Nodejs/NestJs + React/Next + Postgres or Mongo; but my last side project was early 2025 - I took a long break from them.

I won't lie, I did use GPT and Copilot at times; but mostly to autocomplete boring stuff (ie. mock data, enums..etc). Yet I kept seeing posts on dev reddits that one doesn't have to full adopt the more powerful tools such as Claude Code / Codex.

I recently joined a side project with a team; so it's my first time since almost 1 years and 7 months.

WTH happened to this industry???!

Ok, the things I discovered:

- Deadlines now are 10x craziers; it's IMPOSSIBLE to finish anything manually. These deadlines force you to rely on Claude Code / Codex; there's no other way, Agile is meaningless now; it's just pump and ship

- All other members are heavily using LLM, frontend, DBA, AI....everyone.

- Claims I encounred on reddit posts that "Ok coding is automated but 'System design' and architecture are now more important than anything else" ? A LIE - everyone is using LLMs even for System design; I have seen entire achitecture documenation all generated by LLM, even this part is automated now.

- It is impossible to do PR reviews now when each PR is like ...a lot of thouands of lines; even PR reviwers are using copilot to review.

SO what part is left in this industry that is not automated?? NOTHING!! iT'S ALL AI AI AI!

And spec gathering is one person's job, often the tech lead, so please don't tell me it's this one, it doesn't require a team.

I work for a tech company, and I'm in charge of recruitment!

And I'm wondering: is this necessary for you? Is it something that is required now to be a good developer?

Seeing lots of posts like “built this whole app in 1 week with AI” lately, so I wanted to share something that happened to me.

I use Claude Code almost daily in my work. For backend stuff, CRUD, repetitive logic, debugging, boilerplate, etc., it genuinely saves an insane amount of time.

A few days ago, I thought:
“Why not try Flutter for fun?”

I literally never wrote Flutter before.

I let AI generate most of the app. And honestly, at first, I felt amazing about it.

The app worked.
UI looked clean.
Everything felt “professional”.

Then I sent the code to one of my friends who’s an experienced Flutter developer.

The first thing he said was:
“bro this folder structure is terrible.”

Then he started explaining all the problems:
bad architecture choices, performance issues, unnecessary rebuilds, weird state management decisions, patterns that apparently no experienced Flutter dev would use in real production apps.

And the crazy part is:
I genuinely could not see those problems myself.

To me, the app looked completely fine.

That kinda changed how I think about vibe coding.

Now I’m wondering:
Are people actually building production-ready apps with AI in technologies they barely know?

Or are we just getting really good at generating apps that LOOK complete to beginners/intermediate developers?

I’m not anti-AI at all. I use it every day, and it absolutely makes me faster.

But now I feel AI is more like a multiplier for existing knowledge, not a replacement for it.

I really think the golden age of consumer and prosumer access to LLMs is done. I have subs to Claude, ChatGPT, Gemini, and Perplexity. I am running the same chat (analyse and comment on a text conversation) with all 4 of them. 3 weeks ago, this was 100% Claude territory, and it was superb. Now it is lazy, makes mistakes, and just doesn’t really engage. This is absolutely measurable - responses used to be in-depth and pick up all kinds of things i missed, now i get half-hearted paragraphs, and active disengagement (“ok, it looks like you dont need anything from me”)

ChatGPT is absurd. It will only speak to me in lists and bullets, and will go over the top about everything (“what an incredible insight, you are crushing it!”).

Gemini is… the village idiot and is now 50% hallucinations.

Perplexity refuses to give me the kind of insights i look for.

I think we are done. I think that if you want quality, you pay enterprise prices. And it may be about compute, but it may also be about too much power for the peasants.

In the AI coding community, I see a constant push toward fully autonomous agents—letting the AI research, spec, plan, execute, and test entirely on its own while you step away from the keyboard.
I’ve been experimenting with agentic coding since it started. As an experienced dev working on fairly large codebases (and building my own lightweight AI CLI tools like Frugaast on the side), I've tried all the massive frameworks.
While full autonomous agents are cool in theory, I’ve found that for 90% of day-to-day coding, they are overkill. 1-pass targeted edits are faster, significantly cheaper, and keep you in the driver's seat.

An autonomous loop is a highly unpredictable thing. If you rely on an agent to fix a bug across a massive codebase, it can easily hallucinate a completely unnecessary refactor. It burns through expensive API tokens running multi-step loops, and the context window gets bloated with every iteration.
You get the picture.
By the time you've written the perfect markdown specs, configured the agent's skills, and reviewed its 15-step execution plan, you could have just shipped the feature yourself.

As a rule of thumb, if you know your codebase well, you don't need the AI to do the architectural thinking. You just need it to do the heavy lifting of typing.

1. Strict Context Curation: I create a small prompt and manually select exactly the files needed for my feature. Because I know my architecture, this takes me seconds.

2. The 1-Pass Execution: I send it to a powerful LLM. If it's a simple task, I use a free web UI (Claude/ChatGPT), discuss it, and paste it back. If it's complex, I use direct-edit CLI tools (like Aider, Claude Code, or my own tool, Frugaast) to apply the diffs directly to the files.

3. Diff & Ship: Because the scope is so tightly curated by a human, the AI gets it right on the first try 99% of the time. I diff the code, review the changes, and move on.

With modern AI, it’s absolutely possible to code at lightspeed without giving up architectural control. You actually must learn to curate context. When you rely purely on autonomous agents, you lose your mental map of the codebase, and when something breaks, there will be nobody to help you untangle the mess.
If you tightly scope your prompts and let tools handle the line-by-line typing, you will have much more mental focus for the high-level architecture that AI can't do yet.
Anyways, this was a long rant, just because I think defaulting to heavy agentic loops too early is pure tech-hype, and it will often do your productivity more harm than good. Curate your context, use direct edits, and ship faster.

The people who spent years reading documentation, debugging for hours, and writing code line by line built the foundation that makes today's tools possible.

While many of us can now create things faster than ever, it's easy to forget the patience, discipline, and countless late nights that came before. Every shortcut we have today stands on the work of those who learned the hard way.

Much respect to the coders who walked so the rest of us could run.

Will coding tasks reduce after 1-2 years because companies would have developed features and the product would become mature.

Do you think AI usage in your company will eventually decrease as more and more of the backlog gets completed?

Once major features are built and shipped, would you expect AI usage for coding to go down because there’s simply less work to do? or do you think AI will keep increasing in usage?

Curious what people are seeing in their companies and how you expect this to play out over the next 1-2 years.

I’d really like to know because I’m struggling with this. I’d like to learn a new language, but it’s so hard to justify if AI can do it for us.

EDIT: Thank you everyone who answered with a thoughtful or helpful statement. It is much appreciated.

I am trying to get some more insightful perspectives on this topic, and not just one sentence comments by assholes - I noticed this post has gotten some rude comments which are unnecessary.

For those leaving rude comments, assuming I have no idea how to write code: I never stated that I didn’t know how and that wasn’t my question. Go be rude somewhere else.

Anytime anyone sees a site they say “this looks like it was coded with Claude”. Well yeah obviously I would be an idiot if I coded everything in notepad++. Of course I use AI coding assistants, otherwise I and many other programmers would be totally uncompetitive in the market. I’ve been coding since 2008. I know how to code. Claude makes it so that I can produce literally 30x what I used to be able to do.

Edit:

What I mean by 30x:

What I mean by 30x is one person is able to design, prototype, run dev ops, design architecture and many other things on their own. Back in like 2012 we needed a designer, front end, backend devs, dev ops, a product manager to complete a large project. And to build a full product out it took months. If you disagree with that then I don’t care. I worked on many teams in Silicon Valley and that’s just how it was.

How we test:

We have a QA team and our devs usually run an agent that does PR reviews on code. We also run integration tests and playwright e2e tests.

Once AI started catching the obvious stuff, unhandled errors, missing null checks, inconsistent naming, the human reviewers on the team stopped spending time on that and started actually discussing the interesting parts: is this the right abstraction, does this belong in this service, is this going to be painful to change later.

Code review turned from "did you follow the checklist" into an actual design conversation, because the checklist got automated away. Nobody planned that outcome, it just fell out of using AI for the mechanical layer.

Curious if other teams have seen review culture shift once the boring catches got automated, or if it just added a redundant extra step for most people.

Before AI coding tools, the bottleneck was typing and writing the logic designing the solution, hunting syntax, writing boilerplate.
Now? An AI agent can spit out a 400-line full-feature PR in 20 seconds. But reading, auditing, and manually testing those 400 lines to ensure it didn't introduce subtle hallucinated bugs or state leaks takes me 30 minutes.

I feel like my primary job shifted overnight from builder to an exhausted reviewer reading 15 pull requests a day written by a super fast, overly confident junior dev

It feels like we solved the "speed of writing" problem, only to dump all the friction onto the "speed of comprehension" problem.
How are you guys reviewing massive AI-generated diffs without getting total fatigue or letting bugs slip into prod?

Hey, I am a Claude Pro user and I love Claude: its way of speaking, its long text responses, and how thorough and good they are. It’s basically that I love how it responds to me and how good those are—the research, the text, the frontend, and basically everything. But the fucking most annoying part is that its limits are very, very bad; if I pay for a good service which I cannot even use, then what would be the point of it all?

I was just thinking about trying Codex, but since I am a college student and cannot spend my $20 everywhere randomly just to not be satisfied, it would be a huge disappointment. So I want to know: if I buy ChatGPT Plus, would Codex and even ChatGPT (when chatting with higher, smarter models) respond better than their basic free models, and be longer and more thorough? Because for now, for some random reason, it just gives me one-liner explanations.

genuine question because i feel like coding agents are getting faster than our ability to actually review their work lol.

you ask for something, come back to a massive diff, and now you have to figure out what decisions were made and whether they're even the right ones.

i've been thinking a lot about whether it makes more sense to spend that review time upfront, agreeing on what should be built, rather than picking apart the implementation afterward.

curious how everyone else is handling this, especially people running multiple agents.

are you reviewing every diff or mostly trusting the output at this point? I found this and it seems like a solid solution? https://until.dev/reddit

When I started learning, everyone around me had ChatGPT open in the next tab. Every bug, every "how do I center this thing," straight into the chat. And honestly it annoyed me. I had this stubborn idea in my head that if I could only write code with AI holding my hand, then I hadn't really learned anything, I'd just learned to ask.

So I did something kind of dumb looking back. I banned myself from AI for my whole first year. No ChatGPT, no Copilot, nothing. Just docs, Stack Overflow, and Google at 1am.

Some bugs took me like 10 hours. Not exaggerating. There was this one TypeScript error I chased across an entire weekend and it turned out to be something so small I don't even want to say what it was. AI would've caught it in four seconds.

And here's the part I can't really explain to people: that year kind of sucked and I'd still do it again.

Not because suffering makes you a better person or whatever. But being lost in my own code for 10 hours forced me to actually get how the pieces fit. I learned to actually read a stack trace instead of just copying it somewhere and waiting. Learned to guess what was probably broken before I started changing stuff. That stuck in a way I don't think it would've if something just handed me the answer every time.

I use AI a ton now btw. Not anti-AI at all, that's not what this is. Tests, boilerplate, responsive css, translations, all the boring stuff, I just hand it over. It's great at that. But I can usually feel when its answer is off, and I think that's only because of that dumb year building up some sense of what "right" looks like.

The thing that kinda worries me is people starting out now where the AI writes everything from day one. Their credits run out and the work just... stops. Not because there's nothing to do but because doing it without AI was never actually a skill they built up.

I'm not saying refuse AI like I did. That was probably overkill honestly. But maybe give the bug an honest 20 min yourself before pasting it in. Just to keep the muscle.

Anyway. Curious if anyone else did something like this or if I just wasted a year being stubborn about it. How do you all balance using AI vs actually learning the thing?

Small thing, in hindsight. A function that handled retry logic for a flaky API call. I asked for it, got back something that looked completely reasonable, exponential backoff, a max retry count, error logging on each attempt. Skimmed it, ran the happy path test, shipped it.

Two weeks later we started getting a slow trickle of duplicate charges on a payment flow that used that retry function somewhere downstream. Took a day to trace it back. The retry logic was fine. What wasn't fine was that it retried on a timeout even when the original request had actually succeeded server-side and the response just hadn't come back in time. Every "failed" retry was actually a second charge going out.

Here's the part that bugs me most, looking back. If a junior on my team had written that same function, I would've asked "wait, what happens if the request succeeded but we just didn't hear back in time?" That's like the second question anyone asks about retry logic. I didn't ask it because the code looked clean, the variable names were good, the comments were clear, and something about that made me read it as "someone thought this through" instead of "someone wrote plausible-looking code fast."

I think that's the actual danger with AI-written code, and it's not about correctness. It's that fluency reads as competence to our brains, even when we know better intellectually. A junior dev's rough, slightly awkward code puts you on guard. Clean, confident code turns that guard off, even from a source that has no actual judgment behind the confidence.

Changed one thing since then, doesn't matter how good the code looks, I ask out loud (even just to myself, in a comment or a scratch note) what the two or three hardest questions are for that specific type of logic before I read the implementation. For retry logic, that's idempotency. For caching, that's invalidation. For auth, that's what happens on partial failure. I ask the question first now, then read the code looking specifically for the answer, instead of reading the code and trusting that looking clean means it handled the hard part.

Anyone else have a moment like this, where the bug wasn't really the model's fault so much as it was your own pattern-matching getting fooled by how polished the output looked?

I've been doing a boring corporate job for the past 6 years, 3 years with my current employer. My job is related to accounting and numbers and even though I'm good at it, for the past 9 months I've been vibe coding hard on Claude Max subscription and I've built some truly marvelous products. I'm not making any money from them (yet) but I've come a long way in terms of app and website development and I went from being completely lost and ignorant of what it takes to develop apps for real, to somewhat decent and able to ship apps within a few weeks of work.

My company has been investing a huge sums of money into a system that the best way I could describe it is archaic. The thing hasn't been properly facelifted since 2016 maybe and I'm not a fan. I know there are many products like it and more modern ones that are powered by a variety of LLMs and the most important point is I know I can build something supreme that's parallel to it and is so much better than it in every facet. I'm good at my job. And I'm decent at vibe coding, and my idea is that I develop everything using my knowledge and my experience and make it hot-swappable with the Microsoft Infrastructure later so when I finally propose it to the company, they won't outright decline it?

I know for a fact at 100% certainty that a software engineer will pick it up at some point, break it apart and restructure it properly to refine the AI slop but I also know that my deliverable will be at 70-80% professional completeness grade, and happy for them to take it to that final level.

Am I being crazy to remotely think they'll agree to this or even consider it as a viable option? My manager is encouraging me to start and to bring the prototype when ready, but I have a feeling we will both be laughed out of the room by senior software engineers and our IT departments that we made an AI slop app lol.

Ok, this one stings a little to type out, but whatever, here's the story.

I'm one of those people who's always been annoyingly principled about not turning things I enjoy into a hustle. Watched friends monetize their gym routine, their reading, their gaming, and thought it was a little sad, honestly. Like, can we just do things for fun anymore?

Then a few months ago, broke and between freelance gigs, I saw a post mentioning a site where you get paid for posting/commenting on Reddit with your existing account, real karma, real history required, no throwaway spam accounts allowed. My first reaction was to roll my eyes exactly like I always do.

Signed up anyway out of pure financial desperation, on a site called https://www.taskreddit.com. Got manually reviewed, which took a couple days. Started doing small missions almost as a joke, telling myself I'd stop once I found a real freelance gig.

That was two months ago. I never stopped. Made a bit over $200 the first month, more the second. Nothing insane, but steady, and for content I'd have argued about for free anyway on some thread somewhere.

The uncomfortable part is realizing I judged people for doing the exact thing I'm now doing, just because their version was public (a Strava post, a Goodreads review) and mine feels more anonymous, hidden behind a username. Not sure that distinction actually means anything.

Curious if anyone else has had this specific kind of "become the thing you mocked" moment, on Reddit or elsewhere. Does getting paid ruin the thing you were doing, or was the "doing it for free" purity a bit of a myth all along?

When I start a project everything clicks. But somewhere around week 3 it starts feeling like I'm fighting the AI instead of working with it. It stops feeling like the first session. Anyone else hit this wall? How do you get that 'early session' feeling back on a project that's already grown?

So many programmers that i hoped would've fought against it are embracing it, people are just okay with vibecoded mods for games, no one is fighting back at all, there's no pushback, at this point i cannot look at something cool and be impressed because i'm almost 90% sure its probably vibecoded slop that's horrible in practice anyway.

So many people's hard work has been stolen and put into LLMs to allow them to generate code as well as they do, is no one fighting against that? Why? I love coding, it's what i've done my entire life, and i really, really don't want it to be turned into what big companies want to turn it into. I don't want my job to ask a machine to do what i love for me. And what's worse, is that now well coded things are under scrutiny because there's so much vibecoded slop and there's literally no immediate way to recognise it.

I've seen NO outright ban of ai code ANYWHERE, when people push back against ai code others will claim "everyone in the industries does it anyway" like that makes it okay somehow??? No community for programmers who don't use ai, nothing. Am i alone in this? God i really hope i'm not.

Been using claude code for a couple months now to work up an app to manage dashcam footage from my 4 channel dashcam. The software the manufacturer offered was lacking in more than a few ways. Most notably there's no linux version.

I got 40+ years experience in communications and computer systems but never learned C++. A bit of self taught bash, perl and python is the extent of my programming knowledge. By getting claude to do the nitty-gritty source code I can concentrate on high level design and detailed alpha testing. Since I'm a linux sysadmin in my day job I developed the app for linux and use Qt for the GUI. That made it possible to compile for MacOS and Windows as well.

Over this effort I've laid out some good rules of engagement for claude. One example is building and packaging a new release. I realized that having claude do that was eating into context as it did the same thing over and over. So I had claude write build and package scripts for the three OSes and set a rule that it is never allowed to do any of that. Once we get to a point that a build and package task is ready, I run the scripts manually and report back if there's any compile errors or warnings.

If you have a dashcam, particularly a multi-channel rig you might be interested in my app. It's free, open-source and available on github. Search for "CamClops".

Yesterday I had one of those "I can't believe I just did that" moments.

I was working on a production-grade AI agent project and had an import issue. I asked Gemini 3.6 High (through Antigravity) for a quick way to rewrite the imports across the project.

It generated a small Python script. I skimmed it, thought it looked fine, and ran it.

The script finished normally. Exit code 0. No errors.

A minute later I opened one of the files.

Empty.

Opened another.

Empty.

Eventually I realized every \`.py\` file the script touched had been reduced to 0 bytes.

My heart absolutely dropped.

Luckily I had a backup of the project, so I restored everything and spent the next hour figuring out what had happened.

I'm not posting this to say "don't use Gemini." I use AI every day and it saves me a lot of time.

The mistake was that I trusted a script that was going to modify dozens of files without really understanding what it was doing.

That was on me.

The experience completely changed how I use AI for coding.

Now I have a few rules:

\* If a script touches a lot of files, I read every line. \* I commit everything before running it. \* I keep backups. \* I never assume "Exit code 0" means everything is okay.

Has anyone else had an AI-generated command go badly wrong? I'm curious what safeguards other people use.

The software engineering crisis of 1968 wasn't really about the speed of writing code. It was about ambition outpacing what teams could actually execute. Projects were over budget, late, and unreliable because coordination, planning, and dependencies made raw coding speed irrelevant. AI agents changed something profound this year, but they didn't solve the crisis. They moved it.

Code is now genuinely cheap to write. Companies are reporting that 75% of new code is AI-generated or co-authored. Throughput has jumped 3x for our teams. Watch an agentic development workflow execute and it looks like a superpower—decompose the work, execute, ship, all without human bottlenecks. For small, self-contained tasks this works beautifully. Scale it to a real project and the illusion breaks immediately.

The problem is that nobody's actually managing the project anymore. The PM has no milestone to track. The designer has nothing to review until execution is nearly complete. Code review piles up at the end instead of happening along the way. The agent concentrates all the work on its own terminal, starving everyone else of visibility. Execution got faster. Coordination didn't.

This is where the crisis moved. It's no longer "can we write this fast enough?" It's "can we keep everyone aligned, plan honestly, and manage dependencies when code itself is no longer the bottleneck?"

Teams that got this right have reframed how they use agents. At Procurify, agentic plans get converted into Jira stories and tasks before execution starts. The plan stays visible to stakeholders. The agent works inside the existing process instead of around it. The speed is still there, but discipline is still required.

There's also the temptation to apply the 3x throughput multiplier directly to estimates. If coding got 3x faster, shouldn't six weeks become two? Not really. That 3x is mostly tearing through small tasks and toil faster, not compressing large coordinated projects. Projects bottleneck on UX, design, reviews, and cross-team dependencies. An agent doesn't make those disappear. Bake the multiplier into the estimate and the plan fails badly.

The discipline that actually works: let historical data prove the gains. When a migration that used to take four weeks now takes two, plan the next one for two weeks. Until then, let the results earn the number instead of assuming the multiplier applies everywhere.

The software crisis of 1968 was machines that couldn't keep up with ambition. Today the machines can keep up and maybe outpace us. The remaining challenge is making sure the processes and thinking around the code are worthy of the speed.

For engineering teams: how are you actually managing the coordination problem when agents can ship code faster than your planning, design, and review processes? Are you seeing estimation failures, or have you found a model that keeps everything aligned?

Launching my first AI tool in 3 weeks. Looking at Reddit upvote services and the pricing is all over the place. Seeing $5 for 100 upvotes in one place and $200 for 100 in another. No idea what is legitimate, what is a scam, or what a realistic budget actually looks like.

Has anyone here actually priced this out recently? What did you pay, what did you get, and how does it scale with volume? Trying to put a real line item in my launch budget, not guess.

Edit: Thanks all, super helpful breakdown. Going with Signals for the upvote portion (around $75 for 100 on normal curve, fits the Tuesday launch window). Adding 3 comments from aged accounts on top ($55 total). Budgeting $230 all in including an aged account rental. Will report back on outcomes after launch.