Omg you still have to use your brain and wear a million hats from PM, product designer, content designer. qa tester, web designer (coz you gotta get that website up with the right marketing message that explains your app), graphic designer (coz you gotta get some nice pics ready for the App Store) etc. etc.

I wouldnt dare add developer to the list because omg, I have sooo much respect and now understand why they make the big bucks. Who can do a job for something that doesn’t work or has the ability to wrong at any point in the chain, just chasing for that one time stuff works or works in cohesion. Omg you really have to be built for that coz it sure ain’t easy. I mean I enjoy solving puzzles lol but that’s a whole other level and I was just getting frustrated from writing prompts lol so I can’t imagine.

What was hilarious was that even the Ai got stuck and packed me off to discord for an answer from a human. At that point I was like every tech CEO needs to vibe code an app so they can get it. That thing literally has 0 memory too.

Anyway was a fantastic, extremely frustrating at times but rewarding experience and fingers crossed (at build 16) I’m very close to launch.

Oh and another thing, don’t be a perfectionist and try and do this ish coz that’s a whole other problem. I’m in UX so just couldn’t put out slop so I’m happy with what I’ve got and if someone calls it simple that’ll be the most praise I could get coz I literally don’t even want you to know the apps there (other than opening it) coz I made every effort to get outta your way.

If I sound a little delirious it’s coz I been at it 6 weeks straight literally night and day (did I say it’s also kinda addictive, lol) but yeah just wanted to say hi, share a little of my experience and tell folks to keep on keeping on.

I recently posted on reddit about making a full saas with ai with full security, maintenance is a scam they are just getting angry on me and I was like wtf... You have to tell me how can I improve if there is a way... I think we need professionals to leke at our code first before delivering as a respected ai professional

Been thinking about this for a while...

Why are we all just accepting that coding agents need insane context windows and millions of tokens just to build a decent app?

It feels like every new AI release is basically:

«"We made it smarter by making it bigger."»

But what if that's the wrong direction?

What if the next generation of coding agents is built around efficiency, not brute force?

Imagine an agent that:

\- Understands an entire codebase without burning through your token budget.

\- Writes production-quality code in any language, not just the popular ones.

\- Knows when not to generate unnecessary code.

\- Optimizes before it generates.

\- Uses memory intelligently instead of rereading everything every prompt.

\- Feels closer to working with a senior engineer than an autocomplete.

I genuinely think token efficiency is becoming just as important as model intelligence.

If an agent can achieve the same (or better) results while using 10x fewer tokens, that's lower latency, lower cost, better scalability, and something you can actually run continuously instead of worrying about your API bill.

Open source has already beaten closed systems more than once.

So why not build the next generation of coding agents together?

Not another wrapper.

Not another chatbot with a fancy UI.

A real engineering agent that's modular, transparent, language-agnostic, ridiculously optimized, and built by the community.

We're at a point where a few passionate developers can genuinely change the direction of AI tooling.

If you're into compilers, inference optimization, agent architectures, memory systems, code analysis, or just love building cool stuff...

Let's stop chasing bigger models for a second.

Let's build smarter ones.

Curious to hear what everyone thinks—what's the biggest thing today's coding agents are still getting wrong?

The problem

Every time I spun up a Laravel project, I'd do the same dance: copy phpstan.neon from the last repo, copy pint.json, wire up Pest, set up a pre-commit hook, write a CLAUDE.md full of engineering rules… and then drift would set in. Repo A was on PHPStan level 5, repo B on 7. One had architecture tests, the others didn't. The "rules" lived in my head.

Then I started leaning on AI agents (Claude Code, Cursor, Copilot) and a new problem showed up: the agent would happily ignore the conventions. It'd write untyped code, skip tests, invent patterns the rest of the repo didn't use. A CLAUDE.md full of rules helps, but rules a model can choose to skip aren't guardrails — they're suggestions.

What I wanted

• One command to set up a fresh Laravel repo with the same guardrails every time.
• A quality gate the agent literally cannot skip — not just docs it might read.
• The same gate running everywhere: at commit time, at the end of every AI turn, and in CI. If it passes locally it passes in CI, no surprises.

What I ended up building

A small Composer dev package. You run:

composer require --dev mohamed-ashraf-elsaed/claude-kit
php artisan claude-kit:install

It detects your frontend stack (Inertia+Vue, Inertia+React, Blade/Livewire, or API-only), asks what you actually want (PHPStan? which level? strict-rules? Pest or PHPUnit? coverage
threshold? arch tests? git hooks?), and scaffolds it — without clobbering existing files (composer.json / package.json get merged, not overwritten).

The part I care about most: the quality gate is one shell script (Pint + PHPStan level 7 + strict-rules + Pest with an 80% coverage gate + frontend lint) that backs three things — the git
pre-commit hook, Claude Code's Stop hook, and the CI workflow. So the AI agent's turn doesn't "finish" until the gate is green. Same script everywhere = no "works on my machine, red in
CI."

There's also a hybrid update model: the machinery (the gate script, the hook) is referenced from vendor/, so a composer update propagates fixes to every project. The content you own
(CLAUDE.md, linter configs, skills) is written into your repo so you can edit it freely.

It's MIT, PHP 8.2–8.4, Laravel 11/12/13. Repo + docs: github.com/mohamed-ashraf-elsaed/claude-kit

The actual question I'm curious about: for those of you using AI agents on real codebases — how are you enforcing conventions? Just prompt/CLAUDE.md, hooks like this, CI-only, or something
else? Genuinely want to hear what's working, because the "agent skips the rules" problem feels underrated.

I noticed this first in myself so I'm trying to see If you're facing it too...

Back in the day, I had to first plan things, do the

- wireframes, test them, improve

- audit the IA flow (information architecture)

- map the user journey then test again

- real band building

- extract the design system

- plan my component system and make sure it is modular

- build the component system

- build one page at a time, finish then test it

Today? Few prompts. Done.

I feel like I'm getting dumber and lazy overtime.

My muscle memory is no longer the same. I'm at fault cause I could've simply constrain it from the start, however as we're all racing to deliver faster sometimes we just forget and things just start slipping bit by bit.

You assume that it got it right, or you have your system in place then you realize as you're building (vibe-coding) your app, you are not getting your results, and you end burning tokens for no reason and you get nowhere.

The AI slop is slapping you with each output. I have personally degraded in my skills, that is my fault but now I literally do not start any code until all of my laws in place and this has put me back to where I was in terms of production quality.

Just imagine people that have 0 experience in IA, branding, ux, ui, and code?

The results would be terrifying I would assume.

What about you? Faced the same issue?

I have been trying to make a saas with totally AI agent whenever I ask it to solve a bug it just started giving me fake tests that create more problems tho

Hey everyone,

For the last 6 months, I’ve been building ReCode, a dedicated AI web workbench for developers. But because I've been building in a silo, I need a brutal reality check.

I know inline IDE tools like Cursor and Copilot dominate right now. But my goal was to build a separate environment specifically for boring, multi-step tasks that usually clutter up an IDE chat tab. It also doesn't store any of your code (processed in memory) so isnt a vibe coding tool at all.

Here is what it handles right now:

Mock Data Factory: Parses your SQL/Prisma schemas and generates complex, relational mock data with proper foreign key mapping.

Code Auditor: Runs a deep health check for architecture smells, security flaws, and visualizes Big O complexity.

Universal Translator: Accurately migrates logic and whole files between languages (like Python to TS, C++ to Rust).

And there are more tools but they are less important.

My Dilemma:

I know developers hate breaking their flow.

Is context-switching to a standalone web app a total dealbreaker for you?

Would you ever use a separate tool for complex tasks like DB seeding or deep code audits, writing boring RegEx, translating CSS between its frameworks and so on, or is your IDE chat genuinely enough?

If I need to swallow my pride and pivot this into a VS Code extension, tell me. Be brutal. I’ve attached a few screenshots so you can see what the UI looks like. Thanks!

P.S. If anyone actually wants to poke around the UI and tell me why it sucks, the alpha is live here: recode-alpha.vercel.app

When I run Claude Code, Codex, Cursor, or Aider side by side, I sometimes lose track of which session is still running, waiting for input, looping on an error, or changing risky files.

My current setup is basically terminal tabs, git diff, and occasional notifications.

For people using multiple coding agents: how do you monitor them?

Do you use tmux, IDE tabs, custom scripts, logs, dashboards, or just keep it in your head?

What signals are actually worth watching?

- waiting for input

- repeated errors

- changed files

- touched migrations/auth/env/deploy config

- token/cost

- elapsed time

Curious what workflows are working in practice.

I'm trying to understand whether Al coding agents create different problems for solo builders vs engineering teams.

For solo builders, it seems easier for one person to keep the full context in their head, run multiple agents, review everything, and decide what to merge.

For teams, I'm wondering if the bottleneck changes: multiple people may each have their own Claude Code, but the work still has to come together through tickets, PRs, reviews, Cl, Slack, Linear/Jira, etc. Maybe my Claude code has made different assumptions to my colleagues

On top all of that PRs are 10k LoC

For people here:

\- Is agent-assisted coding easier as a solo dev than in a team?

\- If you work on a team, where does the workflow break down?

\- Do teammates get enough visibility into what an agent is doing before the PR appears?

\- Do agents ever duplicate work, make conflicting assumptions, or miss context that another teammate had?

\-Have good tickets, worktrees, Cl, and code review mostly solved this for your team?

\- What process have you added because of Al-generated code?

I feel when we give these tools access to our codebase, we aren’t just getting help with code refactoring or code generation, we’re feeding a system that learns structure, behavior, and context about our engineering environment. This ready to eat meal becomes the training set for the next AI model. The real troubling thing is that it can potentially expose our IP to the world.

I recently downloaded my data from one of the major AI providers. I expected chat logs.

What I got was significantly more structured and detailed than I anticipated. Instead it was more like a behavioral dataset describing everything - what I'm building, how I'm building, how many potential employees, even what laptop versions I am using.

That made me think - While all that structured and sensitive information may be helping the AI tool to get me better responses quickly but what happens to it after that?

I think vibe coding i.e. creating websites, apps etc. Using AI is still alien, not much explored or common, not as decentralised and localised as content creation became once YouTube, even tik tok came in okay

Many companies like v0, anything, tried to create social platforms for these things but failed. Is it not getting more common?

Okay so I keep trying AI app builders and I keep running into the same shape of problem. In a weekend I can get a gorgeous frontend. Nice dashboard, clean forms, smooth transitions. I show it to friends and they are impressed.

Then the backend wall.

Oh you want users to sign up? Now you need auth. Auth needs a database. Users need different permissions so theres access control. Oh you want to charge money? Now there is paid-access state, billing config, and some server-side place that decides who gets what. Want people to actually keep access after they pay and lose it when they should? Now you need backend logic you can inspect before serving content.

The UI takes days. The backend takes longer. Sometimes the tool helps, sometimes I end up wiring a separate backend, and sometimes I just realize I do not understand the permission model well enough yet.

I've now tried this cycle with three different builders and hit the same handoff question each time. Am I just expecting too much? Or is there a workflow where the backend parts are first-class without making me feel trapped?

I keep seeing posts on here from people saying they completely lost everything they built after an AI went off the rails. (I know some are bots, but still).

Seriously, do you guys not know how to use local Git?

Just initialize a repo. Commit your stable code before you start building out a new feature. When the AI inevitably breaks something or turns your codebase into spaghetti, just revert back to your last working state and learn from it.

It takes two seconds and saves hours of loss work.

Edit:

For those who actually don't know the commands, it’s just three lines in your terminal once your code is working:

1. ⁠git status⁠ (to see what files were modified)
2. ⁠git add .⁠ (to stage all the changes)
3. ⁠git commit -m "working state"⁠ (to save your progress)

Done. Now you have a restore point for when things go south.

Disclaimer: I'm not anti-AI and I'd like to hear from people who are finding success with code reviews. Ideally, I'd like to have a discussion that's a bit more involved than just "AI bad lol".

So I've been giving AI code reviews a shot for the past couple weeks to try to help with the code-review bottleneck that we, like many other teams, are facing due to individual contributors being able to generate code much faster than before. However, I don't get how they are useful. Here's a typical workflow I've experienced :

1. Junior sends in a PR, it's obviously mostly AI generated code. Such is life these days.

2. I ask them if they asked for reviews to their AI agent of choice before sending it my way. Their answer: yes, three times.

3. ok fine, I give it a shot. I also ask for a review from an AI agent, with the supposed added value that, since I have more experience, I'll be able to judge the quality of that review better. I'm not sure I agree with this assumption I've been hearing, but let's just assume this is how it works.

4. The AI review comes out with 4 comments, 2 of them i find neat and the other 2 a bit nitpicky. Fine, but im curious... what if I implement these changes locally, and ask another agent to review this new code version in the context of the PR?

5. New agent finds 5 new "issues" to correct. Wtf? Ok now im curious, so I start looping for 5-6 iterations just to see where this goes. Every single time, it digs deeper to find something to complain about. Turns out the AI reviews can be chained pretty much infinitely and a new model or agent will always find something to "improve" on the PR and find a reason to not accept the merge... at least in my limited experience with them.

Am I missing something? People have been praising code reviews as an "actual good usecase" for AI, but I don't see it. In my experience, the agents just find too many ways to simply chain-critique the code. The agent's job is essentially to find holes in the PR, and it will dig relentlessly until it finds something to complain about. I have not seen a single "yep, that PR looks 10/10 now" from any agent in the 2 weeks I tried them.

I've been working for quite some times now in my first software development role. Been using gpt to handle all my tickets. At first it was plain old copy past, test, paste output, re-run, and repeat. It is.a huge codebase, but now as I've worked on a few things, they are making more and more sense, I'm able to apply my own thought process, and do my own analysis on how things work (but only to a certain extent, it's a net positive difference but doesn't mean I'm independent and competent), maybe here and there I'll write a line of code myself if I can reference something similar nearby. I was able to put out some good output, but again, heavily dependent on gpt. But I don't think this is sustainable, I really want to develop the skill of being able to write my own code. I am incredibly dependent on using ai for generating code. But with a codebase so complex, where can I even begin to learn code to a degree where I can contribute to it, I barely even know regular syntax if I was asked to write a basic program for some simple algorithm or build some basic tool, and I dont know how to write any of this myself. My current ability is understanding how to use all these tools and just understanding the interactions between various things, its an understanding of things from a high-level perspective, but going further down into the more technical layers, you lose me.

I am wondering hie professional programmers and developers think about that and the particular case I want to present here: I am developing a TT RPG and created a very basic webapp to have a digital charactersheet that can print to pdf using java script. While I learned the basics of that language through an it module at university and never had to program things professionall, I am by no means capable of programming without help. So I used A.I. to learn what is necessary to finish my project. By the examples given I could finish the program (functional, very basic but ugly looking).

I showed it to my friend who codes for a living, but only after changing to that field through an online course. We discussed it would be cool to have my webapp on android and add functions more akin to d&d beyond. He claimed that using the ai tools provided by his company, we could finish the app fast. So he fed the ai with the necessary part of my game rules and he quite fast achieved what we needed. However he does not know anything about doing that by himself. He does not know the code or wether there are unnecessary lines of code. He does not know how or why the ai does something. He does not want to learn how to do things, he just lets the ai do everything. He even does that in his job. Many mistakes are made by the ai and many things simply get ignored. My friend always claimed he needs more data (i provider json files), but the ai simply ignored many things. I spend time on understanding everything in my code, my code was way smaller (i know its just a webapp) and if I looked for something, I knew where to find it. In short while the finished app was fine (while still incomplete), I really am uncomfortable with the way my friend handles this. He did not write one line of code, everything is done by ai. Am I old fashioned? Is this the way to work these days? Does my friend make himself useless at work?

Thanks for your time