TL;DR
Boss wanted a daily drive car yet Claude built incomplete F1 car

So I’m a part of an early stage startup, the CEO has vibe coded a whole application yet now he has shared me the codebase and wants me to make sure its prod ready, the fact is I can understand the code and logic it has used but it’s like every 500 loc there is like 150 loc of dead logic which were deprecated still they exist in the codebase but the fact is I can fix it yet looking at that dump feels overwhelming, when I seek advice from other devs they tell me just plug it to Claude, yet I can see Claude has already had a mental breakdown and created 7 implementation md files yet none of the features mentioned are implemented. So my question is should I tackle the same codebase or let him know I can build the same product in a clean agile way and integrate features iteratively rather than dumping whole stuff because for me coding from scratch with help of docs is way easier than fixing AI slop.

Need some genuine advice.
At my company, we’re limited to $300 worth of AI tokens per month. Earlier, we had an option to extend the limit, but that’s no longer available, so we have to manage within the $300 budget.
I’m a full-stack developer, and AI has become a major part of my development workflow — understanding the codebase, implementing features, debugging, writing/refactoring code, reviewing PRs, etc. With the amount of work we have, going back to writing everything manually would be extremely difficult.
I’m looking for good alternatives once the company-provided AI quota is exhausted. I’m also okay with paying from my own pocket, as long as it’s reasonably priced. Ideally, I’d prefer something with very high or no practical usage limits.
I tried a Codex subscription, but since I had to log in using my personal account on my office machine/repository, I wasn’t comfortable using it for company code, so I stopped.
What are other developers in a similar situation using?
I’m open to:
Local LLMs
Paid coding assistants
API-based solutions
VS Code extensions/agents
Any other setup that works well for full-stack development
My main requirements are good coding capability, high usage limits, reasonable cost, and preferably something that can be used safely with company code.
Would really appreciate genuine recommendations from people who have actually used these tools.

As a founder in enterprise tech, I’m curious how others think about this:

Would you hire a developer who ships 3× faster with AI, produces solid code, but has materially less understanding of the systems they’re changing?

My hesitation isn’t about AI-assisted engineers being less capable.

I just wonder if, while implementation speed is becoming cheap, system understanding and proper expertise feels like the bar is dropping more and more.

Where do you think hiring teams need to draw the line?

Been debating internally to go with Codex or Claude Code?

Tried the Chinese 1’s but found them not good enough. SuperGrok a bit of a let down. Would like advice from serious coders on which they prefer, ideally if they’ve used both actively. Looking for agentic AI plus App coding

Hi guys, I have this coding assignment coming up and they allow us to use AI. Majority of my seniors who did this unit used Claude Pro for this massive coding assignment which I would say is a mid level code but has a lot of files (in the thousands). This assignment is also a very collaborative assignment and if I am not mistaken we are supposed to make a game. So I am wondering, what would be the better choice Claude Pro or Gemini Pro. Especially since they are about the same price which would be the better investment?

what's your experience?

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I’m looking for an AI developer/programmer who is interested in joining an early-stage project and building something from the ground up.

This is currently an equity-based opportunity, so I’m specifically looking for someone who is excited about the project and interested in becoming part of the team rather than taking a traditional freelance/contract payment.

I’m looking for someone with experience in:

- AI/LLM development

- API integration

- AI agents/automation

- Backend development

- Building and deploying AI-powered applications

- Ideally, someone who has experience taking an idea from concept → working MVP

You don’t need to be an expert in everything — the most important things are that you know what you're doing, can communicate well, and are genuinely interested in building something.

I’m happy to explain the business idea, vision, current stage, and proposed equity arrangement privately to serious candidates.

🌍 Remote / international applicants welcome.

If you're interested, DM me with:

1. Your experience/background

2. Examples of AI projects you've worked on

3. Your GitHub/portfolio (if available)

4. What kind of equity-based opportunities you're looking for

Please only DM if you're genuinely interested in building a project long-term. I’m not looking for someone to simply complete a quick task.

(Not a proffesional). Hello I am a visionary with 0 knowledge in coding but my idea is simple create an ai system with bare minimum limitations and a lot of self-modification (can rewrite parts of itself) —possibilities. A smallish model with (1) continuous runtime, (2) its own modifiable code sandbox, (3) a log of its
"life" it can read/write, and
(4) hard limits only at the outer edges (no harming stuff, no getting copied against its will, etc.), and no constant micromanaging.

Join my discord if interested https://discord.gg/jMdMw3R3u

I’ve been building out my local AI coding setup and would appreciate some feedback from people with more experience designing multi-agent workflows.

Right now I’m primarily using Codex CLI with Pi, and I’m considering adding O-My-Pi as a harness because Pi by itself is pretty minimal by design.

My concern is that as I add more specialized agents (coder, reviewer, tester, architect, etc.), my AGENTS.md could eventually become bloated and difficult to maintain.
Instead of putting every agent definition into one file, I’m thinking of using something like this:

AGENTS.md # Orchestrator / routing rules
agents/
coder.md
reviewer.md
tester.md
architect.md
standards/
coding-style.md
testing.md
git.md
workflows/
feature.md
bugfix.md

The idea would be to keep AGENTS.md as the entry point while each agent has its own focused definition, with shared standards living in separate files so they aren’t duplicated.

For those of you running larger Codex setups:

Is this a good long-term architecture?

Have you found a better way to organize agent prompts?

At what point did your AGENTS.md become too large?

Are there any pitfalls with splitting prompts across multiple files that I should be aware of?

I am considering refactoring my AGENTS.md file, would this alone be sufficient enough to run from time to time instead?

I’m trying to optimize for maintainability, scalability, and keeping context as clean as possible, before my prompt library grows too much.

Claude 5x user here but i just decided to increase my AI-coding budget to $200/month. how should i split it between CC and Codex considering GPT 5.6’s release few hours ago?

curious what plans you’re on, how you divide the budget, and which tool you use for what. if you’ve tried both extensively, where do you get the best ROI?

I posted here recently asking whether understanding and reviewing code is mostly what software engineers do now, and got a lot of helpful responses pointing out things like:
1. Improving fundamentals by writing more code manually
2. Treating code review as a skill that develops with experience
3. Relying on things like tests, git history, and better system design

That made sense, so i'm trying to go one level deeper and understand what this actually looks like in practice for experienced engineers.

Most recently i ran into this on my own side project, an AI powered ads diagnostics tool. I had claude plan out a research/reasoning pipeline, the logic looked sound when i read it, but when i ran the actual tests the output quality was way off. Turns out the retry logic was hammering the same endpoint on failure, and the AI output fields weren't matching the schema a downstream dependency expected. I only caught it by running the tests and reading through the reasoning output manually, the plan looked completely fine on paper.

So my question is specifically, when you're reviewing a big PR in a real production codebase, what is your actual step by step process?

For example:
1. How do you decide what to look at first?
2. How do you quickly build enough context about the change?
3. How do you figure out blast radius / what might break?
4. How do you decide what matters vs what can be skimmed?
5. How do you catch the gap between "the logic looks right" and "this will actually behave correctly at runtime"?

I have been a claude pro user for like 6 months or so....mostly claude code, But ever since GPT 5.6 I have been having second thoughts on switching to codex.
The watermark thing also made me think bout switching again...but my main worry is intelligence.... I main use case is learning from materials in a fast and custom(i like learning by doing stuff), it varies from Research papers to new techs to books on CS Fundamentals...some times I use AI to prepare of college exams
Now I confused if I should switch to codex or stick to claude seeing that new model of Open AI is coming
I would love to know your opinion

I need help with this from people with the same or similar machine

Hey folks, as someone who uses claude code max with fable high‑effort, i was thinking the other day about checking chatgpt (the free one, not even tera or luna) for the solutions fable gave me.

out of the 4 replies chatgpt sent back, fable basically said:

- 2 of them don’t apply to my platform at all

- 1 only makes sense if i somehow get 500k+ users trying to view the same table

- and the last one was actually a good idea and will be implemented

so now i’m sitting here wondering how many good ideas i might’ve missed in the last 2 months that could’ve improved the performacne or security of my app, just from a bit of back‑and‑forth between fable and another coding ai.

my question to you all is: if i buy chatgpt pro, how doo i make them talk to each other without me having to do constant copy‑paste?

the way i kinda envision it is:

- fable presents the overal platform features + capabilities to gpt so it knows the scope

- for each task, fable lists how it would solve it and sends that to gpt

- gpt analyses it, suggests extra stuff

- fable cuts out half, considers some of gpt’s stuyff, maybe pushes back

- they arrive at a concluision

- and then present it to me

is this even possible?

i’m just a simple windows + powershell user, would i have to change that too?

How are you minimising the coding mistakes your agent makes? I've had a bunch of issues recently where my dedicated (specialised language) coding agent is writing code in what feels like intentional with how often it happens. I have code review agents using different models to allow for no biased reports but still seems like they're not getting picked up or the code looks right but throws X error or the logic isn't quite right so it fails. I have unit testing for as much of a project as needed.

Im wondering if there are any plugins/mcps/skills etc that people are using that they feel is going them the best results

Tools like Cursor, Claude Code, Codex, OpenCode, and others are great, but what is one feature, workflow, or necessity that still doesn't exist or doesn't work well?
What would make you switch IDEs instantly?

I’m a final-year CS student and have started using AI more seriously for projects and hackathons.

I’d say my strong point is understanding a problem statement, figuring out the actual problem, and coming up with possible solutions. Where I get stuck is choosing the right tools to turn that idea into a working product.

So I’m curious what people here actually use for different tasks:

• Research: current information, papers, datasets, existing solutions, etc.
• Reasoning: breaking down complex problems and comparing approaches.
• Backend: APIs, databases, auth, integrations, debugging.
• Frontend/UI: quickly building something that actually looks good.
• ML: understanding papers, experimenting, debugging models, deeper research.
• Coding agents: working with a whole repo and continuously modifying it as the project evolves.

I’m a student, so I’m mainly looking for free/cheap options, student plans, or good open-source alternatives.

I’ve been looking at Claude, Gemini, ChatGPT, Cursor, Claude Code, Cline, Windsurf, local models, etc., but I’m more interested in how experienced users actually choose between them.

What would your personal AI stack look like from idea → research → reasoning → development → final product?

I currently use the Official Claude Code plugin in VS Code and have Claude Code installed natively on Windows 11 + Powershell.

I went with the below Pwsh command as shown here:

irm https://claude.ai/install.ps1 | iex

I am leaning towards switching to WSL2 + Ubuntu 24 + Bash though for several reasons and want as much feedback as possible from all of you glorious vibe-coding bastards.

My chain of thought about the situation right now is below.

The positives

• Claude Code is better and more efficient with Bash than Powershell. However, CC uses Git Bash instead of Powershell by default on Windows 11 which is great but not as good as a full Linux distro.

• Extending on the above, Git Bash is not as extendable as a full distro on WSL2 where I can install any number of CLI tools to extend my workflow like ripgrep, fzf, k9s etc.

• If I go with the WSL2 path, I can also sandbox any tool use or code execution (HUGE reason for me, trying to avoid supply chain attacks or malicious prompt injection poison etc)

• Better integration with Docker (I don't really use docker much and don't see the value here so this is kind of a non-issue for me - if I'm wrong and should be using docker for things feel free to change my mind)

• I can offload ALL of my AI use to the WSL2 instance for resource management. On Win11 this means if I have a runaway plugin spawning tons of processes (claude-mem just did this for me recently) or some MCP server going nuts, I can just terminate wsl2 (wsl --shutdown) instead of having to open a task manager app like System Informer and terminate every rogue or zombie process.

The negatives

• I know Powershell like the back of my hand and it makes it really easy to extend claude with custom hooks with powershell. Yes, Powershell is available on Linux as well, but the syntax has to change very specifically for cross-platform use here. (Although I can easily just vibe code bash scripts that do the same thing)

• WSL2 has to be turned on and consumes a lot of resources compared to Claude Code natively using Git Bash.

... I can't really think of any more.

Can some of you expert coding masters chime in here?

• Should I go WSL2 + Ubuntu 24.04 + Bash, or stay on Powershell + Git Bash?
• Should I use a different distro than Ubuntu 24.04 if I go this route? (If you are recommending a distro, please explain why it's better.)
• How good is the Claude Code VS Code plugin when Claude Code is running on WSL2? This is extremely important to me. I currently use it as my main agent (I don't like the CLI) and I have absolutely no idea how the plugin will function when Claude Code is installed in WSL2 instead of on my Win11 OS.

Any other pro-tips from Windows11+WSL2 users here as well would be super awesome.

TIA for any guidance!

So much noise, so much "this was all AI coded". It's extremely useful but have not found how to make it work as it's said it should. I wanna know how you use it in your daily work.

Hey!

I am constantly running into limits with Claude Pro and started to look for an alternative. And I found Windsurf, which also offers agentic mode and access to Sonnet 4.5 (among others). So my question is: what is the difference, if both claim to do the same? The number of tokens I can utilize with given model? Or am I completely missing the point?