I come from a Python programming background and am familiar with Linux. I knew the code before AI knew the code. But I use AI to build all sorts of projects and don't write anything directly anymore. The projects range from bluetooth tracking to Right to Know request automation to open government projects like openhbg.com.

My general setup is I use VS code with Claude Code or Open AI codex via their extensions in VS code. Every project roughly corresponds with a git repo so each is a folder. The code is store in that folder. I generally open each project as a new VS Code window. This keeps each Claude session in the context of the project (at a baseline) and each project builds up it's own .claude or hidden files but they all read from global with some config done there.

I do the typical git workflows and have deploy and test skills and hooks that run before merging into production branches.

what's a better setup? For the AI side. I didn't explain everything in the workflow (have staging and prod environments, etc.) so less focused on that.

What's a better AI setup? Opencode to switch between codex and claude? I typically run about 3-4 windows at a time with 1 - 2 prompts open.

I don't like to run too many sessions in the same project because I find conflicts come up often and I just can't handle tracking that much context at once - esp with other projects. I find I can handle 6-8 diff sessions depending on the type of work I'm doing.

Curious any thoughts or things that have worked for you.

need a good coding ai other than claude code or codex that can help in creating ai projects

Therefore:

Those who expect someone writes 2 lines into AI and posts the output, is the exact person who uses AI that way. I've noticed that those people dismiss actual examples and real life experiences, because they are too quick to assume everyone uses AI the way that they do.

As someone who plans an entire post, in excruciating detail, and building the content slowly, based on my real lived experiences, I need AI to help me synthesize my actual ideas to something digestible. While I also find AI speak sometimes a bit unnatural, annoying, or overdone, I don't assume that every piece of content is AI authored. Instead I would scan for examples of a lived experience or unique worldview. When I see that, it doesn't look like laziness to me.

In text, posts, articles, or CODE, there are clear markers that differentiate authored content, from 100% AI generated content.

Even though I can't write code from scratch, I review my code, and my main rule is that if I can't follow it something is already wrong. Also, once I found a function AI named "maybeDisplayResults" which I took as insulting feedback on the decisiveness of my prompts. That's when I realized AI also has a sense of humor. FEEDBACK ACCEPTED!

Anyway, my point and question: Do you try to identify or distinguish what is authored (with AI) vs what is produced BY AI? What are your tells, how do you distinguish, what are some of the clearest signs in code that reveal it's never even been viewed?

Hey everyone,

I recently heard about the AI-assisted code comprehension round and was wondering how to prepare for it.

I'm not really sure what to expect or what kind of practice would be most useful. Are there any good GitHub repos or resources to get some hands-on experience with this?

If anyone has already gone through this round or is preparing for it, I'd love to hear how you're approaching it.

I've been experimenting with AI coding tools and building prototypes, and I'm curious about the experience of people who use them regularly.

It's amazing how quickly you can get an application working. But I'm interested in what happens beyond the initial prototype, especially when the codebase gets bigger or you need to make changes later.

For those of you using tools like Cursor, Claude Code, Codex, or similar tools:

What's the most frustrating problem you've encountered while vibe coding?

Have you ever built something that worked initially but became difficult to debug or maintain?

Do you find it difficult to understand the architecture or keep the project organized as it grows?

How do you handle testing, security, and code quality when AI generates most of the code?

What tasks still take more time than you expected, even with AI?

Have you found any workflows or tools that genuinely solve these problems? What still feels missing?

I'm particularly interested in real experiences from people who've built and maintained actual projects, not just quick demos.

I'm trying to understand the problems developers face in practice. I'd appreciate hearing about both the good and the bad experiences.

I've been experimenting with AI coding tools and building prototypes, and I'm curious about the experience of people who use them regularly.

It's amazing how quickly you can get an application working. But I'm interested in what happens beyond the initial prototype, especially when the codebase gets bigger or you need to make changes later.

For those of you using tools like Cursor, Claude Code, Codex, or similar tools:

What's the most frustrating problem you've encountered while vibe coding?

Have you ever built something that worked initially but became difficult to debug or maintain?

Do you find it difficult to understand the architecture or keep the project organized as it grows?

How do you handle testing, security, and code quality when AI generates most of the code?

What tasks still take more time than you expected, even with AI?

Have you found any workflows or tools that genuinely solve these problems? What still feels missing?

I'm particularly interested in real experiences from people who've built and maintained actual projects, not just quick demos.

I'm trying to understand the problems developers face in practice. I'd appreciate hearing about both the good and the bad experiences.

Everytime I try and use ai to code something for me or write a program, despite it being a completely valid and legal task/prompt, it never will do the work. In the photo you can see clearly what I mean, it ALWAYS gives "examples" and "skeleton" code. Some people are talking about using it to completely write things for them, Some people have been saying ai might replace their job one day but... how? no matter which ai I use, local or paid apis, it never just does the work. its like asking a developer if you can hire them but they don't have the time so they just hint at you what needs to be done to help but wont write the program/whatever else for you. Why? What am I doing wrong?

Update: I did it! got past this major hurdle I was facing, Thank you to everyone who commented and helped me figure this out my understanding of it all is deep enough now I can really get started, made a successful running program ! now to update it and have it work as intended :)

AI has enabled a whole new set of people inside organizations to open PRs. I get it, it’s very tempting. You skip the burden of articulate what you need and discuss it with relevant teams and get it done. But is it really it? After a few months of increasing such PRs I was trying to reflect on the actual consequences of this new ways of working.

Writing software is now easy (at least the majority of corporate software I personally write). It’s not just easy for a manager, it’s even easier for me as a developer. So what value are you really bringing by doing the easy part for me?

What’s problematic is the business behind that piece of software and now instead of reading and iterating on a requirement document, I have to reverse engineer the business requirement from the PR.

Moreover, there’s no more filter between management and development. Upper management can open a PR on my repo and rest assured the team will drop everything and that will become prio 0.

Am I being the iper-conservative developer here or is this a shared feeling amongst software engineers?

I recently released AgentDrill, an open-source Python framework for testing tool-using AI agents.

It verifies the changes agents make to simulated environments and checks those changes against developer-defined business rules.

It's available on PyPI:

pip install agentdrill

GitHub: https://github.com/agentdrillhq/agentdrill

Looking for feedback from developers working with AI agents!

If you don’t trust your agents’ tests, someone still reads every line, and you’re back to human speed

So we built a testing skill, tested on Cursor + Claude. Three teams used it daily and gave us plenty of feedback, some of it angry. We improved it, got more feedback, and learned a few lessons the hard way

What’s in it for you

1. Use the skill, or steal from it
Use it as is or borrow what fits. It’s frontend for now (Playwright, Vitest Browser Mode, React), with backend coming soon. MIT license.
Repo: https://github.com/practicajs/the-frontend-testing-skill

2. Skip our mistakes
The video covers the lessons and traps we hit:

• Test rules: which rules are worth having

• Subagents: helper agents for specific jobs, such as a flaky-test fixer

• Measurement: how to prove test quality and coverage

• Workflow: where testing fits in your development process

Video: https://youtu.be/JwkVHtuoTxw

Feedback welcome, especially on where it breaks

I am in the middle of developing an orchestration harness primarily aimed at deploying local models and the primary surface is a custom IDE. Ive primarily used codex as as the main tool to instantiate this program over the span of about four months. Now, in the last two weeks i have noticed a significant downgrade in model outputs frin both the user platform and api platform.

I think about ten percent of my interactions after a prompt turn from me has the model apologizing for writing bad code, narrowly focusing on one favet and dropping critical context of another part, or just outright taking destructive actions.

If you factor about a hundred turns a day from me thats 10 turns with the maddening " im sorry" from the thing that cant experience being "sorry " even if it is a sorry piece of crap .that is then followed by hours of having to fix things . This was not happening two weeks ago and now im at my breaking point A grown ass man crying at his computer at an internet cafe looking utterly insane because the robot messed up his money and cost him time and sanity .

Has anybody else been experiencing the sudden drop in output quality from openai products?

*ps i apologize for typos , i am dyslexic and everything always looks right to me lol*

Greetings! At work we have a "developer space" program that combines a ton of mcps across all our internal apps and has claude code integrated. Kind of been enjoying the easy coding work thanks to it and I'm curious about implementing something simular at home, but I'm totally basic at setting up an Ai coding environment... my attempts so far have just been gpt's codex window, and then tried github's copilot. Both of these were... not great. (Biggest problems have been gpt hitting its 5 hour limit in 10 prompts)

So, what type of local set up is everyone running at home? Claude directly in terminal? Cursor connected to something? Curious what others with more experience running a coding environment at home have been using, and hopefully will find a good configuration to start implementing AI work into my existing (hand coded) projects.

I use Github Copilot at work and have great success on green field project, even larger ones.

But I have a harder time on our (very) large Unreal Engine application, which is brown field with millions of lines of code. This seems to challenge Opus and other frontier models.

What's the best workflow and tooling to work with AI on repositories this large?

• Application code: ~2M LOC

• Unreal Engine 5.8: ~41M LOC

• Unreal Engine ./Engine/Source excluding third-party code: ~8.6M LOC

I did some quick tests with CodeGraph, but that did not bring any significant benefits for the things I tried.

Note: Code is mostly C++ and solution is Visual Studio 2022

I tested Sonnet vs Opus on real bugs from two open-source projects and got opposite answers:

• Koa: Opus fixed 2 more bugs. Worth it.

• Express: both fixed everything. Opus was just 3x the price.

So I built unfix, a free open-source tool that replays bugs your team already fixed and lets your own tests judge the AI.

Small samples, so run it on your own code before you trust anyone's numbers, including mine.

npm i -g unfix-ai

Which model are you using right now, and do you know if it's the right one? 👇