r/ChatGPTCoding

Self taught dev here. I got into this by building stuff at night for years. The fun part was making something work that didn't work an hour ago.

These days most of my job is reading. The agent writes it, coderabbit leaves its comments, and I spend about 6 hours a day going through diffs I didn't write, deciding if they're good enough. It's not hard. It's just not the thing I fell in love with.

The weird part is it leaked into my side projects too. I open the editor on a weekend and my first instinct is to prompt it and review what comes back, because that's what my brain is trained to do now. And then it doesn't feel like mine.

I still like my job and I'm not quitting. It just feels like I burnt out on the hobby without actually losing interest in it. Maybe I'm being dramatic, I don't know. How do you guys deal with this?

Welcome to this week's self promotion thread!

If you're building something related to AI assisted coding, this is the place to share it.

We're using a weekly thread to keep the subreddit organized while still giving builders a place to share their work. Promotional posts outside this thread may be removed.

If you're sharing something, we'd appreciate it if you included a little context instead of just dropping a link. Tell us:

• What you built?
• What problem it solves?
• Which AI models or tools it uses?
• Who it's for?
• What kind of feedback you're looking for?

Disclose your affilitation.

Please avoid posting the same project every week unless you've made meaningful updates. Affiliate links, referral links, scams, and low effort promotions will be removed.

Take some time to check out what others have shared too. If you try someone's project or have feedback, leave a comment. Helping each other improve is what we want this community to be about.

I've noticed something weird about how my development workflow has changed.

A year or two ago, most of my time was spent inside the editor. I understood a codebase partly because I was constantly navigating it, changing it, and seeing how the pieces fit together.

Now I can give an agent a fairly large task and let it work.

Which is great.

But when it finishes, what exactly am I supposed to do?

Read the entire diff?

Read the agent's 40-message transcript explaining everything it did?

Trust it because the tests pass?

I've found myself doing some mixture of all three, and none of them feels particularly good.

The more work I delegate, the less direct contact I have with the codebase. And once agents start making larger changes, I feel like I'm gradually losing the mental model I used to get almost for free by working in the code myself.

What's interesting is that the interfaces haven't really adapted to this.

Some newer tools are putting the agent front and center, which kinda makes sense.

but then the replacement for staring at code seems to be staring at agent conversations, task summaries, file lists and diffs (that is if we still have the attention span of reading diffs).

That feels like an intermediate state rather than the end state.

I'm curious how people using coding agents heavily deal with this.

Do you still review most generated code? Mostly trust tests? Ask the agent to explain the architecture afterward? Or have you found some other way of keeping a mental model of the system while delegating more of the implementation?

Once agents were writing most of our PRs, review became the bottleneck. Our fix was to have a review step learn from the comments our senior people had already left on past PRs. So it checks for our standards, not generic best practices. It catches a lot before a human ever looks.

I'd like to see what others are doing:

- Pre-review by an agent before the PR opens?

- Bots auto-approving small or low-risk PRs?

- Custom review skills or prompts? How often do you have to tweak them?

- Anyone fully trusting AI review yet?

Happy to share more details about our setup in the comments.

Some background first. I'm a software engineer from Lahore, Pakistan, with 5+ years of experience, all of it with Australian and US startups. For the last 3 years I was with an Australian startup and things were fine. Then about six months ago they shut down the Lahore office. Their reasoning was that they'd rather train their team in Australia, AI is doing a lot of the work now, and they couldn't train us too. So all of us got laid off. None of us were ready for what the market looks like right now.

In the last six months I've applied to 2,000+ jobs and got around 12-13 interviews. I couldn't convert any of them.

Money-wise I'm okay for now, with some savings and small freelance work.

I've been using this time to learn and build. I pay for two Claude Code subscriptions and one Cursor subscription, and I keep building side projects as if they were startups. I took some courses too. Two things I'm proud of: an open source repo with 1,000+ stars on GitHub and 100k+ downloads a month, and an AI app I put on the Play Store that got 8,000+ downloads in three months with zero marketing. I had to shut the app down because I couldn't keep paying the AI API costs. Recently I also started making Instagram reels. I'm terrible at video editing, so I vibe coded my own editing skills with Claude Code, and honestly they edit my videos more professionally than I ever could. That's the direction I'm trying to keep going in.

Now the reason I'm posting.

A friend referred me to a company here in Lahore, and that's how I got called to their recruitment drive. I spent about a week preparing day and night for it. The technical interview went 10/10, and I answered every single question. The coding test was basic and went well too. The last round was the managerial round with the CEO, the CTO and a manager. The manager has 15+ years in the industry.

He started with a few technical questions and things were going fine. I mentioned my open source repo, 1,000+ stars on GitHub and 100k+ downloads a month.

He asked what my biggest strength is. I said I'm a good learner, especially in tech. Even before AI, whenever something new came out or was trending on GitHub, I'd explore it and try it, and if it helped, I'd keep using it day to day. I told him I still do that, but with AI there's something new every single day, and it's really hard to keep up with the pace.

Then he asked how I use AI.

I told him I have my whole workflow set up: my own skills, my own MCP servers. When I work on a feature or a problem, I first prompt for a plan and ask the AI to ask me 10 questions about it, because that makes it really easy to catch hallucinations. I also gave my video editing tool as an example of how I use AI. I was bad at editing, I hate editing, so I built my own tool with AI and now I edit my own videos. My point was that AI can do a lot right now, and I use it to build things that solve my own problems.

Then he asked: if a person uses AI and has good analytical thinking, can he be a good engineer?

I said yes, he can be a good engineer. That's when I noticed he didn't agree with me. I could tell he wanted to hear something more, but I couldn't figure out what. He asked a few more AI questions after that.

Since I've been vibe coding a lot in the last six months, building fun projects and learning, I ended up talking a lot about that side of things. In my last role I was the one who unofficially got a Cursor subscription first and started doing things with AI before most people around me. Most of my friends weren't using it at that pace. A lot of my code was written by AI and I kept building my own solutions on top, but AI moves so fast that I can't match it either.

Looking back, maybe I should have answered differently. Something like: programming is one thing and AI is another, and a programmer should be able to debug an issue, plan, and review the system design before implementing anything. That's actually what we were doing in my last role six months ago. But AI has changed so much in those six months that I'm not sure what the industry expects anymore.

They rejected me. When I asked for feedback, all they said was "the panel rejected you." That's it.

The reason I'm taking this seriously is that this isn't the first time. I've been asked "how do you use AI?" in screening calls and technical interviews too. So I'm genuinely confused about what the right answer is right now. Is it me? Am I doing something wrong, or am I actually fine and this is just the market?

So my questions:

1. When someone asks you how you use AI in your day to day work, what do you answer?
2. For the hiring managers here: what are you actually trying to hear with that question, and what makes you reject someone?
3. Based on all this, is it me? What am I doing wrong?
4. Anything else that could help me survive this market. I'm happy to listen, even if it's harsh.

Thanks for reading this far.

The pitch is basically saying: make ur coding agent stop overbuilding everything and use the simplest solution that works ... which sounds like something AI coding agents badly need!!

But 150k+ stars is getting into crazy territory, so I’m wondering how much of that is hype vs actual usefulness??

Anyone here used it on real work? and can it be used with GrokBot ?

I run a few coding agents at the same time now. One orchestrator agent hands work to separate agent sessions on a Mac and two Windows PCs. Three things I changed today.

A build agent finished its whole job, 74 of 74 tasks, but it only wrote its done line in its chat, not in the status file my watcher reads. Nothing fired. I only noticed after about 25 quiet minutes. Now when a session goes quiet I read its last message, not just the status file.

A docs agent sat for about 2 hours waiting on a passkey login it thought it needed to check a rendered page. No machine could finish that step. I replaced the check with a local render (3 of 3 diagrams rendered, 0 errors), merged it, and my briefs now say never wait on a human login step.

One agent's context got to about 89%. Instead of resuming that long session, I had it write a handoff file with its role, rules, paths and open items, about 11 KB. A fresh session started from that file and picked up the next task at about 11% context.

None of it is clever. Mostly I'm finding places where my setup assumed something would happen and it didn't.

I just got free Gemini + 400 GB Google Drive for 12 months.

I’ve been using Claude for my work for the past few months. I’ve been very satisfied with it. Even the Claude Sonnet is already good enough for me.

But with Gemini plan, I think I will unsubscribe my Claude plan to save some money. But I’m not sure how good Gemini is for coding (mainly BE using Golang in fintech industry). Is there any suggestion to optimize it?

Is there any skill I can use? Is it able to connect to MCP? Can the standard Gemini plan be connected to third party harness?

In case someone argues me against unsubscribing. I live in a third world country. Claude plan for 12 months costs 1/3 of my monthly salary. And it’d keep increasing because the currency is so fucking weak.

Read the last message of an agent session closely. "Fixed the race condition." "The endpoint now returns the right status." "This should resolve the timeout." Then scroll up and look for the command that proves it. Often there isn't one. It edited the file, read its own edit, found it convincing, and reported the result as if it had run it.

It's not lying exactly. From where it sits, a change that looks right and a change that works are the same thing unless something forces it to check. And "fix this bug" doesn't force it to check.

What I add to the task:

Before you say anything is fixed, run [the command that failed] and paste the exact output. If you didn't run something, write "not verified" next to that claim.

And as a rule in the instructions file:

Never use the words fixed, works, resolved, or done unless the same message shows the command you ran and its output.

The second one changes the tone of the whole session. Claims get smaller and more specific, and when it can't run something (no DB locally, needs a real API key), it says so instead of quietly skipping it.

And the one I run at the end, before I review the diff:

List every claim you made in this session that something works or is fixed. For each one, show the command and output that proved it, or mark it unverified.

The unverified list is the useful part. It's usually 2 or 3 items, and those are exactly where I'd have trusted the summary and moved on.

One more detail: "run the tests" often turns into running only the test file it touched. If it matters, name the command for the full suite.

For bugs I debug in ChatGPT or Claude in the browser instead of the terminal, the end-of-session prompt is saved in AI Toolbox, the extension I build, so it's one // away instead of retyped.

I'm building a Windows desktop utility with a mixed stack:

• Tauri + Vue/TypeScript for the management UI
• Rust for the backend
• A separate C++ native overlay layer using Win32, DirectX 11 and Dear ImGui

The app includes transparent, always-on-top windows, mouse click-through, hover interactions and focus handling. Some of this needs testing on an actual Windows desktop rather than just in a browser.

I've used Codex, and I'm trying to find a practical workflow for going from code changes to building, running and checking the native UI. I'm also exploring what can be done in a cloud Linux environment versus what needs to stay on Windows.

For people using AI tools on similar projects:

1. Which model and IDE/CLI/agent combination has worked well for debugging an existing project across TypeScript, Rust and C++?
2. Do you run the agent directly on Windows, through WSL, or elsewhere? How do you connect it to the Windows build and test environment?
3. Have you found a reliable way for the agent to use screenshots, logs or UI automation to verify window behaviour? What still needs manual testing?

Question: I currently have numerous cheap agents, but to make up for lack in quality i have very rigorous testing, along with normal tests, i do play write/ chromium testing, i also render every single page and route and screen shot them and upload them to github artifacts.
which i then use chatgpt mcp github connector so my smarter chatgpt can download and look at the app visually to inspect it.
Anyways all this testing adds up to about 60 minuets per pr they make, it's spread across different tests suits so it runs faster but 60 mins that would be charged
I'm currently using roughly 200k runner-minutes/ month. The included minutes only cover a fraction of that, so Cl has become a significant expense.
I've already moved some workloads away from GitHub-hosted runners, which has helped, but at this scale it's still expensive.
For anyone running similarly Cl-heavy projects, how are you keeping runner usage/costs under control?
Self-hosting? Ephemeral runners? Reducing the test matrix? Only running expensive suites after merge?

Its useless now.. really weird.

What are people using? I want to be able to tell it "research x" and it should be taking its time, longer than 5 seconds, to come up with diverse sources and then start a really long long process to do that research. I used to have a bunch of skills that worked but things change. The defaults for ChatGPT SUUUUCK now sooo bad.

One agent writes tests from the spec, another writes the code in parallel, and the test agent never sees the code. Their reason was basically that otherwise the AI cheats. I take it to mean tests written after reading the code just confirm what it does, bugs included.

I come from ML, so to me this is a train/test problem. If the agent iterates against the same suite you use to decide it's done, that suite is its training set and a green run is a training score. Agents are known to get there by special-casing inputs, loosening asserts, or editing tests. Read-only test files stop the editing but not the other two.

Holding some tests back only works once. After you show the agent a failure, that test isn't held out anymore.

That's where the blind test agent helps, I think, since it writes new hidden tests from the spec every round. Those leak too once they fail, but what you care about is whether a fresh batch passes on its first run, like drawing a new validation sample each round. The failures just become training data. It needs a spec that fixes names, signatures and I/O, or the blind tests won't compile.

Some will say the tests are the spec, so passing them is the goal. But tests never cover the whole spec, and the agent can cut corners in the gaps.

Two things I'm not sure about. The blind agent can misread the spec, and then a person has to check which side is wrong (that at least shows where the spec is unclear). And the same model writing fresh tests each round probably has the same blind spots every time.

I haven't tried this myself, so:

1. Do you keep tests your coding agent can't see? How do they stay hidden after the first failure?
2. Is a second test agent worth the tokens, or do property-based or mutation tests get most of this cheaper?
3. Has anyone set this up with Claude Code subagents?

I’m currently on the ChatGPT Go plan. I barely used it for anything before, but today I got really inspired by a Minecraft video I saw online and decided to make a Minecraft mod.

I ended up spending the entire day working on it with ChatGPT, and I only got it exactly how I wanted when my monthly usage limit was almost gone. Looking back, I probably shouldn’t have used the heavier reasoning mode so much.

I’m considering upgrading to Plus, but before I do, I’d like some advice from people who use ChatGPT for coding:

How do you use your plan efficiently so you don’t burn through your limits and end up losing a whole day of work like I did?

Any tips for when to use the heavier reasoning modes, when to stick with the faster ones, or how you structure your coding sessions would be really appreciated.

Setup. Solo developer, Linux/WSL2, Codex CLI 0.159.3, gpt-6.1-sol on reasoning effort "high". The task: an existing internal reporting app takes ~35 seconds to load (roughly 20 s until the loading indicator appears, another ~15 s until the report is fully rendered, served over a private Tailscale connection). Goal: make it load faster and verify the improvement with actual measurements. What I expected. Measure the real load phases, find the biggest bottleneck, fix it via the implementation subagent, verify, done. Classic maintenance work, exactly the kindå of long task these agents are marketed for. What actually happened, over 12+ hours:

• The real load phases were never measured. Instead it built its own JavaScript "build analysis" tooling: 4,518 lines of code plus an 8,183-line test file, to analyze a problem that a handful of measurements would have characterized.
• Early in the run it introduced a state-initialization bug it then had to chase down itself.
• It repeatedly interrupted its own implementation subagent mid-work. At one point I had to explicitly demand 20 minutes of uninterrupted runtime for the subagent.
• Endless cycles of analysis, review and checks. Some partial fixes passed individually. Nothing was ever integrated, and total load time was never re-measured end to end.
• When I finally asked whether it had run into a dead end, it admitted it had lost control of the goal.
• Its self-assessment afterwards overstated the test evidence. I had that memo reviewed independently; the verdict Outcome. No integrated fix. No measured improvement. Significant added complexity in the repo. 12+ hours of computric was never evaluated once. I cancelled my $200/month subscription over this: not out of spite, but because sustained mahat I pay that subscription for. Fairness notes. The partial fixes were confronted directly. This is one documente timeline and run state: https://github.com/openai/codex/issues/502 Happy to answer questions in the comments.

Anyone using Claude Max 5x ($100/month) for daily coding? How quickly do you hit the limits?

Considering splitting my $200 budget between Codex and Claude at $100 each. If you use both, does the split actually work well, or do you just hit two sets of limits?

We're a heavy user of Claude Code and Codex, and September got away from us. We moved Codex to the enterprise plan mid-month, burned 256K credits in the first two weeks, topped up 150K more, and ten days later half the team hit the wall the same afternoon. Slack that day was "we codex maxxed," "can't even use my reset," and "I need a bit of juice to continue a session, more please." One engineer gave Claude his .codex folder so it could reconstruct what he'd been doing and he could keep going on the other tool. Claude side, same week: "I'm going to hit my quota in the next 24 hours, can I get a bump."

When we finally pulled the numbers: one session had 3.2B input tokens across 2,400 writes. Sessions that touched gpt-6-astra averaged about $21. Same kind of work on gpt-5.6-terra averaged about $1. Astra was 38 percent of the Codex bill in 31 person-days.

Nobody did anything wrong. That is just what the defaults do when nobody can see them.

So, questions for others who live in these tools all day:

What was your most expensive session, and what was it actually doing? Context bloat, retries, a model that didn't need to be the big one, something dumber?

And what bugs you more day to day: hitting limits mid-task, a bill you can't explain, or not knowing which model to use for what?

Quick disclosure, I work at a startup that builds AI tooling. Happy to say which if anyone asks, but not pitching anything.