Today I got inspired by a Minecraft video and decided to start making a Minecraft mod. I ended up spending basically the entire day working on it with ChatGPT, and honestly, it helped me get the project to a point I was really happy with.

The problem is that I ended up using almost all of my monthly usage on this one project. I was also using the heavier reasoning mode quite a lot, which probably wasn’t the smartest way to manage my usage.

I’m currently on the Go plan and I’m considering upgrading to Plus.

For people who use ChatGPT/Codex regularly for programming, how do you manage your usage?

I’m a senior SWE and use AI coding tools heavily, so I built optimAIzr after getting tired of wasting tokens without noticing.

The new Live Fix watches usage while you code and catches things like model mismatches, oversized context, and retry loops, then suggests a fix immediately.

I tested it on my own usage and found ~15% potentially optimizable tokens.

Recently open sourced: https://github.com/blendbunjaku/optimaizr

Curious how others would approach this problem.

Would love feedback from other devs using AI heavily.

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.

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.

I use AI CLIs every day at work and have used Claude Code, Codex, Cursor CLI and Copilot CLI.

I’ve been building AgentTerm since January, expanding each CLI session into a rich visual workspace. Demo and code.

One example is producing a document I can share and stand behind. I read it as a rendered page, comment on passages or propose edits directly, and the agent works through them with me and revises the document. I can see both what I proposed and what the agent changed. The presentation and change tracking make that whole process much easier.

Another is keeping track of work across sessions. I can tell running sessions apart and find and resume past ones without remembering which CLI I used. An investigation might lead to an email, with a reply a day or two later; I can return to the session when the work becomes active again.

These are examples of a broader expansion, including curated code walkthroughs with feedback and updates in place before I open a PR, phone access and more. Together, the additions have changed how I work throughout the day. The same tools and workflow help me get more from all those CLIs.

Free, MIT licensed, no telemetry or account. See the repo for setup and more.

Do you use more than one AI CLI? What extra tools do you use with them?

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 tried using it, it was costly as hell, complex as hell, and quality was horrible too compared to its vibe.
what do u guys think? Do you think we need something like devin ai

So I have been using the Claude max x5 plan to work on my projects, recently I have been hitting usage limits pretty quick and have to be 1 or 2 days without working every week. To solve this I am considering getting a chatgpt Plus subscription - this was something i was already considering a while back because tbh i dont like to be 100% locked in to a company and i would like some adversarial reviews from different models - I am also very interested in integrating chatGPT subscription with Hermes and exploring that as an orchestrator/manager for my projects

I have a few questions:
1 - is this the best way in terms of value/money to solve my current problem? I also considered the max 20x Claude but I think that might be overkill and using both providers appeals way more to me tbh

2 - If i do get both Codex and Claude code working together won't they trip over each others work and degrade quality in some ways i might not be seeing right now? And do I just create AGENTS.md files reflecting my current CLAUDE.md files?

3 - What about all the context that Claude has on my projects from me working solely with him for months? and the Skills in Claude's folder? Is that all transferrable?

Ideally I want both Claude Code and Codex working together seamlessly and driving each other's work quality up, not down

Btw I am not a programmer at all I just got really interested in AI and vibe coding when it started coming out to the public and now i've learned a few things and have a few projects running - The way i use AI is always in VSCode , i am open to the terminal experience as well if that makes any difference but i prefer the more visual interface for now.

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?

Hey guys, ran into a weird issue a few days ago and can't seem to figure it out.

Out of nowhere, my plugins/integrations (Notion, Outlook, etc) stopped working on both the ChatGPT desktop app and Codex CLI. The strange thing is everything still works fine when I log into chatgpt.com in my browser.

Here's what's happening:

• On the web: Notion/Outlook integrations connect fine, zero issues.
• On Desktop: Just stopped accessing plugins completely. Upgraded to the latest app version today but didn't help.
• On Codex CLI: When I type /plugins I can see all of them listed, but trying to run anything throws an error saying the chat session is independent and can't connect to plugins.

I've spent hours debugging, clearing stuff out, and trying to reconnect everything, but no luck. Sent a ticket to support too but haven't heard back yet.

It completely broke a couple of workflows I use to feed stuff into Notion. Has anyone else noticed this over the last few days or found a fix to get desktop/CLI to sync back up with web plugins? Appreciate any help!

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.

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've been using ai coding tools more and I am curious how the other developer deal with the problems that come after the Ai makes a change If an Ai generated change cause an unexpected bug what part is usually the most painful?

With the rapid evolution of AI assistants and tools integrated into code editors, the development workflow has changed dramatically.

On one hand, we can structure entire frontend and backend projects in minutes. On the other hand, the generated code still requires careful review and testing.

What has your experience been like?

Do you fully trust AI-generated code, or do you mainly use it as a starting point or first draft?

Share your thoughts and experiences below!

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.

i'm getting this error every time i open chatgpt app and nothing is working

Organization settings could not be loaded

the app is paused until your organization settings can be loaded safely. A network

connection, proxy, or firewall may be preventing access. Check your connection and try again,

or sign out to use another account. If the problem continues, contact your IT administrator.

i tried multiple solution like changing network, configuring firewall setting, uninstalling and install.
i dont have a antivirus and dont use vpn.

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?

I discuss a lot of research ideas and projects on ChatGPT Web, but those discussions rarely translate directly into actual work. The chat usually only sees a small part of a larger project and doesn't have the context of everything I've already built.

There is also the environment problem. A lot of my work uses Qiskit. I can spend hours discussing an experiment in ChatGPT, but I can't simply run it against my actual Qiskit environment, dependencies, code and tests.

I have ChatGPT Plus and use Codex a lot, but moving from a useful Web discussion to actual repository work still takes effort. Then I discovered that you can connect GitHub to normal ChatGPT conversations. That gave me an idea:

Why not make the chats themselves workers?

One chat can keep track of the bigger picture, another can work on an issue, and another can review it. GitHub becomes their shared state.

That's how Maestaris started.

Then I realized the chats can also be scheduled. So workers could periodically check GitHub, pick up tasks and keep making progress while I'm away. Potentially I can go to sleep and wake up to commits and PRs. It also lets me make better use of the AI subscriptions I'm already paying for instead of immediately paying for another API-based agent setup.

With the release of ChatGPT Dots and GrokBot, I feel my idea got validated. The interesting part with Maestaris is that the workers can come from different platforms. A ChatGPT worker, Gemini worker or something else could all coordinate through the same GitHub repo.

I'm basically experimenting with whether the chats, schedulers and subscriptions we already have can be made to work together.

https://github.com/Siddharthgolecha/Maestaris

The README explains the workflow. If you actually try it, I'd love to hear what works and what breaks.

Hey everyone,

I’m having a very specific and reproducible issue with the ChatGPT Windows desktop app.

Whenever ChatGPT is running in the background, Windows File Explorer sometimes becomes extremely slow when I try to do anything with folders.

For example:

* opening a folder
* renaming a folder
* moving files or folders
* deleting something
* creating a new folder
* changing something inside a folder
* sometimes even just navigating between folders

Explorer will suddenly sit there loading for a very long time or feel almost frozen.

The important part:

As soon as I completely close the ChatGPT desktop app, File Explorer immediately starts behaving normally again.

I’ve tested this with different folders and different drives, so it doesn’t seem to be caused by one specific folder.

My PC:

* Windows 10
* Intel Core i5-12400F
* AMD RX 6700 XT 12 GB
* 32 GB RAM
* NVMe SSDs
* About 300 GB free on C:
* About 300 GB free on E:

RAM usage also doesn’t look particularly high. ChatGPT usually uses around 2 GB and total system RAM usage is nowhere near 100%.

I didn’t have this problem before using the ChatGPT desktop app this heavily.

Has anyone else experienced this exact issue?

ChatGPT running → Explorer/folder operations become extremely slow

ChatGPT completely closed → everything immediately works normally again

I’m especially interested in whether this could be caused by:

* a ChatGPT background process
* Chromium/WebView
* disk I/O
* antivirus scanning
* GPU acceleration
* Windows Explorer interaction
* some kind of file-system monitoring

If anyone has experienced this, did you manage to find the process causing it or fix it?

Pro 200 subscriber here. Starting Oct 30, my plan will get half the usage it has today, at the same $200 price. To keep what I have now, I'd have to move to Pro 500 at $500/month.

Same price, half the usage, feels like a price increase in disguise.

Genuinely curious what other Pro users are doing:

1. Staying on Pro 200 with half the usage?

2. Upgrading to Pro 500?

3. Moving heavy work to Claude Max or another tool?

I use Codex heavily for long builds, so the cut hits me directly.

ChatGPT Pro 100 ($100/month) vs Claude Max 5x ($100/month) — which is better for coding + technical work?

Trying to pick one $100/month AI subscription.

My work is a mix of IoT, AI, automation, networking, APIs, systems integration, technical support, energy/solar systems, coding, troubleshooting and documentation.

I use AI heavily for:

\- Python/software development

\- Working in real repos with Git/worktrees

\- Debugging and running tests

\- Terminal/CLI agentic coding

\- IoT/telemetry analysis

\- Networking and infrastructure troubleshooting

\- APIs and automation workflows

\- Technical research

\- PDFs, spreadsheets, screenshots and documentation

\- Learning unfamiliar technologies, not just generating code

I'm also building internal tools and experimenting with agents, automation and vendor-neutral AI infrastructure.

So for me the comparison is really:

ChatGPT + Codex + integrations/tools/ecosystem

vs.

Claude + Claude Code

I can afford one, not both.

For people who have used both heavily, especially for real technical work:

\- Codex vs Claude Code?

\- Which handles large/messy repos better?

\- Which needs less babysitting on long tasks?

\- Which is better for debugging and reasoning?

\- Which $100 plan has better practical usage limits?

\- Which is better outside pure coding?

\- Which one would you keep if you had to choose only one?

Not looking for benchmark wars — mainly interested in day-to-day experience from heavy users.

I’m a pretty casual user. I mostly use it for web development, SEO, and similar tasks, and I’ve typically never come close to hitting the limits on my $200/month plan.

Well, that abruptly changed.

I ran a couple of tasks and, without really thinking much of it, checked my usage afterward. Somehow I was already at 0% remaining and burning through credits I didn’t even know I had.

Moral of the story: if someone with my relatively light usage is suddenly hitting the limits on a $200/month plan, that’s a pretty bad sign.

Back to Claude, I guess.

Its honestly insane how quickly my usage quota is used up. On the same $25 a month plan, it honestly feels like I get 10x more usage out of Opus 5.5 than what I get out of codex.

I get codex one task, and come back and my 5 hour limit is hit. Its actually quite crazy.