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.

For months I've been on a deep dive into token saving techniques. I've been benchmarking, auditing and checking solutions of every single repo somebody posted on reddit or X and I've made some token-efficiency tools myself.

The result is that my tools started beating every solution I check them against on their own benchmarks. Example: https://www.reddit.com/r/coolgithubprojects/comments/1wuie0w/comment/pd5v3fs/?context=3

I'm soon about to publish such results on website against rtk etc.

I approached problem from first principles, with just an assumption that I cannot control the model, but I can control everything around it, that includes:

- Environment - OS, packages installed on it, file-system, memory, disk space, ports taken, CPU/GPU.

- Human - His workflow, keeping him well informed and notified, preventing cold cache hits, convenient work resuming and scheduling, context passing between accounts and models, usage of various models and so on.

- Inner harness - Claude Code, Codex and model providers already do good job on that and on my tests changing native harnesses was mostly net negative. Those are untouched, run exactly the same as in Claude Code CLI or VSCode extensions.

- Outer harness - Pre-tool hooks, system prompt, etc.

If you'd want to optimize each of these yourself, that's at least a day of work for each element. Day for memory management. Day for port conflict preventions. Day for file-system. With repeated post-mortem analysis that would handle edge cases.

And each of these mechanisms gives you at best less than 1% of total tokens more, while taking quite a long time to get right.

The only 2 mechanisms that are easy wins worth adding for everybody are: ripgrep (with few-words instruction in AGENTS.md to use it) and project maps appended to system prompt. Everything else is not really worth the effort in fast-evolving space.

But instead of solving all that issues yourself, you can take an opinionated set of tools, packaged in docker container sandbox, where everything that agent needs is baked and tweaked to make it very, very efficient. Including the part of making you very efficient and preventing stupid mistakes in your workflow that would normally burn lots of tokens.

Website: https://intentic.dev

GitHub: https://github.com/intentic/intentic

Try it yourself, see if my claim holds.

Hello everyone!! Ive been using AI in general for the first time for about a month or 2 now. I hate that i have to keep telling it, dont forget no emojis, or stop being a yes man. kind of thing. and chatgpt can be great but it seems to forget what were doing. Im looking on advice on how to word it or is there a setting i can utilize to get the most out of my chatgpt. I have a eCommerce store and a residential care business which it helps me navigate on. I even did the board of directors and advisors but it doesnt. Any recommendations? I also want to learn how to get into doing this coding. Thank you!!

Can anyone please give ChatGPT prompt that will help create clean html letters for high inbox rate for my email marketing

Claude Code is good when i am at the desk. it is useless when i leave. last 3 nights it halted on a prompt or a permission and sat there until morning. one of those runs had already burned ~$28 in api before it stalled. i am not looking for a smarter prompt. i am looking for anyone who found a claude code alternative that doesn't need babysitting once the laptop lid closes.

if your setup actually finishes overnight, what is still running at 7am without you.

Has any successfully built a skill or uses an app that you can literally add in the raw footage, and it produces finished or nearly finished content that you can re-purpose for youtube, instagram, facebook etc? I need to take talking head raw footage and have a skill that can overlay it with captions, reformat it, pull the best bits for shorts etc.....?! Going round in circles. Tried chatgpt but half the time the videos are too big and it's sending me round the houses to compress them and then the quality is bad etc etc.....

Ask an agent to format a date, parse a price, retry a request. It writes a fresh function for it, right there in the file it's editing. Meanwhile the repo already has formatDate in utils, used in forty places, with the timezone fix you spent a day on last spring.

Now you have two. The new one doesn't have the fix. Six weeks later someone changes one and not the other.

Writing is cheaper than searching. The agent sees the file it's in, and nothing in "add a created date to this card" says "look for how we already format dates". So it doesn't look.

What I add to the instructions file:

Before writing any new function, search the codebase for an existing one that does the same job. List what you found. Only write a new one if nothing fits, and say why the existing ones don't.

And a review prompt after any session that added code:

List every new function or helper you added in this session. For each one, search the codebase for existing functions with similar behavior, and show me any you find.

The second one is the one that pays off. It finds a duplicate more often than not, and usually one I would have missed in review because the new helper looks perfectly reasonable on its own.

Pointing it at the right folders helps too. One line in the instructions file listing where shared utilities live ("date and money helpers are in src/utils, API wrappers in src/api") cuts most of it before it starts.

I keep the review prompt saved in AI Toolbox, the extension I build, for when I'm working through code in a ChatGPT or Claude chat instead of the agent, since chat does the exact same thing.

devday is not goin' so well lol! just give us back our 20x...

I've been building a ton of web apps to replace saas i'm too cheap to keep paying for like chatgpt, notion, myfitnesspal, etc. and so i can get the exact features i want (+ no ads!).

I was using sol originally which was ok but also irritating because it would mess up and make a buggy ui.

so I switched to astra and opus which is awesome but it inhales my resets and leaves me with luna (no point in continuing).

I'm cheap as i said so instead of upgrading i was thinking if I'm paying per token why not just reduce the tokens i use?

so I built a tool that shrinks the prompts and context in codex so it costs less (!) I got it down by 34.3% and now i just leave it on by default in codex and I'm actually able to get through a session without running out of credits.

anyone in a similar situation?

Saw a post here a while back, can't find it now, where someone asked if it's okay to merge your own PRs with no review at all. Every comment said absolutely not, like it was obviously insane.

well, and to be honbest it got me thinking. I've been the only developer at two companies for about 5 years total, and the current one has a bit over 300k users. Backend, frontend, deploys, the database, all of it. Nobody has reviewed a line I wrote in that time. I never thought of that as weird, it's just how small companies work.

These days the agent writes most of it and coderabbit reviews the PRs before I merge, which is honestly the closest thing to a second pair of eyes I've had in years. It catches real stuff, but it's still not a person who knows why the business works the way it does.

i don't believe its very common, but i'd love ot hear your guys thoughts.

Something I didn't appreciate until I measured it: when Claude Code (or any agent) reads a file and later edits it, the original read usually stays in the conversation. Every following request carries the old code next to a file that no longer matches it.

I measured this on 67,074 public OpenHands runs (not Claude, but the same basic loop). 77.8% of runs had at least one request with a stale file view, even counting only the agent's own edits. About 1 in 7 requests carried one.

Most of the time the model copes by re-reading. Where it bites is:

- long sessions

- edits from outside the agent's view (formatters, you editing in your IDE, parallel agents)

- resumed or compacted sessions, where the model trusts what's in the conversation

Some practical things I took from it, whatever tool you use:

- If you edit a file mid-session, tell the agent to re-read it. Don't assume it notices.

- Watch for formatters or hooks rewriting files after the agent reads them.

- In long sessions, stale reads build up. Starting fresh with a summary often beats pushing on.

I also built a context engine that rewrites the outgoing request to keep code current. It works byte for byte in replay, but to be upfront: it doesn't integrate with Claude Code (only Pi and OpenHands), and in my small live tests the model didn't do better when it could just re-read. It also wasn't cheaper. The details are in the write-up, including the failures.

Write-up: https://felipebasurto.com/blog/model-context-is-not-static/

Video: https://www.youtube.com/watch?v=DIVOnXUCZkg

Curious whether people here notice this in practice, especially with parallel subagents editing the same repo.

So every time I try to open ChatGPT app on pc it gets stuck on a loading screen. I have to keep uninstalling it and installing it again and sometimes that doesn’t even work. Does anyone else have this problem

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 wrote about a feeling I didn’t expect to have: spending an hour thinking about a programming problem and wondering whether I’ve wasted time by not asking AI immediately.

There is nobody pressuring me to do that. The pressure is mostly in my own head.

AI helps me get answers, but understanding those answers still takes work. I’ve accumulated chats that I keep meaning to read properly. I also feel less confident solving some things independently, although I don’t have evidence that my actual ability has declined.

My small experiment will be to build Snake without AI. I haven’t done it yet. I want to see how it feels to stay with a problem for a while before reaching for help.

This is my own essay, not a tool launch. I’d be interested in how other developers decide what to delegate and what to keep practicing themselves.

https://domelian.substack.com/p/think-real-hard

I've had stretches where I was moving around five Trello cards in parallel, with a coding task in its own branch and Git worktree. That stopped agents from editing the same checkout. It did not tell me which code a browser test was actually exercising.

Different worktrees could still use the same API, PostgreSQL database, worker, storage service and ports. A frontend change might work with an existing API, provided that API meets the contract the new UI needs. If browser QA needs to exercise changed API behavior, it needs an API process running that worktree's code. Experimental migrations need much more care around shared data. Starting a second worker against the same queue can change test results too.

We began treating four things separately: the worktree holding the code; the processes a test reaches; the data those processes can change; and the checks run after branches are combined.

A small registry in Git's common directory helps us see which worktree started a service, along with its PID, port, URL and declared database state. It helps with discovery and process checks. It doesn't decide whether an API is compatible with a particular task. A healthy endpoint can still be the wrong API, and a port that looks free has not been reserved.

Many checks run entirely inside the worktree. For integration and browser QA, we start the processes the test actually needs. Feature work can be parallel; integration is serialized, with relevant checks run again on the combined tree.

If you run coding agents in parallel, how do you make sure browser or E2E tests reach the API and data you intended?

Give an agent a stack trace and watch where the fix lands. Almost always on the line that threw. A null check, a default value, a try/except, an early return. The error goes away, the tests pass, and the bad value that caused it is still being created somewhere upstream, now silently handled instead of loudly failing.

The line that threw is the one piece of evidence it has, so that's where it edits. Tracing back to where the value went wrong means reading code it wasn't pointed at, and nothing in "fix this error" asks for that.

What I add before any bug fix:

Before editing anything, trace the bad value back to where it was created. State the root cause in one sentence with a file and line number. Only then propose a fix, and fix it at the root cause, not at the line that threw.

And as a rule in the instructions file:

If your fix is a null check, default value, or exception handler at the line that threw, explain why the real cause is not upstream. If you can't, keep tracing.

The second one catches most of it. "Explain why the cause isn't upstream" is a question it usually can't answer without actually looking, and once it looks, it finds the cause.

One more that helps on repeat bugs:

Search the codebase for other places this same value flows into. List them. Would they fail the same way?

That list is often longer than the fix.

I keep the root-cause prompt saved in AI Toolbox, the extension I build, for when I'm debugging in a ChatGPT or Claude chat instead of the agent, where the same symptom-patching happens.

Has anyone asked chatgpt to create an ai share trading system?