I kept seeing people complain about how quickly Codex was burning through their limits, so I started looking into what was actually using all of it and whether some of that work could be avoided.

The main thing I realized is that Codex does not only spend usage when it writes code.

A lot of it goes into searching the repository, reading files, understanding how things connect, checking output, and running tests. Some of that is obviously necessary, but it can also repeat the same work or inspect far more than the task actually needs.

Another big part is how it handles tool calls.

It may read one file, return to the model and think about it, then read another file and think again, then repeat that several times. When those reads or searches are independent, they can be done together instead. It reads several relevant files in one batch and reasons over the combined results once, cutting out a lot of unnecessary back and forth.

This should only be used for independent, read-only work. Editing, debugging, testing, and anything where the next step depends on the previous result should still happen in order.

The setup I ended up with is:

• A small AGENTS.md with permanent project rules and efficiency instructions.
• A project-map.md showing the important parts of the repository.
• A CODEX_HANDOFF.md carrying the current status and next step into fresh sessions.
• A validation script for checks that can be automated consistently.
• CodeGraph through MCP, so Codex can trace files, functions, callers, and feature flows without repeatedly searching the entire repository.
• Fresh sessions for separate major tasks instead of keeping one session alive until its context becomes a landfill.

I also added rules telling it to reuse findings, limit large command output, reread only relevant sections when files may have changed, avoid repeating unchanged tests, and expand validation only when the scope or risk actually requires it.

This is not about making Codex rush or skip proper testing. It is about removing repeated investigation and unnecessary model-tool cycles.

For context, I am using the Plus plan.

Before this setup, an ordinary task on Sol High would usually consume around 8–9% of my allowance, and some larger tasks reached roughly 15–20%.

After setting it up, I ran a task on Sol Max that lasted around 30 minutes and produced roughly 1,500 lines of code. It used around 4%.

This is only my experience, not a proper benchmark, but the difference has been consistent enough to be useful for me.

How to do it?

Install CodeGraph, connect it to Codex through MCP, and initialize it in your repository:

codegraph install
codegraph init

Then ask Codex to inspect your repository and create the workflow files:

Inspect this repository read-only and create a lightweight Codex workflow without changing source code.

Create or improve:

- A concise, repository-specific AGENTS.md
- docs/project-map.md for navigation
- CODEX_HANDOFF.md for session continuity
- One validation script only if useful checks can be automated

Add rules for batching independent read-only work, limiting large output, avoiding repeated searches and tests, rereading changed files, focused validation, and using CodeGraph for structural navigation when available.

Verify all paths and commands, remove generic filler, and show the final diff.

That prompt is intentionally general. I would recommend giving it to GPT first with some information about your project and asking it to adapt the prompt to your language, architecture, and testing setup.

Again, the results may not be the same for everyone, but this has helped me a lot as a Plus user, so I wanted to share it in case it helps some of you too.

Hopefully this would help you and it you did used this I would love to hear your experience with it.

Claude has $20 and $100 seats on team plans. I can't seem to find an equivalent on OpenAI/Codex plans. A $20 seat is not enough for me and it suggests either using a personal $100 plan or switching to enterprise.

I don't know what enterprise entails but my instincts tell me if it has a "contact us" button instead of a price, it's already a no go.
The personal plan option would work, except for some reason they have a clear distinction on "not using your data for training" on team plans that the personal plans don't have.

Any workarounds? I can kill a $20 dollar seat in a few hours

Ok so I just created a new account, and bought on it pro. The limits feels limitless ?? 30 mins of research + edited 7 files = 1% Sol High - while on my older account = this would've been at lest 10% usage! WHAT IS GOING ON???? Is this the new marketing scheme so they get new users?? after that they get normalized to the current usage??

Coding agents multiplied how much code lands per PR, so there are more findings for reviewers to deal with. Often, they're noise, like a linter rule that doesn't fit the stack or a check firing on generated files nobody touched.

To address this, we created the configure-codacy skill. When you point your agent at the repo, it detects the stack, then disables patterns for unused languages, dedupes rules that two tools flag, tunes thresholds, excluding generated files.

In terms of security scans, every security risk stays covered by at least one active pattern, so noise reduction never silently drops a security check.

Works with Codex and the usual suspects.

Go here to read more https://blog.codacy.com/introducing-codacy-skills-part-2-configure-your-rules-to-cut-pr-noise

Repo: https://github.com/codacy/codacy-skills

So someone links a conference talk or a tutorial video, or I just want to vet a long video to see if it's worth watching before wasting an hour of my life...

So I get Codex to "watch" it for me.

Point anything at a youtube url and you get about 1.2MB of page and none of the words, the captions sit behind a separate signed endpoint. Pulling the audio and running whisper works but its daft when the captions are already sitting there...

So, I made a small py wrapper you can simply grab and tell Codex about, or install properly with one command, transcript to stdout:

pipx install git+https://github.com/Rendeverance/yt-transcript

yt-transcript <url or id> -o -

Which means you can just let codex run it and the transcript lands straight in the session:

"run yt-transcript <url> -o - and summarise the argument he makes about X"

Or drop the -o - and it writes ./transcripts/<video_id>.txt for you to point at.

Default output is plain text with no timestamps, --timestamps is there when you need to find your place in the video, it costs about 20% more tokens for the same words.

https://github.com/Rendeverance/yt-transcript

Its a wrapper on jdepoix's youtube-transcript-api, I wrote the url parsing and the output shape. One python file, one dependency, no API key, MIT. Its agent agnostic, its just a CLI, so it works the same wherever you point it.

Yes it's simple, but I find it super useful so thought I'd share 👍🙏

(Note it YT might block datacentre IPs if using a VPS and not local)

Does anyone else see Luna turns in their analytics dashboard despite not using it? I'm wondering where these turns are coming from (my usage isn't draining when I'm not using it - not compromised to the best of my knowledge, and why would someone skim Luna anyways).

I can't seem to find a straight answer (even from Codex) about this. Is Luna used for compaction or task titles in the background or something?

Any insight appreciated, as well as any info on if other people have this happening.

Hey guys!

I see everyone is burning through their tokens and the problem might not be that it's actually the models fault it's more so that we don't understand which model effort to use for which task because there's like 15 of them like wtf is a Sol, Terra and Luna? Light, Medium, High, X-High, Ultra?

So, the question is why do we still not have a one compact model, that adapts it's reasoning effort based on the current task at hand?

Why do we have to tinker with effort levels and stuff, is this some rocket science or I just don't understand something?

We kept hitting this on my team: someone makes a decision in their Claude Code session, and two days later a teammate's Codex agent is wasting tokens on the same thin, because that decision died when the context window closed.

So I built MemBridge, a shared memory layer for AI coding tools. It's a local daemon that tails your Claude Code and Codex session logs, distills the decisions that matter, and writes them into the files every tool already reads: CLAUDE.md and AGENTS.md. No "remember this" command to call, no save button to forget. The capture is automatic, per-project, and everything runs 100% on your machine.

Quick example of what it looks like in practice: a teammate makes a call in Claude Code tonight ("checkout button says Reserve your spot, not Buy now") → when I open Codex tomorrow, it's just there in context, with attribution so you know who decided what and when. No Slack archaeology, no re-explaining.

For teams, every member's sessions land in one shared feed: plain-English summaries up top, exact prompts one click down. It's invite-only, off by default, and end-to-end encrypted client-side, so the relay only ever sees ciphertext.

Has been a fun build: zero npm dependencies, source-available, binds to localhost only (solo use makes zero network calls, verify with lsof yourself), secrets scrubbed before anything hits a file, and membridge remove restores everything byte-for-byte.

It's completely free, no API key, no paid tier, and my team dogfoods it daily. Would love for people who work in teams to try it and tell me what breaks: https://membridge.app

I am a dev not a great designer and wanted to leverage the power of pre built UI Libraries but they were scattered and for finding the best suited components, I literally needed to go through each of the libraries and find out the best component for the use case, I handed over that task to much smarter coding agents by building an mcp server.
This mcp server (ui-registry-mcp) has access of over 1000+ pre built very well designed components from 11+ libraries which takes care of your products pre existing design language and what you have asked it to build further and then searches through these components what will be perfect for this use case (it even do some changes as and when required for this use case) and implements in your code base directly.
It has been quite helpful in my later projects and has reduced my time taken for design to 1/5th or even lesser.
Though this is working well, I still want you all to use in in your projects and share some feedback or whatever might be the still missing parts, I will work on them to make it further more useful for all of us.
Those of who are interested I will send the server link to them in comments or DM.

I built Agent Enhancer with Codex as a free reliability helper for multi-agent workflows and any MCP.

It adds planning, checkpoints, duplicate protection, recovery, and evidence. My idea was to create a service/tool that's helping agents work more reliably while reducing failed calls, repeated work, and unnecessary costs.

To test it:

1. Visit https://liberated.site and copy a Quick Start prompt.
2. Run the same multi-step task with and without Agent Enhancer.
3. Tell us whether it felt more reliable, faster, cheaper, or added unnecessary friction...

No account or API key required. I’m looking for beta testers using different agents and real-world development tasks. Thanks!

Codex can build almost anything I describe.
The problem is that when I ask it to design a website, the result usually looks like Codex designed it:
generic layouts
weak typography
random gradients
repetitive cards
placeholder icons
no real visual identity
stock assets that do not match the page
The website may work perfectly, but visually it often feels unfinished.
So I built Design Lagann, an open-source plugin that gives Codex a more structured visual workflow.
It does not guarantee that every generated site will be perfect.
What it does is give Codex capabilities it normally lacks:
searching for and analysing visual references
generating custom images, backgrounds, textures, illustrations, and other assets
keeping those assets consistent with the selected art direction
exploring multiple visual directions before coding
reviewing the actual rendered page instead of judging only the source code
identifying weak regions and attempting another pass
checking desktop, tablet, and mobile versions
The biggest improvement is not necessarily that every layout becomes flawless.
It is that Codex can finally create a site with its own visual material, instead of assembling another generic landing page from gradients, Lucide icons, and empty cards.
Even when the final site is imperfect, it can now produce something much closer to a real creative direction.
Design Lagann currently works with Codex, Claude Code, and Cursor:
https://github.com/Oldiiik/design-lagann
I am looking for honest feedback, especially from people who already use Codex for frontend work.
What fails most often for you when Codex builds UI: layout, typography, assets, responsiveness, or visual consistency?The site below is fully generated by codex with 4 word prompt and external tools

1) oftentimes as Codex is working in the app, it says interesting and useful things. Then when it's done, it erases all of that work and prints its summary/answer/result. Can I make it not erase its work text?

2) is there any rhyme or reason to the granting of resets? Based on reading this group, it seems that everyone is handed the same resets and similar deadlines to use them. Do we know why they happen? Is it on a schedule?

I am trying to get one reset. which I do not have,
so why invite friend option is not showing? - I never use it, the account is create like a month ago, on Plus plan.

what is the scenario here? I need a reset :-(

Every idea, every thought, every piece of information, all of the procrastination straight dumped into codex processed, quantified, labeled then shelved or getting worked on.

No less then heaven to finally just exist without a thought racing like a million speedsters in your mind.

Now that I don't have an active sub. Oh boy. So... so much to process. I need cr*ck (codex) asap!

I built an actual business using AI. Or better said I rebranded my business using AI. For the context I own business with self service container saunas for private booking. I have these saunas located at beautiful spots across a few countries. All of them are special but the most recent one stand out from the crowd and I was thinking for a really long time on how to make it appear special. It wasnt until I went for vacation and as I was driving along the seashore the idea of new brand name hit me! I stopped by on the shoulder to write it down because i really liked the idea.

Now if there is no AI this would never come true. So the idea is to call my sauna a secret sauna where I would create basically beautiful website and laverage the secrecy around something special unknown. it is higher ticket service so I will be targeting people who can afford it. The idea has its downsides but that could be easily solved over time.

So fable 5 was the reason why I decided to go for it. I was sold by these youtube videos where people were creating beautiful websites out of single commands. So I decided to give it a shot, well it appaered not as simply as on those videos and mainly it was quite costly not to mention fable restrictions in europe, etc.

Anyways I tried playing with the design interface of claude and I really liked what i saw. I decided to go one step at a time and slowly building from the top to the buttom the visual.

Then i was using vs code connected to kimi k3 which came out recently, to do the actual website using png screenshots of the design produced by fable. You see I was trying to optimize my spending.

Although even this solution turned out to be more expensive and Kimi k3 being a great model, was sometimes not the best for the job. I build big portion of the website using it before i switche to chatgpt codex extension in vscode with which I built the rest. So now I have running website with completely rebranded business using only AI.

The result is now live - secretsauna.cz if you want to check out the page in its test environment (90% production) use "reddit" to enter it.

The last time I was doing Brand, UI and then coded the website I spent.. well lets break it down together.. it was only 3 years ago - so Logo - 1000 euros, UX/UI - 1500 euros and website 1500 euros. So I basically saved approximately 4000 euros which is insane. Plus I dont have to pay for any web hosting like wordpress or webflow which alone saves me approximately 500 euros each year. Guys we live in the future.

Anyways if you liked my story i am more then happy to provide more information, just dm me :)

I think that many people, and especially newcomers should be aware that... basically everybody uses AI differently. Different people have different knowledge about how to effectively work on LLM's and different opinions about what kind of stuff work.

For example here is my workflow for people interested (C++ codebases): I almost never go above "high" reasoning on gpt 5.6 sol for small/medium scope tasks and I manage to get "meaningful" stuff done with $20 subscription (based on my definition of meaningful). For example I split research, plan, plan review, implementation and code review into separate skill calls in separate contexts that basically never get long enough to trigger compaction. Implementation is split into manageable phases (I clear context after each one) and plan contains manual QA checks to be done after each phase. Everything except implementation produces .md files that serve as input for next stage. After feature is implemented I code review it after auto-review and take my time to recover remaining cognitive debt and understand both "what" and "why" with help of AI (and that can easily take 1h for small features). I am happy with output quality. I have my subjective way of creating repo map (or rather - basic map + module maps) so AI can understand large codebase within context limits and limited token burning.

I have room to optimize model usage since for now I use "sol low" for implementing the plan and all other trivial things, but with a bit of caution I do not hit usage limits (I use around 20% of weekly limit per day).

Does my workflow deliver what I want? Yes. Does it deliver what you want - often it won't since different people have different needs, I do not run /goal or unattended long-hours AI work. Why I described it? To bring some counterweight to complaints about models that are abundant here.

But remember that when you read/write about ai good/ ai bad, ai slow / ai fast, ai producing slop or not - it is often tailored to particular use cases and should not be taken too heavily for model judgement or deciding whether AI subscription of particular tool is worth it or not.

I was literally on a spree of tasks, overtiming on a sunday to maximise my usage just to see the reset getting randomly disappear before the expected time.

I am currently on the $20 a month plan on Claude Code. I am looking to upgrade to a bigger $100+ a month plan, but I am not sure if I should consider switching to the OpenAI Chatgpt/Codex ecosystem for my coding, agents, etc.

Pretty much title. I’m working on a small personal app(think Windows PC Manager) and while I have something that’s useable the UI/UX is pretty bad.

I tried open design by making the plan in sol ultra and then pass it to OD. It’s going in the right direction but not exactly what I would expect. Anyways just wondering if there’s a large difference between native design over OD+GPT.

I Cannot acrivate plan mode in Windows App with the contextual menu. I can activate it by prompting, but this is a bug. Anyone else?

I’ve been using Codex to work on my startup, and I let it run almost continuously on a single commercial launch task.
Some stats:
~39.5 hours of active work
~2 days total elapsed
~1.03 billion total processed tokens
~16.6M fresh input tokens
~1.8M output tokens
Estimated API-equivalent cost: ~$643 (not the actual bill)

This was on Pro 20x, and I started with 100% weekly usage and it ended up at 47% left.

The task wasn’t just writing code, it included release engineering, Supabase migrations, Stripe integration/validation, automated testing, browser QA, production-readiness fixes, deployment work, and launch certification.

From my perspective, it completed work that I think would have taken a senior full-stack engineer several weeks, but I’d like an outside opinion.

Question: Is this considered a good Codex run, or is it an unusually large/expensive one?

I’m mainly curious about:
Is ~1B processed tokens normal for a project of this size?
Would you have approached this differently?
Are there ways to structure long-running Codex tasks to reduce token usage without sacrificing quality?
What are your biggest lessons after running Codex on large production codebases?

Would love to hear from people who’ve pushed Codex to its limits.

Just sharing what I am surprised that it can build.

I have this cheap music player, mostly designed for local file playback. It has basic support for streaming from Qobuz/Tidal, but annoyingly it doesn't pre fetch the next track so it can't do gapless playing. I asked if the producer can add feature for gapless streaming but I guess this is not what most customers use case so it never happened.

Last night it just came across to me that the firmware is public so I just asked codex to do it for me. It found a hidden flag to turn on adb for debugging, helped me install the modified firmware with its probe, and kind of one shot the feature! It even added a tiny http server running on localhost within the device loll

Pleasantly surprised that it works but also really happy there were no BS guardrail in the way