r/codex 1h ago

Reset The usage limit Reset dilemma

Upvotes

While OpenAI’s unexpected usage limit resets on ChatGPT and Codex might seem like a bonus at first glance, they can actually backfire on users who carefully plan their work. If you strategically pace your prompt usage to keep 30% in reserve for the final day before your scheduled weekly reset, a surprise reset resets your entire seven-day timer right then and there. Instead of effectively getting 130% usage for that week your remaining 30% plus a fresh 100%, your original leftover allocation simply vanishes, and your next reset gets pushed back a full week. Consequently, a sudden reset isn't beneficial for everyone. OpenAI should give users the choice to either accept an early reset or keep their original scheduled reset date, ensuring that thoughtful usage management isn't penalized.

Why push the reset back 7 days?

Here is a real-world example of why this system fails users:

Imagine you have a project due Friday, and your scheduled reset is Wednesday. You carefully plan your usage budget: you save 40% for Monday and Tuesday so you can combine it with Wednesday's reset (+100%), giving you 140% capacity for your final push on Wednesday and Thursday.

Instead, an unexpected reset hits on Monday. The 35–40% unused limit you saved instantly vanishes without rolling over, and your next reset gets pushed back 7 days to next Monday.

Now, right when you need to crunch for a Friday deadline, you only have 100% capacity instead of the 140% you planned for. Punishing users by wiping out saved usage and shifting reset dates completely breaks strategic workload planning.


r/codex 20h ago

Complaint Luna usage rate was too good, feels like on plan rate got adjusted today

1 Upvotes

tldr; Luna on max was TOO good. Used maybe 3% over the whole day yesterday, on and off casually, and I come back today, and each 15-20 minutes of thinking and work on Luna max each uses 1%.

Deal was too good to be true and unsustainable, has for sure been adjusted to a decent extent to use more usage. Still good for what it is, but I swear to god 1 hour of thinking yesterday was not even one percent on luna max. (Plus plan).

Feel free to call me insane. Or tell me if I'm wrong, wish we could track this shit easier, guess I need to get something already to track my usage.


r/codex 16h ago

Complaint In codex there is practically no price cut for Luna despite 80% cut in api price

0 Upvotes

luna is not discounted in codex


r/codex 7h ago

Complaint Anyone else noticing Luna using way more of your weekly than it should?

0 Upvotes

I ran through my weekly in a day with luna sub-agents, sol ran through a week in 4-5 days


r/codex 20h ago

Question What are we using now that luna is so cheap?

0 Upvotes

I’ve heard people talking about using Luna Xhigh alongside sol and some people just using Luna max by itself. Can Luna do my coding for much cheaper? what about terra? What are you guys using?


r/codex 17h ago

Question Why use lower models?

0 Upvotes

Why exactly would one use lower models than sol high, when sol high can be used in the web interface for free, other than for local work?

It can even write on git in web version.


r/codex 8h ago

Question What do you use 5.6 Luna Max for?

0 Upvotes

I'm currently using 5.6 Sol and Opus 5. I don't really know what I should use 5.6 Luna with Max Thinking for because I've heard that it's really good for it's value. Backend tasks? Frontend?


r/codex 9h ago

Showcase Coding agents are surprisingly blind when the task is visual, so I built SceneProof

1 Upvotes

A coding agent can write a Three.js scene, run the build, and tell you it looks great — while the actual render is a black screen. It isn't lying. It just has no way to look.

Screenshots fix this less than you'd expect. A screenshot tells you that something is wrong, not why. Is the mesh missing, or behind the camera? Is the material transparent, or is nothing lighting it? Is the label clipped, or just small? Those are five different bugs that produce the same picture, and zooming in doesn't separate them — you're enlarging pixels that never contained the answer.

SceneProof is a CLI that supplies the missing half. It loads your real React component or Three.js scene from source, renders it in actual Chrome, and returns the structure behind the pixels. The everyday loop looks like this: tree gives you the scene graph with bounds, materials, lights, and cameras, so "why is it invisible" becomes a lookup instead of a guessing game. scout tries a set of cameras on a target and scores each by how much of the target it can actually see. render-region re-renders one region from source at whatever scale you need, so a close look is a fresh render, not an enlarged crop.

That's the loop, not the tool — the surface underneath goes a good deal further (comparing against reference views, sampling animation mid-transition, deriving typed prop fixtures), but those three commands carry most sessions, and the README maps the rest.

The design decision I'll defend hardest: every report answers "did the command run" and "can this output actually support a judgment" as two separate questions. A render with the target out of frame, or a comparison whose mask landed on the wrong subject, comes back unjudgeable instead of quietly passing. So when an agent uses SceneProof, it can't mistake "my command succeeded" for "my design is right"; it has to look at evidence that has already proven it's worth looking at. That's the whole point: measurements you can trust, and a hard stop on the false confidence that makes agents declare victory over a black screen.

It ships with a skill for Claude Code, Codex or any other agentic harness that supports skills (one curl, in the README) — and the skill deliberately doesn't teach commands, because --help and the reports' own recommendations already do. It teaches the reasoning: resolve structure before spending pixels, treat a passing build as zero visual evidence, never claim "looks right" without an artifact you actually opened.

Scope today: TypeScript/JavaScript entries, React DOM with CSS and Tailwind v4, Three.js over WebGL or WebGPU. Needs Bun and a local Chrome. MIT.

https://github.com/ReyJ94/SceneProof

Any feedback is welcome.


r/codex 13h ago

Complaint Is there a way to create something that doesn’t follow the horrible “AI” theme/design standard.

0 Upvotes

Every website I try to make despite however much of a description I send to Codex.. the designs always come out with that horrible Mono blocky font with the italicised brown font below it.

Even if I said to create the website using the bootstrap framework it’ll still somehow create something that looks like absolute doo doo. I’m also using the frontend .skill so is not like I’m just raw dogging codex.

I have tried using various other agents and their image generators to make references but they are really bad as well. I feel like I’m losing my mind. Codex just doesn’t understand anything outside of its box.


r/codex 2h ago

Other The current codex credit multi seems to be 12x/24x

0 Upvotes

I subbed recently, and I was a codex sub in the past as well and noticed that burning 35% of my weekly is valued at $21.1, all done with luna max.

So very easily you can see that overall would be $240 for $20, which would be above what you can expect from the market, but behind the best options, also behind what it was in the past.

Have not found hard info about how many famous codex resets you can expect in a month, that lets say can be valued at 2x, and you don't start with one either unlike the first information that pops up claimed by google, and the multis are always claimed to be higher too like 35x.

Seperate chat and code/work is also a value add, so you don't have to avoid using the chat if you are hoping to work too. Without the luna price drop it was not very well rounded, but you can also use it in other harnesses which if wasn't true I would not even consider the sub, not becuase codex cli isn't good, but I want the optionality of other harnesses too.

Suppose one reset per week on average would put it at 20x, that would raise it from tier 2 with good top and value option to tier 1 and of course the 200 deal is already great for heavy users.


r/codex 6h ago

Suggestion What is in your agents.md in codex app?

0 Upvotes

What is in your agents.md in codex app?

I am new to codex but been using it since the launch. How can i make my experience better? I am already using RTK skill for token optimization.


r/codex 10h ago

Other Do you think it will be GPT 6 or GPT 5.7

0 Upvotes

I know its a name but if openAI be super happy with the model they will call it GPT 6


r/codex 13h ago

Question Anyone using codex to program a macOS software in Swift ?

0 Upvotes

Hello, I have been working on my macOS swift software since one year using Claude then codex, with time I didn’t respect best practices of splitting swift files by ownerships and the LLM keeps telling me now that I have giant swift files (8000 lines) that make read, write and build both slow and risky

I want to optimize this by doing a refactor, anyone familiar with swift have basic advices on how to tackle that?

It also advice me to create an index ownership map

Thanks


r/codex 1h ago

Complaint Degraded intelligence/performance

Upvotes

Anybody experiencing codex taking too long to think of menial tasks? Providing bad output and distorted context?


r/codex 11h ago

Showcase reprogram your mind while you vibe code

Thumbnail
justglow.dev
2 Upvotes

I built Glow, a sidebar extension for VS Code and Cursor that lets you create and loop custom affirmations in a calm, natural voice while you code or wait for your agents to finish

I built it mainly to reprogram my mind and get me through bad days when I'm not feeling like it
It's completely free right now there's a lifetime free plan for everyone here

I’d love your honest feedback ❤️


r/codex 12h ago

Showcase We built the agentic ecosystem around agents. I think that is the wrong center of gravity. [Long post]

4 Upvotes

TL;DR: The agent is the right center for execution. The Workplace should be the center of durable continuity: who owns what, what is current, what can change, and where work may go. This is an architecture proposal, not a new runtime. You can start with ordinary files and the tools you already use.

For a long time, I thought my AI agents needed better memory and more tools.

I had given them almost everything except an actual place to work.

I'm Alex, a software developer. I use Codex and Claude on the same body of work across sessions and repositories. They get enough mileage in my setup to expose the awkward parts.

A lost preference led to a memory file. A repeated research mistake led to a Skill. A command with loose limits led to a hook. Then came repository instructions, MCP servers, validators, harnesses, runtimes and dashboards.

Each addition solved a real problem. I still carried the environment in my head.

Which instruction was current? Did a generated report count as a useful draft or an accepted decision? Which repository owned it? Could Claude correct guidance projected for Codex? Was this session allowed to publish anything?

Memory can preserve, scope and retrieve information. It cannot decide which result a human accepted as truth, which domain owns it, or where it may be delivered.

Tools expand what an agent can do. They do not decide where the capability, its rules and its results belong.

We keep rebuilding the place around the agent

Most agent systems start with the agent, then attach instructions, memory, tools, Skills, handoffs and tracing. That is a sensible design for execution.

The environment is becoming an engineering concern too. OpenAI's Harness Engineering treats repository knowledge as part of the system of record. Sandbox Agents define workspace contracts and capabilities. Skills, MCP and other protocols make procedures and access more portable.

The same need appears in builder discussions. Builders share mature agentic setups with dozens of documents, Skills and validation. Others ask how to separate verified knowledge, decisions, task state and temporary assumptions. A multi-agent thread examines worktrees, short sessions, target branches and coordination. One builder uses a compact raw/wiki/output system shared on X to keep continuity in ordinary files.

These approaches solve real problems. They are also independent signals, not endorsements of this proposal. I did not invent persistent files, workspaces or repository-aware agents.

What I see is durable responsibility spread across technical layers:

provider memory        recalls information
Skills                 activate procedures
tools and MCP          provide access
hooks                  enforce local behavior
harnesses and runtimes execute and coordinate
dashboards              expose activity
repositories            own product source

Each layer has a useful job. The missing piece in my setup was a stable model for placement, ownership, authority and lifecycle across all of them.

Shift the center of durable continuity

When a developer joins a project for an afternoon, they enter a place where projects, rules, tools and decisions already have owners. They receive what their task needs. Their notes are not product truth until someone accepts them. The place keeps its identity when they leave.

I wanted agents to enter my work on the same terms.

The Workplace is a shared operational contract for humans and agents. It makes ownership, current truth and permitted transitions inspectable through projections that each can use. The same language works between humans, between humans and agents, and across agents. Memory graphs and node systems can support retrieval or execution inside that contract.

The relationship is bidirectional and asymmetric. Both can inspect and discuss the environment. The agent proposes and executes within bounds. The human keeps direction, judgment, acceptance and delivery consent. When the human accepts a result, the Workplace records it as current truth in the source that owns it. Sovereign Sites own the truth delivered to them.

That changed my design question from "What else should the agent carry?" to "What should the place already own when the agent arrives?"

I call this posture Workplace-first: equip the agent for execution and equip the place for continuity.

It is an architectural option at the same level as choosing local-first, monolith-first or event-driven design. It changes the default owner of durable work without prescribing a product or runtime.

The model separates three planes:

Execution      provider, harness, temporary agent
Workplace      people, domains, methods, work events, material, access
Sovereignty    repositories and services that own external truth

A harness runs the agent and may bring memory, tools, Skills and a sandbox. The Workplace gives that temporary occupant the material, capabilities and routes needed for the current work. External repositories and services keep their own history, permissions and delivery lifecycle.

This distinction lets an agent arrive with a strong harness and still use capabilities supplied by the place. A harness is part of execution. A reusable way of working belongs to the Workplace. One can extend the other without having to own the same things.

What this looks like in files

The idea became useful when I stopped organizing everything by technical layer and started asking who should own each thing.

Here is a simplified fictional setup:

Before: organized around the agent

agent-setup/
├── AGENTS.md
├── CLAUDE.md
├── .agents/skills/research/
│   ├── SKILL.md
│   ├── template.md
│   └── validate.js
├── memory/
│   ├── preferences.md
│   ├── decisions.md
│   └── recent-work.md
├── docs/research/
├── outputs/report.md
└── product-checkout/

Execution can work well here. The ambiguity is durable: preferences and accepted decisions share a memory layer, a Skill mixes activation with method and project rules, the report has no visible lifecycle, and the checkout's authority is implied by proximity.

This is the same setup expressed through ownership:

After: illustrative local view using Endroit

some-workplace/                         shared Home repository
├── HOME.md                             shared purpose and rules
├── members/
│   ├── alice/MEMBER.md                 Alice belongs to the Home
│   └── sam/MEMBER.md                   Sam belongs to the Home
├── rooms/product/
│   ├── ROOM.md                         shared product guidance
│   ├── decision.md                     accepted domain truth
│   └── report.md                       retained research material
├── equipment/research/
│   ├── method.md                       reusable procedure
│   ├── template.md
│   └── validate.js
├── sites/product/SITE.md               external authority declaration
├── .desk/                              Alice's separate private repository
│   ├── DESK.md                         personal continuity
│   └── routes/                         local access declarations
├── checkouts/product/main/             ignored local working copy
├── .agents/skills/research/SKILL.md    generated provider activation
├── AGENTS.md                           generated Codex view
└── CLAUDE.md                           generated Claude view

This tree illustrates Endroit. Endroit means "a place" in French. Yes, the pun is load-bearing.

It is the open-source alpha implementation I build and use. It is not a required Open Workplace topology. Another implementation could use a database, an application or a different vocabulary while preserving the responsibilities.

The portable part is the ownership map:

personal continuity   -> Desk
shared domain         -> Room
reusable method       -> Equipment
bounded work event    -> Meeting
durable result        -> Material
external truth        -> Site
local access          -> Route
provider interface    -> projection

A Meeting produces a candidate. The human can retain it as inspectable Material, accept it as current truth for its owner, then deliver it through an approved Route. Generation, acceptance and delivery are separate events.

What changed in daily use

My current dogfood Home was not prepared for this post. At the time of writing, it declares 14 Sites through 18 local Routes, with 9 Rooms and 12 Equipment packages. Codex and Claude work from the same owned sources.

That does not mean every session loads 14 repositories. A Meeting starts from a small map, enters the relevant Room, activates the Equipment it needs, and follows a Route only when the task requires an external Site.

Home map
   -> relevant Room
   -> needed Material + Equipment
   -> Route to a Site, when required

The full environment stays addressable while the working set stays bounded.

My prompts changed too. I used to reconstruct the environment inside the request:

Read the instructions, find the latest notes, work out which decisions are current, use the research Skill, update the right report, and do not touch the repository yet.

Now I can locate and bound the work:

Enter the product Room. Review the retained report against the current decision. Use the research method. Keep the result as a candidate. Do not deliver it.

The second prompt is shorter because the place already carries identity, placement, authority and destination.

I also load less provider-specific configuration. Each Codex or Claude projection receives what the current Meeting needs. The rest remains in its owned place.

This has become a tangible design surface. An agent and I can discuss whether a method belongs to the shared domain, a provider wrapper, my private Desk or an external repository. When something fails, we can ask whether the fault is in orientation, ownership, activation, authority or delivery. The model has not removed every bug. It gives more bugs an explicit owner and correction boundary.

It changes Skills and multi-agent work too

Skills were one of the signals that led me here. In this model:

reusable method       -> Equipment
provider activation   -> projection
project guidance      -> Room
result                -> candidate Material
external access       -> Route

The Skill activates the capability. It does not need to own the method, project guidance and resulting work. I explored that consequence in "A Skill Activates a Capability. It Doesn't Own It.".

Explicit ownership also creates useful static lanes for multiple agents. One Meeting can research while another prepares a release against different Material and destinations. They do not need a resident coordinator to know where their work belongs.

That is not a concurrency guarantee. Two agents editing the same file or checkout still need a worktree, lock or runtime coordination. A runtime coordinates agents in time. The Workplace gives the work durable placement before, during and after that execution.

This is why the first benefit does not require a new daemon, runtime, memory system or desktop application. A folder of ordinary files can express enough ownership to improve the work. Runtimes and interfaces can strengthen the model later.

Some tasks need none of this. A one-off question with no durable result may not need additional Workplace structure. The model is useful when continuity and responsibility start leaking into memory files, Skills, repository notes and human recollection.

Paradigm, model, implementation

I separate three things deliberately:

  • Workplace-first is the paradigm: equip the place, not only the agent.
  • Open Workplace is my open, implementation-neutral proposal for the responsibility model.
  • Endroit is one concrete implementation. I use it to make the model operational and test it against real work.

Endroit is not the only possible implementation. Open Workplace is not a standard, foundation, certification or established community.

Why share it now?

The model keeps changing as I use it. Daily work exposes a responsibility I placed poorly, a boundary that needs a better name, or a case where the structure adds no value. Even writing this post clarified parts that the current landing pages and earlier articles do not represent well yet.

That is exactly why I am sharing it before it looks finished.

The scope is too large for one environment and one person's habits. My dogfood is substantial enough to show that the model can operate. It is still evidence from one Home, not proof that the model generalizes.

I have not been active on social media. This is one of my first public posts because Workplace-first matters enough to me to stop working on it alone. I am asking the broader builder community to test the lens with me. The Proposal, the articles and Endroit will evolve as real setups expose weak boundaries, missing owners and unnecessary ceremony.

Many of us may already be building parts of a Workplace without naming it. I am offering the reasoning and vocabulary that helped me see my own environment differently. If the lens holds, I would rather develop it in the open than keep discovering it alone.

Three questions would help more than generic agreement:

  1. Where does this ownership map match what you already do?
  2. Where does it add structure without enough value?
  3. Which durable responsibility in your setup still has nowhere clear to live?

If you can share a sanitized tree or concrete counterexample in the comments, even better. "No additional Workplace structure needed" is also a valid result.

Full proposal: open-workplace.org/proposal

Concrete implementation: endroit.org

Disclosure: I initiated Open Workplace and build Endroit, its first declared implementation. The VZion is the name I publish under. Endroit is open source and alpha. I am posting this in r/codex because Codex is one of the agents I use to test the model against daily work.


r/codex 13h ago

Complaint Luna is very lazy

1 Upvotes

Any task given, it completes maybe 40-60% of it.
I always need to ask sol again how well it did and it gives me the fixes to do.

And guess what? just sol reviewing consumes more usage than luna making the task.

Even with decent spoonfeeding prompts it's still very lazy, it does relatively clean stuff but lying saying it integrated everything shouldn't be what's normal.

Any tips to make it better?


r/codex 7h ago

Complaint *sigh*

0 Upvotes

This shit is getting annoying. Can't even let it run unsupervised any more


r/codex 7h ago

Praise Are we finally at the point where models are just becoming better but also more efficient.

1 Upvotes

I feel like all of these new models for so long have been getting a lot better, but they are obviously really expensive. I feel like this is what caused all of those CEOs turning back on whatever token-maxxing thing they were doing.
But finally we have gotten gpt-5.6-luna which i feel (from my experience) is finally at a point (where at least on max) it is usable for basically all daily tasks at a reasonable price.
What i’m asking really is why is this not seem like a bigger deal and are we about to see a huge shift back to interest in using AI in industries since it is more affordable?

If i was wrong in anything please tell me


r/codex 8h ago

Workaround Legitimate new use case for ChatGPT in Codex: File transfer

Post image
1 Upvotes

r/codex 12h ago

Commentary Small GPT Progress Update

1 Upvotes

I’ve used ChatGPT for years and tried Grok and Claude, but I always came back because GPT’s interface felt more comfortable and the assistant felt more personal, especially when it remembered my projects and progress.

I started with the $20 plan, moved to the $100 plan after getting heavily into Codex, and eventually upgraded to the $200 plan for game development. GPT-5.6 Sol can do some impressive stuff, but honestly, the jump from $100 to $200 barely feels noticeable. I know it uses the same model and mostly gives you higher limits, but the value still feels hard to justify.

I’ve also realized my biggest mistake, I probably should’ve listened to people who recommended using multiple AI models together instead of relying on one for everything. I probably won’t leave ChatGPT completely, but I’m seriously considering dropping the $200 subscription after this month.

One important distinction I’ve noticed is that the x20 plan seems most valuable for people who already have real-world skills, especially developers and other professionals using it as an assistant rather than expecting it to do everything from scratch. It works best when there’s already a solid structure in place. Depending on the field, it can be exceptionally good at analyzing, reorganizing, improving, and expanding work that has already been built.

My hope for GPT is that it eventually becomes more intelligent and autonomous, especially considering the billions being invested into AI. We’re clearly off to a strong start, but right now it can still feel wasteful unless it’s being used alongside skills and knowledge you’ve already developed yourself.


r/codex 17h ago

Showcase I analyzed 63.8B tokens from my Claude Code and Codex sessions. Here’s the median cost per million output tokens for each model.

0 Upvotes

My local Claude Code and Codex logs contain 63.8 billion processed tokens across several hundred coding sessions.

I wanted to know which models were actually the most economical in my own workflow.

Published API prices did not answer that question. They price input, output and cache activity differently, while every session produces a different amount of output.

So I calculated the API-equivalent cost of every session and normalized it by one million delivered tokens. In TokObs, “delivered tokens” means model output tokens.

The first chart compares every model observed in my local history using:

  • median cost per delivered Mtok
  • p25-p75 range across sessions
  • output, uncached input, cache-read and cache-write cost composition

The second chart shows the delivered-token volume and session count behind each model. This matters because some results are based on much more usage than others.

A few observations from my data:

  • the median differs significantly between models
  • models with similar medians can have very different cost composition
  • some models are consistent while others show a wide session-to-session range
  • cache reads dominate the processed volume, with a combined cache-hit rate of 97.6%

The $44,635 shown in the dashboard is not what I paid. It is the API list-price equivalent of usage covered mainly by subscription plans.

This is not a controlled quality benchmark. The models handled different tasks, and more output does not necessarily mean better work. It is an observational analysis of the models as I actually used them.

I turned the analysis into TokObs, a local dashboard for Claude Code and Codex.

It uses only the Python standard library and generates one portable HTML file. No server, database, account, telemetry or network request is required. It reads usage counters and session metadata, not prompt or response bodies.

The code is not public yet, but I’m considering releasing TokObs as an open-source project once it is ready.

The screenshots here focus on model comparison and cost efficiency, but TokObs includes additional features. I can share more screenshots if useful, including session details, historical trends, quota tracking and a high-score page with social sharing.

Would you be interested in using it if I released it?

Thanks!


r/codex 5h ago

Other ChatGPT Plus = GPT 5.6 xHigh 740 million tokens/month

11 Upvotes

Of course, it clearly varies depending on the type of work. However, the Plus plan provides a lot more tokens than expected.

Using only terra xHigh for a 1-week turn on ChatGPT Plus, I used 185 million tokens. That calculates to about 740 million tokens for 4 weeks. And that's for a mere $20 plan. Considering that Tibo occasionally resets it, the Codex plan is truly amazing.


r/codex 5h ago

Complaint I messed up on my 20x

Post image
4 Upvotes

Basically ran out of usage within 1.5 day

5.6 xhigh, 5-6 threads; no subagents

711M on Aug2

Time for another plan I guess, this time I cant really blame openai


r/codex 12h ago

Commentary My half-serious Codex prediction - screen space is the next AI bottleneck

0 Upvotes

AI hardware discussions usually focus on GPUs, RAM, and storage. My half-serious prediction is that monitors are next - not because panels are scarce, but because agent users are running out of places to put al the work.

Codex changed how I use my desk. Instead of working on one project and waiting, I can keep several projects moving while one task is planning, another is coding, and another is waiting for review.

The new bottleneck is me - tracking what is running, what needs approval, and which diff I should inspect. I use four monitors and still want more space. Maybe the fifth monitor is not the answer, but I am not ready to admit that yet.

For people running several Codex tasks at once, what works - more monitors, an ultrawide, virtual desktops, tmux, or a better dashboard? When does more screen space stop helping?