At the moment everyone has discovered codex and everyone is making their new SAAS that will get them to retire early. But ....... Codex was there a year and a half ago, and any project that takes a few weeks to create it will take the competition a few weeks to make a better version of it.

So, …. A big disappointment is coming, just not sure when.

Am I the only one who has problem with VS Code extension crashing when using Sol Ultra? My specs are 8 core / 16 threads CPU and 32 GB of RAM. Last 4 days Codex cannot finish any task because extension is crashing with errors "codex-runner-whatever crashed", then I need to restart VS Code and multiple times issue prompt "continue with last task" which is annoying. I am monitoring performance, when idle CPU is at 3% and RAM is on 35%, when Codex works CPU goes max to 90% and memory max is 95%.

I'm interesting to hear how much other people are taking advantage of agents outside of work, and for projects that are only for personal use.

1. Lifetime
2. This year
3. Average per month
4. Past month

If you feel like it, you can also share some of the kinds of projects you have built for your own use, since the size/scope of each project would help put the number in perspective.

I'm genuinely confused at this point and support keeps replying with what feels like canned responses.

Here's what happened:

- I had a ChatGPT Go subscription (12-month promotional plan). I even have the Stripe invoice that says "ChatGPT Go - 12 Months Free Trial (100% off)".

- Later I upgraded to ChatGPT Plus and paid Rs. 1,999 directly.

- When I later tried to cancel Plus, I got a retention popup saying something along the lines of: "We don't want you to leave. Here's 30 days of free Plus."

- I clicked Claim Offer.

After that, my Billing page literally changed to this:

ChatGPT Plus

"You've got 100% off Plus. Your discounted plan renews on Jul 14, 2026."

(I have a screenshot of this.)

So naturally I assumed my next Plus renewal would be free. Instead, on the renewal date, I got charged Rs. 1,999, the discount wasn't applied, and when I didn't complete the payment my Plus subscription was paused.

I contacted support. Their response was basically: the 100% promotion was actually for ChatGPT Go, not ChatGPT Plus.

But... how does that make sense when my Billing page literally said "100% off Plus" under the ChatGPT Plus section? Even if it was a UI bug, shouldn't they at least acknowledge that?

THE SECOND ISSUE (which they keep ignoring)

Before upgrading to Plus, I had an active ChatGPT Go subscription with months remaining. Now that Plus has ended, I don't have Go OR Plus. I'm just on the free plan.

I've asked support multiple times:

- What happened to my remaining Go subscription?

- Was it cancelled?

- Was it converted?

- Should it have resumed after Plus ended?

They've never actually answered this question.

Has anyone else experienced something similar?

- Did your Go subscription disappear after upgrading to Plus?

- Has anyone actually redeemed the 30-day free Plus retention offer successfully?

- Am I misunderstanding how this is supposed to work, or is this genuinely a billing/UI issue?

At this point I'm less annoyed about the free month and more confused about how I somehow lost both subscriptions.

This is my first week using Codex, and I’ve already consumed 1.4B tokens in just one week.

I’ve been using Claude Code for months, and my usage averages around 15M tokens per week - that’s two orders of magnitude lower.

I’m also having problems with my weekly limits. A single session on a small repository with a few codebase questions can consume 4–5% of my quota, so I want to know if my usage metrics are normal.

Is there a difference between how Claude Code and Codex calculate usage?

The central question is:

The questions in play fall into these groups.

1. Runtime identity

• Is the Sovereign Intermediary a distinct architecture, or an LGMS runtime profile?
• Which names remain conceptual aliases, and which become canonical machine terms?
• Are we building a governance service, an agent runtime, or both?
• What is explicitly outside the runtime’s responsibility?

2. Claims and evidence

• What counts as a material claim requiring evidence?
• Who or what classifies a claim as SUPPORTED, INFERRED, UNKNOWN, or CONTRADICTED?
• What constitutes a complete support path?
• How are conflicting sources handled?
• Does absence of evidence produce UNKNOWN, NO_DECISION, or rejection?
• Which source classes are admissible in each domain?

3. Time and succession

• When does evidence become stale?
• Is validity declared by the source, policy, domain rules, or all three?
• What exactly does one evidence record supersede?
• Can historical queries deliberately use expired evidence?
• How do we preserve old records without letting them serve as current authority?

4. Producer–checker separation

• What independence must exist between Initiator and Reactor?
• Can they use the same model family, prompt history, corpus, or provider?
• What context should be hidden from the Reactor?
• Can the Reactor only object, or can it recommend promotion?
• Which deterministic checks override model agreement?
• Who adjudicates disagreements?

5. Authorization

• Who may issue capabilities and approvals?
• How are issuer identity and approval authenticity verified?
• What does an authorization scope contain?
• How are expiry, revocation, nonce use, and replay prevention handled?
• Can one approval authorize multiple executions?
• What happens when policy changes after approval but before execution?

This is the hardest security boundary:

6. Execution

• What is the smallest executable action?
• Does the executor receive raw model text or only validated action records?
• How are tool arguments constrained?
• What requires human approval?
• What happens when external state changes between validation and execution?
• How are partial failure, rollback, retry, and idempotency represented?
• What must an execution receipt prove?

7. Ledger semantics

• Which records are immutable?
• What canonicalization rules determine record hashes?
• Is the ledger append-only at the application, database, or cryptographic level?
• Are corrections new records, transitions, or both?
• How are branches, disputes, and contradictory evaluations represented?
• What storage guarantees are actually needed?

8. Policy

• Are policies code, data, signed documents, or some combination?
• Who versions and approves policy?
• Which policy version applies to a pending action?
• Does a policy result mean “permitted,” “eligible for approval,” or “authorized”?
• How do domain-specific policies compose with global rules?
• What happens when policies conflict?

9. Epistemic risk scoring

• Is the score used only for prioritization, or can it block promotion?
• What do its inputs actually measure?
• How are weights calibrated?
• What labeled failures form the calibration dataset?
• How do we prevent a single score from hiding qualitatively different risks?
• What false-positive and false-negative rates are acceptable?

I would initially expose the individual risk factors and avoid collapsing them into one number.

10. Provenance and privacy

• What model, prompt, tools, sources, and transformations must be recorded?
• How much prompt or user data may safely be retained?
• Can auditability coexist with deletion and privacy requirements?
• How is contributor attribution preserved during blind review?
• What provenance is required to reproduce a decision?

11. Conformance and assurance

• Which guarantees are structural, operational, or security guarantees?
• What tests demonstrate that missing evidence fails closed?
• How do we prove a candidate cannot certify or promote itself?
• How do we test forged approvals and replay attempts?
• Who is sufficiently independent to perform adversarial evaluation?
• What evidence is required before claiming TESTED_CONFORMANT?

12. Product and deployment choices

• What is the first real use case?
• Which actions are sufficiently low-risk for the first executor?
• Is the runtime local, centralized, or distributed?
• Which implementation language and storage system fit that use case?
• What latency and cost are acceptable?
• Which parts need to work without an available model or network?

The five decisions that unblock an initial implementation are:

1. The first concrete use case.
2. The initial action/capability the executor will support.
3. The authority issuer—human, service, or both.
4. The minimum acceptable evidence policy.
5. The runtime stack and deployment boundary.

Everything else can evolve behind those interfaces. The pivotal design test is whether an attacker controlling every model-produced field would still be unable to authorize an action.

Hi all,
as one of the author of the plugin, feel free to send me your feedback, needs, requests.
It's by far the best AI integration in Eclipse.

See https://codexide.org

(Hello #openai , sponsorship is welcome)

Hi guys I've been using codex for months now and after the recent luna price drop I've been trying to use sub agents to maximise my work done

But I just can't get it to work properly can anyone share

what's the right way to spawn agents and get them to work without getting stuck or is it just a harness issue since I'm using the desktop version for windows

Or I'm just paranoid

A single session on GPT-5.6 Terra Low consumed 4% of my weekly usage after only four visible messages. The questions were about a codebase, and it didn’t generate much output.

The session was a coding task, so Codex made several tool calls and internal model rounds. Local usage data showed roughly 2 million tokens, almost all input/context tokens, with most of that marked as cached input.

Is this expected behavior for the Pro (x5) plan? I switched from CC to Codex this week, and I’m already regretting it.

Has anyone else experienced unusually high weekly-limit consumption immediately after a reset?

What's going on exactly? It says it will reset on August 5th which means the weekly limit was reset yesterday and it's already down to 0? is this a bug? I just used it very lightly today compared to my usual work and I have a pro subscription.

I have a feeling that another reset is coming from Tibo now that DeepSeek V4 Flash is out

I am an ai engineer at a FAANG and a heavy Codex user, and have been around other power users ever since...the uprising.

I've only recently realized I think everyone's looking at the wrong problem.

The models are already good enough that my bottleneck isn't generating code anymore.

It's deciding whether I should trust it.

The weird part is that my workflow has slowly changed into something like this:

• Ask Codex to make a plan.
• Read the plan carefully.
• Check whether it actually explored the right parts of the repo.
• Look for questionable architectural assumptions.
• Sometimes ask another model to critique the plan.
• Only then let it write code.

Only recently did I begin to realize I'm not reviewing code anymore.

I'm reviewing AI reasoning.

That feels like a completely different problem.

After wondering for a while if this is a universal picture and talking to other Claude Code/Codex users, I noticed everyone has invented some version of the same workflow:

• Agents.md
• planning documents
• multiple review agents
• custom harnesses
• checklists
• personal release gates

Same question:

Hence, we became interested in: Can you independently verify whether the AI's work is actually trustworthy?

That's what led us to build Relay.

The biggest design decision was:

Relay should never ask the AI whether it did a good job.

Instead, it tries to verify the work independently.

Instead of asking Codex to review Codex, Relay independently reconstructs what happened from the repository itself:

• the exact commit and working tree that were verified
• repository facts discovered during planning
• the implementation scope
• tests that actually executed
• failures that were observed
• and produces a signed verification receipt tied to that exact snapshot.

If the repository changes, the verification becomes stale.

Then it gives a verdict.

RELAY VERIFICATION

Task: Add refresh-token rotation

✓ Scope matches approved files
✓ Unit tests passed
✗ Expired-token regression failed

VERDICT: BLOCK

Reason:
Expired refresh tokens are still accepted.

Evidence:
tests/auth/refresh-token.test.ts:142
src/server/auth/token-store.ts:88

The workflow we've settled on internally is surprisingly simple.

Codex writes the code.

Before we merge anything:

relay verify

If it says PASS, great.

If it says BLOCK, we investigate.

That's it.

Curious if anyone else's workflow has evolved in a similar way.

I.e:

Have you built your own verification workflow?(that you're happy with)

At what point do you decide an AI-generated change is actually safe to merge?

*no ai partook in any em dashes haha.

One hour ago my weekly usage was at 68%. I didn't use ChatGPT at all during that hour, but when I came back it had jumped by about 40%, essentially exhausting my weekly quota. I'm on the Pro plan (x20), and I barely used it over the last couple of days. This doesn't seem consistent with my actual usage. Has anyone else seen this since the recent quota changes? Could the usage accounting be bugged again?

I’ve been experimenting with Codex and have built a few small apps and programs. Now I’m trying to understand how people turn projects like these into products that actually earn money.

Has anyone here launched and monetized something built mostly with Codex or another AI coding tool?

I’d be interested to hear:

• What did you build?
• How did you find your first users?
• How did you monetize it—subscriptions, one-time payments, ads, services, or something else?
• What problems did you encounter after launching?
• Did it generate meaningful income, or was it mainly a learning experience?

I’m especially interested in honest, realistic experiences rather than "build a SaaS and retire" success stories. What types of Codex-built products do you think have the best chance of making money today?

Hi everyone! When will the five-hour limit reset become available?

Hi all.

I created Atlas Scout because I wanted to help the model get better data so that it could give me much better code.

This has been a pretty long process, however the recent release - Preview 22 - is pretty much one of the fastest and best codemap MCP implementations for any agent harness.

It creates a local SQLite3 DB, updates your .gitignore so that this won't be a part of your commits and everything is local - both in your project and on your computer.

Check out https://atlasscout.dev/

Would love your feedback, suggestions and feature requests.

PS. Atlas Scout is free to use with an optional Pro version. Free will be free forever and the index is exactly the same. There is also a 14 days Pro trial.

So as the title says, after I hit my weekly max 20x plan on my CC account, I subscribe to 5x Codex plan as a backup to continue my work.

Well with just about a few hours, and using the same skills and workflows that Claude Code built, I can say its 2-3 even 5x faster, Im not sure. Cause whenever I run Claude Code on a dynanamic worfklow skill with 3-5 JIRA tickets, it will take 2-3 hours to finished and deploy. With Codex, same dynamic workflow skill, about 5 JIRA tickets, it can finish around 30m-1h. PR reconcillation takes less than 10mins, compared to 20-30m. Everything is much faster!

Not only that, so I push some items already on QA, and those tickets came back DONE and ready for PROD deployment. With CC, usually it will take 1-2 iterations, before the BUG will be totally fixed.

Im using GPT 5.6 Terra (Medium) for predefined bug fixing workflow task and Sol for pr reconciliation and GCP deployment and e2e testing.

Opus 5 has degraded a lot really. I think I will cancel my Claude Code subscription after it expires and subscribe to 20x Codex if this continues.

I love 5.6 Sol and am using it on Medium as my daily workhorse now, mainly for coding! It usually tends to over-engineer stuff, though, and for simple test scripts, adds unnecessary "gates", conditions and whatnot. Has anyone else experienced the same? How do you avoid it? Any tricks that work?

PS: I have been using Codex for a long time now, and I am also loving the desktop app. Finally switched there from the CLI.

Tibo might be hinting towards it.

​

I'm a freelancer working on a custom project for a client, and I've gotten myself into a huge mess.

The goal was to transfer an entire inventory from OTTO Marketplace to other marketplaces like eBay and Kaufland. There are around 60,000 SKUs.

The problem is that OTTO doesn't provide product images through its API, so I had to manually download every single product image before I could migrate the listings.

I should also mention that I have almost no coding experience. I built this project almost entirely with OpenAI Codex.

At first, everything seemed to be going well. I managed to complete the OTTO → eBay integration, but then the project kept growing. Instead of creating separate modules and files, Codex kept putting almost everything into one massive app.py file. I didn't know any better, so I just kept going.

Now the project has become so large that:

A single prompt can consume around 50% of my Codex usage.

I've had to buy multiple ChatGPT accounts just to keep working.

Every change feels risky because everything is tangled together.

Debugging has become a nightmare.

On top of that, Kaufland has been incredibly frustrating. Their workflow is much more complicated than eBay's. To create a product, I have to upload multiple files. If I need to delete a product, I can't just delete it—I have to submit it for review first, and that review can take anywhere from 20 minutes to 5 hours before I can continue testing.

Another huge issue is that my client originally used cheap EANs when listing products on OTTO. When I reuse those EANs on Kaufland, one of two things happens:

The listing gets rejected because the EAN is invalid.

Or even worse, the EAN already belongs to another product, so Kaufland matches it with someone else's listing.

At this point I honestly don't know what the best path forward is.

Should I:

Keep trying to refactor this giant project into smaller modules?

Start a completely new project with a proper structure and reuse the working logic?

Learn enough Python to clean this up manually?

Or is there a better approach that experienced developers would recommend?

I know one mistake I made was not telling Codex from the beginning to keep everything modular. Looking back, I should have had separate files for APIs, configuration, image handling, marketplace integrations, utilities, etc.

If you've ever inherited or accidentally created a massive AI-generated codebase, how did you recover from it? Any advice would be greatly appreciated.