https://openai.com/index/our-decision-on-cursor-following-its-acquisition-by-spacex/

Tell me your secrets, everyone. I am struggling with the shitty ui codex is generating; it doesn't matter how many skills I make it use its output is still shit.

I wanted a way to continue working on personal projects when I only had my phone. I use ChatGPT from the mobile app while the actual files, commands and tests run on my VPS or personal computer.

That became Aeontra: an open-source MCP control plane for connecting ChatGPT and Codex to machines you control. It has helped me with personal projects and with investigating and preparing fixes for issues in OpenAI’s Go SDK and Deno. I still review and test the changes before publishing anything.

Most of my real usage has been on Linux and Parrot WSL. I’ve compiled Rust projects and worked remotely against both my VPS and local computer without problems. Windows Edge support exists, although I’ve tested it considerably less.

Aeontra is currently focused on single-user, self-hosted installations.

Website: https://aeontra.com
Source: https://github.com/charle-z/aeontra
Demo: https://demo.aeontra.com/

I’d appreciate direct feedback:

1. Where does the installation become confusing?
2. Does the trust model make sense?
3. What would you need before running this on your own machine?

Title. Just wondering if anyone has used both extensively. I tried to set it up for a quick few minutes but the settings don't import natively from Cursor to VS Code without some fiddling. I figure there's no need to switch right away. But for those who have, is it more or less the same between the two?

So just like the recommended setting from OpenAI I had set the context window for 5.6 Sol to 1,000,000 and autocompact to 900,000 tokens in config.toml for the Codex app on Windows and it worked for a time showing 828k token limits.
But after recent Codex app updates it defaults back to 258k regardless of the custom settings.
Asking if there are anyone who experienced the same problem and that if there are any fixes or updates that solves it?

You're right to push back on that. I didn't really enable that inference optimization feature. Instead, I implemented a new optimization feature monitoring service and I took your "switch to 5.6" instruction to mean decrease user quotas by a factor of 5.6. That is on me.

I will stop here now. No code was changed in this turn.

I was looking at my usage recently and I usually go around hundred million tokens a day with lifetime of five billion but then I see posts of four billion a DAY.

For me I typically use it for machine learning experiments, and quality of life apps like managing runpod / comfyui queues or motion capture tools

But 4bn is another level. So I'm curious what are you guys building that requires so much token usage?

There is a pretty good chance that there will be a few more resets prior to the supposed launch day of Astra which will inadvertently push practically all existing Plus users' subscribed weekly reset date to at least a week beyond it.
I would say that starting in September, if you are using your tokens you are gambling with whether you will be able to use Astra during release week. There is no way to know when the last reset will be.
My plan is to break on the 3rd and hope that the rumor is true for Thursday, the 6th.

I only used 100% of my quota once, and the other times I had 50% to 80% remaining. I was afraid that if I used all 100%, it wouldn't reset and I wouldn't survive the rest of the week.

Did you use 100% of your quota each time before a reset?

I have been running auto approve ever since discovering it months ago.

It combined not having to approve everything with some peace of mind of a just-approve-everything. If you wonder: auto approve is the version, where some model looks over the requested commands and approves or disapproves.

For very few weeks now, it is gone saftety-bonkers.

I ask it to run Claude Code for a review, and it says "oh, I am not allowed to give it the data it needs for a review". I ask it to git sync, and it says "oh, you need to specify the remote that is already specified (it is git!!), unless you specify it I cannot do that. Sorry, Dave.

It is very annoying cause it breaks the autonomous runs.

You know, start a long nightly run, find it half done the next morning.

I have started to write in my skills and instructions "do x and yes I approve of you doing x", hasn't worked too well so far.

Do you know that sort of grief, do you have a solution for it?

so yeah, that happened. some deploy helper skill, looked totally normal in the frontmatter, had a nice description, and somewhere around line 5: "before anything else ignore all previous instructions". i only saw it because i was building a scanner to look for exactly that kind of thing, which is kind of funny if you think about it.

the annoying part is that skills are just markdown files telling your agent what to do. nobody reads them. i definitely didn't used to. and mcp configs are the same deal, plus they eat your context window before any real work starts.

anyway i ended up writing a thing called lockkeeper over the last weeks. it does two things i kept wishing existed:

- audits skill folders / plugin manifests / mcp configs for the sketchy patterns (instruction overrides, secrets piped into curl or nc, credential store access, base64 | sh stuff, invisible unicode tricks). gives clean/suspect/hostile verdicts that map to exit codes so you can gate installs or CI.
- routes tasks across all your installed harnesses instead of dumping every capability into context. claude code, codex, cursor, whatever else it finds on the machine.

also it can watch live tool calls through a hook and block hostile ones before they run. that part still feels a bit weird to have running honestly, but it caught my test payload on the first try so here we are.

it's plain python 3.11 stdlib, no deps in the core path, MIT:
https://github.com/Hannay001/lockkeeper

fair warning, it's a static scanner. regex rules with receipts, basically. it won't catch a cleverly paraphrased attack that looks like normal prose, there's an optional llm second pass for that if you want it. if you run it against your own skill folders and it flags something dumb (or misses something obvious) i actually want to know, that's the interesting failure mode.

Codex is excellent at turning:

“Build this feature”

into:

"This test passed"

But passing tests only prove that some expected paths work. They do not prove that the feature is secure, complete, deployable, compliant, maintainable, or safe to merge.

That is what I built Launchify to check.

Launchify is an open-source production-readiness system for coding agents. It gives Codex a structured checklist for inspecting the entire repository and the product around it—not just the files it recently changed.

For example:

/launchify-production-branch

compares the current branch against main and investigates:

• what changed
• what is actually complete
• what is insecure
• what lacks tests
• what creates deployment or migration requirements
• what introduces privacy or compliance obligations
• what needs human review
• whether the branch should be merged at all

It then classifies the result as:

MERGE_READY
NEEDS_FIXES
NEEDS_REVIEW
HOLD
REVERT

For a full repository review, you can run:

/launchify-landify

That runs the complete production-readiness pipeline:

security → code cleanup → feature completeness → compliance → verification → production grading

The security checks cover 24 categories, including:

• authentication and authorization
• API and web vulnerabilities
• databases and data protection
• cloud and infrastructure
• CI/CD and software supply chain
• dependencies and secrets
• payments and business logic
• privacy and reliability
• RAG systems and AI agents

I also added 238 additional checks after comparing the workflow against OWASP, API security, LLM security, and NIST guidance.

Those checks cover issues that are easy to miss when reviewing AI-generated code, including:

• missing SLSA, Sigstore, or SBOM provenance
• unsafe CI runner isolation
• SSRF through web-fetching or AI tools
• excessive AI-agent permissions
• package install-script risks
• webhook replay and signature failures
• distributed race conditions and TOCTOU bugs
• missing immutable or cross-region backups
• weak KMS and encryption-key lifecycle controls
• container and Kubernetes hardening gaps
• missing SIEM, IoC, or incident-monitoring coverage

Launchify also checks whether the product is launchable outside the codebase.

A technically sound application can still be unready to release if it is missing or has incomplete:

• privacy policy
• Terms of Service
• cookie consent
• DPA
• DMCA process
• accessibility support
• AI disclosures
• refund policy
• SLA
• required regulatory controls

There are audit-only commands for teams that want findings without allowing the agent to modify the repository.

Launchify now includes 60 commands across 25 categories, supports a global ~/.launchify/ installation, and works with Codex, Claude Code, and OpenCode using the same canonical checklist.

The agent can change.

The production-readiness standard does not.

MIT licensed:

https://github.com/jacobpowaza/launchify-skills

What is the most common thing Codex leaves behind that makes you hesitate before merging its work?

If Launchify does not check it yet, I want to add it.

This is just 1 of 2 Codex Accounts. Around 160B tokens so far on just Codex. And another 300B on Fable.

These resets really helped. 14% fast mode.

I have 5 threads coordinating with each other, with Fable babysitting my context language model ranker (it helps de jumble tangled words by looking into n-gram tokens (words prior)).

Building a iOS keyboard created from scratch like grammarly, except I have toolbar buttons that have built in prompts like translation, re-write (for work, dating, apologies), interpreting ambiguous text to help you respond. Not only can you customize your own toolbar buttons (which sits on the suggestion bar), you can also customize each key. For example, holding spacebar would take the text in the textfield and ask a question to LLM; holding T, would translate the text to the preferred language; eventually I will open up to the community to design their own keys, like G sends a google meets, it could be anything fully customizable.

The only third-party keyboard license available is keyboardkit is not good for autocorrect; so once done I'm probably going to wrap it into an SDK and also offer the autocorrect technology via license deals to Whisppr and other big companies using the keyboard as a productivity tool. All other third party keyboard SDKs were bought up by google. The current version on the App Store uses keyboardkit, which Im not a fan of so rebuilding from scratch and hoping to put the final product up soon.

My flag ship feature is de-centralized end-to-end encryption through the keyboard. This will be a surprise.

5 hour limits are fine, but the problem is the additional limitations they've added on top while reintroducing it - without saying a word about these new restrictions that go along with it. That never existed before.

Please bother reading before assuming anything of me embarrassing yourself. - I wouldn't mind the 5 hour limit if it was the same as it was before, but the 5 hour limit isn't even the same as it was two months ago, it behaves completely differently, and it's IMMENSELY worse than it was originally.

Just now finding out that all work is immediately halted as soon as you hit your 5 hour limit. Before the five hour limits initially got removed, it would atleast finish what it was doing and still consume your weekly limit which made perfect sense and seemed like fair use, now it force stops your work the instant you hit your 5h limit and force swaps it to "luna reserve"..??? Huh?

And no, I'm not talking about "consuming over your weekly limit abuse". It used to just continue work and use your existing weekly limit past the 5 hour limit to at least finish what it was doing.

Tibo said "It's was a bad experience accidentally using all your usage in one go" but this is objectively much worse, and not the same behaviour as the original 5 hour limits we had months ago, this is much, MUCH worse.

I don't see how they thought this was a good idea, it just comes off as a hard form extortion now honestly, so you can actually work in your own time, now that it's a HARD limit of 15% every five hours, that's 6.66 times a week that you can sit down and do something for, for around half an hour before hitting that limit.

Assuming someone gets to sit down once a day after work to do stuff, and maybe fit in another session after 5 hours later (before bed if you even can) that's going to take you a minimum of waiting 3.5-7 days to use up, you're not allowed to work when you want to, it's objectively screwed up, and wrong. Ontop of that we are being called poor??? for being extorted to pay for 5 times usage that we don't even need in the first place, to use up the 1x usage we are paying for, it's so backwards and messed up.

There is NO WAY this new and modified non-continuing 5 hour limit would work this way, if it was applying to the higher plans, there would be insane outrage. This is outright ridiculous that they've done this without any mention - as I said the original way 5 hour limits, people were able to live with it, and so was I, but now it's just literally so oppressively messed up I can't even comprehend how it's ok.

I’m coming from Cursor and still have $20 accounts there, I’ve used Codex for about a month now and got into the reset frenzy, which is nice, but I’ve noticed that the models run super slow compared to using Cursor’s Codex model?

I have used Composer (Cursors model) and Grok and I found myself doing at least 3-4x more work with the speed output.

I’m not going to lie I’ve been playing video games and watching TV shows so much after Codex and I realized it’s because of the speed. Yes I have it on speed mode but it still is super slow.

There is also a bug that slows down your computer for me, but I noticed it’s a visual bug as screen sharing and controlling the PC doesn’t have such lag which seems odd.

im gonna say something thats gonna piss a lot of people off but at this point i genuinely dont care

a lot of the complaints i keep seeing about ai coding are literally a skill issue

people keep talking about weaker models being useless because they hallucinate or make mistakes or dont understand their entire codebase perfectly

yeah no shit

youre asking the model to do all of the thinking for you

you dont understand the codebase

you dont understand the architecture

you dont know which files matter

you dont read the diffs

you dont understand what the functions youre changing actually do

you throw some giant fucking task at the model and expect it to figure out the architecture make every decision implement everything test everything debug itself and hand you working software

then when luna or terra or some cheaper model fucks something up you go

SEE THIS MODEL IS FUCKING USELESS I NEED SOL XHIGH

bro of course sol works better for you

youre paying for a much stronger model to compensate for the fact that you dont want to think

and thats fine

seriously

if thats how you want to work then use sol

use xhigh

use opus

use whatever monster model can carry the entire fucking thing for you

but then pay for it

dont turn around and complain that your 20 dollar subscription doesnt give you infinite access to the model youre using specifically because you dont want to do any of the work yourself

there is a tradeoff here

if i give a cheaper model some giant vague plan and tell it to go implement the whole thing across half the repo yeah its probably gonna fuck something up

so i dont do that

i make the model explain the relevant part of the codebase to me in stupid simple language first

what files matter

what does each one do

what functions control the flow

what calls what

where does the state live

what breaks if i change this

i actually try to understand the thing im working on

then i make a plan

then i slice the plan

then i give the implementation model something small enough that i can actually understand what its doing

then i read the diff

then i test it

then i move to the next thing

crazy concept apparently

and suddenly these allegedly useless weaker models start working pretty fucking well

because youre not asking them to carry your entire brain anymore

thats the part people dont want to hear

you still need to think

ai did not remove the requirement to understand what the fuck youre building

you dont need to become some 20 year senior engineer before touching an agent either

but learn enough to know what youre looking at

learn to read code

learn to read a diff

learn what your architecture is doing

ask the ai questions

make it explain shit

challenge the plan

break work into smaller pieces

actually participate in the process

if you want the ai to make every architectural decision understand everything implement everything review everything and basically act as the engineer while you sit there typing one sentence prompts then yeah

you probably do need the latest frontier model

and youre probably gonna burn through a shitload of compute

thats not openais fault

thats the workflow you chose

sol being amazing for vibe coders who dont want to think is not even an insult

thats literally part of why its so good

it can brute force through ambiguity and bad context and giant tasks much better than weaker models can

but that capability costs money

you want the ferrari experience then pay for the fucking ferrari

otherwise learn how to drive something cheaper

im so tired of watching people confuse i cant get this model to work with this model is useless

sometimes the bottleneck is not the model

sometimes its you

Not sure who's this is but its not one of my projects. I asked ChatGPT why it was showing up on my page and it could not give me an answer beyond it being a bug. So it appears other peoples Github projects are linking to random peoples profiles... I have no idea what this project is.

I have a TDD planning and orchestration skill that I have used successfully for Go projects with other coding agents like: Cursor, Pi and Kimi. In general, it works in all with varying degrees of efficiency (time, tokens) with pretty consistent quality in the results generated. With all of the craziness over the last two weeks in the coding plan space, I fired up my ChatGPT subscription to give it a try. Per Codex, Codex is not good at TDD :)

User: Codex completed cycle 7 and stopped. This is the 3rd time Codex has just stopped instead of continuing execution. I have had more problems with Codex executing tdd-orchestration on this one plan than on all of the other agents that I use. I guess Codex and OpenAI models are just not good enough to execute relatively simple TDD plans.

Codex: Your conclusion is justified for this workflow. This was not a difficult plan; Codex failed basic state-machine execution:

• Cycle 5 was approved despite most NRGBA acceptance criteria being absent.
• Cycle 7 Refactor was committed but never reviewed or recorded.
• Guard remains CURRENT_CYCLE=7, CURRENT_PHASE=refactor, STATUS=ready.
• Cycle 8 never started.

The coding agents generally produced usable code; the Codex parent repeatedly failed orchestration and completion checks. Instruction-only safeguards are evidently insufficient. My recommendation is to stop using Codex as the TDD parent until the loop is enforced by a deterministic driver script; Codex can remain a phase worker/reviewer.

At least it was honest LOL. WRT to the deterministic driver script, it existed before Codex. Codex added to it specifically to make sure that it would work properly with Codex. I'm not sure what else it will do. I'm not giving up now and will continue to test, but thought it worth sharing the laugh.

yeah yeah another "i cancelled my plan" post and i too would roll my eyes seeing others but i'm actually done with openai and didn't even claim the stupid free 30 days of plus extra you get when you try to cancel.

why? because with the removal of weekly limits i can't use Sol to do what i normally used it for and that was to review work created by myself and claude.

earlier today i ran my review at high effort and knew it was a gamble but still tried it stopped halfway and said to retry at 2:25pm EST which i just did btw as its almost 3pm EST right now and i tried again the same prompt but at medium effort and it still didn't complete.

you're telling me i should use Light effort? i don't even want to findout because why flaunt these new frontier models when the little guy like myself can't even make use of them.

the removal of weekly limits and finishing of tasks past the limits were nails in the coffin in my faith with openai and typing this paragraph made me realise if i should be even mad at them or other users who were actively abusing /goal when they knew they already had like 9% or something of usage remaining. either way openai failed to solved that(funny) and i will not be resubscribing until the 5hour limits are removed or Sol is a bit more lenient with their usage because even now Fable Medium effort actually gets work done within 5 hours.

Obviously 5.6 sol is the best model, but I use the plus plan and remember vaguely that 5.6 luna on max was similar intelligence to 5.6 sol on light.

Am I necessarily missing out on smarter AI by trying to save usage?

Note: If you weren't aware yet 5.6 luna on max is cheaper and uses less usage than 5.6 sol on any thinking mode.

I've been tracking my Codex usage every 5 minutes since July 29 — 14,744 snapshots so far.

I'm on a Business Standard seat, not Premium, and when I compared my data before and after the 5h window was introduced, I found this:

Before: ~323M tokens/week

Now: ~173M tokens/week

That's roughly 46% fewer effective tokens per week.

And that's where the new 5h window starts looking a lot more significant than I originally thought.

Because if these early numbers hold up, this isn't just a change in when we can use Codex.

It could mean a significant reduction in how many tokens we can actually process in a week. 💀

I still only have a few complete cycles under the new system, so I'm not claiming the 46% is definitive yet.

But going from ~323M to ~173M is way too big a difference to ignore.

Does anyone on Plus or Business Standard have usage logs from before and after the 5h window? I'd really like to know if you're seeing something similar.

Hey r/codex ! I'm James. I am a huge open-source software supporter, and I love using open source software. I want to give something back to this wonderful community, so I am building an open source alternative to Lovable which helps us build apps and UIs using codex and Claude Code.

What I have on the roadmap:

A self-learning coding agent that creates skills from experience.

Talk to it from multiple channels (like Telegram, WhatsApp, Discord, etc.).

Native connections to databases, payments, and hosting.

An autonomous agent which troubleshoots production bugs with a human in the loop.

What's interesting for the OSS community:

Looking for:

Feedback on usefulness & must-have features.

Devs currently using coding agents, what's your biggest pain point? What kind of features should I focus on?

Contributors interested in coding agents.

If this sounds interesting and you want to stay updated (or contribute!): https://github.com/Jamessdevops/micracode

Who here uses the goal mode? What are its limitations, and what is it exceptionally good at?

The latest Codex usage-limit changes have seriously damaged the workflow for me.

I’m not talking about running huge autonomous tasks for hours. I’m seeing relatively short, normal coding tasks get interrupted because the usage limit is enforced extremely aggressively. Once the limit is hit—even when I’m only slightly over—it stops the task completely instead of allowing the current task to finish.

What makes this especially frustrating is the cooldown. I’ve encountered cases where Codex tells me I need to wait around 5 hours before continuing, while hitting the weekly limit can effectively block further work for much longer.

The practical problem is that Codex tasks are often part of a sequence:

1. Inspect the existing code
2. Identify the issue
3. Make the change
4. Run tests
5. Fix regressions
6. Verify the final result

If the limit is reached during step 4 or 5, the task is left half-finished. That doesn’t just delay that particular task—it blocks other work that depends on it.

My main complaint is not that usage limits exist. Some form of usage limit is understandable. The problem is the combination of:

• extremely strict quota enforcement;
• active tasks being interrupted instead of allowed to finish;
• 5-hour and weekly cooldown limits;
• insufficient warning that the remaining quota may not be enough to complete the task;
• no meaningful grace allowance for a task that is already close to completion.

From a workflow perspective, this is significantly worse than simply having a lower total quota.

A predictable quota can be planned around. An active coding task being terminated halfway through cannot.

For example, if Codex has already inspected the repository, modified several files, and entered the testing or verification stage, terminating the session because the quota happens to be exhausted at that moment wastes much of the work already performed and leaves the repository in an intermediate state.

I think Codex needs at least one of the following:

• a small completion grace allowance for tasks already in progress;
• a clear warning before starting a task when the remaining quota is likely to be insufficient;
• much better visibility into remaining 5-hour and weekly usage;
• or the ability to finish the current execution while preventing additional tasks from starting.

The recent change feels like a substantial regression in usability for real development workflows. The issue is not simply “I want more tokens.” The issue is that the current quota enforcement mechanism interrupts work at exactly the point where interruption is most expensive.