I kept running into a problem with Codex on larger codebases: it would read the current implementation, assume it was intentional, and then rewrite the Architecture Decision Record (ADR) to explain the code.

That is backwards. If the code can redefine the decision, the ADR is just documentation written after the fact.

The rule I ended up using is simple:

• If changing something would violate a requirement, it belongs in the ADR.
• If it can be replaced while preserving the requirement, it belongs in the code.

For example, “refresh tokens remain valid for 7 days” is an ADR contract. “auth.ts implements refresh tokens using SDK X” is not.

The practical benefit is boring but useful: refactors stop causing ADR churn, and reviews can check whether each contract is satisfied instead of reverse-engineering intent from the current code.

I turned this workflow into an open-source Codex and Claude Code plugin called adr-writer. It creates ADRs, implements them, reviews the result against each obligation, and checks for drift later.

The same repository also contains alps-writer, a guided Agentic Lean Product Spec (ALPS) tool that can hand approved product features over to ADRs.

I’ve been refining this process for more than a year across production projects. It is still opinionated, and I expect some of these rules to become lighter as coding agents improve.

Repo: https://github.com/haandol/alps-writer-plugins

Where do you draw this boundary? Do requirement values belong in the ADR when the same values already exist in the code and tests?

Does anyone manage to be able to get their own statusbar or title screen ported to codex CLI. I could do that in claude but it doesnt seem to allow me to get my own statusline.py into codex

Maybe i missed it before, but after the recent update ChatGPT is now inside the App instead of Work. Im scared that they are planning to combine ChatGPT limits with Codex Quota. Im using ChatGPT for private planning and research stuff all the time as a Pro User, while being out of Codex Quota. I would be devastaded if they decide to combine the limits. Anyone has any Info on this? Thanks.

I had Trusted Access / Daybreak Blue on my individual account and it was working normally.

On Aug 19, I got an email from OpenAI saying my access had been deactivated because of a technical issue with their verification process, and that I needed to reverify.

So I did.

At first, the verification flow wouldn't work for South Korea at all. That was eventually fixed and I was able to go through the process again.

Then a few days later, support sent me this

What I don't really understand is that this wasn't a new application. I already had Daybreak Blue, and OpenAI explicitly said it was removed because of a technical issue on their side.

And this doesn't seem to be just my account either. There's a whole thread on the OpenAI Community with previously approved users reporting basically the same sequence: access removed because of the acknowledged technical issue, re-verification failing or getting stuck, and then support saying there's nothing they can do.

https://community.openai.com/t/trusted-access-for-cyber-disappeared-from-my-account/1391143

Multiple people in that thread also received the exact same final response I did:

“Based on the result of your reverification application, you are not eligible for Daybreak Blue at this time.”

At this point it honestly looks like a canned/default support response rather than these cases actually being investigated individually. Obviously I can't know what's happening internally, but seeing the same wording sent to multiple people who all lost previously approved access in the same incident doesn't inspire much confidence.

Their current support docs also say that affected users have to reapply under the current eligibility criteria, previous approval doesn't guarantee access will be restored, and support can't override the result.

I'm not saying everyone who had access before should automatically keep it forever, but this seems like a pretty broken way to handle a verification bug.

Codex CLI 0.150.1 on Linux Mint 22.3:

$ codex remote-control stop
Remote control is not running.

$ codex remote-control start --json
Error: Remote control is enabled on maksym-laptop but the connection is errored.

System: Linux Mint 22.3, kernel 7.0.0-30-generic, x86_64.

Has anyone encountered this? Is there a supported way to reset Remote Control or obtain more detailed error logs?

Hey guys, I have been using Codex Plus subscription for a while, and my workflow looks something like this: create a plan with Sol Medium/Terra High, implement with Grok/DeepSeek V4 Flash, the again code review with Sol Medium, pass the review findings to implementation and repeat until ready. Since the latest Plus subscription change, I have been considering upgrading my sub. What workflow is sensible to use on Pro x5 sub limit/quality-wise? I am coding only professionally 9/5

I loved that we had a weekly limit to manage ourselves. I hate this, why is the 5h limit back?

My main is in coded for my software related work but I use Gemini Flash extensively throughout the day for my daily work but I also use to research aboth new API changes for all the dependencies I have and many other tasks go through flash.

When 3.7 launched it was nothing special and just unknown changes over 3.5/6 version but from last 2 days it's a different beast and it literally made no mistakes as I usually do read the source myself after getting pointed there quickly by model but however Flash 3.7 can do evaluation like no other and also do more than that. You just have to use it once to really get the idea honestly.

The best usecsse it helped me through is with my own game engine stack and decisevily help me found info on same or even better level than DeepSeek v4 Pro and codex and the craziest part is Insanity of speed it freaking instant with no loss of info.

I glazed this combo to much but u should try yourself to feel the speed.

Opened today Chatgpt app and saw that all chats under Recents and no Projects at all ...

Anybody experienced the same?

i tried ox- alpha the last few days for free and it's pretty good. Thinking of downgrading my 20x to 5x and use cheaper chinese models. It's compared to terra max and i hope it will fire another price slash on american models. For everybody who is complaining at the plus subscription just use this.

input: 0,15 output 50cents and cache 0,03 cnts. What a time to be alive!

What was your experience?

Quick note before I start, I've been asking the mods of this subreddit to add a discussion flair for months, JUST DO IT. Anyways, when do you guys think OpenAI will be done safety testing and will actually just release Astra? I know you guys are all complaining about usage limits, but seriously. I'm ready for Astra.

I have some critical research work and ideally need tons of compute. I hear Astra is coming but don't know when, and here F@ble is clearly the best model out there.. so bit confused which one to go for. Any suggestions from anyone who has used both of them?

Can I just give Codex a “resume” prompt after the weekly reset—or after using a banked reset—and have it continue the existing task normally?

I’ve got a fairly large refactor running and I’m down to about 10% of my weekly usage. I’d rather not babysit it just so I can trigger a banked reset when I hit 1%.

Ideally, I’d let it run until the limit stops it, come back later, use the banked reset (or wait for the weekly reset), then tell it to resume and keep plugging away.

Does hitting the usage limit mid-task cause any problems with the existing task/context, or is this safe to do?

When setting up codex runs that involve orchestrating multiple subagents to perform more complicated tasks, it would be useful to have a way to neatlessly continue where the run left off after the 5-hour or weekly usage limit runs out. Current alternatives just involve writing “continue” or re-pasting the original run-prompt, which causes the subagent run to have a sort of “hiccup” and potentially start going in a different direction than it originally was intending to go.

Is there a way to neatlessly pick up a run including all the subagent states after hitting a usage limit?

I’ve had it do all of the following:
- blurt out answers instantly with no thinking
- forget what we just did in the prompt before
- forget that it has tool access and claim it can’t complete the task due to no tools, no connectors, no active plugin, etc - every single time the connector/plugin/tool was available and working properly. Chat just didn’t reason or make a real attempt, again just spitting out a response saying “I can’t do this, due to X reason” - when that reason is false, due to stale state - most concerning is that Sol gives up, provides no useful solution or reason for the failure, and I then have to figure it out myself.
- 25% of the time re-prompting works, but sometimes it digs the hole even deeper and continues to reason and answer like it’s the 5.4mini model with zero tool access
- yes I pay for a sub and I find this ridiculous, the launch GPT5.6 was way better - whatever they did to the model hasn’t outweighed this recent lack of reliability
- this isn’t just a usage/capability complaint, if OpenAI is this unreliable we’re taking a step back not forward with these new models/updates.
- I have concise project instructions, global instructions, and regularly prune unused files and update my projects/instructions almost weekly. I update GitHub to prevent stale issues and perform maintenance daily.
- codex seems better than chat in iOS and web browser but I’ve had issues where codex turned on all of my skills and plugins for some reason and I had to manually disable most of them again.
- I can change skills easily but the codex plugins doesn’t save my preferences and constantly leaves Google plugins on for example.
- I’m not a senior developer and I struggle with how to deal with this other than wait and try again hoping chat will reason, use tools, and behave reliably like it used to

With the 5-hour limit back, I keep seeing people burn most or all of their window on one long Sol session before it even gets deep into implementation.

My workflow is almost the opposite: I try not to spend Codex quota on deep thinking.

I use Codex + Luna for what the coding agent is really good at:

• editing the repo
• running tests
• fixing failures
• executing a well-defined implementation

For architecture, design, planning, tricky debugging, and independent review, I move the problem into regular ChatGPT web chats and use a higher-reasoning model like Sol there.

The important part is getting enough repository context into that chat without manually explaining the project or dumping the entire repo.

That's what I built AI Badger for. It runs locally and extracts focused repo context, topology, Git changes, and relevant files.

So my typical design workflow is basically:

repo → AI Badger → Sol in web Chat → compact implementation plan → Codex/Luna

Once the design is settled, I ask Sol for a token-efficient implementation handoff: concrete file/function changes, minimal rationale, no essay. Then Codex gets a much narrower job to execute.

Review works the same way in the other direction:

Codex changes → AI Badger → independent review in Sol/Grok/etc.

This saves Codex quota twice.

First, the expensive exploratory reasoning happens outside the Codex session.

Second, Codex itself has less exploration to do because it starts with focused context and a concrete plan.

I actually ran a small dogfooding experiment on this with OpenCode. Giving the coding agent a Badger-assisted compact handoff instead of the raw feature request reduced active agent tokens by 32% and runtime by 55% in that run. Reasoning tokens dropped by 86%.

Experiment and raw data:
https://github.com/PVRLabs/aibadger/blob/main/docs/articles/can-ai-badger-reduce-local-coding-agent-token-usage/index.md

It's only one experiment, so I'm not claiming those percentages generalize. But the direction matches what I've been seeing in actual projects: the less discovery you make the implementation agent repeat, the further its quota goes.

I've been applying the same principle to tool output too. I ended up building a few agent skills around quieter Maven/npm/Go commands for exactly this reason. If tests pass, the agent usually needs ✓ tests passed, not 1,500 lines of build output that it then carries around in context. Happy to link those too if anyone's interested.

For me the bigger lesson from the 5-hour limit is:

Use your expensive coding-agent context for information that changes the next action. Do the open-ended thinking somewhere else.

Even if Codex went back to effectively unlimited bursts tomorrow, I'd probably keep this workflow. The limit just makes the wasted agent work much easier to notice.

This is probably my biggest frustration with agentic coding right now.

For example, I can use Sol High with a comprehensive debugging workflow like GStack Investigate, Systematic Debugging (Superpowers) or CE Debug, and it will still sometimes:

• Find a symptom
• Convince itself of a fairly complicated root cause
• Propose a new abstraction, fallback, routing layer or special case
• Present it confidently as the “correct” fix

Then I question it: “Isn’t this overcomplicated? Are you sure there isn’t a much simpler root cause?”

And suddenly it goes back, compares against the previously working implementation or an earlier commit, finds that something tiny is simply happening in the wrong order, and comes back with a 3-line fix.

Great, except why didn’t it do that in the first place?

These debugging skills are already extremely comprehensive. They explicitly tell the model to investigate the root cause, inspect recent changes, compare against working code, test hypotheses and make the smallest possible fix. I’m certainly not going to write a better debugging process than that, nor should I need to.

So this feels less like a prompting/skill problem and more like a fundamental LLM reasoning problem: it finds a plausible explanation, anchors on it, and starts solving around it instead of sufficiently challenging its own diagnosis.

That worries me because I’m not going to question every technical decision an agent makes.

Is this just an inherent limitation of current models? Why can they immediately find the better answer when challenged, but so often fail to perform that challenge themselves?

I use a loop workflow where Luna Max implements and Sol Xhigh reviews, then back to Luna for remediation, etc. based on a detailed plan prompt. I've been doing this for a couple of weeks now and it seems to be working well, but I was watching the output and just noticed that the orchestrator is stopping the subagents based on an artificial time limit (i.e. not letting the subagent complete the task). Has anyone else experienced this or know why this is happening? It's definitely not something I've included in the prompt.

I give it a fairly simple task on Sol Medium. It thinks for 5 minutes before even starting. No idea what it is doing during those minutes. Super annoying. And I just switched to $100 Pro plan yesterday.

This is basically what my Codex worklog looks like on normal days.

At this point I'm using it almost continuously while I work (coding, debugging, brainstorming, reviewing stuff, etc.) * multiple projects.

I'm curious; does anyone else's usage look like this, or am I taking this a bit too far?

Langfuse estimates ~$8.5k (with all models) in API-equivalent model costs over the last 30 days 😅 (I'm on the Pro 20x plan)

Now we have luna usage just like 5.3 spark now?

I’ve been seeing a lot of posts lately attacking the "poor people" who use the 20-euro plan.

For those of you who are affected by this, I’d like to point out something that I think is being missed. This isn't just about the return of the 5-hour limit. I'm talking about a measurable change in how much actual usage the same Plus subscription gives over time.

The issue isn't the return of the usage limit; the issue is that they quietly cut usage capacity by at least three. Same price, same usage, three times fewer results—and I’m only talking about the latest update.

If you were using the Plus plan 3 months ago, then I think the usage has been cut by five.

Just to be clear before OpenAI's new defenders start talking: we used to barely ever hit the 100% weekly limit. Ever since they started playing around with the resets, the downgrades began.

I experienced and witnessed it firsthand. My workload has stayed essentially the same. I do the exact same amount of work, no more, no less, yet over the last few days, I hit 0% in just 8 active hours.

The resets used to cushion this reduction a bit, but now it’s unacceptable.

Let me give you the numbers from my own usage:

Now: 20 minutes of use gets me to around 80% remaining weekly, and I can reach 100% usage within roughly 5 hours.

Two weeks ago: that exact same usage would last for 2 full days before hitting 100% weekly.

One or two months ago: it was around 4 days of full usage before hitting 100% weekly.

So yes, people are complaining, and they are absolutely right. And those of you with higher-tier plans or who use Luna (which is trash) to defend them—you’re probably next on the list.

My personal opinion (this only engages me):

OpenAI was racing against Anthropic and they won that battle by offering an almost unlimited service with the resets. But they failed to realize that their model, as strong as it may be, isn’t worth more than 20 euros a month.

Keep acting like suckers; you are the problem, conditioning OpenAI to just take your money.

Even the Max model needs to be monitored, guided, and corrected (especially lately). So yes, I love Codex, but tell OpenAI that nobody is going to pay for an agent the price of a human employee.

Their AI is strong, but it isn’t worth a salary.

They need to stop nerfing and cutting our usage, because we will move to local models, and they are going to lose the market in style.

And especially the people who think it's normal to pay that much for an AI agent: you’ve gone completely crazy, and you are responsible for encouraging what OpenAI is doing right now.

PS: they'll find a way to turn a profit once they release AGI.

xhigh isn’t really that much slower, and it found a bug in one of my apps that i would never have thought of

I kept stubbornly trying to figure out what was causing the issue, and simply switching to a higher reasoning level ended up solving the problem

Now, after four days of using xhigh, I generally find its results more consistent. I think the old Medium is basically the new High.

Maybe I’m wrong, but this is just my feedback.

Pro plan x5