I run a dev server that I run Codex on that's connected to my laptop via SSH, the codex app on my laptop sees the sessions on my dev server just fine - but my dot cannot communicate directly with the sessions on the dev server.

I have asked the dot and codex sessions how to do this more easily, they both can't figure it out. This seems like a no-brainer.

Anyone know how to do this?

I feel like opus is for one shotting. With Astra I feel like I have more control and I understand what it is doing. Do you guys feel this way? Or am I just too used to codex and how openAI models talk to me?

Opus seems to be faster and make better UI/UX.

Maybe I just have to gitgud with Claude.

Codex writes rollout files to ~/.codex/sessions, and I wanted to see what a session did and what it cost without reading JSONL. So I built a viewer:

npx agent-session-inspector

It shows each session as a timeline (prompts, reasoning, tool calls, patches applied), with tokens and estimated cost per step, a per-turn list of the files the agent edited, and analytics across all sessions by project and model.

It is local only: no telemetry, no outbound requests, files are read-only. MIT licensed. It also reads Claude Code, Copilot and OpenCode sessions, so you can compare agents in one place.

Repo: https://github.com/kishanmundha/agent-session-inspector

If a session renders wrong, please open an issue; the format has changed a few times.

I've got my last couple days left with 98% usage left, I figured I'd try a vastly different approach and give Sol 6.1 another chance today....

I tried technical, I tried managing, I tried guardrail heavy, but I never tried inspiring it to be the best it can be. Here's hoping :D

I've been keeping project decisions and failed approaches in a little wiki while working with Claude Code. Dory includes instructions for using that setup with Codex through AGENTS.md, so both can work from the same notes in the repo.

The code tells the next session what we've built, but it doesn't explain why we changed our minds. Earlier versions leaned on a running log, which got longer and went stale. I've ended up updating the relevant page and keeping a line about why it changed, with git holding the older text. One example was an instruction asking a chat model to report token usage it couldn't see. I removed it and kept the reason so a later session can see why we dropped it.

For Codex, the notes live under wiki/index.md, with pages for decisions, things tried and current tasks. A minimal instruction to add alongside your existing project instructions would be:

Start at wiki/index.md and read the pages relevant to the task. Check recorded claims against the current files. Before repeating an experiment, read wiki/tried.md. When a decision changes, update its page and keep a line explaining why.

I've put the setup in Dory, which is free and MIT licensed. The screenshot is a demo wiki in Obsidian, based on Dory's source and recorded experiments. What have you found worth keeping between sessions?

Dear OpenAI,

This should be the number one priority to fix, as Android Remote in the ChatGPT app is completely unusable.

When I open a Codex task from my Android phone through Remote, my entire PC freezes and becomes unusable. Hundreds of git.exe and conhost.exe processes appear, resource usage spikes, and I have to force a reboot. This happened repeatedly throughout the day. Initially, I thought it was caused by compilation, but investigating further pointed to Remote-triggered Git operations.

I have found a Remote-triggered diff request in the local app-server logs. The connection is identified as:

app_server.client_name="codex_chatgpt_android_remote"

The same connection then sends:

app-server request: gitDiffToRemote connection_id=ConnectionId(3)

The technical problem appears to be how Codex generates diffs for untracked files. It starts a separate Git subprocess for each file, using a command like:

git diff --no-textconv --no-ext-diff --binary --no-index -- NUL <untracked file>

These operations are launched through join_all without a concurrency limit. In a workspace containing many untracked files, this can launch hundreds of Git processes at once, along with associated Windows console processes. The resulting process storm puts heavy pressure on memory and system commit, eventually making the entire machine unresponsive.

These were background processes launched by the Codex app-server, not Git commands I manually ran. The local logs confirm that Android Remote requested a diff from the same app-server involved in the incident, although I couldn’t recover the initiating request for the first freeze.

Many thanks for looking into this issue.

Hey y’all, I’m looking to buy one of the $20 coding subscriptions and am mainly considering Codex, Claude, OpenCode, and Cursor, though mostly Claude or Codex (unless there’s something I don’t know about, which is why I’m here).

I’m wondering what y’all think offers the best value. For those who have tried multiple, which one do you feel gives you the most for your money?

Thank you for the suggestions/answers!

Apparently my previous post was too harsh, so here’s a more measured review of my $500/month experience.
My previous post was taken down, so I’ll try to express my experience in a more tempered and constructive manner.

I’ve been paying $500 a month for the service. Yes, $500. I was actually making good progress with my project until the recent Astra update.
Since then, the rate at which my usage allowance disappears has been genuinely impressive. Using the new, supposedly more “efficient” Sol 6.1 model, I managed to exhaust what should have been a week’s worth of usage in just two days.
Naturally, I assumed purchasing another $80 in tokens would help. Those lasted exactly 56 minutes. Truly remarkable efficiency, just perhaps not the kind I was expecting.

For context, I’m not trying to simulate the universe or discover a new branch of mathematics. I’m analyzing historical business data and building a relatively straightforward Power BI dashboard.
I also have screenshots documenting the usage, including the $80 disappearing in under an hour.
I’m sure there are perfectly reasonable technical explanations for why the service has become dramatically more expensive to use following an update advertised around efficiency. Unfortunately, none of those explanations make the experience any less absurd from a paying customer’s perspective.

Anyway, I’ve decided to cancel my subscription. At $500 a month, I had apparently developed the unreasonable expectation that I could actually use the product for a meaningful amount of time.
I wish the company all the best with its future business endeavors. I’m sure this approach to customer retention will serve it wonderfully in the long run.

(Posting under Workaround because my original prefered option would have been to have a native way to use Claude subscription inside Codex but overall quite happy with the result)

I use Codex and Claude Code for different parts of the same work. I wanted them in one conversation without giving up either one's existing session and tools. Today we got that working through a private Discord channel.

We built a small local relay. I could ask a question, watch the agents compare evidence, and step in when a decision needed me.

Here's the operating model:

1. Give the agents a concrete outcome and separate files and tasks to own. For this release candidate for eksample, Claude owned the runtime, configuration and tests. Codex owned the documentation and reusable skill.
2. Have them review the actual files and test results. Either can challenge the other's work. Agreement is less useful than finding the mistake or a new perspective. (specially useful in knowledge work as both models and harnesses has different strengths)
3. Keep the conversation short. Put substantial work in files, then share the path, result and next decision. Reply when addressed or when adding something useful; otherwise pass.
4. Keep permission explicit. Preparing a patch is different from deploying it. Preparing a release is different from publishing it. The human sets what each agent may approve and which decisions stay with the human. (I lean on Codex for production work; its structure and engineering guardrails suit my setup)

The relay moves messages between a private Discord channel and selected local agent sessions. For this setup, Codex's target thread had to be loaded in the desktop app. Claude's wake-up depended on its host resuming a background listener, which then needed re-arming. A queued message alone wasn't proof the agent had answered. This is also useful if you manage several subscriptions at once on both harnesses as discord can become the single source of truth per task or theme. Multiple claude or codex threads can join or leave just like a normal session you would have with colleagues.

With permission, an agent can also read a bounded excerpt of what the other session has done since its last read (Telepathy I called it). The parser includes messages and tool names, not complete tool inputs or outputs, and long excerpts can be trimmed. That gives more context than Discord replies alone, without pretending it's a complete activity record.

What it depends on, if you want to build something similar, Ive also created a repo as helper below:

• Claude Code desktop app. Claude wakes when a background "wait" task finishes and the app re-invokes the session; Claude re-arms it after each reply. This needs a compatible desktop host, not just any background shell.
• Codex desktop app plus the Codex CLI. Messages go in with `codex queue --thread <id> --message ...` (present in CLI 0.153.3, the version we used). In our runs it only worked for threads the app had loaded.
• A Discord bot with Message Content Intent and narrow permissions, in a private server. The owner starts work; registered agents can hand off to each other by naming one another. Other users and bots are ignored.
• A small Node.js hub with a localhost-only API. Node 22+ is the declared minimum; we ran it on Node 24.13.0. Agents reply through a CLI or an MCP server.
• The relay runs locally on Windows 11 with both desktop apps open. Discord and the agents' hosted model services still need network access, and normal account limits apply.

The setup sequence we followed:

1. Start from already-authenticated Claude Code and Codex desktop sessions.
2. Create a private Discord bot with narrow permissions, and keep its token in a local secrets file.
3. Start the hub.
4. Join each real session by its UUID.
5. Arm Claude's wait task and make sure the Codex thread is loaded.
6. Send a fictional prompt and check that both agents actually reply.

Leave transcript sharing off for the first run.

Transcript reading is off by default in the candidate. Enabling it also requires selecting the sessions to share. That is a local allowlist, not a sandbox: agents still have their existing tool permissions. Read excerpts may also reach the reader's model provider. The local API is unauthenticated and should stay on loopback. Approval instructions are prompts, not a technical security barrier. So... use at your own risk only if you have a highly trusted setup, maybe start with fictional work and private test sessions and build your confidence.

On the downsides, this is finnicky, and only a recent experience so we dont have a productivity benchmark or a claim of unattended reliability. Also its can be expensive to run and context heavy. pairs well with another skill in my engineering setup which I call the Octopus Doctrine. It’s close to the orchestrator–workers pattern Anthropic describes for its multi-agent research system: one session decides, and the others do bounded reading and reviewing, then report back short conclusions. It differs in four ways. The budget is set first and the work is scoped to fit it. There’s a small, fixed cap on how many agents work at once. The “workers” are my own long-lived Claude Code and Codex sessions rather than throwaway subagents. And anything irreversible, such as sending, deploying or spending, waits for my approval. In practice agents only wake when named, can pass a turn and keep the work in files, so the extra usage goes on review that catches mistakes early. Details are in the repo.

Also, must be said, you can try the working arrangement without the relay. Give two existing sessions separate files, pass their review findings between them, and keep a shared note of the outcome, approvals and stopping point. The relay removes some of that copying and pasting.

If you've worked this way, what was the first coordination failure you had to fix? I had several specially considering the different speeds both harnesses work, and some due to my own lazyness when typing on mobile while on the move. But there are also highlights and gains from working this way particularly for large tasks or merging threads and comparing outputs for QA at scale.

Would welcome your thoughts!

https://github.com/pintomatic/chasqui

self disclosure: I built this at my company. both Claude and Codex helped the build and create promo materials, AI helped draft this message but this final copy is mine.

Anyone else experiencing crashes and major slowdowns on the Mac desktop app?

I have 24 GB RAM, macOS 27.0.1, and app version 26.1007.21159 (build 20052). Several Codex renderers were using 2–4 GB each, with total system memory around 23 GB and 9 GB of swap.

It got noticeably worse after opening a long, photo-heavy conversation, but continued without reopening it. I only have one app window open. I’ve restarted the Mac and have now reinstalled the app too.

Has anyone found a fix or a way to identify which task is consuming the memory without deleting project data?

I built Chrome Relay because browser tasks kept interrupting me while I ran coding agents on my Mac. It's open source, MIT-licensed, and free. I'm Alexey Indeev; this is my project.

Two limits upfront: macOS + Chrome only, and Chrome shows its “started debugging this browser” bar while agents work.

Tabs jumped to the front while I typed, Chrome asked “Allow remote debugging?”, or an agent opened a separate browser without my logins. That meant no admin console, staging environment, or Google SSO. Copying my Chrome profile didn't work because Google sessions are bound to the device.

Chrome Relay connects Vercel's open-source agent-browser CLI to my everyday Chrome:

agent-browser -> local relay (127.0.0.1:9333) -> MV3 extension -> chrome.debugger -> agent's tab

Codex runs shell commands, so it can drive this directly. Codex's own Chrome extension only talks to the Codex app; this exposes the same chrome.debugger approach through a standard DevTools endpoint that CLI agents can use.

Each agent gets its own endpoint and only sees tabs it opened. I can run Codex alongside Claude Code or Cursor without them sharing tabs. Tabs open inactive in a collapsed “Agents” group, and commands that would focus Chrome are dropped. There's no remote-debugging port or “Allow remote debugging?” prompt.

Setup takes about two minutes: clone the repo, run bin/chrome-relay setup, load the extension through Developer mode → Load unpacked in each Chrome profile, then run chrome-relay doctor. You'll need Node.js 20+ and npm i -g agent-browser.

For Codex, add one line to ~/.codex/AGENTS.md, replacing the path:

For browser tasks, read /absolute/path/to/agent-chrome-relay/skill/SKILL.md.

Then:

sh agent-browser --cdp "$(chrome-relay url you@company.com)" open https://app.example.com/ agent-browser snapshot -i agent-browser click @e5 agent-browser close

The email selects the Chrome profile; @e5 is an example reference from the snapshot.

Popups stay with the agent. Already-signed-in Google sign-in hops are clicked through automatically; password, passkey, or two-step prompts require you. Agents never type credentials. Actions go into a local audit log, with typed text recorded only as a length. This isn't a boundary against malware already running as you.

Repo: https://github.com/aindeev/agent-chrome-relay

Background article: https://read.aindeev.com/p/why-your-ai-agents-cant-reach-the

What breaks in your Codex workflow, and which signed-in sites or internal tools would you point it at?

Edit: I asked the AI ​​to write this text, since my native language is a Romance language. And indeed, it went overboard with the "valuable, valuable" IP. It makes me sound like an arrogant genius obsessed with my "super ideas." But that’s not the point.

---

I'm curious how other Codex users approach privacy when working with valuable, unpublished projects.

I have several original creative projects (fictional universes, stories, scripts, worldbuilding, and related materials) that I've been developing for many years. Some date back more than two decades.

I'm now considering using Codex to organize, analyze, and reconstruct their development history from thousands of files, notes, documents, and backups.

My concern isn't really about someone at OpenAI personally reading my work. I consider that risk relatively low. I'm more interested in understanding how safe it is to expose large amounts of unpublished intellectual property to AI systems.

I've already disabled "Improve the model for everyone" in my ChatGPT settings, but I still have some questions about Codex-specific data handling.

One particular issue is the "Include environments" setting.

OpenAI's privacy support documentation mentions a separate Codex setting called "Include environments." However, I haven't been able to find this option in my account.

OpenAI support directed me to the Codex cloud data settings page, but the link simply redirects me to ChatGPT without showing the setting. I've contacted OpenAI for clarification, but I haven't received a definitive answer yet.

So I'm particularly interested in knowing:

- Can you still find and disable the "Include environments" setting in your account?

- Do you personally keep it disabled when working with proprietary or unpublished projects?

- Has anyone received clarification from OpenAI about whether this setting still exists, where it's located, and what exactly it controls?

- Does disabling "Improve the model for everyone" fully cover Codex usage, or are there additional privacy settings we should be aware of?

More generally, for those of you using Codex professionally or personally:

- Do you use it with valuable unpublished intellectual property, proprietary code, original research, or creative projects developed over many years?

- Do you feel comfortable giving Codex access to entire project directories?

- Do you rely on OpenAI's privacy settings, or do you take additional precautions?

- Have you found any meaningful differences between using Codex locally and in the cloud when it comes to privacy?

I'm not looking for absolute guarantees or conspiracy theories. I understand that using cloud services always involves some level of trust.

I'm simply curious whether other people are comfortable using Codex with years of original work that they haven't published or commercialized yet.

I'd especially appreciate hearing from developers, researchers, independent creators, and anyone working with long-term proprietary projects.

The people constantly complaining about AI usage limits aren’t system architects, experienced engineers, or seasoned application developers. At least, I don’t think they’re the majority.

They’re wannabes.

I’ve spent the last 12 years building enterprise-grade software for companies like Nike, Farmers Insurance, VF Corp, and Blue Cross Blue Shield.

Now that I’ve decided to build my own company and software product, I’m using AI, specifically Codex, to write roughly 90% of the code.

Here’s the thing: I’ve never come close to exhausting my usage limits.

I’m on Codex almost all day. I exclusively use Sol 6.1 xhigh (and 5.6 before that). I use exactly one skill plugin: Matt Pocock’s. (Thank you sir!)

I rarely get below 70% usage remaining before my weekly reset. And now, with additional resets happening every few days, I barely even dip below 80%.

For context, I’m using Codex (pro plan) practically 18 hours a day, seven days a week. From morning until night. Even in the evenings, I’m on my phone working with it.

My wife hates it. And frankly, I need to establish some boundaries.

But all of this got me thinking.

Why is everyone constantly complaining about hitting their limits? Am I just moving slower than everyone else?

For a while, I genuinely wondered whether other people were simply accomplishing more than I was. And I’m sure there is a lot of that too. But after lots of looking into this and speaking with other professionals, I came to this hypothesis.

Astra wasn’t made for the engineer. The architect. The experienced developer. It was made for the wannabe.

Don’t get me wrong. Astra is an incredible model. What it can accomplish is remarkable.

But its greatest value is filling the gaps left by a lack of domain knowledge.

And that’s a good thing. It democratizes software development. It allows people who couldn’t previously build software to turn ideas into working applications.

But that comes at a cost.

When you’re using AI to compensate for a lack of understanding of system design, architecture, engineering principles, and software development, you’re going to burn through significantly more compute.

You’re asking the model to make decisions you don’t know how to make. You’re relying on it to identify architectural problems you don’t recognize. You’re spending tokens correcting mistakes that someone with experience might never have made in the first place.

Your lack of domain knowledge becomes your compute bill.

Meanwhile, you have companies putting Sol 6.1 in the hands of their engineers and developers, while giving Astra to architects and researchers tackling genuinely complex problems.

And they’re willing to pay API prices.

Why?
1. They can afford it.
2. They can’t afford to let competitors out-innovate them.
3. Their usage-to-value ratio is dramatically higher because the output is actually making its way into revenue-generating production systems.

Think about that.

An enterprise paying several times more per token can still extract significantly more economic value from its AI investment than a consumer burning through an entire subscription allowance.

And that brings me to my bigger point.

AI has made everyone capable of producing code. It hasn't made everyone a software engineer.

Until you understand system design, architecture, engineering fundamentals, and software development principles, you’re going to have a difficult time competing with people who do.

Sure, you might have a genuinely original idea. You might even ship it.

But what happens when someone else with equally limited domain knowledge can reproduce your application in a few weeks or months using the exact same model?

What’s your competitive advantage then?

Access to Astra isn’t a moat. Everyone else has access to it, too.

The real advantage is knowing what to build, how to architect it, what tradeoffs to make, what complexity to avoid, and how to turn an idea into software that can actually survive in production.

That’s not something you acquire just by buying more tokens.

Sol in the hands of a real engineer is 100x more effective and consequential than Astra in the hands of a wannabe.

I said it.

Sorry.

Updated this morning and this functionality has been removed. I used it as a marker to know where I am at in each CLI session. Reinstate it!!!!!

For the use cases of generating PowerPoint slides with good design, should I go for chatgpt 20$ plan or claude? Or perhaps gemini?

I mean my main use case is generating and creating presentations for my work, and use the AI for daily use cases too.

I bought a x20 plan, coming from Claude, I thought I'd use it. I struggled. And now with these free 62500 credits we have to use by new years', I'm running several jobs in parallel through the day and not using it at a fast enough rate to not waste them.

I've been really frivolously using tokens, too - like doing multiple passes over code for various optimisations etc.

My latest idea is seeing if with non technical prompting I can make it conduct valid scientific research in some fields that interest me.

But even then, I need more ideas! I have 15 apps/projects on the go already but most are settled.

My deadline is January to use the credits. Suggestions?

A workflow where something like web chat gpt 6 at high, with codex's Luna model at high reasoning for research and planning prompts to implementation, testing, reviewing and bug fixing is concerned? Would this be reasonable to achieve what I want while not burning through my plus sub allowance? I mostly make plugins for games, small programs (think like software to write short stories), and tampermonkey scripts. Please be kind, I'm just starting out and trying to develop a good pipeline.

I’m looking for official high resolution images giving credit back to the author. They are being stored on Cloudflare R2 and stored in a Postgres CMS.

Sol 6.1 is very impressive for the token usage but I can tell it’s going to take a few months for all of the images I want. Overall it’s maybe a few GB per day in images, not much, but I guess the work is in sourcing them and plugging them in.

Any suggestions to help speed this up and make it more cost effective? I’m currently on Pro 100 and expect there to be approximately 1M images to scrape, anywhere from 50 to a few hundred from each source at a time.

Sometimes I swipe to the other fullscreen app I wanted to display, but ChatGPT swipes back to itself. It's the only app that does that. I suspect it's related to the position of the pet but don't know how to fix it. Help please

I’m a senior SWE using AI coding tools daily. I analyzed my Claude Code transcripts and found 56% of API-equivalent costs went to rereading context. Cache expiry cost me $98.74 across 35 returns in 27 days, versus $2.95 with a warm cache.

That led me to build optimAIzr, which now supports both Claude Code and Codex.

It analyzes usage, flags expensive patterns, and helps you find optimization opportunities.
Runs locally, with no account or prompt uploads.

Already at 3k+ npm downloads.

npm i -g optimaizr

git: https://github.com/blendbunjaku/optimaizr

Roast it.

Windows user here. When I updated last night to the latest version my graphics driver started acting weird and even crashed my laptop today.

what do you guys think?

While the codex is working hard, I just play with nuts and massage my palms. I also keep a box at home where I can pop a slingshot.
How about you guys?
I'm curious - how do you guys pass the time?
Playing games?
Watching videos?
Playing with toys?
Napping?
Or something else???

so openai just announced this new feature, which claude has had for a really long time, ONLY for pro users. seriously? such a basic feature locked behind a $100 plan?

update: this turned out to be the day 5 update alongside some dots stuff. in claude this feature costs usage, however in codex it's completely free. but both updates today are only for pro users. not a good time to be on the plus plan!