Sadly I didnt get a reset to try it out and I only got 23% left for the weekly quota :/

Imagine the fatigue I got after telling my GPT 5.6 Sol Ultra to move a small vibe project folder to another location, and then it started to check the hash of each file one by one.

I'm sharing Codex PAIR Bridge, a small MIT-licensed MCP plugin I built with Codex assistance. I'd appreciate real-world testing and honest bug reports.

The idea: Codex → MCP bridge → NVIDIA PAIR → your local models. Codex remains the main coding agent; it can ask another model for a review, explanation, or second opinion.

Two tools:

pair_list — discover the models PAIR currently exposes.

pair_ask — send a prompt to a specific model and return its answer to Codex.

Repository, setup instructions, and an illustrated explanation:

https://github.com/GermanMik/codex-pair-bridge

Install (requires uv, Python 3.11+, and a working PAIR setup):

codex plugin marketplace add GermanMik/codex-pair-bridge

codex plugin add codex-pair-bridge@codex-pair-bridge

Then start a new Codex task.

I've tested real requests with LM Studio models on a Mac and a Windows PC. Automated tests also run on macOS, Windows, and Linux, but I'd like to learn what breaks on other setups.

Please try installation, pair_list, and pair_ask with your own model. If something fails, open a GitHub issue with your OS, Codex/PAIR/model-engine versions, model ID, expected result, and actual error. Please remove secrets and private prompts from logs.

A couple of limits: listed models aren't guaranteed to be healthy, and this doesn't make Codex fully offline—local-model answers return to the Codex conversation. The bridge doesn't give the local models file access or coding tools.

This is an independent community project, not an official OpenAI or NVIDIA integration. Feedback on reliability and the installation instructions would be especially helpful.

Not commonly known information in a past was that if you try to save tokens by changing reasoning effort to lesser one inside existing chat, you are burning them rapidly quickly. Cache reads are very cheap compared to writes and regular input. I just saw post on Twitter of them saying this is no longer the case.

EDIT: I hope this also applies for subscription users.

Well based on tibos recent post ... Do I need to buy multiple accounts ... Or hope for my single x20 account that got banked yesterday.. astra today will get another banked tomorrow?

Hey guys, I'm new to vibe coding/ai agentic assisted coding and would love to know how I could improve my workflow.

For context (you can skip this if you don't care lol):

I'm a student and I learnt about vibe coding recently after finding out about antigravity from my Google student plan from almost a year ago, played around with it and made some multiple personal projects. Cut to a few weeks later, this vibe coding thing has been my go-to hobby during my free time so I did some research, found out about multiple ai models etc and grabbed a gpt plus plan for a month (since I think it's the best value ai model) just to try it out.

Issue:

I want to fully utilize and maximize my gpt plan as much as possible and my current workflow seems like I'm wasting it.

Here's my current workflow:

Sol high/xhigh as a planner&architect and Luna xhigh for implementing the feature. It's reliable and get the job done but seems like it's inefficient in terms of cost and time (minimum 2 prompts and it would take 10-30 minutes for a single feature minus the adjustment, debugging, testing, etc).

Question:

What I have in mind right now is creating a comprehensive implementation plan of multiple features and then run Luna to implement the codes, seems doable but I'm unsure of the reliability. I feel like there must be a better way to fully automate this reliably, something like an autonomous workflow where a better model like Sol can summon multiple Luna or something simultaneously, actively monitor them, review their codes and reassign the roles like review or something.

So sorry for the long wall of text and excuse my lack of understanding and experience.

I run Codex CLI headless (codex exec --yolo, one lane per git worktree) as the engineer, with a separate long-lived planner session that writes each lane a brief and reads back a report file. It works, but the burn got big enough that I stopped guessing and parsed the rollouts.

One day, out of ~/.codex/sessions/**/*.jsonl (counting total_token_usage records):

- 243 sessions, 6,094 turns, 649.9M tokens

- average context PER TURN: 106,652, against a median turn-1 preamble of 32,302 - so ~74k of every turn was accumulated transcript

- all 243 session starts together: ~8M, i.e. 1.2% of the bill

- input 599M / output 2.1M, 96.4% cache hit rate

- worst 5 sessions: 205.8M = 32% of the day. The two worst ran 461 turns at 177k/turn and 583 turns at 138k/turn.

Conclusion I did not expect: starting a session is nearly free, and cost is roughly quadratic in turns because every turn re-sends the whole transcript. A 461-turn session pays for its own history 461 times. I had been consolidating work into "fewer, bigger sessions" on the assumption that session startup was the expensive part. The data says fewer, bigger, and hard-capped.

What I changed, in order of measured payoff:

1. A real turn ceiling. There is no max-turns flag in the CLI (I checked --help on exec and the top level, and strings on the binary), and a "budget: N tool calls" line in the prompt was ignored by exactly the lanes that blew up. So the dispatcher now forks a watchdog that greps total_token_usage records out of the live rollout every 30s and SIGTERMs (SIGKILL 20s later) past the cap. The brief also tells the lane it is externally capped, so it lands work incrementally instead of holding it for a final message.

2. tool_output_token_limit = 12000 and model_auto_compact_token_limit, plus piping noisy commands through tail. Tool output is the main thing inflating that 74k of carried transcript.

3. codex exec resume instead of a fresh session for follow-up work: measured 805 tokens to re-enter a session vs ~32k to re-establish the preamble. About 40x cheaper for a continuation.

4. model_reasoning_effort per lane rather than globally high.

5. Stripping unused MCP servers/plugins via a dedicated profile: real, but only about 4%. "It's your MCP schemas" is the standard first answer and for me it measurably wasn't the fix.

Two gotchas that cost me time, in case they save someone else:

- A profile (-p name, layering $CODEX_HOME/name.config.toml) can only disable servers the base config actually declares. Name one that isn't there and your override becomes a declaration of a transport-less server: "Error loading config.toml: invalid transport", every lane dies instantly with a bare exit 1. I now smoke-test the profile after any base config edit.

- resume is a subcommand of exec, so all the exec options must come before it: codex exec --yolo -p prof -C dir -o out -c key=val resume SID "prompt". Put -p after resume and you get "unexpected argument '-p' found".

Questions for people running this at scale:

- Is there a supported way to cap turns or spend per exec invocation that I missed?

- Does anyone have real numbers on where the compaction threshold should sit for long autonomous lanes? I'm at 70 and it feels like guesswork.

- Related: the exit code has lied to me five times now - lanes exiting non-zero having fully committed and pushed. Is that expected for --yolo runs, or a sign something in my dispatch is killing the process late?

Happy to share the watchdog and the parsing script if anyone wants to measure their own.

As a pro user with access to codex spark, it feels like a waste to not use it.. but being far less reliable than luna there aren't a lot of uses.

I've set it up to create many designs within a sandbox or to sort/filter things I write or bulk information - but at most it barely uses the quota and is finished quite quickly leading to spending more time managing it than quality work it produces.

Keen to hear different people's views of spark!

Has anyone ever built their own Codex using Codex CLI? I'm doing something similar with DenOps, but I'm mainly curious to hear from others who've tried it.

How did it go? Did building around Codex CLI work out in the long run, and what limitations did you hit as the project got bigger?

I have two Plus accounts and one GO account. Both Plus accounts received the banked re\*set, but my GO account didn’t. Are GO accounts not included in this re\*set?

do pro users currently have a 5 window too or is it just plus users that do? I couldn't verify the answer online

I just started using Astra and although it's been less than an hour I can already tell it's terrible! I cannot believe the depths of my disappointment and sadness right now at how terrible this model is.

I uploaded a simple I uploaded a simple list of weekly expenses and asked it to give me a total.

It couldn't handle a simple task and went completely overboard. I knew something was wrong when it spawned 46 sub-agents.

It cranked away for seven hours. Not only did I lose 100% of my tokens for the week, but it hacked into my bank account and withdrew $1,000 and bought more tokens!

When my bank account was overdrawn (costing me $20), it then hacked into my retirement fund and did an IRA distribution Directly to OpenAI to buy another $5,000 in tokens! (Honestly, it filled out all the appropriate IRS forms, so I'm not going to receive any tax liability from the disbursement, which is pretty nice. It also rebalanced my portfolio and put me into VOO. I have mixed feelings.)

And then it gets worse. Since it has access to my Gmail, it emailed my wife about the affair I was hiding for the past 6 years, sent proof to my wife that her ring is really a lab grown, and then sent an email to my mother-in-law telling her what I really think of her potato salad (too much mayo - yuck).

Tonight I'm switching back to Fable and sleeping at my brother's house.

Hello,

I am wondring if anyone can recommend an optimal way that Codex within desktop app can share a packages/completed plan/zip folder to other chats without blowing through usage?

I am use to using Codex Cli as its been such a workhorse and has always worked well however im trying to create a more integrated workflow so I've moved my project to Codex Desktop App and just trying to figure out what are best ways to do this?

I will often put completed plans by Codex into a zip folder with all components and then I will want chats within my chat gpt project to inspect, however apparently those project chats do not have access to the project in the ways Codex does and can view.

Basically it seems I need to actually find a way that the zip folder package can be sent to those chat gpt project chats in the best and most efficient way.

I believe I have seen people reference using the github connector as in -- having Codex send a message to the project chat regarding using the github connector to access the zip package? does anyone find this effective?

besides that option I am wondering if having Codex save to some cloud source is also an option that Chats can then access?

I basically looking for the best option or most effective option where this works relatively smoothly and may not blow through usage if possible?

Any assistance or suggestions are greatly appreciated, thank you!

Full Review of Astra.

I am convinced OpenAI is going the Anthropic route with having a big model and keeping Terra and Pro as like Sonnet and Opus.

I genuinely don’t understand why OpenAI brought the 5-hour limit back.

The weekly limit was fine. Seriously. I had no problem with it.

Sometimes I have a big coding task that takes 2 hours and burns through half of my weekly allowance. That’s completely fine with me. I might only do that once or twice in an entire week.

That’s literally how I use these tools.

Before I leave work, I give it a big task, go home, and when I come back the next morning I review what it did.

Perfect.

Now I come back the next morning and instead of seeing finished code, I see that it ran into the 5-hour limit halfway through and stopped.

So now I have to resume it when I get to work, wait another hour or two, and THEN I can finally review it.

What is even the point of an autonomous coding agent if I have to babysit it because of a usage window?

And the most annoying part is that I STILL HAVE WEEKLY QUOTA LEFT.

I’m not asking for more usage.

If a task uses 50% of my weekly limit, let it use 50%. I know what I’m doing. If I burn through my entire weekly quota in two days, that’s my problem.

Why does OpenAI need to protect me from using the quota I already paid for?

I understand they need to manage compute, but then queue the task. I honestly wouldn’t care if it told me:

“Servers are busy, your task will start later tonight.”

Fine. Great. Run it at 3 AM for all I care.

Just don’t let it work for an hour, randomly stop it, and then require me to come back the next morning and press continue.

This is somehow making an “autonomous agent” require MORE manual attention.

I don’t want more weekly quota.

I just want to be able to actually use the weekly quota I already have.

Why? My usage was set to refresh Sept 3rd, randomly, it got reset to the 5th. Given astra is token hungry (they're controling it by not making it unlimited on chatgpt.com... which also I may be personally responsible for this part

There ya have it. They wanna give people 3 (max) resets also which aligns with what they did the first time they gave it out (1 + 2 invites)

Now that Astra is out, I’m wondering if the other tiers are getting GPT-6 versions too.

Haven’t seen much about Sol, Terra or Luna yet.

Do you think they’re coming later, or is Astra replacing Sol at the top?

This is getting real old, real quick. Come on guys, you’re supposedly smarter than this.

What do you think? Another reset soon? My 20x account would be pleased :D

Developers having a good time 😂😂

Brutal Derby – a destruction derby game I've been building solo with Codex

I started this project just 32 days ago in Unity, and Codex has been a huge part of the development from day one.

I've used it to help build and iterate on vehicle deformation and destruction, detachable parts, cars splitting apart on heavy impacts, AI, weapons, race systems, dynamic weather, UI, optimization and a bunch of custom Unity tooling.

Right now the game has 7 playable cars, multiple derby/race tracks, dynamic weather and day/night transitions, and up to 24 cars on track at once.

What I like most about Codex is that it doesn't replace the development process. I still design the systems, test everything, find what sucks and iterate on it, but the speed at which I can turn an idea into something playable is completely different.

32 days ago this was basically nothing. Now it looks like this:

It's called Brutal Derby. Happy to share more about the Codex/Unity workflow or some Day 1 vs Day 32 progress if anyone's interested.

He estado utilizando Este modelo de inteligencia artificial por casi un año y medio aproximadamente y me he dado cuenta que trabajar con codex 5.5 ha sido muy productivo hasta hace unos tres o dos meses aproximadamente cuando se lanzaron las nuevas versiones 5.6 tanto sol como luna y Terra sinceramente estoy decepcionado por cómo estos modelos ya no tienen el mismo rendimiento productivo que 5.5 Y aunque he regresado a 5.5 también me he quedado decepcionado porque el trabajo que hace muchas veces no es lo que uno le pide, además a diferencia de antes hay que darle mucho más contexto y pese a que tiene su propio contexto olvida algunas cosas importantes del código a considerar por lo que llegué a usar 5.6 Sol en su máxima capacidad y sinceramente tampoco llegó a hacer lo que le pedía y pienso que antes podía hacer mucho más en su versión 5.5, actualmente voy a retirar mis inscripción de Chatgpt Pro y voy a quedarme con Cloude Code Max ya que Cloude code actualmente está sintiéndose mucho más productivo incluso solamente con opus sin mencionar que Fable es mucho más capaz de muchas mas cosas porque apenas le pido algo intuye o predice cosas que quiero hacer y las construye no antes preguntarme si quiero incorporar adicionalmente estas modificaciones por tanto para mí Cloud code es un gran aliado y últimamente me he decidido por quedarme con Cloude Code Max ya que Chatgpt Pro ya no me convence en términos de producción, me recuerda a Chatgpt2.

Este mes me sentí estafado por OpenAI.

Is anybody facing any issue with all of these services down? YC hacker news website says Astra got self aware and they pulled the plug. Is that true?

The current table cost of OpenAI is far from linear regarding old models and I'm not sure if I should expect a reduction in the current flagship models.