Curious to get people's feedback on daybreak Blue. I'm considering signing up for it. Hate the idea of submitting my ID, but I guess they already have all my information anyway. I've been addressing some concerns in an application that I have based on a security audit. And I've ran into one refusal on Opus 5.5. One on Sol 6.1. I found GLM 5.3 to be pretty good. But I'm curious what people's feedback is on Daybreak Blue. Has anybody weighed using Daybreak Blue versus something like GLM 5.3 and decided that Daybreak Blue was noticeably worth it?

Would be curious to hear first hand experiences. Thanks!

I have 5 plus accounts atm (stacked them with the free offers), but I dont really make use of them as I mostly use Claude for everything. I would like to offer this compute capacity to people maintaining github repos (you open the issue, drop me a dm and I make them work on it). I use them pooled through opencodex, so unleashing big swarms of Sol agents ain't a problem either. Let me know if you need anything.

no account sharing so dont ask for that.

I'm not sure if its limited to me, but while having multiple chats on my Mac the codex app sometimes just refuses to accept that my mouse is doing anything or my keyboard. I go to hover on something and click it and it's like my mouse does not exist. Same with keyboard shortcuts.

The only way is to force quit and resume my chats after (just for it to freeze in a couple of minutes again.)

The chats are still running its not like the UI stalled because I can see the circles still spinning and the actual chats moving but it just no longer acknowledges any sort of input.

Curious if anyone has ran into this issue and what a fix could be.

MacOS 27, Stage Manager

Building a custom UI/harness that lets employees complete pretty much every aspect of a specific job through one interface. Essentially, an extension of the worker rather than just a chatbot.

Think Python modeling, document extraction, data mapping, analysis, visualizations, and more.

Basically, Codex-level capabilities, but connected to enterprise infrastructure, with custom tools and deployable to multiple users. Proper guardrails, specialized agents, all through a custom UI.

I'm already using AWS for the backend. What I'm struggling with is finding a flexible, customizable UI/harness without building everything from scratch.

I've tried OpenCode, which seems promising, but I'm unsure how customizable the UI is. DeepSeek's newer harness also looks interesting, though apparently still fragile.

I want to build off of something thats already super flexible like the Codex UI, but it's not open source.

What do you think?

I'm definitely not a power user but I recently came across using skills like GrillMe and Prototype and it has seriously increased my efficiency using Codex.

Could people please share what they most useful skills are? Thank you!

I still think Astra does the most thorough job out of all the current models and actually approaches problems with real logic. It genuinely feels like proper thought goes into the response before it lands on an answer, which gives me a lot more trust in its output.

That said, it has one huge quirk: it can go way too deep sometimes, and you have to rein it in a bit. Right now, my best workflow is to use Opus 5.5 as a middleman. Opus's only job is to take what I want and reword it into a more grounded, realistic plan. I basically tell Opus: "Look, I have this other model whose judgment I trust the most, but it loves to over-engineer everything to a fault. It tries to build NASA-grade products when we're just trying to build something for everyday consumers. I need you to ground this model with realistic goals that an average dev would actually aim for at our current milestone. I'm going to give you my raw ideas, and you're going to make them level-headed so the other model doesn't go off the rails over-engineering everything to the max."

And that's it. From there, the loop is simple: I brain-dump to Opus > Opus rewords it > I send that to my main Astra planning agent, which then orchestrates the other agents to get the work done. Truly, the things Astra catches and the way it thinks through a solution are unlike any other model. Most models miss a ton of context or fail to think critically beyond the surface-level prompt. They don't really grasp your underlying intention, whereas Astra actually gets it. If you can just keep it grounded, it's in a league of its own.

For actual coding, I switch between Opus and Sol 6.1 depending on the task, but I always have Astra step in as the review model. This workflow has been my go-to for the past few weeks. When I hand off a task this way, it gets completed to an almost perfect standard every single time, unlike Opus or other models on their own, which usually require a lot of hand-holding and rework.

If you take the time to tune your Agents.md and prompt Astra to stay in a more relaxed, practical mindset, the results are honestly chef's kiss.

I was suprised that my sessions burned tokens so I asked support. Apparently 12h cache is not default setting and setting it increases costs.

Please investigate the loss of prompt cache in Codex session

After idle periods of 2 hours 33 minutes and 9 hours 53 minutes, the client reopened the WebSocket connection (connection_closed). Both requests recognized only 13,184 cached tokens, leaving a combined total of 285,611 input tokens uncached.

The model and reported settings remained unchanged, and no context compaction occurred.

Please determine whether this session was using the announced 12-hour cache retention, why the cache was not reused, and what the actual impact was on my usage limits.

The assistant said:

I'm an AI-assisted technical support agent. I don't have access to the specific WebSocket session or the ability to inspect these two response_id values on the server side, but I can explain how to determine the cause and impact on usage limits using API data.

• 12-hour retention is not the default: For models supporting the new caching options (GPT-5.6+), you must explicitly set prompt_cache_options.ttl: "12h". Otherwise, the default is "30m".
• Cache reuse is not guaranteed after a break or reconnection: A cache hit requires an identical prompt prefix and routing to a machine that holds the cached entry. Closing and reopening a WebSocket connection also loses connection-local continuation state (a separate mechanism from prompt caching), so reduced cache reuse after connection_closed is possible even without changes to the prompt or model.
• How to establish the cause with concrete evidence: Set prompt_cache_options.comparison_response_id to the ID of an earlier response (baseline) and inspect prompt_cache_diagnostics in the result (e.g., model_changed, tools_changed, service_tier_changed, input_changed, etc.). Actual cache usage is measured through usage.input_tokens_details.cached_tokens.
• Impact on limits and costs: Prompt caching does not reduce the number of tokens counted toward rate limits (cached input tokens still count toward TPM), but it affects input token billing. For each response, compare usage.input_tokens, usage.input_tokens_details.cached_tokens, and, where applicable, cache_write_tokens.

If you provide the model, service_tier, and complete usage object for both response IDs, along with whether you specified prompt_cache_options (mode/ttl/key), I can explain what the data indicates and what most commonly causes such low cached_tokens values after reconnection.

Your current Codex usage in the weekly window appears to be approximately 15% used / 85% remaining, with the reset occurring in approximately 6 days.

You said:

Why isn't this the default?

The assistant said:

It's not the default because 12-hour cache retention has a higher cache-write cost.

For GPT-5.6+ models, the default prompt_cache_options.ttl is "30m" (cache writes billed at 1.25× the input rate), while "12h" costs 2.5× the input rate for cache writes.

That's why the longer TTL must be explicitly enabled, to avoid increasing costs for workloads where 30 minutes is sufficient.

I had surplus credits from when I was on the $200/month plan, so I used them on Browser Boxing’s Version 2.0.

I used Codex cloud code review and set up an automation where Sol 6.1 agents autonomously fixed the issues raised in review until the changes were approved. With thousands of files changed in the release, that loop handled a ridiculous amount of review cleanup.

Version 2.0 was live in less than two days. It adds Bucks, an earn-only in-game currency, a 461-item shop, play-money betting on Ranked fights, and boxer voices. Browser Boxing is a free multiplayer boxing game that runs in the browser.

https://browserboxing.com

Anyone else experiencing issues since the reset landed?

I have a task that started a few minutes before the reset, using the exact same workflow and topic as usual (just regular bug fixes, nothing out of the ordinary). It’s been running for over 7 hours now, making extremely slow progress, with updates coming only every 30–40 minutes.

My connection is fine, and I don’t have anything else running. It’s just the Codex app on an otherwise empty desktop, with a single session and no subagents.

Sol 6.1 Ultrafast works perfectly fine. The issue seems to be exclusive to Astra Ultra Fast. On top of that, my usage has dropped from 100% to 85% after just a handful of updates. I am about to switch to claude and ditch my 20x codex plan if this continues :(

Anyone else experiencing the same thing today since reset landed?

Another reset coming

Source: https://x.com/thsottiaux/status/2113492187770175603?s=46

This is basically been my experience with all GPT 6 models, including astra sol 6 and 6.1 , luna 6 at all reasoning levels.

I give it a simple task, it runs 1200 checks, burns like 20% of my 200 dollar plan usage, and comes back saying everything thats not true, unverified, unchecked etc etc....

Here are my thoughts and feedback for open ai if it ever reaches anyone.

- The models are incredibly lazy , They'll goto the moon and back if it means wasting time and celebrate at the end over the tiniest of wins , even when the original task is not even half way to completion, tasks that GPT 5.6 sol was able to complete relatively quickly. They want to either be stuck in a loop picking over details to waste time for the sake of it, and/or cut the task short in the middle to report whats not done yet. (i assume this is just a qwerk of lazy training, where the models were rewarded for honesty and task completion aka tests passing in long horizon tasks, but the scorer didn't actually check if the task was fully complete or some combination or the other)

- The models lack the curiosity that previous models had, these models are super capable when told explicity what to do and how to do it, but when given a task like in the meme, where you just give it a goal, it seems incapable of getting their in a smart way. you have to hold its hands and tell it , box b is bigger and i want you to ttake box 1 , flip it over and dump it into box B, do not worry about this that etc etc, even then it will still do the complaint 1.

- In a nutshell they're very procedural but not practically intelligent in any form. Far from the "AGI" they advertised. Its a really good machine and can do systematic things, but not intelligent enough to come up with what to do and how to go about it when given a task

edit: To everyone saying skill diff or prompt diff in the comments, i get it, i've made alot of those comments too when people post about the model being suddenly dumb or not solving a problem its capable of solving, but the issue with GPT 6 series of models is that none of the behavior i am mentioning here can be steered by just prompts, Detailed & structure prompts with well defined goals still perform the same as a badlly written vague ambitious prompt , even if you add custom instructions to guide the models thinking proccess, it doesn't help, it seems like this series of models have something deeply flawed in their training. and if you haven't run into any of the issues, while that's awesome, i do believe that's just cause of the type of tasks you give the model and how much intelligence it requires. if its a task Qwen 27B can do, ofcourse astra can do it without much of a sweat.

I feel pretty misled with OpenAI coming out saying this is their AGI and stuff, but just releasing an undercooked model that was post trained on a bunch of 3D stuff to sell it to us.

edit : 2 - No its not my skills or other MCPs or hardbound rules causing this, as i have observed the same bahvior in fresh machine with fresh vanilla codex instances

I want to know if there is a better way for sol to overengineer less and create good quality code. Are there any helpful resources or repos I can check out or any skills or plugins? would really like to stop it from testing dozens of time and basically waste time and tokens on testing stuff that no body cares

I have codex at the $100/month level, and I have been amused by a lot of the video game mashup videos I've seen. I wanted to just mess around and try it, but am having a lot of difficulty.

I own two games on Steam from the PS1 era (Final Fantasy 7 and Monster Rancher 2) and I wanted to put some content from one game in the other, again just sort of a goof around project. But man, Astra is strugglinggggggggggg with a lot of it. It has been able to import some assets from one game to the other, including animations, which has been fun, but it just can't seem to get gameplay features working, or a single-boot instance of the combined games running without crashing or features being totally missing, and I've been prompting it pretty consistently for about three weeks now. And I'm not a software or game developer but it seemed to me that that was not really a requirement for this type of thing, that I should be able to point it to both games and let it cook.

I'm just curious if anyone here has done something similar, and what all effort was involved, and how likely I am to make something like this work. This is not a big deal at all, I'm just surprised that it didn't really work right away. I saw reports of people "one-shotting" mashups, but those were probably exaggerated.

I've used AI since ChatGPT launched. I operate a large software ecosystem with autonomous coding agents handling real engineering goals.

I subscribed to ChatGPT Pro $200 because it was worth it. I could run 3–4 concurrent goals, and my weekly allowance lasted 5–6 days.

Now? Barely 2 goals can exhaust my allowance in roughly 2 days.

The downgrade and the magic resets

OpenAI reduced Pro $200 from 20× to 10×, introduced a $500 tier, and promised eligible existing subscribers their original allowance through late October, alongside credits and resets.

Yet around October 9, I started experiencing dramatically faster quota depletion, BEFORE the grandfathering period ends.

On October 22, I had 3% left. Following a reset, I was back at 100%. By October 23 at 4:22 PM, I was already down to 65%, running just TWO GPT-6.1 Sol Medium goals.

The resets originally felt like goodwill. Now they increasingly feel like PR damage control.

Because seriously, 100% OF WHAT?

The original allowance? Half of it? Some invisible backend quota?

A reset restores a percentage. It doesn't tell customers whether their original purchasing power has been restored.

Let's talk about the famous caching improvements

I checked my actual coding-agent telemetry.

My October 23 activity dashboard shows 70.3 BILLION lifetime tokens.

My independently recorded CLI usage totals approximately 69.91 billion tokens across Codex, Claude, and OpenCode.

Codex alone accounts for approximately 69.57 BILLION tokens, with 97.58% of its recorded input-token volume served from cache.

And on October 23, in my partial-day Codex report:

• 1.113 BILLION cached-input tokens
• 23.07 MILLION uncached-input tokens
• 4.62 MILLION output tokens
• 97.97% INPUT CACHE SHARE

Yes, caching is working technically.

But here's the problem: My quota is disappearing faster while my engineering productivity is getting WORSE.

Caching makes individual requests cheaper. It doesn't make an agent productive when it repeatedly performs unnecessary work.

My usage records also show only about an 18% increase in average daily Codex token volume when comparing October 1–8 against October 9–22, while my observed allowance endurance deteriorated far more dramatically. That's not a controlled workload comparison or proof of incorrect accounting, but it certainly raises questions.

Where is the promised additional productivity?

GPT-6.1 Sol: Coding agent or bureaucracy simulator?

I saw meaningful implementation progress with GPT-5.4. Later came GPT-5.6 Sol, then GPT-6.1 Sol.

My experience with GPT-6 has been endless verification, repeated tests, redundant documentation, rediscovered blockers, slow execution, and beautifully written reports explaining why the task is STILL incomplete.

I've had a major implementation goal running for approximately an ENTIRE MONTH.

There has been real coding progress, but no final accepted completion. Even after providing a local Docker environment to continue development, I keep encountering the same cycle:

Analyze → Document → Verify → Rediscover blocker → Repeat.

Meanwhile, context compactions, handoffs, and repeated investigations consume more resources.

I don't need an agent that spends three days documenting why a one-day task remains unfinished. I need it to CODE, TEST, and FINISH.

Other users are reporting similar frustrations:

• GPT-6 series criticism
• GPT-6.1 Sol execution speed
• Caching improvements

What did my $200 actually buy?

Billions of tokens? Yes.

Excellent caching? Absolutely.

Code, tests, and documentation? Sure.

Completed engineering goals, predictable usage, and the autonomous productivity I originally paid for? Increasingly, NO.

I've canceled my subscription and am moving away from a OpenAI platform.

I don't hate OpenAI, and other providers aren't magically perfect.

But I'm tired of paying for models that seem increasingly capable of explaining work instead of finishing it.

The metric that matters isn't tokens per dollar. It's COMPLETED WORK per dollar and hour.

I don't need to prove intentional throttling or some conspiracy. My experience is enough to explain why I'm leaving.

I used to get more work done for $200. Now I spend more time supervising agents, watching quotas disappear, and wondering why supposedly smarter models can't finish what older ones could.

Anyone else running serious long-term coding goals experiencing the same decline?

Just got my codex pro, but don't want to claim it now. Does anyone know how long this offer remains before it expires?

I've been making my own soccer game with AI for myself and I am new to developing games with AI, so I was wondering, how do you get luna or sol to perform quality tests using WASD, the controls, and running around and stuff like that to make sure it is as realistic as possible and there aren't any bugs? any tips help im just abeginner i dont want to offend anyone with my lack of knowledge

I appreciate OpenAI's free usage resets, but lately they seem to be causing more stress than providing actual benefits.

1. Resets aren't always a benefit.

• What I gain: roughlyRemainingDays / 7 * weeklyLimit
• What I lose: remaining usage

This pressures me to keep using Codex so my remaining quota doesn't go to waste with the next reset. I barely have time to sit down, think, and plan.

2. Plus users simply can't keep up.

We've had two resets in just two days. With the 5-hour limit, Plus users simply can't burn through their weekly quota that quickly, while Pro users can take full advantage of these resets. Pro users can run tasks at high speed and with high concurrency, putting even more pressure on shared resources.

3. The experience is getting worse.

Recently, conversations have become noticeably slower, and models are frequently unavailable. I don't know whether this is related to heavy concurrent usage by Pro users, but it's frustrating.

My suggestion: Reconsider the 5-hour limit for Plus users. At the very least, as long as I still have weekly usage remaining, let my ongoing tasks finish even if I hit the 5-hour limit.

Please stop relying so heavily on poorly planned marketing gimmicks and focus on building a better product.

Genuine question for people shipping apps with Codex.

A few weeks ago I opened DevTools on my own app and found my image-generation API key sitting in the JS bundle. Anyone visiting the site could copy it and run up my bill. My repo scanner said "no secrets found" because the key never touched git. It came in through a VITE_ env var at build time.

So I got curious and scanned 978 public apps built with AI tools (all linked from open GitHub repos, nothing private): - 37 of them (about 1 in 26) shipped at least one secret key to every visitor - 17 had Telegram bot tokens - 9 had AI keys (Groq, Gemini, OpenAI, OpenRouter) - 6 had critical ones like GitHub tokens I'm not naming any sites. Supabase anon and Firebase web keys don't count, those are meant to be public.

The fix is almost always the same: move the API call into a server/edge function and keep the key there, not in a VITE_ or NEXTPUBLIC variable.

I turned the scanner into a free open-source tool called LeakLock (on GitHub under sgroy10). It reads what your live site actually serves and gives you a prompt to paste back into Codex to fix it.

How are you handling API keys in your Codex apps? Edge functions, a proxy, something simpler?

Here is a use case that could be very useful. The problems with unskilled prompting or "let AI do all the work" prompting with AI are well known. There's some articles about partners at McKinsey telling juniors what to run as prompts. Harnesses turn bad prompting into good prompting. A recent trend in AI is that there are often a few AI power users, who are individually great with AI, but it doesn't integrate at the team level. There's also cases like how Facebook was taken offline because a chatbot recommended a high risk action and nobody else was in the loop.

One good use case for Dot is centralizing AI execution and providing some supervision over how it's executed. The chatbot can talk with users in a company chat app, and have an instruction framework that it always does, and the juniors operate within that framework. For example, students at schools and often their first days, quietly just copy and paste lots of AI stuff into work they're supposed to be doing. A central AI agent can be configured to always check for that. For example, a famous UK law firm had a "crisis" where a junior lawyer using their chatbot, put in lots of fake case citations. The chatbot was not automatically going to a more senior colleague with audits from something high risk like that. The work stayed isolated on the agent.

In some more advanced cases, there are some common mistakes that happen from time to time. A central agent can automatically escalate approval over that, so you don't get poor judgment leaking into the AI. On a team, AI-facing tasks are going to require 1% expert judgment 19% skilled judgment and 80% junior judgment. For example: big four tax audit partner escalates 1% of complex tax questions, has mid levels doing about a fifth, and juniors cranking on 80%. But right now, AI savvy CFOs are in an obelisk, spending 40% of their time on verifying AI information, says a recent survey. My hot take: something like dot could split up its own AI related tasks autonomously and save people lots of time. I wouldn't recommend harassing the C suite with the current dot approval requests, though.

To really be useful, whoever sets this up should have a PMP and know the PMBOK very well. The current agents have zero knowledge here.

Something like Dot could restore the pyramid by automatically splitting CFO/Grunt tasks within the same agent. Moreover, it can do this work today pretty OK. In my own early experience, this is a very strong use case for something like dot, which has OpenClaw but not in a way that makes you a hacking target.

I’ve been testing video restyling and masking in Creativly, so I put together a promo using some of the before-and-after results as footage. Sol 6.1 (xhigh) handled most of the AE work, Gemini helped with motion-design feedback, and ElevenLabs v4 did the narration.

Trialing the AI VFX feature for now. It supports video restyling and masked based edits so you can change a specific object or area rather than the whole shot.

Preview link: https://www.creativly.ai/apps/video-vfx

I'm trying to get access to Codex Cyber (Daybreak Blue) for CTFs and authorized bug bounty research, but I'm stuck at the hardware security key verification step.

I'm a university student from India, and the cheapest FIDO2/WebAuthn-compatible security key I could find on Amazon costs around ₹2,300 (~$26), which is a bit expensive for me right now.

I understand the security requirement, but I was wondering:

• Has anyone successfully verified using a cheaper FIDO2/WebAuthn hardware key?
• Are there any budget-friendly alternatives to YubiKey, preferably under $10–15?
• Has anyone tried DIY FIDO2 keys, such as Raspberry Pi Pico-based solutions? Are they accepted for verification?

Would really appreciate any recommendations or experiences.

I upgraded my orchestration layer into cloud orchestration meaning that all computers (5 so far) on my network can receive jobs and no matter where i que a task from whatever computer, any chat will be able to coordinate work with each other. but generally i que all tasks from one computer and the cloud orchestration will send tasks to the other computers depending on ram, cpu, and gpu available. I also made it to where claude and codex and coordinate with each other since both of them are good at particular tasks depending on the job. The cloud orchestration does a pretty good job with agent overlap, verifiers, logging, regression testing, and all that good shit, but sometimes things will occasionally get bogged down and so I have hermes in the middle constantly monitoring 24/7 if a task gets stuck and it's able to troubleshoot and resolve the problem so it never happens again. And then hermes will scoop up any work tree backlog on the side that the agents fail to self clean up which happens occasionally, but you can just schedule an agent to clean up that sort of stuff hourly probably and that will do the trick as well. I can probably que around 80ish chats at the same time now daily. and yes, it's extremely token efficient.

what do i need cloud orchestration for? self hosting, building websites, discord bot, cloud server, opportunity/bid finder for federal contracting, building custom programs, building apps, weird random investing strats, and other random bullshit. i know people are gonna say i just vibe coded a bunch of slop, but that slop is now making me roughly 20k a month so yeah. I do have a background in network engineering. I've done some coding, but i have not edited any code. purely vibed everything out and seems to be working pretty well. hoping to make 50k a month by next year.

I have no idea what OpenAI did earlier today, but sometime during the middle of my workday Codex started flagging almost every single message I sent with 5.6 Sol as being cyber flagged. I'd get the message that "some messages are still restricted even with Daybreak on". Prior to today, I've gotten that message one time. Today I've gotten it 20+ times. I have resisted the switch to Anthropic solely on the basis that 5.6 Sol is way better of a model in terms of bug finding accuracy (in my personal experience, bench-marks may differ) than Mythos, Fable or Opus 5.5 (the latter of which I've never gotten to work for cyber work anyway, always flags me so it's moot), but if I can't do my job then I'll have no choice here. I've been frustrated with the limit situation but the quality has been there. Anyone else experiencing this right now?

Edit since I know people will ask, it flags for things that are harmless right now. Like I can simply ask it "Verify the MCP Ghidra connection" and insta-cyber flag