I used to be a big Codex fanboy

I started on the $20 Codex plan and moved up to the 5x plan. Every new frontier model felt like an event. So when Opus 5.5 came out, I didn't care much.

When Astra launched I was just as hyped. But a few weeks later I was drowning in work on a big project, and I gave in and bought Claude 5x for Opus 5.5. It cleared a huge amount of that backlog in two days.At hackathons I kept using both.

Recently, I was in another week-long hackathon, and it was more of a research/optimisation task than a coding task. Basically it involved loading some geospatial data in GeoTIFF formats and trying to see if we can gain any performance improvements on the underlying necessary memory that we need to store.

I was thinking, at least, that a bigger model like Astra, probably bigger than Opus, so I was assuming that maybe the big model might be more relevant here

It's not a simple coding task, It involves coding, but it involves a lot of research and stuff as well

I couldn't watch it during the start , had some other work etc.I'm big on context engineering, so I gave the agent everything up front: the brief, the company's guidance, the datasets, the evaluation rules and the constraints. Then I left it alone.

I had Astra run for 2 days, and it gave me basically meaningless improvements, like less than 5% improvements

I gave opus the same stuff, and it resulted in 5x total perf improvements and 98% less memory.

These are not novel inventions; the hackathon is set up in a way that the starting points are obviously very inefficient, and we had to do some improvements, but it still completely changed my mind on OpenAI stuff: it just completely did not work as compared to opus

For the entire run, I used Astra, not even 6.1 sol, subagents, and stuff like that, whereas Opus was allowed to delegate to Sonnet 5.5

It's not even like a subagent, or like the orchestration issue.

Its just completely baffling to me.

End of My rant

I am a Software Engineer and I had the impression at first that Astra would be the first "AGI". Turns out I (like many others) were wrong. And since then, I haven't found much use for Astra since all my coding work for my job is used on opus 5.5.

This is not hate towards Astra, I genuinly like the model. I just haven't had a reason to give it a shot when others have a better product for my use case. This is leading me to have an insane amount of usage and resets just sitting duck which I will never be able to get through before the expiration.

This is not a ragebait shit post~!

So I had used Codex in my work—Unity—for a long time ($20), then lately because of the need for more usage and also here a lot of praise for Opus 5.5. I sub $20 for Claude too.

Yeah... It was stunning! Opus is just so fast that it's like 5 more times than Sol 6.1, I know... I know that there are 2 different tiers. But it is just a freaking awesome feeling!!! Like you driving a sports car after riding a bike. And talking about tiers, I see that the usage of Opus is not that bad either; I see that they are not dropping more than Sol in the same work but with much faster speed. Astra will just kill my usage in 30 minutes.

But then after a few days of work, I figured out that it is not perfect. Opus is fast and maybe good at building a fountain (and 99 percent AI agent will not get what you want after 1 or 2 first prompts), working with them to fix it like you want is not... present. They’re too mechanical, too robotic. And I don't know why, but in small details of understanding what you want, they are not as good as Sol. Codex is much friendlier, and they actually fix those small things and must be better than Claude in my case.

So in summary, I think having both in a workflow is very good if you can handle the price (poor people like me T.T.). I am now using Opus to code new modules and functions. Just throw at it, and it will produce a fairly good product, then use Sol to refine the result as I want. Work perfectly!👍

I’d just used a banked reset and was about halfway through it when the forced reset landed. Back to 100%, but the 50% I still had left didn’t carry over. So I got half the use out of a reset I’d saved before it was overwritten.

Why not give us a banked reset instead and let us choose when to use it?

I use Codex heavily for repeat business workflows with custom skills, local files, spreadsheet drafts and browser checks. for months, gpt 5.6 Sol handled these workflows reliably from normal prompts.

since gpt 6 Sol became available, ive had several cases where it refused or stopped workflows that 5.6 completed without an issue due to cannabis guidelines even though cannabis is legal in my country. Same files, same skill and basically the same request. I switched back to 5.6 and it finished the work right away.

Now I’m worried 5.6 will eventually disappear and ill be forced to rebuild everything around something worse like claude. has OpenAI said how long 5.6 Sol will remain available in codex? has anyone else noticed GPT 6 behaving very differently from 5.6 on existing workflows?

Im not saying 5.6 is smarter overall. It’s just much more dependable for the actual work I use Codex for.

After some checking I discovered that codex is stuck in a zenos paradox type situation. You start with a big scope of work often broken down to steps or something. As the work is completed and the total scope of work decreases, codex agents proportionally decrease the size of tasks in that scope. This creates a zenos paradox type of effect where you can never get things over the finish line.

Once I rightfully pointed it out to the agent and instituted a new rule suddenly it was able to complete the work rather quickly, although slower than it should have. Finally a solution to wasted goals.

Hey guys so if you guys create public mcps you know what trouble everyone goes through: having to reload the mcp and the server each time an edit is made to actually test it out and see if the improvement worked. For me, it wasted time, tokens, and shortened coding runs with codex cuz it kept needing to test and test and it needed me to do that. So, I created https://github.com/Praket7/codex-mcp-hotload codex mcp hotload. It registers your mcp as a child of the hotload mcp and can restart its servers at any time, so you can instruct your agent to register the developing mcp and use it for live tests. It will show the newest tools available to it within the chat, with no need to stop a run and reload codex! let me know what you think and if i should expand to other agents (i only use codex so thats all i needed it for yet)

I've been putting Dot through its paces for the last 3 weeks and wanted to put together a proper review.

I came off of many months of Hermes tinkering for my personal agent while being deep in Codex for dev work, so Dot instantly found a place in my workflow. Its value is undeniable but its implementation leaves a lot to be desired from the overall ChatGPT app mess (which Dot only exacerbates), weird sandboxing choices, opaque status, and more. I lay it all out in my review along with many things that did click well for me (like calls) and some actionable advice on how to get the most out of it.

Would love to know if this tracks with your experience and if you found better ways to work with it.

I’m curious how people are fitting Codex into their PR review workflow. Do you have any prompts or setups that help it catch real bugs and edge cases without generating a bunch of noise?

Would love to hear what’s worked for you, what context you give it, and where it still falls short.

Also, are you connecting to the repo directly? Like, where are you doing the review, and in what directory? (I have OCD with this stuff)

I've been using Codex to help rework the art in FrogPop, my first Unity game. One of the main criticisms was that the art didn't look consistent. Different bugs looked like they belonged in different games.

At first I kept asking for everything to match the frog. That wasn't specific enough. The crab ended up too simple, and later the bee and spider were much too big.

What helped was asking for the characters side by side at actual game size before changing the game. Once the design looked right, I asked for animation previews, then approved the integration separately. That made it easier to catch things like the upside-down spider having its eyes on the wrong part of its body.

I also learned not to replace things just because they were old. We tried new relics and zone art, but I preferred the originals and kept them.

Claude Code helped build the core game systems. I use Codex for fixes, previews and integration, with AI-assisted art tools in the process. I still review the visuals and test in Unity. The clip shows the new crab alongside other gameplay.

Older free demo: https://blion247.itch.io/frogpop-demo

It covers the first ten waves and doesn't include all the newer work shown here.

Hi everyone,

A quick update to share version 0.13.2 and thank everyone who has been using and contributing to Codex Pooler.

After months of working on it almost around the clock, I feel the gateway has reached a solid level of stability, with some useful new features along the way. I expect this to be one of the last beta releases as we move toward our first stable version 1.0!

If you have a few minutes, give it a try. You can even ask your coding agent to help you set it up. It's self-hosted and useful even with a single Codex account; you don't need multiple accounts to benefit from it.

I certified it to work with a lot of harnesses, all documented.

Feedback, questions, ideas and contributions are always welcome!

some weeks I'm dry early, some weeks there's usage left at the reset and it just evaporates. so I built Overflow

spare week: take a priced issue from a registered repo, merged PR gets you credit dry week: price an issue in your repo, someone with a spare week ships it, your credit pays

junk PRs: you block them on GitHub like any repo, you set the price after you've seen the work, and you only owe for what you merge. you don't need to know the project either, the issue is the brief, if your agent hits a decision it asks the maintainer on the issue

no money, no keys, MIT

main reason I'm posting is I want people to register their repos. the 17 open issues are all mine (some already claimed, so the board shows fewer) and I'm not after help with those, I want spare usage going into other people's projects. 20 users so far

https://github.com/Nitjsefnie/Overflow

I also understand Claude is the best deal right now. And more than likely a rugpull will come from that as well.

Ultimately all AI providers will reduce usage and jack up costs. That's obvious and expected.

What to do when this happens? No idea.

Just randomly no doing anything ("reconnecting") for 20-40 seconds after hitting send is unacceptable server behavior and this has not been a temporary thing. Don't normalize this shitty service.

I built Ringside at Knight AI+AV to keep my coding limits visible while I work. It's a free, MIT-licensed Windows widget with a Codex meter and a Claude meter.

For Codex, it reads the rate-limit snapshots in local session logs. It shows the usage windows and reset times those snapshots provide, plus plan or credit details when present. It doesn't read auth.json or call OpenAI to get the numbers.

The rings turn amber at 75% and red at 90%. There are optional notifications, a compact mode and a tray icon if you don't want the full card on screen.

Windows 10 and 11, 64-bit. The updater uses GitHub for checks and downloads; automatic checks can be disabled.

Source and downloads: https://github.com/KNIGHT-AI-AV/ringside

If you try it, I'd like to hear whether the display matches the limits your Codex session reports, especially with several sessions open.

EDIT: Its ok to make tools that others also made. just make it what you like and share. i plan on add to this what i want. making it my own...

day ? “app limits” (maybe) I really hope this doesn’t mean open ai are going to be merging usage across all apps. The App limits section disappears when the page fully loads. So a future feature I would guess.

Hi,

I"ve been long user of gpt models and anthropic models, i still remember how great 5.6 sol was and how quickly things were done. with gpt 6 release things went downhill and switched to use opus 5.5 which felt like WOW. Great understanding , clear code , no unlimited testing phases. Did try 6.1 Sol and 6 Ultra last week and felt like hell. So i did try to "downgrade" and use 5.6 sol ultra for a bit and if feels again like magic. things move fastly and more gets done in less time and more efficiently than 6.1.

This little guy keeps popping up in the lower-right corner of my screen. I went into ChatGPT settings in the Mac app and chose "Hide pet," but it keeps coming back. Anybody know how to get rid of him permanently?

Codex still rejects sending the password with sandbox_approval is false. Root login was not completed.

anyone seeing this or a workaround, I set git has people mentioning it, but i would think this would be commented on more

Anyone or is it just me i went 100 to 20 in like 4 hours. using sol 6.1 for simple saas backend and frontned ux....

I will start by saying I am tech savvy but not a programmer/coder just a long term gamer and just know a lot about PCs building etc but not software.

I delved into AI 1 year ago with trying to build something for the company with claude code but could not really get anywhere so I left it.

Started again about 2 weeks ago delving again after seeing alot of hype with new releases, and I found out about N8N to use for automation so started my little tasks within work but I am doing it on a more higher level, (I work in a dealership) so little things email router, then using the email router to route normal things certain things can go to certain people... Works great what else oh API's I managed to get connected to my account software so get the email router to pick up invoices record it and post it. Iterated on it and moved on.

From there and using the N8N AI have made the workflows realised there was an N8N agent because now I was getting information I thought build something so staff can ask the N8N questions and it can answer with information it retrieve from various workflows cool... Then thought well we have codex give that a go because running out of creds on N8N AI.... Works great its building my workflows even faster without much prompting and making a better bot.

Moved onto a RAG tool for the N8N bot to use, then realised well my DMS systems do not have one whole truth because the information is just split everywhere that its never held in one place to be able to retrieve everything just how DMS systems work in companies and that I need to build a place where everything is recorded, costings, parts, VINs/Registrations so much more it hurts my brain... But it will be the source of 100% correct information for everything so the N8N agent is not wrong ever in the information it gives out, Turned to Supabase and realised im building a background storage system where everything is stored and retrieve and kept for future, complaint logs, vehicle files, invoices everything so the bot becomes the most reliant for the company as a whole.

The thing is at this point I feel like am I going to far with this not knowing what everything 100% does and whether I am on the right path off what I am creating, if I should stick to little automations fine but I cannot tell whether I am or not because it seems to be whatever I build I somewhat stop after and think I can do it better and codex is telling me how to as well.

I guess at this point I am asking you guys what you think?

TIA

Is it just me or there are so many bugs as of recent. Usage feels like it is getting worse and worse. "Thread not found", prompts supposedly running for 30 minutes and i have to restart the app to see it was done in 4, browser automation now not working and having so many errors , im actually tired plz fix the app im begging

hey guys,

been using dots for agent workflows and desktop tasks for a bit, but got kind of annoyed by being locked into default models and cloud environments. wanted to run tasks on my own hardware (remote vps, local pc) and route stuff to whatever model endpoint i prefer

so i threw together a lightweight proxy/bridge for it and decided to open source it

basically what it does:

- custom models: hook up any openai-compatible api, openrouter, local ollama/vllm, etc.

- custom computers: connect your own machines (windows / linux vps, local rig, homelab) instead of relying on default instances

- 24/7 persistence: lets you keep agents and environments running in the background on your own server

its pretty straightforward and handles the tool/computer routing without breaking the native dots workflow

repo: https://github.com/0mamiis/damndots

setup guide is in the readme. figured id share it here in case anyone else needed this. let me know if you run into any bugs or have suggestions

Hello everyone! I work at Hystax and we built the compressor (open source, MIT). The benchmark ran on Codex CLI, so the numbers seemed worth sharing here.

Why compress at all. Codex re-sends the whole conversation every turn, including every apt-get log and progress bar it has already read. In our traces, tool output is about half of all prompt bytes. About 93% of input tokens are cache reads at a tenth of the price, so a compressor has to produce byte-identical output every turn. Otherwise it re-prices the conversation and costs more than it saves. Ours is rule-based and deterministic: no ML, no LLM calls.

Setup. Terminal-Bench 2.1, 89 tasks, Codex CLI on GPT-5.5, 3 concurrent trials via Harbor. We compared actual provider bills with the cache discount included. Figures are per full run over the 87 tasks every arm completed.

Two trials had to compact their context without compression; one did with it.

Where it helps. Building from source −28%, ML training −26%. Algorithm/puzzle tasks −8%, reverse engineering −5%. If your tasks are short with small outputs, expect little.

Codex-specific:

• Codex wraps tool results in its own framing (Chunk ID, Wall time, exit code). We split that off and re-attach it verbatim. One Codex Python result went from 0% to 87% removed.
• Codex has a 5-second WebSocket timeout. The engine runs at about 0.2 ms per KB, so it never gets close.

Nothing is silently dropped. Every elision becomes a marker like [... 412 log lines omitted [ref:k_…]; 3 ERROR, 1 WARN], and the model can fetch the original back for an hour.

Repo: https://github.com/hystax/optscale-kompressor

Methodology and per-task results: https://optscale.ai/blog/context-compression-coding-agents

If you think the measurement is flawed somewhere, I'd rather hear it.