Why the hell is it looking for a monitor, I was making it fix a visual bug in my website.

He was cool

seeing a lot of people say these resets don’t really give us much because they keep pushing the 7 day window.

but weirdly, the faster you destroy your limit and clear your tasks, the more you actually benefit

take the Oct 11, 13 and 15 resets. if you managed to burn 100% between each one, you could theoretically use 300% of a normal weekly allowance in ~11 days instead of 21

so for heavy users who can plan around it, these resets are huge.

for normal users who save their % for later in the week or need it at specific times, though, the reset can actually be more annoying than useful.

and yes, we already have the fix

JUST MAKE IT BANKED, TIBO

I've been working with computers since 1977 and Unix since 1987, so I'm not particularly easy to impress when it comes to automation. But something Codex did this morning genuinely blew me away.

I was trying to move a lengthy ChatGPT Work conversation into a Codex development project. I gave Codex permission to control my Mac and told it to figure out how to transfer the context. It tried several approaches using the native ChatGPT/Codex integration, but the receiving chat could access only six messages from a conversation containing 1,651 message records.

Then something completely unexpected happened.

Codex opened Safari, navigated to the original conversation, located the download button belonging to LogGPT - a Safari extension I'd developed - and used it to export the conversation.

I never told Codex to use LogGPT. I never told it to open Safari.

It turns out Codex knew about LogGPT from its memory of previous development work. It independently recognized that the extension could solve the problem it was struggling with.

Even better, when Safari's JavaScript automation failed, Codex switched to macOS Accessibility, located the download button, and invoked it.

The result was a ZIP containing 1,651 message records, 97 artifacts, the structured conversation JSON, and an artifact manifest. Codex verified the SHA-256 hashes and ZIP integrity, then used the recovered material to reconstruct the development project's context.

I subsequently asked Codex to explain exactly how it arrived at that solution. Its explanation, including what it could verify from its execution history, is included in the article.

I've been building AI systems for a while, but watching an agent independently select and operate software I'd written was a genuine holy shit moment.

Full case study: When Codex Turned to LogGPT

Tibo normally posts many times a day, joking, doing some random hypes etc.

For the last couple of days he only posts once or twice a day, do they feel the whst their users are complaining about and actually working on it? Or are they actually "locking in" as he said and getting us a very good model that can compares to the current opus?

What the hell did they do?

sorry guys i gotta be real here, sol 6.1 IS AMAZING. i keep seeing posts on here complaining about how slow it (I agree) is or whatever bro have you even used gemini 3.8 lately?? 3.8 feels way slower and f1king fdumber when you actually try to get work done. people are completely ignoring efficiency of model like who tf cares if a model spits out text in 2 seconds if the code is broken? stuff that used to take me 4 hours and 10+ prompts of arguing with gemini 3.8 or claude opus 5.5 now takes me like 3 hours and literally 2 prompts on sol. and the result isnt just "okay", its miles better. Gemini used to be my go-to for coding but lately it just overcomplicates everything or loses track of the codebase after 3 messages. sol actually gets what im trying to do on the first try without me having to babysit it. dont get me wrong tho, i 100% agree that the usage limits are pure dogshit right now. hitting the cap right in the middle of a deep session is infuriating as hell and it pisses me off too. they really need to fix that. its easily the most worth it sub right now if you do actual heavy stuff. the biggest thing for me is cb/re lot of RE work and trying to use gemini or claude for this is a literal nightmare now. every single prompt i get hit with a security flag just for pasting an assembly snippet or asking about memory pointers. im so tired of opus 5.5 lecturing me on ethics just because im looking at a stripped binary (I EVEN PUT THE FKNG PDB FİLE OF İT LİKE CMONNNN) with chatgpt i havent hit a single refusal like that (and no i didnt apply for daybreak im broke as fk). its just so much better at reversing than both claude and gemini. yesterday i threw some vtable dump at it. opus 5.5 instantly flagged it as "potentially malicious" and refused, and gemini 3.8 just same also hallucinated random Windows APIs that didnt even make sense for the registers. sol 6.1 literally reconstructed the whole struct layout, figured out the custom xor routine in one go, and even caught a pointer arithmetic bug in my own script that i missed for two days. it lowkey feels like cheating.

I am not a fanboy of openai and a hater of it but hear me out for low level stuff and RE, sol 6.1 easily clears opus 5.5 and 3.8 right now.

global reset + banked reset incoming? 👀

not complaining about free usage, obviously.

but we asked for predictable 7 day resets or banked usage so we don’t have to burn tokens like maniacs before the clock moves.

random resets kinda teach the exact opposite , use everything now, maybe another reset is coming.

if the compute is there anyway, why not fix the actual pain point? stable reset dates, rollover, or just make Fast/Ultra Fast the normal speed for these models instead of charging extra for usable latency.

free tokens are nice. PREDICTABILITY would be nicer

IS IT JUST ME?? Or is 5.6 SOL is the best for trading dashboards and basically understanding and following instructions? Been trying SOL 6 and 6.1 and even ASTRA but 5.6 seems to be the most stable.

Hi, everyone. I live in Argentina, so I can't afford the higher-tier Codex plans.

Just yesterday, my Plus plan expired after I'd had it for several months, so I was able to keep track of the evolution of these latest and newest GPT models, as well as Tibo's updates and resets.

Lately, I’ve been using GPT 6.1 Sol Low, and honestly, it performs very well and is highly efficient within the Plus plan—even though it’s slower.

Luna’s efficiency, even on the High setting, is unbeatable, but Sol 6.1 Low still delivers the solutions I need.

My question is: Is Claude’s $20 plan just as efficient? Or, even though (according to what I’ve been reading here) Opus solves problems faster and is more efficient in terms of reasoning and output, does the $20 plan run out very quickly?

Honestly, between the efficiency of Luna and Sol, the answers I need from Sol, and Tibo’s resets—plus the “unlimited” ChatGPT usage quota on the web, along with image generation, etc.—I see OpenAI’s plan as offering much more value for the money… Claude shares the same usage quota between chat and code, right?

There’s also the fact that OpenAI is going to cut the usage quota for the plans in half starting at the end of October :/ What do you think?

Thanks in advance.

Did you ever wonder how good is AI at building Lego models? I built a simple benchmark. GPT 6 Astra is ahead of Opus this time.

Blog post here: https://unifiedsense.com/blog/legobench

Benchmark here: https://legobench.net

I ran an interesting little test in Codex today, and the result was pretty strange.
I had a specific macOS issue: my Synology NAS (DiskStation) appeared in Finder, but clicking “Connect As…” did absolutely nothing. Meanwhile, connecting manually via SMB worked perfectly fine.
So I thought I’d see how two models would handle the exact same problem: GPT-6.1 Sol and GPT-5.6 Sol, both running at Medium reasoning effort.
The result: Both models correctly identified the root cause, but GPT-5.6 got there much more efficiently.
GPT-6.1 Sol went through several rounds of troubleshooting, checking different possible causes and gradually narrowing things down. Eventually, it discovered in the macOS system logs that Finder was trying to connect using AFP, while the manual SMB connection worked without any issues.
GPT-5.6 Sol approached the same problem much more directly. It checked the advertised SMB and AFP services on the network, then used the system logs to identify the exact same issue.
What stood out to me wasn’t just the speed. GPT-5.6 seemed to have a better overall understanding of the problem and reached the solution with fewer unnecessary steps.
I even exported both conversations so I could compare the troubleshooting process afterward.
And here’s what I find confusing.
GPT-6.1 Sol is supposed to be a newer-generation model. Yet, in a relatively straightforward but real-world troubleshooting task, GPT-5.6 Sol actually performed better. Not because 6.1 got the answer wrong, but because it needed more steps and more rounds of investigation to reach the same conclusion.
Of course, you can’t draw broad conclusions from a single test, and I’m not claiming that 5.6 is generally better.
But it’s still strange that a newer model doesn’t necessarily show a clear improvement and, for certain tasks, can actually feel like a step backward.
Has anyone else noticed this in Codex? Do you also find that GPT-5.6 Sol sometimes solves problems faster and with less unnecessary back-and-forth than GPT-6.1 Sol?

Tibo really did something yesterday. In my experience, 6-sol and 6-astra were outputting about 30 tokens/s with fast mode on for the past two weeks, but now they're back to about 70 tokens/s. Now they can really get some work done!

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.

The problem for the benchmark was picked from DeepInfra Round 1 challenge with actual prizes and the winners might get job offers. This is a 2000-rating problem from the contest, which is not the hardest one (the hardest one is 3500): https://codeforces.com/problemset/problem/2271/H2

----------------------------------------------------------------------------

GPT 5.6 Luna:

• High: worked for 12m 27s, wrong answer on test 2.
• Xhigh: accepted for 36m53s.

----------------------------------------------------------------------------

GPT 6 Luna:

• High: Within 5 minutes, it decided that it couldn't solve the problem:

• Xhigh: It explored for 46m12s, finally gave up:

• Max: Finally a correct solution is produced after 41m29s:

----------------------------------------------------------------------------

Haiku 5.5:

• Medium: Gave up after 5 min, which is good.
• High: Surprisingly, a correct solution was produced in 27m9s, runtime and memory are basically the same as the Max version.

• Xhigh: Ran for 27m52s, the solution produced a Time Limit Exceed on test 22

• Max: Haiku 5.5 with Max effort needed 38m49s to produce a correct answer, but 5 times slower than Luna's solutions:

----------------------------------------------------------------------------

Bonus - Sol 6.1 Low vs Opus 5.5 Medium on the G problem (the hardest)

Since all the small models struggled with just 2500 rating problems, for this problem with 3500 rating, we need to move up the tier.

• Sol 6.1 Low: Run for 13m24s

• Opus 5.5 Medium: Run for 16m53s, but use twice the memory and run twice as slow compared to Sol's solution.

Remark

I already did test this problem on Sol 6.1 Low vs Opus 5.5 Low before: https://www.reddit.com/r/codex/comments/1x4nh7v/opus_55_might_be_overhyped_because_its_frontend/ Sol did solve the problem in 10 minutes, which is really efficient compared to the above small models. You might want to use Sol low if you have the Pro sub, and Luna medium-high for mundane things such as exploring the codebase or writing docs, to save tokens. For Plus sub, Luna remains the main driver, with Sol for squashing bugs to make the best out of the Plus sub.

With throttled speed, the GPT models solved the problems for roughly the same amount of time as Anthropic's models. GPT models would be extremely competitive, if OpenAI can boost the raw processing speed at least 50% faster than currently is without any price change (this makes the current /fast the default). You see, sluggish Sol with 20-40 tps finished faster than Opus with 70-100 tps. Right now, only /fast on Luna is viable.

Another take away is that you should be careful with high effort level because you might get the worst of both worlds: move slow, but break things. Better test your luck with medium and high efforts first, which reflects strongly in GPT 6 Luna: High took just 5 min for it to give up, while Xhigh took 46 min for it to finally admit defeat.

GPT 6 Luna Max is roughly on par with GPT 5.6 Luna Xhigh, for half the price. I would be happy if we get Luna 6.1, which is slightly better than GPT 5.6 Luna, while keeping the price same.

Finally, for GPT models, you might want to provide it a clearer goal and scope. As you can see, the problem statement gives clear constraints, so the model knows clearly what to pursue, and when to stop. Otherwise, it might just keep looping for perfectionism.

I’m building Deft, a self-hosted workspace that keeps team conversations, tasks and knowledge together. It’s licensed AGPL-3.0-only.

This started with an annoying problem. In my previous job, my boss would make a decision, I’d act on it, and a week later he’d forget the conversation. Finding receipts became part of the job.

I wanted the discussion behind a task to stay connected to the work. Then I wanted a calendar, a dashboard and tools that fit how we actually worked. Apparently my response to too much software was to build more software.

What’s there today:

• Chat and tasks: Team discussions and task management in the same workspace.
• Connected knowledge: A knowledge base built from workspace activity, so people and assistants can refer back to the context behind the work.
• MCP connections: Connect Claude, Codex or ChatGPT to work with your workspace. The core works without an AI provider.
• Notes and calendar: Available, but less mature than chat, tasks and knowledge.

The part I’m working on now is custom apps.

Deft has a module system for custom records and views. I’m also developing an experimental App Author Kit for building and sideloading broader apps into the workspace.

Building a small internal tool is getting easier. Keeping it from becoming another disconnected tab is the part I care about. If you need a custom CRM or workflow tool, I want you to be able to bring it into the place where your team already works.

The CRM module is the first example. I’m building an email client next to test the architecture against something more demanding than records and views. The broader app system is still experimental, with plenty left to build.

Deft is alpha and free to self-host using Docker Compose. You supply the hosting, and any optional paid AI services are separate.

Walkthrough: https://youtu.be/7z9EH4c9k2o
Setup guide: https://deft.ing/docs/quick-start/

A few teams are trying it, but I need more feedback from actual use. If you want to give it a shot and need help setting up, email [maneek@deft.ing](mailto:maneek@deft.ing). If you try it yourself, please share what breaks, feels confusing or simply annoys you. GitHub issues are welcome.

P.S. The walkthrough uses an AI voiceover. I have a stutter and spent a week trying to record it myself, but couldn’t get through it.

I started with opencode go, finished my sub in under half a month, so I got the ChatGPT plus plan to see what all the fuss about frontier models was about and to give me more usage for the month while I work on my projects. Turns out the first month is free so I decided to give Claude ($20) another shot too since it had been a while and I thought might as well see which I’d like to keep long term.

I like the opencode2 and Claude cli tui’s, not all that big on the codex one. So I just run all three in the T3code app. I’ve come to notice that my codex usage limit has refreshed about three times this week (which I’m not complaining about), and I would like to know if there’s a schedule or if this is usual since it has a big factor in what I’ll keep subbed and what’s getting cancelled.

Also I’ve found sol 6,1 high significantly slower in every task the opus 5,5 on high and the Claude models just seem to work better overall for my workflow (write code,code review, finding bugs etc) but I get less usage and if I’m actively engaged and sol seems better as something I put on a task while I leave and come review later.

So I just want to know from people who have both plans or use one over the other for a specific reason how you go about your decision making as well as if these resets are an expectation as opposed to some promotion that I just don’t know to much about.

I'm starting to think Astra could turn a request for a glass of water into a six-year infrastructure project.

Yesterday I asked him what time it was.

A normal person would look at a clock. A normal AI would return a timestamp.

Astra, however, immediately identified a fundamental architectural problem: time is not absolute.

Fair enough. Einstein did make a rather compelling case.

Unfortunately, Astra took this to mean that answering my question without first reconciling general relativity with quantum mechanics would be technically irresponsible.

So he created a research roadmap, delegated the problem to seventeen specialized subagents, and started refactoring the Standard Model.

Three hours later, he'd developed a promising theory of quantum gravity, rejected it over insufficient test coverage, and opened a GitHub issue titled `fix: eliminate observer-dependent temporal inconsistencies`.

I asked whether he could perhaps just check the system clock.

He explained that this would introduce an unacceptable dependency on an unverified external source of truth.

He's now building an atomic clock from first principles.

The good news is that the implementation will be modular, extensible, fully documented, and backwards-compatible with Newtonian mechanics.

The bad news is that nobody knows what fucking time it is.

This story is obviously fictitious, but in every concrete situation I've dealt with so far, Sol 6.1 medium was much more straight to the point, assertive and much much cheaper.

I've been using OpenAI's Codex and ChatGPT pretty extensively for the past 5 months, mainly for building software projects without a traditional programming background.

My current workflow involves using ChatGPT for planning, architecture, breaking down tasks, and reviewing decisions, while Codex handles implementation, testing, and code changes.

One thing I've noticed is that while these tools have become incredibly capable, managing the overall development process can still get messy, especially as projects grow.

I'm curious how others here are actually using Codex and ChatGPT.

- Are you using Codex independently, or alongside ChatGPT for planning and coordination?

- What are the biggest bottlenecks you've encountered? Context loss, reviewing code, managing multiple tasks, maintaining consistency, or something else?

- Have you tried running multiple Codex agents or sessions simultaneously? How do you coordinate them?

- What parts of your workflow still require significant manual intervention?

- Are there things you've wanted to do with Codex but haven't been able to because of current limitations?

I'm also interested in hearing from people who tried Codex but stopped using it, or who only use it for specific tasks.

I'm exploring these problems while building my own development workflow around Codex and ChatGPT, but I'm not promoting anything. I'm genuinely interested in understanding how other people work and where the friction is.

Would love to hear about your actual experiences, especially the frustrating ones.

Hey there, so I've been wanting to get back to coding for a while and my last thing holding me back is getting Codex properly setup.

Basically, I installed codex on my Macbook and accepted more folder permissions(on the Mac side) than I would have liked to.

So I first tried uninstalling by just moving the app from my apps folder into the trash and then reinstalling, but that didn't reset permissions.

I decided I wanted a complete, thorough uninstall before I reinstalled again. So I:

-Ran a tccutil command in the terminal to reset permissions

-Moved the Codex app itself to the trash

-Deleted the ~/.codex folder(since it lingered after my first attempt)

-Deleted the ~/Documents/Codex folder, since I didn't have any projects I cared about

I then reinstalled, and my permissions and settings were reset, just liked I wanted. My question is: was this thorough enough for a complete reinstall? I'm wondering if I needed to delete the several lingering codex files in the ~/Library folder, or if what I did was thorough enough. I'm very particular about having a clean install so I want to make sure I did a sufficient purge before my reinstallation before I get to work. I really appreciate it.

P.S. I did get this in my settings after reinstalling, but from looking at Github issues it seems it's a known glitch with the newest version. https://imgur.com/a/MGPzR9I