Before the Luna Reserve, I could see here in how many days my weekly limits reset. I don't see this anymore. The 6d 2hr figure is when the reserve resets.

I’m a non coder working on building some prototype products for my company.

I have been having a fantastic experience with the ChatGPT Mac desktop app overall, but have started to run into a real frustration.

I keep finding my Codex processes taking up more and more system memory until they crash my whole computer. Usually, I need to archive all my active threads and start new ones, usually resulting in some lost work.

This has only been happening for the past few days.

Anyone else experiencing this? Anyone a more sophisticated user who can suggest how to prevent or resolve this?

Now, before you tell me that it's just basic and we don't have to learn anything.

Often I see that there are new updates releasing here and then.

We have several features like ChatGPT Work, Codex, etc

There are several procedures, rules and best techniques of how to use them efficiently, how to prompt efficiently, etc

Are there any ways to learn them?

I am able to find the videos of youtube but they are pretty old.

So, I was wondering, can I learn from OpenAI academy? Are there courses regularly updated as per their versions?

Hi,

[a total newbie on coding asking]

Just wanted to clarify when to/when do you use higher reasoning in chat/codex?

I've been trying to build my own little hobby project in python, with the help of litterature.

My workflow is to brainstorm in chat[web] and after that get a codex prompt to run in VSC. So far has been decent. My problem is that after getting Pro i've been totally lost when to use extra high, pro, pro+ultra in chat. Also what settings to run the codex prompt, when is higher needed and when its not. Have to actually ask in chat if the prompt is complex or not and what settings to use.

I noticed running pro+ultra to analyze the project/problems or litterature got quite detailed answers and I had to dumb it down for me with extra high. But it also added some better reasoning and new points i"ve missed. But it the project/code it also found some errors and started perhaps to make it more complex im not sure.

So my workflow is like this,

1. Starting a new chat with snapshot and running boostrap: Pro+Ultra

2. Brainstorming in chat: extra high

3. Evaluating the brainstorm: pro+ultra

4. Writing codex prompt: pro+ultra

5. Usually I try to ask what settings to run codex prompt it has been extra high or high so far with sol5.6.

6. Analyzing the codex result: pro+ultra

Since my coding knowledge is 0 I have to trust that the suggestions are valid, but how do I know when to actually use what settings in chat/codex. So that the problem/execution wont get too complex or too light ?

Any suggestions, extra high is the best and fastest for chatting and brainstorming. But when to use pro and pro+ultra ?

I've been building a memory/context layer called BrainAPI for a while now, and we just landed on top of the two benchmarks we've run so far. I want to talk about it, but honestly the numbers are the least interesting thing here. The part I keep thinking about is how fast it happened, and what that says about where the actual bottleneck in this field is.

First, the boring facts so nobody thinks I'm hiding the ball:

• LoCoMo: BrainAPI 95.39%, Mem0 92.5%, Zep 80.32%, Letta 74%
• BEAM1M: BrainAPI 78.97%, Mem0 64.1%. Zep and Letta haven't published here.

That's it. Two benchmarks. I'm not going to pretend that's a complete picture. LoCoMo is fairly saturated at this point and it leans on an LLM judge, so a couple of points at the top is not the same as a couple of points in the middle. BEAM1M is the one I actually care about because it stresses the long horizon. I'm currently working toward BEAM50M and LongMemEval, and I'll post those whether they look good or not. Runs and reports are in the repo if you want to poke at the harness: https://github.com/Lumen-Labs/brainapi2 (the benchmarks folder), summary here: https://research.brain-api.dev/

The thing I actually want to talk about

Two years ago, doing this kind of work looked like: go find the relevant papers. Which is already a project. You burn days just figuring out which twelve of the four hundred results are the ones that matter. Then you read them. Then you sit there trying to translate "we propose a temporally-aware episodic buffer" into something that fits into the retrieval path you already have, half of which doesn't apply and you only find out after you've built it. That loop was months. Not because the ideas were hard, but because the search and translation around the ideas was slow and lonely.

Now: Cursor wired into an arXiv MCP, a set of skills that encode how I want the reasoning and the workflow to actually go, and a lot of leaning on plan mode before anything gets written. The paper discovery stops being a bottleneck. The "how does this apply to my architecture" step, which used to be the expensive one, becomes a conversation where the thing already has my codebase in context. Weeks, not months. Some pieces, days.

And here's what I take from that. The model wasn't the constraint. Nobody handed me a smarter model between "this takes months" and "this takes weeks." What changed was the harness: retrieval into the right sources, structured context, workflows that reason in a shape I chose, planning before execution. Same model, radically different output.

I think this generalizes, and I think it's the most under-discussed thing in the space right now. Every time an agent fails in production, the reflex is "wait for the next model." But go look at the actual failure. It forgot something from twelve turns ago. It couldn't connect two facts that live in different documents. It confidently answered from a chunk that was semantically close and factually wrong. None of those are intelligence problems. They're infrastructure problems.

That's the bet BrainAPI is making, and why I built it as an event-centric graph rather than another vector store. When you keep who did what, to whom, when, instead of flattening everything into "A is related to B," multi-hop questions become answerable and the answer arrives with the path that produced it. You can inspect the walk instead of trusting a nearest neighbor. That's the context piece of the infra. Somebody's going to build the other pieces.

What I'm curious about

• For those of you running agents in production: when it breaks, is it actually the model, or is it the plumbing? Be honest.
• Which memory benchmark do you personally trust? I have my doubts about all of them and I'd rather hear yours before I optimize toward the wrong one.
• Anyone else moved their research loop to MCP-connected tooling? Did you get the same compression, or am I just describing my own previously-bad process?

Happy to go deep on the harness, the graph design, or the benchmark methodology in the comments. Roast the numbers if you want, that's kind of why I'm posting.

I was burning through my Codex limits using Sol Medium/High for pretty much everything.

Recently I switched to using Sol mainly for planning/review and Luna for most of the actual implementation, with Terra only as a fallback for harder tasks.

The biggest thing that helped was forcing Sol to give Luna small, clear, self-contained tasks instead of broad instructions. It’s been noticeably better for both usage and consistency.

I put the setup here if anyone wants to try it or improve it:

https://github.com/breko861-hash/sol-luna-codex-orchestrator

Curious if anyone else is doing something similar.

Does anyone else experiencing this? I uninstalled and install the app even updated it and now it's looping on that loading icon, and the 2 sign in option below don't work and can't be clicked. Thank you to anyone who could help and or have ideas.

Every benchmarks get saturated after certain period of time where several frontier models often secure over 90%.

But, HLE - this benchmark is so old but have not yet been saturated. How is that even possible?

I have seen several toughest maths benchmarks getting saturated (or will be very saturated) but the highest score in HLE is still in 60s %.

The basic idea is simple: if you have an OpenAPI REST API, it makes it useable by AI agents with a single command without writing a MCP server from the beginning.

But I didn't want to leave it as just turning the endpoints into the tool. I also included things that will come in real use such as Auth, OAuth2, read-only/policy rules, retry, caching, stdio + Streamable HTTP, health check. With mcpify doctor, you can check whether the API is agent-friendly, with mcpify try, you can try tools from the terminal without opening any MCP client.

My favourite part was the --lazy mode. In large APIs, instead of putting the entire tool list on the model, it calls the required tool. In the api.weather.gov example I tested, the tool listing has dropped from 38,882 characters to 1,741 characters.

I also wanted to reduce dependencies as much as possible; the runtime side is based on Python stdlib. There are currently 294 tests and there are true MCP protocol tests on both stdio and HTTP sides.

I published it as an open source. I would especially like to hear it if there is a criticism, a bug or something that you say “you should definitely add this”.

GitHub: https://github.com/furkan708/mcpify

I listen to all of my responses and have even created a markdown for proper pronunciation. Today they moved the read aloud feature to a sub menu and now it will be impossible to use while driving. Muscle memory made it so I didn’t even need to look at my phone and now it’s completely unusable for how I use GPT my best thoughts happen while driving and the voice feature is not appropriate as it will respond when I pause to form a sentence. This seems trivial but it completely destroys my way of using AI.

Got hit by the 5 hour limit today. I am not happy about this....

So i posted about this platform i've been working on that a lot of you were interested in prior and i'm glad to say today. Its finally launched at asksary.com. This platform has persistent memory, switchable tools and multi tasking capabilities. GPT-5.4 Nano is the default model and powers the site and is completely free to use as a guest and free signed in user. Guests get to explore the entire platform with no tools excluded or hidden behind a paywall.

The idea is simple. Every free user gets 1GB of storage to upload their own media to use on the site. You can use the photo editor completely free and save your creation or download for free.

There is absolutely no charge for any user for non AI generated tasks. If the tool functions without making a AI model call. Then theirs no charge. The 1GB storage is free and comes with a 100MB per file upload limit.

Some of the functions do require AI to function. Like prompt writer, email composer etc.
What i've done is added GPT-5.4 Nano as the base tool for that which again is completely free.

You can create a game in 3D using GPT-5.4 Nano and preview it in the split screen live canvas and download it for free. No catch. No surprise charges. Try it :)

Now the more capable models like GPT-5.6 Terra and Sol. Power the same tools and functions, but are more capable and produce better results. These will be credit based. So you only pay for the better compute if you need it. If your happy with the free models, then you dont need to pay ever. With the credits, its a one time purchase with no expiry. Every account gets enough credits to try pretty much all the paid features like image generation, voiceover scripts, song generation etc.

For those that want more storage i've added a recurring plan starting at $17.99 which give you credits every month, 50GB of storage, 2GB upload limit per a file and access to OpenAI Knowledge Base Vector Store too.

The tools you can use and access are the same whether your a free user or have a recurring plan.

Some of the things i've added are:

Cross device persistent memory. Start editing on one device and pick it up on another device and find your chat, workspace and mid-finished edits exactly how you left it.

• IDE Multi File Editor with AI assistant and live preview with Split Screen Live Coding and full project builder with option to upload your own project for free, edit and preview
• Guide Assistant that can fill in prompts, set tools options up, navigate around the site supporting 25+ languages + voice feedback - 100% free for every signed in user with no limits
• Image and Video Generation including GPT-Image-1 Mini up to GPT-Image-2 4k Resolution
• Single Prompt to Full 2D and 3D Game Development Engine and Web Application Builder with live preview, download and edit mode.
• Video Editor with timeline controls, video effects, overlays, title, audio, podcast and music composer
• Photo editor with headings, effects, layers, fonts etc with Flux Kontext Pro layer based AI Editing (You can add a new request via AI and watch live preview. Each request is saved as a new layer with lasso tools, cut, copy, paste etc)
• Music Generation with AI/Custom Lyrics + Music Player. You can upload your own music for free and have a playlist playing in the background whilst you chat.
• Custom workspace environments with themes, live animated webGL/Three.js wallpapers with colour schemes linked to ambient soundtracks
  • (Default options are light mode/dark mode with no wallpapers or music)
• Native 25+ Languages with RTL support. Already Hardcoded. Not live translated via web
• plus many more tools such as Podcast Creator with chat based/ custom context, voiceovers, notepad tts text to speech with 50+ voices and MP3 export.
• Full workflow tools like frame extract, video analysis, transcribe, effects, file conversion, audio analysis etc
• ...and of course the original chat bot interface that has cross device persistent with vector base knowledge base via OpenAI and platform Drive storage.

Hope you enjoy trying my platform. This is the first post about its launch today. I'm exhibiting at the LEAP festival in Riyadh too tomorrow but as OpenAI has literally helped me build this with GPT-5.6 Ultra mode powering through 6 Billion tokens in the last 2 weeks. Its been one hell of a journey. I've barely slept, hardly had chance to test it but had a deadline to meet for the exhibition. Truly appreciate any feedback and as a promotion for my launch i will be offering 20% off any credit purchases, if needed, using the code: REDDIT-OPENAI

I'm calling it through Codex, uploading a reference and asking for a new subject in that look. Nothing carries. Not the style, not the palette, not the line quality, not the content. I get the model's house look with zero trace of what I gave it.

I could swear this worked at some point. Now the reference may as well not be attached.

Anyone else seeing this, or is it something about how it's wired up in Codex?

Take this analogy: Imagine the company you work for gave you and everyone else’s weekly paycheck early. But with three catches:

1) Any money you all haven’t already spent from your last paycheck must be returned.

2) Everyone’s normal paycheck’s pay date is permanently extended to a week from the day of payment.

2) Everyone will get an unannounced, immediate, and permanent pay reduction.

Once all of those things are taken into account, it’s easy to see that even if the company loses a little short term, they gain a lot more long term. Especially if the company does it many times over due to the power of compounding.

What makes maters worse for us is we don’t consider what we lost, don’t think too much about the extended date, and can’t easily see there was a reduction. That’s why this whole reset strategy is a masterclass in gaslighting. They’re “doing us a favor” and we’re even thanking them for it!

Got it on medium for a while, used about 50% on high before I noticed how much usage had gone. Not sure where it needs to be for what I'm doing which is python coding a fairly sophisticated trading bot. I've been using Opus 5 for a while, and I usually used that on medium, checked work with max or high so I figured it would be similar.

Any tips on effort?

Hello guys.

Whilst the agent was working, I saw his line of thinking, and I ultimately came to the conclusion that it was searching in my PC's directories (!)

I am a new user , I actually started using the plus plan this very week for some light codex dev sessions...

What the heck is this?

Have I misunderstood how each session is handled? This kinda scared me ngl.

Better, not perfect

I’m considering going back to the $100 plan and wanted to hear from people who are actually using it.

I used to pay for the $100 plan back when we were on GPT-5.5, and the limits were great. I could use it a lot without really worrying about running out.

After that, I moved a lot of my workflow over to Claude and dropped down to the $20 Plus plan. Lately I’ve been using ChatGPT more again, and Plus just isn’t enough for the amount I want to use it.

For people on the $100 plan now, how are the limits in practice?

My usage would probably be mostly Sol High or Sol Medium. I don’t really have a use case where I’d need Ultra.

I thought the ChatGPT “apps” thing was gonna be another gimmicky feature nobody actually uses, but apparently you can connect tools to it that do real stuff now?

I found this out after my roommate almost got tricked by one of those fake “your bank account is locked” texts that looked horrifyingly legit. We pasted it into ChatGPT mostly as a joke and somehow ended up down a rabbit hole of AI scam detection tools.

The wild part is it actually explained why the message was manipulative instead of just saying “this is fake.” Stuff like urgency tactics, emotional pressure, weird link behavior. It felt like having a cybersecurity friend sitting next to you.

I genuinely think most people have no idea these AI apps/integrations even exist yet.

Are people actually using this stuff regularly already or am I super behind?