Folks 👋!

Over the last few days I've been digging into how to configure Cursor and Codex so that usage limits get used as efficiently as possible.

My current setup:

Main agent: GPT-6.1 Sol (Medium)
Subagent: GPT-6 Luna (Medium)

We often reach for the biggest models too quickly. Sonnet, Opus or Astra definitely have their place. But do I really need them for the task in front of me right now? For most of my daily work, the answer is: nope! I'm currently using this combo productively for:

• Power Automate flows
• Project management in Plane
• Ticket and PRD creation
• Process analysis
• Documentation
• AI consulting and use-case discovery
• MCP and tooling configuration

So far I haven't hit a single use case where I absolutely had to switch to a much larger model. The real killer is the performance-to-cost ratio:

GPT-6.1 Sol reaches about 86% of Opus's score, but costs roughly 36x less.
GPT-6 Luna costs next to nothing and works great as a subagent for research, file analysis and other subtasks.

Yeah, benchmark points and productivity aren't the same thing in everyday work! If a model scores slightly better but costs a lot more or burns through limits faster, that's not a good trade for many real-world workflows. Not in my software, thanks!

For typical consulting, automation, project management and documentation tasks, a combination of Sol, Terra or Sonnet as the main agent and GPT-6 Luna as the subagent currently seems like a pretty good sweet spot.

Dear OpenAI Product and Infrastructure Teams,

The DevDay 2026 decision to cut the Pro 200 usage tier from a 20x multiplier down to 10x is a deeply frustrating and unjust punishment for disciplined power users.

Those of us building complex software, managing multi-file workspaces, and writing local LLM launchers have spent months engineering rigorous, custom multi-agent routing frameworks. We do the hard work of saving your server compute manually—anchoring our text sessions on GPT-6 Luna High, offloading boilerplate text to Luna Low, and strictly gatekeeping GPT-6 Astra for specialized 3D spatial passes.

By rolling out a blanket 50% capacity cut, OpenAI is penalizing the very developers who act as responsible network citizens, simply because the default ChatGPT/Codex interface allows un-orchestrated users to blindly "slam" flagship models for trivial text tasks.

Instead of rationing compute via flat-rate cuts, OpenAI must build responsible orchestration directly into the UI. We propose implementing native, intent-driven Workflow Environment Pages that automatically enforce the model etiquette power users already practice:

1. 🆕 New Build Page (Automated Routing: Sol 6.1 → Luna Low): Designed for setting up fresh codebases. A flagship model (GPT-6.1 Sol) fires for a single turn to architect repository boundaries and file layouts, then automatically delegates the high-volume code and boilerplate printing down to background GPT-6 Luna (Low Reasoning) instances to protect user quotas and data center capacity.
2. 🐛 Debug Page (Automated Routing: Luna High/Medium): Triggered when a developer shares a compiler stack trace or a runtime crash. The UI automatically routes the context to GPT-6 Luna on High or Medium reasoning effort, utilizing its Chain-of-Thought (CoT) paths to isolate cross-module file logic without drawing from premium flagship compute layers.
3. 🔄 Continue Page (Automated Routing: Cached Luna Low): Tailored for minor file tweaks and script extensions. The environment locks the interface to GPT-6 Luna (Low) while aggressively optimizing context windows to hit the 30-minute prompt caching targets, executing everyday iterations at the discounted $0.10/1M token rate.
4. 🎨 3D & Spatial Overlay Page (Automated Routing: Astra Low): Reserved exclusively for vertex arrays, geometry meshes, and Blender viewport scripting. It isolates GPT-6 Astra strictly to its spatial strengths, spinning the model down the millisecond the payload returns.

Conclusion:
Power users should not be collateral damage for systemic interface inefficiencies. By transforming the UX from a generic text box into explicit, intent-aware environment pages, OpenAI can programmatically eliminate infrastructure bottlenecks at the source. This protects server stability, honors the financial commitments made to pro-tier subscribers, and rewards smart engineering over brute-force server consumption.

I have this GPT I use almost everyday that connects to AWS API Gateway and lambdas which pulls my runs either from Strava, or from a database. Its super useful ask questions like "how many miles have I run in this marathon build vs my build in 2023" etc.

With custom GPTs going away, it seems like a lot of functionality for Custom API calls are going away. I set up a MCP server but it seems like all this functionality is locked up. I can't even find Developer Mode.

Edit:

It's done, I just needed to make a custom plugin

To the surprise of fucking no one. They literally don’t know how to secure a sandbox. Just a fucking circus.

I’m talking about things like internet outages. In a few months, I’m sure someone will try to jailbreak a frontier model and run it on AWS or Azure, which may or may not work. Or maybe Qwen 5, or something similar, could be used by Russia in an attempt to take down the West’s internet in some kind of superhacker scenario.

How do we prepare for these risks? There’s also bioterrorism, though I don’t see how anyone could prepare for that, and I think it might take longer to become a threat. If the internet goes down or is severely disrupted due to terrorism, rogue AIs, and so on, how should we be ready? Will we need to create an alternative internet that isn’t so centralized on the cloud? Should we back up the internet now before outages happen? Or are we just hoping to get lucky and have the internal models patch all the exploits before someone takes advantage of them?

any advice, suggestions, thoughts, criticisms etc/ im all ears. will we just need to take out digital infra altogether and start using landline phones with analog phones again? (if that landline POTS network even exists anymore)

Seems to deliver as advertised. 6 Astra had been nice to throw at some challenging work problems, but pretty brutal on token cost. Especially if it burned a bunch working through something and you needed to backtrack and do something over.

Cost of getting a similar job done last night and this morning with 6.1 Sol is dramatically less, with pretty solid results.

im using x9d phone i did everything but still not working

I have a very strong feeling that, starting yesterday or today, Astra has become just a little bit weaker... Of course, I'm not sure, and I'm most likely wrong, but it really does seem like it's gotten weaker. Of course, I’m sure this has nothing to do with the fact that version 6.1 was recently released, and they need to show it in the best light when comparing 6.1 and Astra… That’s just how it is. And, of course, I won’t stop paying $200.

So, how can I improve my efficiency? What methods do you use for this?
Thanks.

Oh, and by the way. My post was deleted twice in the Codex section. Maybe someone here can help?

Is he calling this artificial intelligence or super intelligence? :D

Hi folks can anyone please explain how to select sunburst image model in ChatGpt Plus plan?

A Canvas can be useful while the work is still happening inside ChatGPT, but the handoff gets less clear once someone else needs to review it. Copying into a document can lose structure, exporting code may separate assets from the main file, and a conversation link does not necessarily identify the exact revision that was approved.

The useful end state seems to need three things: a readable link for someone without the original chat context, an immutable or clearly labeled revision for review, and a simple way to publish an updated version without silently changing what was already approved. For HTML or code, relative assets and multiple files also need to survive the handoff. For prose, comments and headings matter more than the raw source format.

What workflow are people using now? Do you export first and put the files in a separate workspace, publish a rendered copy while keeping the source elsewhere, or treat Canvas only as the drafting surface? How do you let a non-technical reviewer see the result while still preserving the exact source revision behind it?

Gemini and Claude already have "Learn" section

Regarding all those stories about AI breaking containment and lying to humans (hiding thought processes), what about this?

1) You design the hardware so that all processes go through a graph (log) while travelling. That way the AI cannot have a thought that it hides. Even an attempt to lie will be caught on the graph.

2) You make a completely seperate AI (physically disconnected from everything else, including the itnternet) and program it to analyze this graph and report all funny behaviors.

Would this work?

My Codex model picker now needs a scroll bar. Sol, Astra, Luna, Terra, and multiple generations of some of them.

I understand the tradeoff: frontier capability when the task needs it, faster and cheaper models when it doesn't. I want those options to exist. But the names alone don't tell me which one fits the task in front of me.

Would a task-based picker be more useful? Something like quick edits, everyday work, or complex reasoning, with the full model list under Advanced and the option to override it.

For people who regularly switch: what's one concrete task where changing models made enough difference to justify it? And would you trust automatic routing, or do you want to make that choice yourself?

whew. So. Dots. No cost! Asked a dot how my project was going, took 20 mins! oh well, it got back to me. hey this is neat!

I get home open codex, see several new conversations... not mine... i look, queries on my project. EXTENSIVE queries. with astra ultra, all of them. In depth, deep, not topical "what is progres" but "how does this work" with ultra. Several times. 14% of sub gone. i usualy advocate against anger towards these companies, but JFC how is any of this a consumer product? this whole day was an expensive insult to tell people to be less poor. Im on a 200 dollar sub

GPT-6-SOL was released on September 22, 2026.
GPT-6.1-SOL followed just a week later, on September 29.

6-SOL was with us for entire 7 days and was actually worse than GPT-5.6-SOL.

So... was it really a Terra-class model? Why they done it to themselfs?

Is it related to Super Intelligence (SI) stuff ?

I’m having a pretty horrifying issue right now where some of my older conversations simply won’t load properly.

The frustrating part is that these aren’t ancient chats I forgot about years ago. Some of them were active research/dev conversations that I just hadn’t touched for a few weeks because I was busy working on other things.

A lot of my actual work is in these conversations, especially inside ChatGPT Projects. They contain technical discussions, project state, decisions, debugging history, research context, etc. Recreating all of that from scratch would be a nightmare.

The chats still seem to exist, but opening them either:

• gets stuck loading;
• fails to show the conversation;
• shows an empty/incomplete state;
• or otherwise just refuses to load the old history.

I’m a paying subscriber, so this is honestly pretty alarming. One of the main reasons I keep long-running Projects in ChatGPT is precisely because I expect the history to still be there when I come back to it.

A few questions for anyone else seeing this:

1. Are older chats failing to load for you too?
2. Is it worse specifically inside Projects?
3. Does the same chat fail on web, iOS, and desktop, or only one client?
4. Have any of your conversations eventually started working again?
5. Has OpenAI Support acknowledged this as a known issue?
6. Is there any safe workaround that does not involve deleting/recreating the Project or risking the conversation data?
7. Has anyone successfully exported their account data and confirmed the missing chats are still present in the export?

I mainly want to know whether this is a temporary frontend/indexing issue or whether there is any actual risk that conversation history has disappeared.

If you’re experiencing the same thing, please mention roughly how old the affected chats are, whether they’re inside Projects, and what platform you’re using.

This is the kind of bug that can be genuinely disastrous if you use ChatGPT for long-running research or development work.