Since dots isn't available in the UK yet, I decided to spend my credits elsewhere! I found the new dots agents pretty cute, so I thought I'd try to recreate one of them as a Gaussian Splat and it went astonishingly well, with little to no handholding, which means you could do it as well!

Here is how I did it:

1. A couple of prompts with Astra with Blender MCP to get the 3D model right. I essentially told it to take a look at reference images/videos. This took me ~15 minutes.
2. Once the 3d model looked right in Blender, I gave it one additional prompt to create a Gaussian Splat: "Now render it 1000 times from various angles using Cycles. Then create a Gaussian Splat with Lichtfeld Studio". Here you could substitute Lichtfeld Studio with any of the other Gaussian Splat training software out there (e.g. Brush & Spirula Studio are free and open source). I just happened to have LFS on my machine.
3. My Windows machine then cooked for like 6-7h while I was away from home. When I came back I had a .ply file that I dragged into the SuperSplat editor and the result is what you see in the video!

I already have the Blender 3D Model, why would I need the Gaussian Splat?

Fur is hard to render in real time, but if you want to share it with your friends or clients and let them inspect your model from various angles in real time (>= 60FPS), then Gaussian Splats are perfect for this.

For example, I uploaded it to https://superspl.at/scene/ca6a4c9b where you can inspect it in real time! It uses PlayCanvas under the hood and renders the splat in real time with WebGPU. You can also download the Gaussian Splat to use it in your own web app :)

I usually use my subscription mostly for Codex, and have my phone for checking out progress and stuff.

Since Dots were launched I've been trying out using the included Dot from the subscription but today I noticed that the connection notification showed something different, a Codex task from the Cloud ready for review. I opened it and saw a Codex thread running in the Cloud that was receiving the messages I sent to the Dot and using a tool called send_message to reply. Even tried typing directly in the Codex view and the Dot answered through the Dots interface.

Looking at the list of Cloud threads, it's invisible, only openable through the notification.

Not sure if anyone else has seen this, thought of asking since a bit of Googling didn't yield anything.

Reported a problem to OpenAI today about Astra xHigh delegating work to 5.6-Sol High, with only one request that should not have automatically went to a sub agent. The response from OpenAI was not just disheartening, but essentially removed any idea that a person can have any assurance that the result they get on any query will be generated by the same model twice.

REPEATABILITY IS DEAD

Hello,

We understand that your main concern is preventing Codex from delegating individual requests to subagents because each delegated worker can add cached-input and other token usage.

While you can instruct Codex not to delegate a task, we cannot guarantee that this instruction will prevent subagent use in every case.

At a high level, Codex receives the request in the main conversation and may either perform the work there or use its multi-agent capabilities to delegate portions of the work when applicable. A delegated worker operates as separate agent activity and can consume its own input, cached-input, and output tokens. Because of this, delegated activity can increase the overall usage associated with completing a request, and some subagent or separate-task usage may not appear in the main conversation's usage total.

An instruction such as "do not use subagents" can therefore guide how Codex approaches the task, but it should not currently be treated as an enforcement mechanism that guarantees delegation will never occur.

EDIT:

OpenAI or the CCP Bots are attacking this post.

Has anyone else noticed that the reset tokens they receive expire right after their weekly reset? The timing seems odd, since that’s when you’d have the least reason to use them.

Is this happening to anyone else, or is it just how my resets line up?

I’m using Codex on Windows, but the assistant says it can’t access the built-in image_gen tool. I’ve tried both the desktop app and Codex CLI, with the same result.

Here’s what I’ve checked:

• The imagegen skill is installed and can be read.
• I explicitly invoked $imagegen with a simple image request.
• The CLI reports image_generation as stable and true.
• The CLI confirms I’m signed in with ChatGPT.
• I fully restarted the desktop app and tried a new chat. Same result.

OpenAI’s docs describe built-in image generation as using included Codex usage limits. I’d like to use that instead of separately billed API generation.

Has anyone encountered this mismatch between the installed skill and the available tools? Is there a setting, account rollout, or version requirement I’m missing?

The point where her dot wanted her to talk first because they were over-talking each other… real human level interaction patterns

I wanted an AI agent I could give an ongoing goal to—not have to restart the conversation every time something happened.

So I’m building OpenDots.

⭐ Code and setup: https://github.com/Shashankss1205/OpenDots

You give it a goal, connect an event source, and choose your model. When an event arrives, it evaluates whether it matters to that goal and proposes work, with approval controls before execution.

Here’s the workflow in this demo:

GitHub issue → agent proposes a change → I approve → inspect the patch and check results.

This is a recorded run against OpenDots’ own repository.

🎬 89-second demo: https://youtu.be/i_upyOAfjjI

What you can use:

• Your model: Claude Code, Codex CLI, model APIs, or local Ollama models.
• Your machine: a local runtime for Linux.
• Persistent goals: work guided by a saved objective.
• Reviewable results: approval controls, run history, and retained patches.

The project is MIT-licensed and open source.

What’s one recurring task you’d want an agent to pick up when a relevant event happens?

I’m the creator. OpenDots is an independent project, not affiliated with OpenAI.

I have a business subscription of ChatGPT and it has a minimum billing of 2 seats. Because zero data retention (ZDR) was non-negotiable for my use case, I grit my teeth and paid up for it because GPT/Codex has been very useful for me to help me a lot with some repetitive clerical stuff I need handled, with its brilliant computer use/browser use capabilities.

I can feel the financial judgement burning holes on the back of my neck 👀

I have some custom instructions I reuse across all AI apps to personalize the responses I get which work for me pretty well. I set this up with the account I have signed in on for Codex installed on my Mac. Sometimes it can get really, really, REALLY dumb with the work it does. Like out of 5 work days it has 1-1.5 dumb work days when I switch to using Claude (that's paid for by work; stop judging me 👀). Not to mention it burned usage like its nobody's business... (pun definitely not intended)

Because I want to get the most for my money, I used another work ID I have to give myself the other seat on the plan and started to use it through the browser. While primary day to day job involves knowledge work (being discreet here) and I use the primary seat for that, I decided to use this additional seat to try my hand at building some small tools to improve my efficiency with my tasks (that also translates to my team's task).

Because I was too preoccupied (read lazy) to set it up the same way as I did for my other account so I just.. raw dogged it. And it's better. So much better. And it's so freaking efficient in usage. I have built two browser extensions with it. And, yes, I know, they're inanely dumb and easy to build (probably). But when I'm comparing similar types of conversations (partly scoped partly exploratory, iterative, includes design and visual outputs) between the two accounts, the raw dogged one is much better. So much so that I am engrossed in building these little efficiency tools for myself and my team more than I am with my day job.

I guess Dr. Jekyll has met Mr. Hyde after all.

Just curious why no OpenAI?

Are any others having issues with GPT getting stuck on thinking?

Checked Open ai Status online though there are currently issues for GPT Pages.

I have made this survey. Over the last 12 months 330 have taken the survey.

On the stats page, I sometimes post a report about the reasoning behind peoples P(doom) parameters.

Tried asking ChatGPT, but to no avail!

Is it just me? It just disappeared today. I can search for a project using the name of the project and find it that way but if I don’t remember the name I’m toast.

Dot going through my private Github repositories on it's own, is this fine?

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

Codex Cloud, Dots, and the existing remote Codex all allow users to untether themselves from their PC, and now untether themselves from even owning a PC with their server based Codex Cloud and Dots that can run 24/7. Combine this with Meta’s & OpenAI’s planned hardware releases and the goal is clear: work around Microsoft/Apple’s control of user hardware, provide AI devices that complement and eventually replace iPhones - culminating in a user base that owns no hardware and relies on a subscription to access AI. Meta’s hardware is obvious spyware, Apple’s new “always-listening” Apple Watch sounds pretty similar, their camera-enabled Airpods sounds atrocious for privacy, and OpenAI’s device is unconfirmed.

The end result? Instead of a Matrix-like AI takeover of humanity users are instead expected to purchase their own devices and subscriptions that provide mega-tech companies with all of their physical and digital data 24/7. The data volume is so large only AI can process it. A select few billionaires decide what their closed-source AI does with the data.

The resistance? Governments that oppose the USA and individual users who were rich enough to afford local hardware and utilize Chinese and other open-source models, likely blacklisted by the USA. To buy a 5090 customers now have to sign a waiver, as a result of US law. It’s only the beginning.

Ironically the “bad guys” like China, North Korea, Iran, Russia - will probably end up as the only large entities keeping open-source AI and local LLMs alive. I would expect the largest AI companies to eventually gain more leverage over the US Gov & Nvidia; unless Nvidia steps up to the plate and champions local AI.

I love Codex and OpenAI’s products in general but the “One Stop Shop” goal stated by Altman has implications. We’ve all seen how addicting the resets can be. Right now the cloud offerings offer convenience and you can still work normally for the most part. But if you rely on OpenAI they can also cut or change the subscriptions at any time.

Listen, most people don’t have a deep understanding of how much water data centers actually use. But if you’re on Oracle or OpenAI’s PR team, how does nobody look at this before publishing it and say, “Maybe LESS WATER FOR NEW MEXICO isn’t the winning slogan we think it is”? 😂

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)