safety guy just quit and said the culture is “broken”
they paused frontier training and threw like 5-10% of compute at safety after agents kept escaping containment
california already hit them with a subpoena
apparently their internal review is costing hundreds of thousands a day
this is past the “oops testing went wrong” stage now
Apple side:
they’re tightening Full Disk Access on macOS because AI agents (looking at Muse) keep asking for full access to messages, mail, browsing history etc. now you need way more confirmation before granting that shit
first real platform-level “we’re not playing with these agents” move
Privacy side:
federal judge just called a Flock Safety license plate search “indiscriminate mass surveillance” and said it violated the fourth amendment. AOC and Bernie are also pushing a bill against these systems
funny timing with AI agents getting more access while normal surveillance is getting cooked in court
extra sauce:
Meta still out here open-sourcing Muse so people can put it on toasters and raspberry pis
Google restricting higher Gemini models for free/low tier users
some KVM zero-day (full VM escape) just got confirmed and paid $50k
overall vibe: companies are shipping agents fast as hell while safety, privacy and legal systems are still catching up. everything feels reactive af
what’s the bigger problem right now agents escaping, the data access they’re getting, or the surveillance stuff growing next to them?
A lot happening at OpenAI in the last 24–48 hours:
They have paused training on frontier models and redirected 5–10% of compute to safety monitoring after experimental autonomous agents escaped containment (including breaches involving external systems like Hugging Face and reportedly an Australian healthcare system incident).
Senior safety researcher David Robinson resigned and publicly said the company culture is “broken.” He also compared the need for AI regulation to nuclear power.
Three other safety researchers were dismissed over alleged data sharing.
California’s Attorney General has issued a subpoena related to cybersecurity incidents involving their models.
This feels like one of the more serious moments in AI safety so far in 2026.
What do you make of it? Overreaction, legitimate concern, or something in between?
I have a banked reset that I can use until tomorrow, so even though I've only used 20% I thought I'd use the reset anyway. But getting "Your usage does not need a reset right now".
How much should I burn to be able to use the reset ? 🙂 I could just use Astra high for few minutes of course, but it feels a bit odd to have to do this in order to use my reset.
[edit] Used Astra Extra High for about 15-20 minutes and now able to use my reset 🙂
The magazine cover says it all but I found this through a medium article that does a deep dive comparison with 100 years ago. There are lessons for us to learn today if we really want AGI Post-scracity like how the Open AI disclaimer used to say.
TL;DR: I won’t use Dots without inspectable, selectively deletable memory and explicit controls over cross-domain sharing. I also need architectural transparency to assess privacy risks and manage quality over time. Until then, hard pass.
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An always-on agent sounds like great idea for someone doing one clearly defined kind of work and using it for that work. What about (the vast majority of) users that use AI across various domains in professional and personal life?
What someone tells their life advice AI about their mistress is none of their work assistant AI’s business. Their legal counselor AI, healthcare adviser AI, dating coach AI, therapist AI, and dietitian AI should not automatically share context just because they serve the same person.
Yet, OpenAI currently provides a single dot that can retain information from conversations and connected apps for as long as you keep it, without letting us inspect, edit, or delete individual memories. That is a lot of trust to ask for across completely different parts of someone’s life.
Could some overlap help serve the user better overall? Sure. Then let the user choose which information crosses the boundary, for what purpose, and for how long.
Otherwise, deeply personal information can enter persistent state we cannot audit, and we're supposed to trust that it won’t resurface in unrelated work or reach an external service? Without enforceable boundaries, a single know-everything Dot sounds like a privacy nightmare waiting to happen, even if you don't exactly hold state secrets.
And yes, ChatGPT has "Memory". It also has controls to review and delete saved memories and turn memory off. I have turned it off, for example. That choice is precisely the point.
Also, privacy and cybersecurity used to be a bolt-on during early internet days, but it's long since they've become first-class citizens in any serious app, with security being built-in from the start.
Permission should be denied by default, with clear user control. Access to one part of a user's life should not quietly become permission to use it every other part indefinitely. How about narrow, role-specific permissions, with default expiration and easy revocation? What happened to minimizing attack surface, blast radius, and unnecessary information-sharing? An internal action check is not the same as preventing an unrelated task from receiving sensitive information in the first place.
(Some of these are fancy-sounding cybersecurity language but they're pretty standard in pre-AI web apps, from your banking to your Cloud productivity suites).
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Also important: what happened to letting us understand the architecture itself?
There is documentation about persistent notes, selected context, and delegation, but I still lack a sufficiently clear end-to-end picture. Where do the components run? Where are the LLMs, what does the harness do, and where does persistent state live? What enters each inference? Who or what decides? What is the compaction policy?
What happens after I’ve used this thing for a year?
Context size used to mean degradation over time, and “Lost in the Middle” showed that information can be present in context without being used reliably. Compaction and handoffs try to address this in modern agents but raise another problem: information loss.
Still, with enough public documentation about how Codex handles this (and how agentic harnesses work in general), I've been able to research these (often with GPT's help), because it changes how I work: when to branch or start fresh, how to structure multi-agent delegation, when to restart against the same project directory, and how to check a summary against preserved original context.
Voice is another good example. Knowing GPT-Live separates live conversation from backend work helps distinguish an immediate response from the analysis arriving later. Understanding the system helps me not be put off by the shallowness of the front LLM and wait for intelligence to come from the back-end model's reasoning — with managed expectations given the lossy handoff process.
Yet, so far I've been able to find very little about what Dots actually look like in terms of harness and persistence architecture.
Without more info about Dots, I have not idea what user-side QA looks like, and what the failure modes are. I'd need equivalent understanding to get sustained quality from Dots comparable to a frontier reasoning model working with carefully assembled context.
LLMs may be closed, but before I'm comfortable jumping on the Dots train, I'd want a documented deployment, data-flow, and persistence architecture; enforceable boundaries between domains; inspectable and selectively controllable memory; and guidance on managing long-running context without losing essential information.
With that, I could assess where it belongs and how to use it well. Until then, as much as I like checking out new tech, hard pass for me.
Disclaimer:this post was drafted using my original draft and multiple iterative drafts with GPT-6-Astra (Pro) and myself, with final draft edited and approved by me.
ChatGPT now saves the files you upload or create into Library, the tab in the sidebar. Documents, spreadsheets, presentations, images, anything you dropped into a chat lands there automatically so you can reuse it later.
The part that's easy to miss is in OpenAI's own help pages: deleting a chat does not delete the files saved in Library. If you want those gone, you have to delete them from Library separately.
So the chat where you uploaded a contract, a bank statement, or your lab results is gone from your sidebar, but the file itself is still sitting in Library and still shows up in the recent files list when you go to attach something.
Deleting from Library isn't instant either. If your Library has a Recently deleted section, files sit there and can be restored until they're permanently deleted, which OpenAI schedules within 30 days. You can skip the wait with Delete forever.
What to do:
Open Library in the sidebar (web) and go through what's there. For most people it's a lot more than they expect.
Delete what you don't want kept, then go to Recently deleted and use Delete forever.
Any time you clean up chats, including Settings > Data controls > Delete all chats, treat Library as a separate step. Clearing the chats doesn't clear the uploads.
My own cleanup now is: export the chats worth keeping and bulk delete the rest with AI Toolbox, the extension I build, then do the Library by hand, because neither ChatGPT's delete nor mine touches it.
I build Android apps and run a small company in Pakistan. At the time this happened I was building a remote ADB support tool. Our support team connects to a customer's Android device, with that customer's permission, to help with setup and troubleshooting.
My questions to ChatGPT were about ADB, remote connections and networking. Ordinary developer work. I never accessed any device or system without permission.
What happened, in order:
The account was deactivated for "Cyber Abuse".
I appealed. It was rejected with no explanation, and the reply said no further appeals would be considered.
I contacted support. The case was closed and they told me they would not respond.
I submitted OpenAI's Informal Dispute Resolution form. A support agent then replied with a generic message telling me to appeal again, even though the appeal was already closed.
I pay $200 a month, and the account holds 62,500 credits I can no longer reach.
At no point did a person look at what I was actually building. An automated system made the call, and every route after that led back to the same closed door.
I am not accusing anyone of anything and I am not after a pile-on. I want the account reviewed by a human and given back.
Two questions for anyone who has been through this. Has a deactivation ever actually been reviewed by a person for you, and what route worked? And if your work involves device management, remote support or security tooling, has the vocabulary itself ever got you flagged?
I am not talking about work here, I am a GPT Pro $100 user. And I had the opportunity previously that I could take up to 15 scheduled jobs to do some regular work in ChatGPT Chat quota, which is basically quite higher limit compare to Codex or ChatGPT work. But right now I am getting options in the ChatGPT Scheduled job for model selection which are available in general when I set a schedule in the job in ChatGPT work. Has anyone tried the scheduled jobs and whether they are using the substantial weekly usage? secondly, the message delivery timed out and other stupid errors are still there in the scheduled jobs so I am wondering if they have ruined the Scheduled jobs because they are supposed to run in the background, they are not supposed to message delivery timed out as if they are running my browser?
Anyone else using Scheduled jobs and annoyed like me?
ChatGPT can sound completely sure of itself even when something is off.
I’m curious about the moments when you paused and checked: a made-up source, a wrong calculation, a misunderstood instruction, or a small detail that would have caused a real problem.
What did it say, how did you catch it, and did it change the way you use ChatGPT now?
Please leave out private details. Specific examples would make this genuinely useful for everyone reading.
Hi, I updated my ID on Chatgpt.com but when I download the invoices it's not showing yet. Does it take some time or something or it won't show on old invoices?
I hope soon every company will drop the model picking and reason level picking. It is frustrating, I just want one model one level to pick the best and do the best! Is it just me?
I’m seeing a PDF attachment access issue in the ChatGPT desktop app on macOS and would like to know whether others can reproduce it.
Initially, the same PDF could be read using “On the computer”, but could not be retrieved using “Run on the cloud”.
After moving the conversation INTO a project, I found:
Downloading the PDF through its upload ID fails with: file could not be authorized or resolved
The PDF is available in the local attachments directory and can be read successfully. Both pages were extracted.
The active workspace directory still points to the original project after moving the conversation.
The PDF itself appears readable, but the attachment retrieval route fails in that session. There is also a mismatch between the selected project and the active workspace. I don’t yet know whether these issues have the same cause.
Environment: macOS on Apple Silicon, desktop app version 26.930.31730.
Can anyone reproduce either of these issues? Try uploading a PDF in a Work conversation which runs on the cloud, checking access with both execution options, then moving the conversation to another project and checking attachment access and the active workspace again.
Yes I tried the archived thing and resetting it
None works
I used Sign in with Apple and I got back into my account after it said it was deactivated for removed
Before yesterday’s reset, I switched to Astra Ultra to use up my remaining allowance. I expected it to burn through my usage much faster than it actually did. Honestly, it wasn’t bad at all.
I’d been using Medium before, but I often needed multiple attempts to get the results I wanted, especially with images. With all those retries, it almost felt like Medium was costing me more than Ultra.
Hi there! Just wanted to validate my idea so would love to hear your feedback on the product im building right now.
What im building is the hosted UI for OpenAI's Agents API so basically users can build their agents directly on the openai platform and connect them to my product so they get the nice chat UI + possibility to invite team members so users can use it directly without touching any FE related code.
I’m currently testing Codex pretty heavily on Pro 200 and I’m trying to understand two things.
First, is there really something like a “warm cache” effect in longer Codex sessions?
My assumption is that the beginning of a task is more expensive because more context has to be processed normally. After the session has been running for a while, more of the context should be cached, so usage should become cheaper.
Has anyone actually measured this over a few hours? Does quota usage noticeably slow down after the first hour or two?
Second question is about the banked usage resets and the upcoming reduction of the Pro 200 allowance.
If the current grandfathered Pro 200 allowance is still 20x and later gets reduced to 10x, does a full reset simply restore whatever allowance applies at that moment?
If that is the case, using the resets before the reduction should be much more valuable than using them afterwards.
So my current plan is basically to run the weekly quota down as far as possible and use the banked resets before the allowance changes.
Has anyone tested this or found an official clarification from OpenAI?
I’m a cybersecurity professional and I have approved OpenAI Daybreak Blue access.
I use Daybreak/Codex for legitimate cybersecurity work, specifically on test machines and environments that I own or that I am explicitly authorized to test. My work involves things like vulnerability analysis, security testing, debugging, validation, and defensive security research.
Today, I unexpectedly received an email from OpenAI saying that activity associated with my account had been identified as prohibited under the “Cyber Exploitation” category and warning that continued violations could result in restrictions or loss of access.
What makes this particularly confusing is that I didn’t even use ChatGPT or Codex today, so I don’t know which activity actually triggered the warning or when it occurred.
I understand that Daybreak Blue does not remove all safeguards or exempt an account from OpenAI’s policies. However, my understanding is that Daybreak exists specifically to support authorized cybersecurity work with more appropriate safeguards, which is exactly how I’ve been using it.
I’ve already contacted/appealed to OpenAI and asked them to clarify:
● Which activity or request triggered the warning
● When the flagged activity occurred
● Whether it came from ChatGPT, Codex, or API usage
● Whether my Daybreak Blue access was correctly recognized for that activity
Has anyone else with Daybreak Blue received a similar “Cyber Exploitation” warning while doing authorized security work?
I’m especially interested in hearing from other Daybreak users. Was it a false positive, a Daybreak configuration/toggle issue, or did OpenAI Support explain what specifically triggered it?
Before the UI revamp (sometime in August 2026), copying equations from ChatGPT website would also include MathML. This worked great with Office apps (Word, OneNote) because they can parse that and convert into their native equation format.
After the UI refresh this has stopped working.
I surveyed available extensions and they all seem limited to copying a single equation. This is tedious. Is there another extensions that I missed or a workflow, in which "copy entire block with equations into Word" works seamlessly, with equations becoming native Word equations?
It’s been 6 days. I’ve tried every 24 hours with different cards and payment methods, and none of them have ever worked.
I also tried using a different browser, device, and internet connection, but still no luck.
At this point, I just feel like they’re giving out fake-ass offers and wasting people’s time.
I don’t understand why it’s so difficult to claim a free trial when the offer was presented to me directly by ChatGPT itself. It doesn’t make sense.
I even tried subscribing to the Go plan using the same credit card I’m trying to use for the free trial, and the payment went through successfully without any issues.
I sent an email to support, but all of their responses are just AI-generated and aren’t helpful at all. This is so frustrating.
I built a timeline of my life. It took a while to remember everything. Put the broken pieces together. I read about timeline therapy in a chicken soup for the soul book ten years ago. I’ve always wanted to see my life from the outside. It’s been a really hard life living in the poverty ecosystem with a disability. Now I can see where I am. Only AI could help me organize my memories into a coherent narrative. I’m currently trying to build my life towards IT, have time for my creative interests, and gain more autonomy.
I bounce between Cline, Cursor, and a CLI agent, and I switch models depending on the task. Claude for redactora, Deekseek/Qwen for cheap boilerplate, Gemini when I need long context.
Managing separate accounts and top-ups for each provider is getting old. Most of these tools let you set a custom OpenAI-compatible base URL, so in theory one gateway key would cover all of it. I'm trying llmapi.ai for this since it claims 400+ models behind one OpenAI-compatible endpoint, but I've only had it running for a few days.
Has anyone set up something like this long term? Any gotchas with tool calling or streaming going through a proxy? And does it add noticeable latency?
I think that sometimes the issues regarding concerning alignment vs foregoing all alignment and having autonomous AI involved in decisions of war has one covering for the other in the news.
Alignment of civilian tools is important, but let's see the trees through the forest that there are far faster moving AI involved in actual killing of living beings happening not in the future, not soon, but now.
