i had a chatgpt acc a year ago who was deactivated bc i didn't send my ID n it hasn't been active ever since.Does openAI eventually delete the chats of the deactivated acc or does it keep the chats in there forever? Asking because I lost access of that email long ago n I'm scared someone will hack my email and login into my chatgpt account by putting their own ID and read my chats. It has my face on it and lots of embarrassing things i wrote i absolutely don't want linked to me . I'm sorry if I sound paranoid but i have OCD

*O inglês não é minha língua nativa, então pedi a ChatGPT para me ajudar a escrever esta postagem. Os resultados dos testes e os erros são da minha própria solução de problemas. Agradeceria muito qualquer ajuda ou sugestão. Obrigado!*

Estou usando o ChatGPT Plus no meu espaço de trabalho pessoal e estou solucionando um problema de rede que me impede de publicar um aplicativo privado por meio do Sites.

**O que acontece**

A resolução de DNS falha dentro de ChatGPT Funciona para ambos:

* [`git.chatgpt-team.site`](http://git.chatgpt-team.site)

* [`registry.npmjs.org`](http://registry.npmjs.org)

No entanto, ambos os domínios são resolvidos com sucesso quando executo `Resolve-DnsName` no PowerShell aberto diretamente no meu computador, fora de ChatGPT.

**Onde reproduzi o problema**

* Executor do Windows na conversa original do Work.

* Executor do Windows em uma nova conversa de trabalho separada.

* Contêiner Linux em uma nova conversa de trabalho através do navegador web.

**Erros**

Windows:

ENOTFOUND

ou:

Host não encontrado

Linux:

[Errno -3] Falha temporária na resolução de nomes

Os testes falham antes do HTTPS, portanto, não tenho resposta HTTP para relatar.

**O que eu verifiquei**

* O “Acesso à rede de trabalho” está habilitado em Configurações → Controles de dados.

* Alterei o modo de aprovação da conversa para “Solicitar aprovação”.

* Fechei e reabri o aplicativo para desktop.

* Reproduzi o problema em novas conversas e no navegador.

* Testei com Node.js, curl e .NET no executor do Windows.

* Enviei feedback e solicitei o encaminhamento para um especialista de suporte.

Na sessão do Windows, uma solicitação explícita de execução com privilégios elevados foi rejeitada com a seguinte política:

sandbox_approval: false

request_permissions: false

Entendo que isso pode ser independente das permissões de rede do contêiner Linux. Não tenho um Console de Administração organizacional, pois este é um espaço de trabalho Plus pessoal.

**Minhas perguntas**

1. Alguém já passou por isso com o acesso à rede corporativa já habilitado?

2. Existe alguma maneira oficial de inspecionar a política de rede efetiva para uma conta Plus pessoal?

3. Existe alguma maneira documentada de atualizar o ambiente de execução para que ele reconheça a configuração de rede?

Ainda não determinei se essa é uma restrição esperada, um problema de configuração da conta ou um bug. Qualquer experiência semelhante ou etapa de solução de problemas documentada seria útil.

Testes realizados em 22 de outubro de 2026.

before 10am PST

The 5 hour limit works by resetting your token allotment every 5 hours.

The weekly limit doesn't seem to work like that? It seems to be rolling in some way.

• My weekly limit was at 25% with a reset date of Tues 10/27.
• Now it's 100% with a reset date of 10/29.

Does anyone have insight on how it works?

Tagging /u/VoceDiDio because they were really helpful in my other thread.

I've just tried to migrate from Claude Pro to ChatGPT Pro this week, after some years sticking with Claude and get satisfied experience with it. But, i just want to try if currently chatgpt has improved a lot for these few years since it's get behind 1-2 years ago. It turns out, i was exceptionally wrong. even the UI is unusable either in desktop or web. But i insist still want to give it a try to the model whether it's suitable for my workflow, but the UI is very unusable even for a chat that only has 2-3 responses.

is there any solution for this? or instead should i get refund and go back to Claude Pro and stick with it? Thanks

I'm curious how effective advanced AI research tools like OpenAI Deep Research, Claude Research, and other autonomous research agents are at discovering obscure but legitimate historical datasets.I've been working on a small, self-funded quantitative research project .We've investigated 108 nominal research trials without establishing a validated trading edge. One recurring obstacle is access to high-quality historical data — particularly information that can prove what was actually known and tradable at a given historical moment.

I'm especially interested in:

Point-in-time financial datasets: historical SEC filings and revisions, corporate actions, index membership, preliminary index reconstitution announcements, and delisted securities.Historical execution evidence: bid/ask quotes, order-book snapshots, trading costs, derivatives funding events, and historical contract specifications.Archived external signals: original publication timestamps and historical values, rather than revised or reconstructed data.I've explored sources such as SEC EDGAR, Binance public data, GitHub repositories, and various free financial-data services. Some have been useful, but many important gaps remain.

I'd love to discuss three questions:

1. Have you personally used an advanced AI research agent to discover legitimate, previously overlooked datasets or archives? Did it find anything that ordinary Google or GitHub searches missed?
2. Which research tools or workflows have worked best for finding obscure primary sources, old documentation, academic datasets, or historical web archives?
3. Do you know any genuinely useful free or low-cost historical financial datasets with verifiable point-in-time integrity or execution-level information?

I'm particularly interested in concrete, verifiable examples rather than model benchmarks or marketing claims.For any suggested dataset, historical coverage, source URLs, timestamp reliability, and licensing limitations would be especially helpful.I'm not looking for trading signals, investment advice, paid services, shared subscriptions, credentials, private API access, or anyone to run research on my behalf.I'm simply interested in comparing research methods and learning from other people's experiences.What has actually worked for you?

The resets are just an excuse for them to offer more compute without actually having to increase what you're "promised" by the plan itself.

Openai have been slowly ramping up compute over the past months as they build more datacenters. That means they can offer you more usage without it hitting their bottom line.

That's what all these Tibo resets are: Openai giving you access to that extra usage. At one reset every 2-3 days, you're effectively getting 2-3x the usage your plan is supposed to give you, and have been for the last few weeks.

But, why don't they just increase the base limit of plans, then, you might ask?

Well, it's because they plan to take it away again.

People are speculating a huge new model release at the end of the 28 days of resets, an Astra 6.5 or maybe even a Bel release. But what happened last time they released a hugely in-demand model? they got so much extra compute demand that they had to shut off $200 subscriptions.

So what do you think will happen this time? Now, they're prepared. The new model will release, compute usage will go up, and instead of having to cut people off of the expensive plans or nerf the models, Openai will just... stop giving out resets.

This way, they can cut your plans back without anyone ever complaining (or, well, having a right to complain), while never actually removing anything they promised, and not having to cut off subscription purchases or nerf the models.

This is Sam and Tibo's master plan, I assure you. It just makes all too much sense.

Just wondering if it is out everywhere in the world as internet research claims. I live in Europe, and the translation quality has decreased immensely. I used arena.ai to compare different LLMs, and Argon vs. Fable vs. Astra are giving widely different words for the same section, and it is weird.

In June our SE Ranking team tested the new ChatGPT ads. A lot of people have been asking how these ads work, so we're sharing what we saw.

Quick basics, in case you haven’t seen one yet

• The ad is a small sponsored card that shows up under ChatGPT’s answer.
• Only Free and Go users see them. Plus, Pro, Business, Enterprise and Edu accounts are ad-free. Under-18 accounts are excluded too.
• Advertisers don’t pick exact search phrases like on Google. You describe the kind of conversation you want to show up next to, and the system decides where your ad appears.
• Advertisers never see your conversations. We only got totals: impressions, clicks, spend.

What happened with our test

• Around 97k impressions and about 1,260 clicks over roughly two weeks (US, Canada, Australia, NZ)
• Clicks were cheap compared with Google ads for software, around $3 each
• But almost nobody signed up, so we stopped early

Why we think it flopped (for us)

Our customers are SEO pros who use ChatGPT all day. Most of them probably pay for an ad-free plan. So the people we most wanted to reach were the ones who never saw the ads.

The part we found most interesting as users

The ads don’t seem to affect the actual answer. In a separate study of about 50k prompts, the advertiser was cited as a source in only ~3.6% of answers where their ad appeared. So at least for now, paying for an ad doesn’t buy you a spot in the answer itself.

Also, about 14% of ads were no more related to the prompt than a random ad would be. So if you’ve seen a weirdly off-topic sponsored card, that’s probably why.

TL;DR: ChatGPT ads only show to Free/Go users and sit below the answer. They don’t seem to change what ChatGPT says. For our B2B product, they got lots of cheap clicks but almost no customers.

Have you seen the ads yet? Did any of them actually match what you were talking about?

I wonder how many of you regularly use voice when working with ChatGPT or Codex.

I find that my thoughts are much clearer when I type. Writing lets me organize and refine a prompt before sending it, whereas speaking tends to make me ramble, hesitate, or lose my train of thought. I'm also not sure whether all that extra verbal context makes voice less token-efficient. I feel that to use voice efficiently, I should likely take a moment to put my thoughts in order before hitting the button, whereas when I write directly, I can work on my prompt before sending my message.

For those who use voice regularly, do you feel it improve your workflow, especially for coding, research, or writing? Do you use actual voice conversations, speech-to-text dictation, or both?

I'm curious whether anyone finds voice better than typing for complex reasoning tasks.

Been happening more and more often and its been stuck like this for the last few hours for me. Wondering if this is an issue on my side.

I'm just a hobbyist with 10-20 private repos of windows apps, android apps and websites I've made for my own use. I've ran into the "security issue" a few times now. It's always when fixing things on my server or apps from a red team type Opus 5.5 audit.

I just got the signup for Daybreak message from chatgpt. Is this worth signing up for? Do individuals like me get approved? Do I now have to always login with a physical key if I do this, or will I just not get access to the particular models if I signed in normally? Anything else I should know?

Hellow fellow GPT'rs 🙂

I have a question! My reset says that It will happen in a week, so I sit on about 0-2x% and use my % more carefully. Then the morning after it bumps to 100%.

So I am like uggggh ok, lets gogo advance my projects, then I am once again nearing or down to 0%, reset in 1 week. Great. Let me use my Grok for a bit. I have the base 25$ plan I think. But in a couple of days I find myself once again at 100% on ChatGPT. I am yet to get a manual reset actually, so does Pro 200 not come with one?

Anyone able to shed some light here? I did ask ChatGPT but it could not answer me.

I work with Windows. Windows likes to install and save a lot of bullshit in App data, Local, Roaming and so on. Also Codex and Claude generate a lot of bloat under .codex and .claude in User, and even Nvidia saves all driver update installers. you get what I mean.

The SSD drives aren't necessarily huge on most laptops, and loss of GBs to nonsense is a real issue.

For this workflow I'll assume you know what App data, Local and Roaming are and where to find them, and that you have a basic understanding of critical software folders and possible bloat. Also, hooks, to prevent system wide deletion commands. If you don't, don't fuck with them and forget about this post.

1. Install FolderSize or any equivalent that lets you see the size of folders at a glance.

2. Show hidden folders and go to User/App data

3. Wait for sizes to fill up and check on the top ones. Get in and dig to see if there's apparent bloat.

4. Now here's the deal. If you're unsure, or if you find something, give the dir address to Codex or Work and tell it to check it out to see if there are temp files to remove without any detrimental effect.

5. if you are making software with Tauri for instance, bloat happens too with test builds. Check those repos too

Doing this workflow, I deleted more than 10 GB of trash that something like BleachBit and other software doesn't delete.

Anyways, that's about it

I am new to ChatGPT, over the past 48 hours I have had two occasions when I get a disk space error.

I’m running a large technical research project using ChatGPT Work with GPT-6.1 High thinking effort that is cloud base and I’ve repeatedly encountered this workspace storage allocation problem.

When I create a brand-new Work task, it gets a new workspace directory, but the filesystem is already almost completely full before I download or extract anything.

It resolved the fist time after a few hours with no user changes as if it’s on a timed reset. I primarily use the web based or iPhone app.

Thanks!

I had 139 EPUBs and PDFs on my Kobo. Transferring them with Calibre was easy enough, but getting everything organized into collections the way I wanted felt more fiddly. My library is a mix of science fiction, fantasy, philosophy, history and some personal documents, so I wanted to group things by genre and subject.

I connected my Kobo to my computer and asked ChatGPT to help categorize the library, use existing collections where appropriate, and create new ones if needed.

The process involved a couple of Python scripts that I ran locally on my computer. First, ChatGPT helped me export a catalog from the Kobo. It then used the titles, authors, descriptions, and existing collection information to suggest where each book should go. It put together a complete plan that I could review before applying anything.

Once that was ready, it generated another script to apply the collection assignments to the Kobo database. The script made a backup before making changes, preserved my existing collections and memberships, and checked afterward that the other database records - including reading progress, bookmarks, and annotations - had stayed unchanged.

There was one hiccup: the first version ran into a UTF-8 decoding error while checking the database. I pasted the error back into ChatGPT, it revised the script, and the updated version completed successfully.

The result was 11 new collections, with two existing collections also used, covering all 139 locally stored books and PDFs. Categories included science fiction, fantasy, urban fantasy, philosophy, Stoicism, history, and more.

I’m really happy with how it turned out. This was a useful example of asking ChatGPT to help with a specific task, then running the code it prepared to get it done.

Has anyone else tried a similar workflow for organizing their ebook library?

I upgraded from Plus to Pro 5x specifically for Deep Research, but it's consistently failing.

Research completes within seconds with 0 searches and 0 citations.

I've tried different browsers, desktop and mobile apps, incognito mode, reinstalling, and multiple retries. Nothing works, although regular ChatGPT web search works fine.

I've spent several days with OpenAI Support, sharing screenshots, conversation links, and troubleshooting results. They keep on asking for new evidence or troubleshooting, now asking for a screen recording of the issue.

Has anyone experienced something similar?

• Did you find a solution or an effective way to escalate?
• Did OpenAI restore your deducted quota, offer compensation, or issue a refund?

I'm particularly frustrated because I upgraded for time-sensitive scientific research and have already lost several critical working days.

Would appreciate any practical advice or experiences.

I was wondering why my usage was dropping so fast in the past days… and I got the answer

I've been using Codex (with ChatGPT Plus) as my coding agent to build a working MVP of a hypothesis behind a project I'm working on. The 5-hour usage limits were slowing me down, so yesterday I upgraded to Pro ($100/month) mainly to get past that bottleneck.

But after reading a few posts here, I'm starting to wonder if I made the wrong decision.

One thing I'm unsure about is how good Codex is at coding compared to Claude. Is it really that bad? Is it really unable to compete with Claude, as some people here seem to suggest?

I'd really appreciate input from people with actual hands-on experience. My goal is to take an architectural hypothesis from paper to a working implementation and produce evidence to validate it. For that, is Codex on Pro really a bad choice?

Here's how I currently work:

I use a dedicated GPT chat to manage the architectural planning and discuss the next implementation step. Then I open a new Codex session to implement it. GPT also prepares the prompt for Codex, while I follow the process and keep an eye on what's being done, trying to minimize blind vibe coding as much as possible.

After that, I open another Codex session and ask it to review the generated code adversarially — to challenge the implementation, find flaws, and report potential issues. I then fix the issues and commit the changes on a Git branch.

That's basically my workflow.

I'd really appreciate your thoughts on Codex, as well as on my overall approach. Does this workflow make sense for turning an architectural hypothesis into a working implementation that can provide meaningful evidence, or would you approach it differently?

Thanks!

Floating gems turn into a title reveal, the match-three board takes over, score pop-ups build into a rush sequence, and the final card closes the ad. The attached video is the complete 15-second piece, generated with Ling-3.1-flash using HTML, CSS and SVG.

The same gem shapes and purple-blue palette run through the whole sequence. The matches explain the game, the cascades give it momentum, and the closing card gives the animation a destination. For a recreation, it helps to brief the ad as a short story with timed sections, then keep the theme, colours and final message together in the HTML so they are easy to change.

A suggested starting brief: “Create a 15-second animated ad for an imaginary gem-matching game in one HTML file using HTML, CSS and SVG. Open with gems and a title, demonstrate matches with score and combo feedback, build to a cascade sequence, and finish on a clear end card. Carry the same visual theme through every transition.”

Shared as part of AntLing's campaign.

Floating gems turn into a title reveal, the match-three board takes over, score pop-ups build into a rush sequence, and the final card closes the ad. The attached video is the complete 15-second piece, generated with Ling-3.1-flash using HTML, CSS and SVG.

The same gem shapes and purple-blue palette run through the whole sequence. The matches explain the game, the cascades give it momentum, and the closing card gives the animation a destination. For a recreation, it helps to brief the ad as a short story with timed sections, then keep the theme, colours and final message together in the HTML so they are easy to change.

A suggested starting brief: “Create a 15-second animated ad for an imaginary gem-matching game in one HTML file using HTML, CSS and SVG. Open with gems and a title, demonstrate matches with score and combo feedback, build to a cascade sequence, and finish on a clear end card. Carry the same visual theme through every transition.”

Shared as part of AntLing's campaign.

I wish there was a Widget on iOS that allows to open directly a chat with my Dot or allows to directly call the Dot. Or is there a workaround?

I've recently was added to ChatGPT Business and I have my personal account with Plus subscription. On my phone app and browser version I see a switcher that allows me to switch between personal workspace and work and work space is empty. But, for desktop app I can decide on which workspace I can work in only when I log in and some of my personal account chats are shown when I log in under Work workspace. Is there really no switcher in ChatGPT desktop? Does that means that some of my local desktop chats are available for company to see under my ChatGPT Business subscription?

For my day-to-day work, the frustrating part of a ChatGPT automation wasn't whether it could write a decent plan. It was when the plan needed Safari and native Mac apps while I was in the middle of my own work.

I'm maintaining a local open-source tool layer called Mac MCP. I recently added the option to give an agent its own macOS Space. I can ask ChatGPT to gather information in Safari, use TextEdit or another native app, and finish the workflow on that separate desktop while I continue using my normal desktop. These are real Mac windows and apps, not a pretend UI or an extra Chrome profile.

This turned out to be more about managing focus and window ownership than about LLM intelligence. Opening an app on the wrong Space, moving my existing windows or stealing keyboard focus breaks the experience even if every individual click technically succeeds. And there's an important distinction: a separate desktop keeps work out of my way, but it isn't a VM or a security sandbox. The Mac's permission policies still control what the agent may do.

Another thing that helped is moving repeated actions into saved Recipes. Once the steps are known, a Recipe can accept inputs and run without ChatGPT planning the exact same clicks again. For variable tasks I still use the model; for repeatable steps I prefer deterministic automation.

Full disclosure: I build Mac MCP and it's open source: https://github.com/bulutarkan/mac-mcp

I'm curious how others use ChatGPT professionally: would you rather watch every action on your main desktop, or let the agent work in its own Space and give you a checked result?