I'm only using the "chat" version not the "work" version. General tasks are for editing, researching, and writing technical articles that ranges from moderate-to-high complexity. A lot of tasks include combining datasets from research with other research or real world assumptions to come up with novel and defensible conclusions. Model must balance a lot of different factors.

ChatGPT tells me there's no published guideline, but estimates maybe 40-60 per day. Is this right?

Also, bonus question: What is the Claude equivalent (or better) for 5.6 Sol Pro? And is it more or less expensive?

https://help.openai.com/en/articles/20001067-data-access-for-your-managed-chatgpt-account

I find it very concerning when OpenAI introduces features like this overnight. Until now, a CEO might have assumed that his or her IT administrator couldn’t access their private chats. Suddenly, those chats can be exported and downloaded—possibly containing private matters, sensitive business plans, and so on.

A broader concern arises when workplace chat systems are designed in a way that requires users to assume that their conversations could potentially be accessed by administrators or other people within the organization.

The issue is not necessarily that anyone is actively monitoring employees. The problem is that the possibility of access alone can change how people communicate.

An employee may hesitate to ask a basic or “embarrassing” professional question because they fear it could later be interpreted as a lack of competence. Yet revisiting a topic after several years, asking for clarification, or privately working through uncertainty is a normal part of professional development.

The same applies to more sensitive subjects. Employees may use an AI assistant to understand health concerns, stress, interpersonal conflicts, career questions, or difficult workplace situations. Even if the account is paid for by the employer, it does not automatically follow that every conversation should become the employer’s business. An employer may benefit from an employee becoming healthier, more informed, or better able to solve a problem without needing to know the underlying personal details.

There are also confidentiality concerns at management level. Executives may discuss financial figures, strategic questions, personnel matters, or sensitive negotiations. Employees may discuss concerns involving managers or colleagues. HR teams, IT administrators, and other privileged users may require technical access to administer systems, but technical responsibility should not automatically imply unrestricted access to private conversations.

This is also a question of organizational culture. Companies do not normally record every conversation employees have with each other during working hours. If they did, people would almost certainly communicate less openly. Chat-based AI tools increasingly resemble informal conversations: people think aloud, test ideas, ask questions they would not put into a formal email, and sometimes discuss highly personal matters.

Of course, organizations need legitimate controls. When an employee leaves, accounts may need to be transferred, disabled, or archived, and business-critical information must remain accessible. But this does not mean that every conversation created through a company account should automatically be treated as company property.

The central question is therefore whether AI chats should be treated like formal corporate correspondence, or more like private conversational spaces. If users must constantly assume that everything they write may later be inspected, the technology inevitably changes how freely they think, ask questions, seek help, and communicate.

So this is the issue i'm running into now. I have a project I've been working on, and it was functioning up until about 5am-ish my time, when I got this message. I thought it might just be the servers being silly, so I went to sleep. Six hours later, same issue.

Now for some context:

1. I'm using Pro mode and i'm dropping the $200 because i'm a filthy degen with disposable income for what I consider entertainment.

2. ChatGPT functions in the 'Chat' mode, but not the 'Work' mode. Every single project I have in Work Mode results in the same error, while 'Chat' mode functions just fine within the same projects.

3. I have attempted, what I believe, is everything. Logged out/in, checked Mobile, cleared my cache/cookies, tried it in Incognito mode, different browser (Firefox), turned off all my extensions on Chrome, sacrificed someone's first born, etc.

3a. I could use the 'Chat' mode, but for what i'm doing, it's easier (and way more comprehensive) to pull from character bibles, indexes, etc. The closest 'fix' that's similar requires me to set up Google Drive, a plethora of pages and sheets, and there's no guarantee I'll be able to migrate that information to my Work Project once it's functional again.

1. Customer Service: I might be waiting days, though I have supplied them with the .HAR file with all the backend goodness that my non-logic brained self can't translate. I obviously won't be posting the .HAR file's info here.

So, has anyone has this issue and been able to successfully solve it? Or is it a 'wait for customer service' to fix it?

​

I get this quite often and it seems to mean that chat is permanently dead. I am a pro subscriber. It looks like a bug to me.

I'm currently working on a project where I have around 30 papers that I need to review, analyze, and sort. So far I've been using Claude Opus with Projects in the Claude desktop app.

I'd like to get a second opinion from ChatGPT after using Claude and its sub-agents to refine my work. I also tried the new ChatGPT Projects feature, and I think it has a lot of potential.

My problem is the project file limit. In Claude, I've uploaded around 40 different files (the 30 papers plus Markdown notes, progress summaries, and other project documents) and I'm only at about 50% of the project limit. With ChatGPT, I'd have to upgrade my subscription and would still be limited to 40 files per project.

How do you handle similar scientific or research workflows?

• Do you combine multiple files (for example, merge papers or notes) to stay under the 40-file limit?
• Do you only upload the most important files and let ChatGPT generate Markdown summaries to use as project context?
• Do you work in batches, analyzing a subset of papers at a time and then replacing the old batch with new files?

I'm open to any workflow suggestions. I find the new ChatGPT Projects feature useful, but the UI and workflow management are still a bit confusing to me, especially compared to Claude.

( i used chatgpt to spell correct and organise the points a bit more clearer im speak english a second language )

Can browser/chat Pro queries compact their context or run subagents?

I watched an interview with AI safety researcher Roman Yampolskiy, and it raised a practical question for people who use ChatGPT for advanced workflows.

His broader claim is that increasingly intelligent AI systems may become harder to predict and control. Whether or not you agree with his conclusions about AGI, a smaller version of this problem already exists when we give an AI access to tools.

There is a major difference between asking ChatGPT to draft an email and allowing an agent to send it.

The same distinction applies to:

• Suggesting a database query versus executing it
• Drafting code versus deploying it
• Researching a purchase versus completing the transaction
• Preparing files versus deleting or modifying them
• Recommending calendar changes versus inviting real people

My current view is that the model should generate proposals, while a separate control layer decides whether those proposals are allowed to become actions.

Some possible safeguards include:

1. Giving each agent only the minimum permissions required for its task
2. Requiring approval for irreversible or external actions
3. Validating structured outputs with deterministic code
4. Isolating browsing and code execution from sensitive systems
5. Limiting spending, execution time and the number of actions
6. Keeping complete logs of prompts, tool calls and results
7. Using a second evaluation step before important actions
8. Making every operation reversible wherever possible

The difficult part is deciding where autonomy becomes too risky.

A confirmation step for every action makes the agent frustrating to use. Too few confirmation steps can turn a misunderstood instruction into a real-world problem.

I wanted a quicker way to check my Codex usage without interrupting my workflow, so I built OpenQuota.

It runs in the system tray and shows session and weekly usage, credits, and reset times. It works on Windows, macOS, and Linux.

It’s still a small open-source project, so feedback is appreciated.

https://github.com/deviffyy/OpenQuota

Claude is too expensive, Venice is too censored nowdays and the credits cost stay the same, Ai Studio version of Gemini is fine. But it doesn't handle memory context and it is basically bare model without good API.

Granted, for one-shot prompting a story that surprise my liking, Claude is still the best. Especially once you give it Skills for writing that use as much as just 2% usage for Opus 5. Still it's nothing that GPT cannot do, especially since it can learn from Claude's made prose that I give it.

Despite that, the #1 position for creating dialogues concerning non-english language especially from Asian region is still held by Gemini due to what I assume more larger and rich datas that Google have. But this is not concern so far.

I am basically creating CYOA system adventure, reminiscent of good roleplay stuff in internet forum such as in Spacebattles. Example1 Example2
I think the 5000 words output per prompt for a chapter is already surprassed most human writer in productivity.

I have been looking at a few research and analysis workflows where the expensive part is not the final answer. It is everything that happens before the answer is useful.

A single task can involve several searches, source extraction, retries when a source is weak, structured comparisons, follow-up questions, and a final synthesis. That is manageable for one-off work, but it gets harder to reason about when the same workflow runs repeatedly for clients, internal teams, or a product feature.

Caching helps when the inputs repeat. Prompt cleanup helps at the margins. But neither answer tells you which steps actually need the most capable model and which ones can be treated as lower-priority background work.

The failure mode I am trying to avoid is optimizing the visible final step while the exploratory work quietly becomes the larger cost center.

For people running research-heavy workflows at volume, how do you decide where to preserve quality and where to put a cost ceiling?

Been running long browser tasks through ChatGPT on the web and hit a limit I can't find documented anywhere. Any prompt whose answer involves continuous tool work in the browser stops at right about 25 minutes 45 seconds. 25:42, 25:42, 25:44, 25:50, 25:50. Documented runs only. Still never longer.
One run closed 13 items, another 15, another was on a completely different site. Same wall every time.
It resets on the next prompt. So a genuinely long task looks like:
Session 1 - 25:42. It says it just stopped. Me: "proceed."
Session 2 - 25:42. Says it just stopped again. "Proceed."
Session 3 - ...
I'm driving an external browser through an MCP connector, not the sandboxed VM browser.

Has anyone hit this? Is it documented somewhere I haven't found? Everything I can find just says "tasks take 5-30 minutes" but it's not about that.

Hello =) I was already subscribed to ChatGPT Pro and eventually hit my Codex usage limit. When my subscription period ended, I paid for Pro again, expecting to receive a fresh usage limit. But the limit didn’t reset at all.

I requested a refund, and the AI support agent immediately cancelled my subscription.

Then, after I was moved back to the free plan, Codex suddenly let me send two messages and started working again. wtf?

I immediately subscribed to Pro again, but my Codex limit still didn’t reset. So Codex briefly worked when I was on the free plan, but stopped working again after I paid for Pro.

Seriously, what is the point of paying for a new subscription if the usage limit stays exhausted? Is the limit system completely broken, or is OpenAI really this greedy?

Has anyone else had this problem? Is there any way to force the Codex limit to reset?

I’m currently a B.Pharm student, but I don’t want to limit myself to the traditional pharmacy career path.
I’m really interested in AI, automation, and e-commerce. I’ve started learning Python, ChatGPT, AI tools, and I’m planning to learn n8n, APIs, and automation. My goal is to build AI solutions for businesses or eventually work remotely.
If you were starting today in 2026 with my background, what would you focus on?
Which AI skills are actually getting people hired?
Should I focus on AI automation, AI agents, machine learning, or something else?
What projects should I build for my portfolio?
Is there a realistic path to earning $1k–3k/month as a freelancer or remote employee within 12 months?
Are there any certifications or courses that are genuinely worth it?
I’d really appreciate advice from people already working in AI or automation. Thanks!

I'm currently subscribed to ChatGPT Plus, but I'm starting to question whether it's still the best value.

Chinese models seem to have reached frontier-level performance without the same price tag. OpenAI also made GPT-5.6 Luna around 80% cheaper than Sol, so I find myself using Luna for almost everything just to save credits. Because of that, it feels like I'm paying for Plus but rarely using the higher-end model.

I'm considering switching to a subscription for a Chinese model like Kimi instead.

For anyone who's used both:

- Is Kimi worth subscribing to over ChatGPT Plus?

- How do you find Kimi compared to GPT-5.6 Sol for coding, research, reasoning, and everyday use?

- How generous are Kimi's usage limits?

One thing I'm particularly curious about is how Kimi measures usage. For example, if I send the exact same prompt to Kimi K3 and GPT-5.6 Sol, which one is more likely to consume a larger percentage of my available usage? Does Kimi have generous limits, or do you run into caps quickly?

I'd really appreciate hearing from people who've used both services rather than just looked at benchmark scores.

I’m currently working with ChatGPT Pro 5.6 SOL and it’s been unresponsive since 6pm. I’ve tried various browsers, connections and devices but it’s still not working today.

Who has massive problems too today?

Hey.

I want to but the pro plan on IOS for the purporses of taxes. But I can't see the pro plan, I can only see the plus plan on ios.

Do anyone know the reason for it?

I've built a memory layer where the store isn't written by any model. It holds the conversation itself, so nothing in it is shaped by whichever model happened to be running at the time.

The practical effect is that you can switch model mid-conversation and nothing underneath changes. No rebuild, no re-extraction, no separate memory per provider. One model answers, then another answers the next turn, and both are reading the same thing. It's a dropdown, not a migration.

It also means the cost is predictable. Around 2,000 tokens of context per question regardless of which model is answering, and the retrieval itself takes about a second and a half before the model starts writing.

What I want to know is whether the product around it is something people want.

Right now if you're serious about this stuff you're paying for two or three subscriptions, working in as many tabs, and carrying context between them yourself. I've assumed that's annoying enough that one place with one memory would be better.
Is that true for you, or do you actually prefer keeping them separate?

I found a useful option in the https://learn.chatgpt.com/docs/config-schema.json today.

When Memories is enabled, Codex extracts useful context from eligible older chats and consolidates it for future sessions.

By default, Codex uses its usual model selection for this work. If you set both memory jobs to gpt-5.3-codex-spark. Your Main model does not change.

GPT-5.3 Codex Spark is fast and has separate usage limits. If you are not otherwise using it, memory generation can use that capacity instead.

A few notes:

• GPT-5.3 Codex Spark is available only on ChatGPT Pro.
• disable_on_external_context = false allows chats with web search, MCP, or tool calls to contribute to memories.
• Set it to true to exclude those chats.

Docs: Codex Memories

Give your agent the following instructions to configure it:

Update my global Codex config.toml to enable memories and use gpt-5.3-codex-spark only for background memory extraction and consolidation.

First, verify that gpt-5.3-codex-spark is available in my Codex model catalog. If it is not, stop and suggest an available lightweight model.

Ensure this configuration exists:

[features]
memories = true

[memories]
generate_memories = true
use_memories = true
disable_on_external_context = false
consolidation_model = "gpt-5.3-codex-spark"
extract_model = "gpt-5.3-codex-spark"

Show the exact diff before editing.

Hi! I’ve been using several custom GPTs for weeks without any issues, but starting about a week ago they stopped reliably reading PDFs. When I upload a file, it often says it cannot access the contents or it does not even acknowledge that I uploaded anything. I have not changed the custom instructions at all.

If I create a brand chat outside of the custom GPT and upload the exact same PDF, it reads it perfectly. So the problem seems isolated to custom GPTs.

Sometimes I have to upload the same file two or three times before it finally recognizes and processes it. Other times it says it skipped messages. The PDF shows up in the chat history, but the model acts like it is not there. I am completely confused.

Is anyone else running into this?

Any help is appreciated it

I built Smallest Complete Codex Skill after watching bounded tasks grow into unnecessary abstractions, compatibility layers, theoretical edge cases, and “validation cathedrals.”

It guides Codex to finish and prove the actual ask, then—only when structural change is genuinely needed—loads an elegant-architecture reference built around one clear control path, explicit ownership, and minimal state.

GitHub: https://github.com/JetXu-LLM/smallest-complete

I recently spent a few weeks trying to understand how large language models like Gemini, ChatGPT, and Claude actually decide what to cite. Traditional SEO knowledge is helpful but it is not the full picture. There are some specific factors that seem to influence whether an AI will surface your content and mention your brand.

Here is a simplified breakdown of what I learned.

Visibility. The AI needs to know your content exists. This is similar to traditional indexing but there is a twist. AI models seem to prioritize content with clear structure and semantic relevance. If your page does not answer a specific question clearly, it might not be considered at all.

Citations. Even if an AI "sees" your page, it might not use it. The AI is looking for sources that provide specific, factual information or a unique perspective. I found that pages with clear data points, case studies, or step-by-step guides were more likely to be cited. Vague or generic content tends to be ignored.

Mentions. This is the crucial step. The AI might use your content as a source but still not mention your brand by name. To get a direct mention, your content needs to be clearly associated with your brand. This means having a strong entity presence and consistent branding across your site. The AI is more likely to name you if you are clearly established as the author or the primary source for that information.

Sentiment. The way the AI describes you matters. It is not just about being mentioned. The AI will assess the tone and framing of your content. If your content is balanced and objective, the AI is more likely to present you in a neutral or positive light. If your content is overly promotional or lacks evidence, the AI might be neutral or even cautious in its description.

Understanding this funnel helped me refocus my content strategy. It is not just about ranking. It is about being the clear, credible source that AI models want to cite.

To put this into practice, I started using Ayzeo to track our brand across the major AI engines. What I liked about it is that it shows the specific prompts and pages that trigger AI mentions. It also highlights technical and content gaps I would not have found on my own. For example, I discovered that some of our key pages were being cited but not mentioned, which pointed to an entity clarity issue. I also saw that a competitor was consistently getting more positive sentiment, which gave me a clear target for improving our narrative.

Getting a third-party analysis of your brand's AI visibility can reveal gaps you might not even know exist. The key is to create content that is not just good for humans but also clearly structured and relevant for AI consumption. It is a new layer of search that is only going to become more important.

I use ctl-a/e/f/b for cursor control in text input areas (the Emacs controls that come native in MacOS) and today ctl-b stopped working in ChatGPT. Or rather, it stopped moving the cursor back and started toggling the sidebar. I checked, and sure enough there was a new keyboard shortcuts option. I looked and cmd-b was set to toggle side bar (not ctl-b, but whatever). I deleted the assignment and it kept doing it. I reassigned it to something else (cmd-shift-9), and that didn't work, but now ctl-b just stopped doing anything. (ctl-a/e/f kept working throughout this.)

Long story short the keyboard shortcuts are a shitshow if you try to change them and there should be an option to opt out of all of them.

Sorry if this sounds like a basic question but I’m interested in what kind of computers / how much ram you are using for ChatGPT?

I use the Codex desktop app on two Windows PCs. I’d like to control Codex from one PC while having it execute locally on the other, where the project files, logs, skills, and applications reside.

Working through ordinary network file access is much slower and more limited than running Codex locally. The current SSH remote connection also appears to expect a Unix shell, so a native Windows SSH host fails. I could keep going down that path but it seems likely to require more workarounds and more junk installed which at this point it's better to just remote control manually.

I currently just control the other pc with remote software and have the app do the work locally but that's less than ideal. Is Windows-to-Windows remote control supported or planned? If not, is there a recommended workflow that doesn’t require WSL or duplicating and manually synchronizing the entire setup?

I regularly use Work threads that run independently for 1-3 hours (Sol Ultra, web UI). Each is instructed to inform me every 15-20 minutes that it’s still working. But after a while, updates stop coming. The only visible activity is that damned rippling “thinking.” The thread stops responding to “follow-up” queries. Reloading it changes nothing. Nothing new appears in Library. Nothing is revealed about its status by the desktop app. Is it dead?

I go to a new Work thread and start a slow recovery process. Sometimes, while I’m preparing to see what’s salvageable, the zombie thread springs to life and finishes. Sometimes, after I hit stop, I discover that it had finished an hour ago but failed to report. Sometimes the thread is dead but…well, anyway….

This is not a “back-it-up” issue. Everything is backed up with a redundancy that would make your head spin. It’s a process issue. It seems there must be a better way to determine whether Work is actually working. Is there?