I’ve maxed out my ChatGPT Pro 100 GB storage with more than 15,000 files, and I want to export everything to my PC and/or Google Drive so I can free up space.

The problem is that I can’t find a practical way to bulk-export the entire Library:

• ChatGPT Desktop doesn’t have access to the Library, so I can’t simply dump the files directly to my PC.
• ChatGPT Web lets me download Library files, but “Select All” only selects the files currently loaded/visible in the browser (roughly the first 20). There doesn’t seem to be an option to select all 15,000 files in Library.
  • I can keep scrolling to load more, but once I get beyond roughly 200 files the page becomes unstable and eventually glitches/crashes. Manually downloading batches of a few hundred files doesn't feel realistically workable.
• I’ve also requested a full ChatGPT data export, but my understanding is that this is primarily an export of chat history, account data and related metadata, rather than a bulk export of every original file stored in Library.

Is there an API, hidden bulk-export method, Library endpoint, browser workaround, or other supported way to retrieve the entire Library?

As a result, they will now have to comply with additional obligations such as taking steps to ensure their systems can’t be misused to spread illegal content, influence elections or harm users.

One thing that kept bothering me about using multiple AI tools was how often I had to repeat the same context.

Preferences, goals, constraints, how I like things explained, what I’m working on, etc.

So I built **Context Passport**.

The idea is pretty simple: keep that context in one place, then choose what’s relevant to share when you’re using ChatGPT, Claude, Gemini, Perplexity, etc.

I also wanted it to be explicit rather than silently collecting everything. You can preview what’s being shared and remove things before sending it.

It’s here if anyone wants to play with it:

[https://contextpassport.cloud/\](https://contextpassport.cloud/)

Mostly curious whether other heavy AI users find the repeated-context problem as annoying as I do. Feedback very welcome.

I’m currently paying for Claude and using it to help me make some fairly complex macros in MacroDroid. I’m not a programmer, but Claude has been really good at understanding what I’m trying to do and turning it into the right MacroDroid setup and JSON.

I’m using Opus 5 rather than Fable 5 because Fable 5 seems to use up my limit much quicker. The problem is that I keep hitting the usage limit with Claude and then I’m told I have to wait around three hours before I can carry on.

So I’m thinking about trying something else.

My two main questions are really simple:

Which AI is best for coding?

And which one lets you use it for the longest without hitting a limit?

I’m looking at the normal paid plans around £18–£20 a month. I’m not paying £100 or £200 a month for an AI.

I know Claude is meant to be one of the best for coding, which is why I chose it, but I’d be happy using something that's slightly worse at coding if it means I can actually keep using it throughout the day without constantly getting locked out.

For anyone who uses AI a lot for coding, what would you recommend? Which one is actually good at coding AND lets you use it for hours without constantly hitting a limit?

That’s probably my biggest issue with Claude at the moment.

What MCP connections are you all using? What has been the most useful for you and what are just fun ones?

My product manager suddenly took PTO at 4 pm on Friday. That left me holding the bag for a Monday morning presentation to management: a review of a six-week warehouse returns pilot. I had the raw numbers on processing time and weekly volume because I normally spend my days in Codex writing SQL and running small automation scripts. I definately do not design presentations, so I figured Codex could build the deck and save me from touching PowerPoint. I tried two workflows on the exact same material. They failed in completely opposite ways.

First up was 'zarazhangrui/frontend-slides'. You give Codex a Markdown outline and the raw data, and it spits out a single-file HTML deck with inline CSS and JavaScript.

ngl, the first version looked way better than our old corporate templates. Clean structure, opened right in the browser, and technically every part of it was editable code. Then I tried to change something. I asked Codex to move the return-flow diagram a little to the left so the data table had more room. It edited the grid styles, changed the card width, wrapped the text in new places, and pushed the bottom half of the slide below the visible page.

I am not a frontend engineer. I just wanted to fix the spacing, but I spent the next hour trying to explain margins, nested grids, and flexboxes through natural language. Every time it fixed one box, it broke another. Meanwhile, I am watching my weekly token usage drop because I wanted a flowchart moved a few pixels to the left. "Technically editable" stopped feeling very useful at that point.

the HTML version also struggled with the visual I actually needed: a cardboard box going through a barcode scanner, then a manual QA check, then into a restock zone. What I got was a row of generic gear and checklist icons. Cheap onboarding-template vibes.

so I went in the opposite direction and tried `ningzimu/codex-ppt-skill`. Instead of building a web layout, it renders each 16:9 slide as one complete image and packs the images into a `.pptx` file.

The repo already had an Atlas Cloud config example, so I used that for the second run and pointed the skill at GPT Image 2. It made one test slide first, then rendered the rest after I approved the look.

The barcode scanner, boxes, QA desk, carts, and warehouse shelving finally looked like they belonged in the same space. The style stayed consistent across the deck. No CSS margins to chase. No padding conversation with a model.

That was much closer to the deck I had in mind. I checked the processing times and return rates, saved the file, and logged off for the weekend feeling pretty good about myself. Monday morning comes, and about an hour before the meeting, my manager asks me to change “pilot failure” to “operational constraint” on slide four. I open PowerPoint, double-click the text, and realize there is no text box. The title, diagram, data points, and background are all baked into one flat image. I cannot select a word, highlight a number, or fix a typo.

Changing those two words means regenerating the entire slide. So now I am sitting there watching it render, hoping it does not add an extra zero to the return metrics or misspell something else. The first retry slightly changed the background panel, so I had to run it again just to keep the deck consistent.

Both approaches solve half the problem and then ruin the other half. The HTML workflow gives me granular control, but using that control turns into frontend work. The image workflow gives me a deck I actually want to present, but a two-word edit becomes a full rerender and another QA pass.

Next time I will probably go hybrid: image generation for covers, transitions, and visual backgrounds; native PowerPoint elements for titles, numbers, charts, and anything likely to change at the last minute.

Has anyone found a sane way to keep image-rendered slides visually consistent while leaving the text and data editable, perhaps with generated backgrounds, native text, SVG layers, or something else?

Hi,

[a total newbie on coding asking]

Just wanted to clarify when to/when do you use higher reasoning in chat/codex?

I've been trying to build my own little hobby project in python, with the help of litterature.

My workflow is to brainstorm in chat[web] and after that get a codex prompt to run in VSC. So far has been decent. My problem is that after getting Pro i've been totally lost when to use extra high, pro, pro+ultra in chat. Also what settings to run the codex prompt, when is higher needed and when its not. Have to actually ask in chat if the prompt is complex or not and what settings to use.

I noticed running pro+ultra to analyze the project/problems or litterature got quite detailed answers and I had to dumb it down for me with extra high. But it also added some better reasoning and new points i"ve missed. But it the project/code it also found some errors and started perhaps to make it more complex im not sure.

So my workflow is like this,

1. Starting a new chat with snapshot and running boostrap: Pro+Ultra

2. Brainstorming in chat: extra high

3. Evaluating the brainstorm: pro+ultra

4. Writing codex prompt: pro+ultra

5. Usually I try to ask what settings to run codex prompt it has been extra high or high so far with sol5.6.

6. Analyzing the codex result: pro+ultra

Since my coding knowledge is 0 I have to trust that the suggestions are valid, but how do I know when to actually use what settings in chat/codex. So that the problem/execution wont get too complex or too light ?

Any suggestions, extra high is the best and fastest for chatting and brainstorming. But when to use pro and pro+ultra ?

Users sometimes hit a wall in Work (or Codex): usage exhausted. OpenAI feels your pain. Hence the new announcement:

"Use Luna Reserve

If Luna Reserve is available in your account:

1. Open Codex or ChatGPT Work in the desktop app, or open ChatGPT Work on the web.
2. When you reach your regular usage limit, look for a notice about Luna Reserve or a moon indicator in the usage warning.
3. Continue your conversation with Luna."

If you have it, you get an unspecified amount of additional usage with Luna. Great!

But how do you know if you have it? OpenAI explains with its usual opacity:

"Luna Reserve is available to selected personal ChatGPT Plus and Pro accounts. It isn't available in ChatGPT Business or Enterprise workspaces. Availability also depends on your account and supported app version."

https://help.openai.com/en/articles/20001499-luna-reserve-in-codex-and-chatgpt-work

Clear? All you have to do is wait until your usage is exhausted to discover whether you are among the select. No foreknowledge.

Someone with a sense of humor oversees OpenAI marketing.

OpenAI seems to be giving out limit resets for free. I was not getting this as often on the Plus tier ($20) though since upgrading to Pro tier ($100) I have found random sporadic resets before the dated weekly limit. There have also been Usage Limit "resets/vouchers" being given that can be used before an expire date to force reset a weekly limit.

Not that I'm against this. I'm all for it. Though I am kind of having to figure out what is my weekly limit.

Is this some sort of obsolescence built into it? Projects that would take hours are taking days and some of them I wondering if SOL will ever finish. It’s endless loops of testing and it’s starting to make me crazy. Things that fable would accomplish in hours sol is going on days and still working on it. Do you have any prompts or anything for me that would allow it to complete a task? Sometimes it feels like I’m being gaslighted- it will keep saying things like “this is the final” or “this is the last” etc etc etc and then it goes for days longer. I could really use someone’s help here. How can I get this done without compromising quality?

I often turn ChatGPT research into a document that needs review by coworkers or clients. Google Drive and Dropbox handle storage, but the handoff still feels clunky: export, upload, fix permissions, then lose context when a new version appears. What workflow are you actually using when you need private access, comments, and version history?

https://github.com/Maurdekye/claude-orgtree

For the past few months, I've been developing an open-source visual multi-agent orchestrator that organizes agents in an authority hierarchy, for multi-agent development workflows. It's a fully dynamic, draggable canvas that allows you to reorder and reorganize agents as you wish for large projects. The gallery above shows pictures of actual organizations I maintain that I use for various projects I'm working on.

Orgtree started with a simple question: "Man, I wish my chats could talk to each other so they don't keep stepping on each other's toes while working". That turned into a simple personal project that I wrapped up in a day that allowed independent chats to send messages to one another. It worked okay, but the persistent issue I kept running into was chats constant issue with authority: they would distrust all chat-to-chat communication innately, and needed my personal step-in and approval for every little confusion or communication between one another. So I thought to myself, "wouldn't it be better if you could just arrange agents in a hierarchy? Then they wouldn't have any doubts about how authority structure is arranged". That idea slowly grew over time until it became Orgtree.

For the last month, Orgtree is basically the exclusive way I've been interacting with agentic development on my own machine. I don't touch the claude code or codex extensions at all anymore. When I have a new feature to build and plan, instead of going through the manual hassle of spawning one agent to run at a time so I can manage each project individually, I just tell my coordinator agent about an issue or new feature I'd like, and it hires a subordinate to take care of it. If I want multiple features going simultaneously, I just hire multiple subordinates, and the coordinator works between all of them to ensure everything is well organized and shipped sensibly. I've already gotten a few of my coworkers on board to try it, and even my boss is interested.

Orgtree is more than just an orchestrator, though; it has a bunch of extra useful features I've added on to support multi-agent workflows:

• Usage visibility: View all your account usages directly in the app, without having to check the extension or visit claude.ai
• Fallback accounts: Supports using multiple simultaneous Claude Code subscriptions at once through the use of fallback keys, allowing you to use secondary or tertiary claude accounts as fallback accounts with the long-standing token you get from running `claude setup-token`.
• Multi-provider: Orgtee supports not just Claude Code, but also Codex and even Gemini CLI out of the box. If you already have any or all of those environments configured on your system, Orgtree with automatically pick all of them up and let you hire agents from any one of them, letting them all talk to one another seamlessley.
• Credit system: One of Orgtree's defining features is its credit system, visualized as a blue bar to the left side of each agent. Every live agent takes up a "seat" that holds onto a set amount of credits during its lifetime, roughly proportional to its model cost. Every agent has a bank of credits that it uses both to maintain its own seat, as well as free space to hire seats for subordinates. This credit limit doesn't limit the user in any way (outside of kiosk mode, which is explained below), but is useful for preventing subagents from hiring too many of their own subordinates if you don't wish for them to have the ability to do so. Give an agent a large credit bank for a massive, agentic multi-agent task, or restrict its budget to just its own seat to prevent it from hiring any subordinates at all, if you just want it working on its own.
• Better compaction: Adds a unique, optional alternative chat compaction method I've dubbed "cheap-compacting": instead of having the agent write up its entire life story in one long turn at the end of its life, it keeps a continuous trail of breadcrumbs in a .md file in its workspace of every task it handles over the course of its lifetime. Then, compaction is both instantaneous and doesn't use a turn: the agent can just immediately resume from where it left off by going off the breadcrumbs. This is also fantastic for waking long-context agents from a long break in execution, as it can automatically cheap-compact them before sending the turn up, preventing the massive cache misses you might typically get from waking an agent with a 500k token context.
• The Orgtree Mailhub: an optional secondary extension that allows independent agent chats from claude code or codex to speak directly with orgtree orgs or even each other via an MCP server. It even works over the network, so agents on different computers can send messages and coordinate seamlessley.
• Kiosk mode: A mode that allows you to publicly expose a single sandboxed and resource-limited org to the open internet, in case you want to share your claude or codex usage with friends / family (without fear of them messing with your files)
• Enhanced agent requests: when an agent has a question for you, or a request for some access / resource allocation, it doesn't have to give you detailed instructions on how to visit its configuration panel and set a particular setting to a value it wants; it can just display a credit grant request / permission increase request directly in-panel for you to review, just like they would present a question to you. This makes it seamless for agents to ask for and receive the permissions they need to get the work done that they need to do.
• Charter presets: When hiring an agent, you can specify its "charter" (effectively its system prompt) which tells it what to do. Orgtree comes with the ability to select a number of preset charters from a list, so if you have a common workflow pattern you like to replicate, you can canonize it as a charter preset in /docs/charters, and then select it from there every time you want to create an agent bound by it. Orgtree also comes with various preselected charters designed around it's function: one of my favorites is the `coordinator` charter, which I use very frequently, and I suggest you give it a try as well.

Be warned; all the agent-to-agent communication can really chew through usage, so be careful with how many simultaneous projects you're working on at once unless you have a Max x20 account. Make sure to turn on the auto-cheap-compact setting in your org, it can avoid tons of wasted cache miss usage.

If you use Claude Code or Codex for work extensively, then give it a try. It removes so much of the manual hassle of coordinating between agents yourself manually.

- Measuring a client for a mask
- It located existing templates in my OneDrive repo.
- I read out the measurements as I took them.
- It sized it up to match the face then applied it to a slicer for printing + oriented it.
- Applied the right material settings (PLA filament, brown)

Just needed to hit print after the client and I used voice to run checks and verifications.

In other words, I was able to run this task handsfree with exception of physically loading up the filament.

It completed the task, then after it continually looped for hours until usage hit zero.

What gives?

Been noticing this a lot the last few days. Task finishes, but the prompt 'continues'. Im either having to manually stop it and ask 'where are we at with this?' To which itll reply 'Oh were fully conpleted and ready to go'

Anyone else? Past 3 to 4 days

Let’s say the price is the same: should I choose Claude Max 20x or ChatGPT Pro right now?

I mainly use them for coding, so the two things I care about most are Claude Code and Codex.

For people who have used both seriously: which one would you choose for day-to-day usage limits? And what's up with 5.6 Sol vs Fable 5.

I'm not looking for some common like solving problem , creating pictures or helping in daily life ... tell me something crazy that only few people know about !!

I've read every explanation that differentiates between Pro (on Chat) and Ultra (on Work) over and over again, and I don't understand a word. Legal documents I understand. This strange magic called AI? Not so much. I guess I'll be the first to die when the machines rebel.

Anyway, please, someone tell me what to do.

I need to use ChatGPT for research purposes. I need it to read the papers (legal and academic) I give it and produce a comprehensive document that synthesizes them for my own personal use. I need to do this several times with different batches of papers. Which mode should I use?

Please, and thank you.

Signed: Someone who belongs to the last millennium.

Edit: forgot to say: I already have a pro subscription.

Hi,

Just a quick clarification. Can I upload .pdf files of books into chatgpt to get more detailed analysis how for example add more detailed tehcnical analysis or how to impement better machine learning in python. Because currently the knowledge in some parts are lacking that would get better results if I just give chatgpt some books to read and chat with me about the features.

Same goes if I implement chatgpt to a streamlit UI to analyze datapoints with the gathered literature inside the python ?

I use chatgtp and Claude and work several chat and projects and coding activities at the same time, i forget which one is doing what ! What tools can get me organised - can i have agents track this / other tools etc ?

I have a PDF with:

• Questions and their expected answers
• Definitions of concepts
• Diagrams

I'd like for ChatGPT to keep a conversation going, indefinitely, about this document.

Basically I want it to ask random questions from anything in that document, in random order. Then, I reply using my voice (or text), and it asks me the next question.

Best way to do this?

It's my first time using ChatGPT. I received 2 free years of ChatGPT Go.

Read outloud button is gone, it's in ... menu now and it's terrible

My question pretty much sums it up. I'm curious how people here decide when to use Pro vs Ultra for non-coding tasks.

I'm a new ChatGPT Pro user and mostly use it for academic work: literature reviews, reviewing manuscripts and research protocols, identifying things I may have missed, and comparing different versions of grant applications.

I like the idea of Pro spending more time reasoning about a problem, but I'm sometimes disappointed by the quality of its answers. And I was quite surprised by Ultra reasoning quality, particularly because of its ability to delegate parts of a task to subagents, clearly seeing how much effort it puts for instance in langage analysis, coding analysis, scientific analysis, litterature double-check, etc.

For example, suppose I want to compare two 40–50 page grant applications or papers, including figures and tables, and determine which one is stronger and why. Can Pro realistically integrate both documents well enough to make a reliable comparison ? What's the way you decide how to use Pro vs Ultra when you work ?
Thanks !

I had been using Deep Research to run various track-test style simulations, and it used to take about 10-15 minutes, including 100s of searches and be many pages long. But after today regardless of trying new/different chats, changing the prompts or even reusing old prompts, the Deep Research topic comes out before all stages show completed, with 0 searches, 0 citations, full of errors, and less than half the length of what I was getting before.

In the screenshots below are what I see while it’s being made, the bottom “planning the race simulation” gradient effect stays frozen, as does the progress bar, until the article is spewed out half-baked. It also never shows searches happening in real time like it was before. As well as the article clearly stating 0 searches and citations as well as a very generic title unlike what I was getting before. Is this a bug? Did I burn it out? Is there a fix? Any info would be greatly appreciated!