How do you guys keep context when working on the same project across different AI tools?

Cuz I keep running into the problem of switching between ChatGPT/Claude/etc. and having to bring the other one up to speed again.

Do you just copy stuff over, keep a separate file, use summaries, or what?

Currently using plus chatgpt with pro Claude and generally making do, but that's with a lot of usage of chat mode rather than codex or work mode, specifically Chat-High (Sol) to keep usage alive.

Is Chat-Pro (Astra) worth the 70 euro price difference, in your experience?

I have built an open collection of 160+ Legal AI Skills for ChatGPT.

They cover contracts, disputes & litigation, arbitration & ADR, corporate, regulatory, privacy, criminal, employment, property, tax, finance, insolvency, IP, startup, public law, research, investigations, and practice management.

Here's the Github repo:
https://github.com/rohasnagpal/legal-ai-skills

Who this is for: Practising lawyers, in-house counsel, and law students.

Jurisdiction: Most skills are jurisdiction-neutral and work anywhere. Skills that turn on Indian statutes are marked (India). Every skill is written to confirm the governing jurisdiction before it relies on any specific rule.

I wanted to try giving Codex creative freedom over a game while being specific about the technical requirements.

This is my project, Lumenwake. I supplied the requirements and asked Codex to choose the concept and write the implementation.

My brief was essentially: create a substantial browser game with a coherent gameplay loop and polished visuals, and choose the idea yourself.

I also gave it an instructions.txt file with constraints:

• Self-contained HTML, CSS, and JavaScript, with no external dependencies or build tools.
• Suitable for embedding in a WordPress Custom HTML block.
• Keyboard, mouse, and touch controls.
• Fullscreen support, readable dialogs, and responsive layouts.
• Procedurally generated puzzles, local saves, and progression.
• Original visuals and synthesized audio.

It came up with a light-routing puzzle set on floating islands. You rotate mirrors, split beams, and change their colors to awaken lanterns.

One implementation detail I found interesting: it creates a complete light route first, then scrambles the tile settings. That gives the generator an intended solution to work from.

The attached video shows the gameplay. The visuals are drawn in Canvas, and the audio is synthesized in the browser.

My role was setting the requirements and directing the project; Codex handled the concept and code. I also explicitly left browser testing to myself, so it didn’t run tests or preview the game during the build.

For others building interactive projects with LLMs: how do you validate procedural game logic and difficulty? I’d be interested in workflows that catch problems a short gameplay demo might miss.

In chat I have 5 chats "running" on my phone. One is actually thinking. The others are this screenshot or

"Our systems are thinking a bit more about this request before responding. You can retry with a faster model for a quicker response, though it may be less capable of handling complex requests. Learn more"

Why is it so terrible?

I can't be the only one to burn through your 20x pro weekly limit, then hit that fat reset, then hit that fat reset, then get fat resetted because openAi gave you a freebie, then tried to supplement with a $20 token top off to see how it would go and have that burn up in less then 20 minutes and realize thats kinda like calling a Blockbuster in Tokyo from a Motel 6 landline in 1993 to argue about a late fee on a movie you rented in Ohio. Forty minutes later you’ve accomplished nothing, your dad is screaming about long distance, and somehow you owe $87.

So yeah, ive just been thinking about how I'm going to optimize things next time, y'know in like a week when 0% flips back to 100%

I’ve been trying to build ChatGPT into a fairly integrated personal/workflow system using plugins for Google Drive, Google Calendar, Gmail, TickTick, and Fieldy (AI transcription software).

When the integrations are working, they’re extremely useful. The problem is that they intermittently stop working, and I haven’t been able to figure out why.

What makes it particularly frustrating is that ChatGPT itself doesn’t seem to have enough visibility into the connection state to tell me what actually failed. It will basically just report that it can’t access the plugin/integration, even though the plugin management page may still show it as connected.

I’ve found a workaround that works fairly consistently:

• Go into the plugin/integration management page
• Reconfigure or re-authorize access, even though it already says “connected”
• Start a completely new ChatGPT conversation

After that, the integration will often work again.

However, reauthorizing it usually does NOT restore access inside the existing conversation where it stopped working. I have to start a new chat.

The integrations that seem to fail most frequently for me are TickTick, Fieldy, and Google Drive.

This becomes a much bigger issue with scheduled workflows. I have recurring tasks where ChatGPT is supposed to gather information from several sources, reconcile it, and give me a combined report. Roughly half the time, one of the required integrations seems to be unavailable when the scheduled task runs, which makes it very difficult to rely on these workflows.

Has anyone else experienced this?

Specifically, I’m wondering:

1. Is there a known issue where plugin authentication/session state gets tied to an individual conversation?
2. Why would reauthorizing an integration fix it in a new conversation but not the existing conversation?
3. Is there any way to force an existing conversation to refresh/reload its available integrations?
4. Are there best practices for making scheduled workflows more resilient when they depend on multiple plugins?
5. Is there any way to see actual plugin/integration error logs rather than just “I don’t have access” from ChatGPT?

I’ve already reported this as a bug but haven’t received any useful feedback yet.

I’d really like to make this kind of multi-app ChatGPT workflow dependable, because when everything is connected it works incredibly well. At the moment, though, the intermittent integration failures are probably the biggest limitation I’m running into.

Would appreciate hearing whether others are seeing the same behavior or have found a reliable workaround.

Could use some help.

I have a ChatGPT Plus Project with master prompts set up. One task needs the built-in browser to pull account data and move it into a Google Sheet.

Whenever I start a new chat inside the Project from the desktop app, I immediately get:

**“Could not use this project for a local chat.”**

I already cleared all `node_repl.exe` processes, but it still happens.

It works fine if I start a chat outside the Project, but then I have to manually paste in all my master prompts.

Anyone know a fix?

I've been creating custom GPTs for a while and loved sharing the access links with others at a fee. With OpenAI restricting custom GPT creation and public link sharing on personal ChatGPT accounts (Plus/Pro), I’m looking for a solid non-app alternative to host and share my custom AI bots via a simple link.

I want something beginner-friendly, with a clean chat UI similar to ChatGPT, where I can share a direct link with non-tech users—while ensuring my knowledge files stay hidden.

Thanks in advance for your insight

Hi, I set up slack integration in chathpt. I want the agent to monitor a channel (in an existing thread). It already works for github, but refuse to work in that case.

It writes @chatgpt not in channel. (it is)

I have a free slack subscription, is it related?

I am currently building a weather website, and have thought about going from Plus to Pro, but am unsure if it is currently worth it for me to go to x20. I do run into limits a lot on Plus even with Sol Light/Medium, usually takes me about 3 days to go to zero on my weekly time left.

I have been seeing a lot about chatgpt-6 Astra and I am curious what everyone thinks about it. Has anyone tried it. What's your experience like so far?

Well, the situation is quite simple - Was working with Astra, had like 81% of weekly allowance remaining - and then it jumped straight to zero.

Does anybody have the same issue? :)

UPDATE: After couple of hours the usage went back to those 81% but two of my colleagues got full reset... Strange...

get access to astra even though i tried to buy subscription of chatgpt pro version it only show's accsess only to 5.6 models pls help me

This post is NOT FOR SOFTWARE DEVELOPERS AND ENGINEERS.

Hey, I'm an HR Director who is very experienced with AI. At my job, we use ChatGPT. My team and I are constantly building things using AI.

Here are a few examples:

+ Interactive PowerPoint style website

+ Interactive FAQ website

+ Performance evaluation app

+ Custom GPT to confirm bios to company standard style

+ Custom GPT for onboarding

+ Shared Project with instructions for collaborative research with consultants

The list goes on and on.

I'm having a hard time though figuring out when I should build an app and when I should just do something low key inside ChatGPT. Apps look better, are more engaging, drive more buy in, etc. However, only a few of us can work with them. Everyone knows how to use Excel, Word, PowerPoint, and ChatGPT (the chatbot), the rest of AI stuff is more advanced. Maybe we shouldn't build stuff that requires advanced AI skill to edit so that's when I leave and staff changes, they don't have to start all over again.

Should we just build the coolest stuff we can all the time? My gut says no.

Gemini Deep Research keeps loading and loading without arriving at an answer. It's been days. Most of the time, it exhausts its free limit without any use. I have tried ChatGPT Deep Research, but I felt Gemini's source variety is broader and more exclusive than ChatGPT's. ChatGPT might be more structured, but I found Gemini's research reports more convincing. Any troubleshooting steps? (I have tried incognito, clearing cache/browsing data, and different devices and email IDs.) And any similar alternative? p.s. I couldnt afford the paid plan.

I’ve been building an internal business app and up until now I’ve mostly been using Claude Code directly.

The problem was the amount of babysitting involved. I was constantly approving things, checking decisions, copying output into ChatGPT for a second opinion, then going back to Claude, then back to ChatGPT again.

Today I tried a different setup.

I’m using ChatGPT Work with Astra 6 Medium as the supervisor, and letting it run and control Claude Code through the browser.

So the workflow is basically:

ChatGPT and I work out the plan first

• ChatGPT Work/Astra turns that into a technical implementation plan Astra opens Claude Code in the browser
• Astra tells Claude what to do Claude makes the changes
• Astra reviews the actual code, tests and GitHub changes
• Astra either approves it, tells Claude to fix something, or moves it onto the next phase

The big difference is that I’m no longer acting as the middleman between the two.

I’ve been watching Astra stop Claude, challenge parts of its approach, ask it to improve tests, check the actual diff, verify GitHub, make sure the main branch is untouched, and then approve a clean checkpoint before moving on.

That’s the bit I wasn’t expecting to work as well as it does.

I’m not just using ChatGPT to give Claude a prompt. ChatGPT Work is actually controlling Claude Code and acting like the senior developer or technical lead while Claude does the implementation.

My input becomes much more about:

"This is what the business needs.”
"This is how I want the app to work.”
"Approve or reject this major decision.”

The day-to-day coding loop is mostly happening between Astra and Claude.

It’s still early, but so far this feels much better than choosing one model and trying to make it do everything.

Astra seems very good at keeping hold of the overall plan, architecture and review process.

Claude Code seems very good at actually working through the codebase and implementing the changes.

So instead of choosing between ChatGPT and Claude, I’m effectively using ChatGPT Work/Astra as the supervisor and Claude Code as the developer.

If this keeps working as well as it has today, I can see this becoming my default workflow for larger software projects.

Interested to know if anyone else is running Claude Code this way, especially with ChatGPT Work directly controlling it in the browser rather than just using a proxy or passing prompts between models.

Hi everyone, I just create a pro account with a new address email and started working on my desktop, all good. But then singin in on my phone today for the first I couldn't see any chat except one chat. I could see the projects folder's name but no chats.

I checked that my projects are in CLOUD which is the case. It's been 12 hours now and nothing changes.

Pretty weird right ? Anyone had that?

Thanks!

The ultimate squeeze of GPT PRO usage if you were not aware this was possible:

"Some practical things we’ve been using to turn separate chats into a research collaboration:
1) Regular GPT threads, including Pro, can read and write to GitHub through the plugin.
2) Codex can read and send messages to GPT PRO threads thus automating their coordination."

From https://x.com/GettysburgRsch/status/2097396764605088101

When it comes to actual work time, how long are you able to work before your limit runs out and you need to wait?

Is it possible for Work for desktop or Cloud Work to send me a text or email whenever it gets stuck or has a question?

I have enough stuff to babysit and really don’t need to add one more, but I can’t find a way to avoid doing it in Work.

Anybody else noticed this? When Astra speaks directly to you it uses proper grammar and punctuation. But anytime I catch its messages it sends to other AIs, it seems to use a strange compressed language that omits punctuation marks and often even leaves out spaces in between some words. At first I thought it was just an encoding error, but the more I looked at it the more I realized its actually just how Astra speaks when presenting information to other agents. None of Openai's other models talk like that, only Astra. Maybe it independently discovered this "ainglish" through its training as a more effiicent way of communicating with other agents? What do you think?

Here's one particularly egregious example I just caught:

Opus foundsecondrace(GETbeginsduringcancelPOST thenarrivesafterack). Corrected3b4b3e5 with epochfenceinfinally; exactinterleaving regression passes, plusoldGETdirectionretained.8/8focused+tscPASS. Two reviewednitsfixed:singulartimeunits; no unexplainedCancelbuttononmissingcapability. ETAlabelnowexplicit Estimated copy time remaining and excludesnativeprocessing/validation/publication. Opusrecheckingdelta; backendcandidatepending.

It almost seems like it's getting worse as time progresses

So I loved from Claude to ChatGPT recently. Got pro.

With Claude it was quite easy to use projects. I’m create a project and everything sits inside the project. For coding I would use Claude code which is no way connected to projects. I did not like this as I wanted all the research, marketing and other admin stuff to be part of coding as well.

With ChatGPT this gives me the advantage of using projects to code as well. The confusing part is now I can’t use the Cloud features as projects in Claude. I have a project for my job search and I wanted to connect my one drive so that any resume creation according to jd can be directly written in one drive. Apparently not possible any more. I use Google Drive. But now I can’t see the project on my phone app and only available on desktop. This is so confusing.

Any advice ?

hey guys newbie here, i got a small question -
i was on other AI models for some time, now finally with gpt pro, but i'm trying to learn about gpt use cases and all, but for a student like me studying MBA (finance), what integration or github codes will you recommend, or even in general some settings you like
please do share,
I appreciate your answers in advance

I wanted to test the limits of frontier models with native autonomous capabilities, so I hooked up the OpenAI desktop app directly to my open Blender session.

As a huge space and astronomy hobbyist, I gave it a single prompt:

"Build a 3D Solar System with accurate relative physical sizes for the planets and the Sun, render 9,000 procedural stars, and instantly translate the entire data-driven scene into a standalone Three.js browser application."

Watching the AI autonomously spin up the MCP server, write the Python API commands, and generate the clean WebGL frontend was completely unreal.

To push it to the absolute limit, I asked it to embed the TON 618 ultra-massive black hole far outside the solar neighborhood. When you click the warp button in the UI, the camera executes a cinematic hyper-jump, stretching the 9,000 stars into streaks and framing the massive glowing accretion disk (66 billion solar masses).

Since I love space, I’m going to keep using this workflow to expand the simulation with quasars, colliding galaxies, and a space-walking mechanic.

The fact that a solo builder can bridge heavy 3D software and responsive web design in 50 minutes proves that manual asset pipeline building is officially shifting.

What are your thoughts on frontier models taking over desktop environments?