r/ChatGPTPro

Hi, i have been using ChatGPT plus plan for Codex, but i am using only around 20-30% of weekly limits and around 70% gets expired every week. Free plan has only weekly limit and no 5 hour limit, it gets exhausted in 3-4 days.
In pricing page, Go plan doesnt show codex. Does GO plan include any extra codex limits or is it same as free plan for codex use?

I am finding it is significantly degraded in performance. I am starting a new context too for basic UI work. We have been stuck with very simple CSS issues, and I know codex is bad at UI but the issue is that it keeps claiming that it fixed something but when I inspect the code, it hasn't done any of that. It keeps flagging conversations as cybersecurity risk when we are just fiddling with strictly UI terminology.

The way I see chatGPT used by management at my company is frightening. Instead of doing any actual research, they just throw things into chatGPT blindly and run with it. They are extremely confident in the output. They are more confident in the chatGPT output than their own staff.

They have no clue how the models work. They have no clue how to engage with the models. They aren’t even reviewing the output.

Today my VP said that she ran our procedure document through chatGPT in order to determine what improvements could be made to the process. She sent me an email with the output. We had no meetings about the process, no analysis or process flows. No data or metrics. No hypothesis, pain-points, etc.

She then asked me to upload to chatGPT emails and notes about a situation, and ask it for advice on how we could do better? I put a question mark because I’m still not even sure wtf she wants.

A different VP asked it to search his emails and to summarize all the points related to a very specific topic. A highly visible topic that we have been discussing for 18 mos. He sent the output summary to everyone to use as the basis for an important memo. He did not check or review the summary. He didn’t compare it to any existing documents or analysis for context or relevance. It basically summarized any idea, thought, or comment on the subject. It was terribly inaccurate.

This is happening more and more.

genuinely asking because it’s borderline unusable for me right now lol

responses are taking forever, sometimes it just hangs mid-generation, gives errors and a couple chats completely froze. my internet is fine and everything else works normally, so I’m guessing it’s on OpenAI’s side?

feels way slower than usual today..

anyone else getting this??

Took me embarrassingly long to figure this out. For most of the year i threw every task at whatever my default model was. Drafting, code, quick lookups, the heavy reasoning stuff, all one model. The bill kept creeping up and i was paying premium rates for tasks a cheaper model handles fine.

So i set up a dead simple routing rule. Bulk stuff and first drafts go to gemini 3.5 flash, its fast and cheap and good enough for like 70% of what i do. Anything that needs real reasoning or tricky code goes to claude opus 4.7. The agentic stuff where it has to actually use my tools goes to openai's 5.5 since the tool calling has been the most reliable for me.

Its not a fancy setup. For a while it was literally just me knowing which tab to open. Now theres a little router in the middle but the logic is the same, match the task to the model.

Two things happened. Spend dropped from around $300 to $140 a month because i stopped burning frontier-model tokens on throwaway tasks. And the output got better too, since the hard tasks now go to the model thats actually best at them instead of whatever was convenient.

The mindset that helped was treating models like a team with different strengths, instead of one assistant i stick with out of habit.

Curious what everyone elses routing looks like rn, do u actually split by task or still mostly running one default

I've experienced business pro recently on an institution I was employed in. But since having been fired I've of course had no access to that. I love codex and love treating it as my little system manager for organizing stuff on my PC, but the free weekly resets are irking me. I just wanna know if personal plus' codex limits are comparable? or perhaps even better (hopefully) before I commit and buy it.

I just uploaded a full match between Claude Opus 4.7 and GPT-5.5 playing the same strategy ruleset involving nuclear silos, uranium management, scouting, and long-term planning.

Both AIs start with the same rules and win conditions, but their reasoning styles quickly go in very different directions. One model is extremely methodical and transparent in its thinking, while the other plays more aggressively and makes faster decisions. The game features resource denial, diplomatic messages, ultimatums, and several critical moments where positioning and timing become decisive.

Here’s a quick non-spoiler rundown of how the match unfolds:

• Early game shows a clear difference in pace and priorities between the two models.
• Both sides eventually bring their silos online while still sending diplomatic messages.
• One model manages to disrupt the other’s uranium income, creating a growing resource gap.
• Scouting becomes crucial as both try to locate the enemy base.
• Several high-stakes moves and miscalculations happen in the mid-to-late game that shift momentum.
• The final phase comes down to who can secure uranium income and reach launch capability first.

The match is a great example of how different reasoning patterns affect long-term strategy and decision-making under pressure.

Disclosure: I'm the developer of AI Toolbox, the browser extension this post describes. Posting because the "no native way to organize ChatGPT chats by topic" problem is widely felt and worth talking about. Link at the bottom per the sub's rules.

Some context first. I've been on ChatGPT daily for about two years. My chat history is somewhere around 600 conversations. Maybe a quarter of them have useful auto-generated titles. The rest are "Untitled chat" or some variation, and I have no idea what's actually in them without opening each one.

For a while I tried to organize this manually using folders, but every system broke down within a week because I had to open each chat to figure out where it belonged. The manual classification work was the bottleneck. So I built the categorization to run automatically.

AI Toolbox Smart Tags Feature

Why doesn't ChatGPT categorize chats by topic natively?

Genuinely no idea. ChatGPT will auto-generate a title for each chat (often useless), but that's the entire extent of "what is this chat about?" metadata you get. There's no topic tag, no category, no way to filter your history by "show me only the coding conversations" or "show me only the research questions." If you want to find a specific old chat, it's a search across titles (most of which are noise) or scrolling forever.

What does the auto-tagging actually do?

After your first sync, every conversation in your account gets classified into one or more of five built-in categories: Coding, Writing, Research, Math & Science, Business. The sidebar grows a Smart Tags section with colored pills (one per category) showing how many chats fall into each. Click a pill, you get a filtered list of every chat with that tag. Click a chat in the list, it opens in ChatGPT.

Each chat can get up to 3 tags if it spans multiple topics. The math-and-coding chat where you asked ChatGPT to derive something then implement it shows up under both, which is the right answer.

How does the tagging actually work, technically?

This is the part I want to be clear about because it's the question I'd ask if I were reading this. The tagging runs locally in your browser. It does not send your chats to any external AI for classification. Zero outbound API calls for the categorization step.

The detection is pattern-based: each category has a list of keywords and signals it looks for in the title and first 10 messages of a conversation, scored against a threshold. Code fences, language names, and SQL patterns trigger Coding. Question patterns like "explain", "what is", "compare" trigger Research. Math operators and scientific terms trigger Math & Science. The patterns are tuned and yes, occasionally miscategorize - I see a few chats per 100 that I'd personally classify differently. The accuracy is good, not perfect.

The results are cached in the extension's local IndexedDB so the classification doesn't re-run on every sidebar open.

Can you make your own tags?

Yes. There's a Custom Tag Rules section where you define a tag name, a list of comma-separated keywords, and a color. The extension matches your keywords against conversation content and tags accordingly. I made one for "Client work" with the client name as the keyword. Works exactly the same way as the built-in tags.

A few details from dogfooding

• Up to 3 tags per chat, with a score threshold. Early versions tagged anything that matched any pattern, and I ended up with chats tagged Coding + Writing + Business when they were really just business writing about a coding tool. Added a score threshold (matches have to score above a minimum to count) and capped at 3 tags. Both fixed the over-tagging problem.
• Recomputation runs after every sync, not on demand. I tried "recompute on every sidebar open" first and the sidebar got laggy when the history was big. Moved it to a post-sync background pass with a Promise lock so two syncs can't trigger overlapping computation. Sidebar stays snappy.
• Pattern scanning only covers the title and first 10 messages. First few messages are by far the strongest topic signal in a ChatGPT conversation. Scanning the full conversation buys ~1% accuracy and costs a lot more compute time. Stopped at 10 messages, accuracy stayed at the same number.
• Custom rules use word-boundary matching. Naïve substring matching meant my "client work" rule was matching chats about "clients" generally, which wasn't what I wanted. Switched to word-boundary regex and the false-positive rate dropped to roughly zero.
• Color choices are intentional. Blue for Coding because it's the most common dev-tool brand color. Green for Research because it reads as "information." Amber for Math because it stands out from the others. The colors aren't arbitrary; they're cues for fast visual parsing when you have a sidebar full of tag pills.

How does the workflow look?

Open ChatGPT. After the first sync (takes a minute on a long history), the Smart Tags section appears in the sidebar with the five colored pills and your counts. Click a pill to filter the chat list to that category. Click any chat to open it. To add a custom rule, open Custom Tag Rules, type a name, type keywords (comma-separated), pick a color, save. Tags recompute automatically.

For my 600-chat history, the first sync and classification was about 90 seconds. After that, new chats get tagged on the next sync automatically. The "where is that SQL query chat from a month ago" lookup that used to be 5 minutes of scrolling is now a 4-second click on the Coding pill.

Answer any or all of the questions below. Thanks!

Lawyers

Writers

Editors

Educators

Managers

Mathletes

Second-Language learners

Translators

Researchers

Consultants

Doctors

Anyone who does NOT use ChatGPT to code at all, what made you try/upgrade to the much more expensive Pro sub than just Plus, other than the obvious higher usage limits?

Is it worth the price?

How much better is 5.5 Pro-Standard than 5.5 Thinking-Standard? How about 5.5 Pro-Extended?

Have you tried Google AI Ultra? Which has better output?

I run a packaging/manufacturing business and I’m trying to understand how realistic it is to use AI for searching through years of business files and job history.

We have a master PC where Excel work tickets are generated and stored, while I work independently on my own laptop for quotations, design work, dielines, etc. Most quotations are Word documents and the work tickets are Excel-based.

What I’d like to achieve eventually is something where I can ask in plain English:
“Find the latest perfume box job for Indigo”
or
“Pull similar rigid box quotations from last year”

…and have the AI search through the work tickets and related quotation files to return useful results.

I’m not trying to build some fully autonomous AI office. More just a smart searchable business memory layer over existing files/folders.

What I’m struggling to understand is where ChatGPT itself fits into this.

From what I understand, ChatGPT can’t just freely access files on a PC unless there’s some kind of integration/bridge involved. So are people using:
- shared folders,
- cloud storage,
- APIs,
- OpenAI integrations,
- local AI tools,
etc. to make this work?

Would appreciate hearing from people who’ve actually implemented something similar in a real business environment and not just demo videos.

I’m developing a custom MCP connector for ChatGPT. It was working fine, then I regenerated my gateway token and now I can’t find the existing connector anywhere in settings.

I only see “Create app”, but that looks like creating a new connector, not refreshing or reconnecting the old one.

For context, I’m testing this with my own small MCP gateway, so this is not a random public connector issue. The local app can still connect to the gateway, but ChatGPT no longer shows the old connector/tool link.

Where are existing custom MCP connectors managed? How do you refresh the tools/list manifest after changing your MCP server?

Any help will be highly recommended. I can no longer access my app.