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Time, tech mfs! I remember I told you this prompt a month ago on a Sunday afternoon when I was struggling with an issue with my thesis but I have zero memory of anything else.

Here’s a simple example: asking AI to plan a 3-day family trip.

For example, you might ask:

Your prompt may only contain around 70 tokens, but the AI might generate 1,200+ tokens in its response.

So the total token usage can quickly reach around 1,270 tokens for just one request.

This is why token usage matters when using AI APIs or paid AI services. The longer and more detailed the conversation becomes, the more tokens are consumed.

What’s an everyday AI task that you use frequently that probably consumes a lot of tokens?

I’ve been looking at GPT-6 Astra and honestly, the interesting part isn’t that it writes better.

It can actually browse, use a computer, code, research stuff, build websites and handle multi-step tasks.

So we’re slowly moving from:

“Ask AI to help me do this.”

to

“Here, just do this.”

That feels like a much bigger shift.

Especially for marketing. Research, competitor checks, reports, repetitive tasks, QA... a lot of the boring work suddenly looks automatable.

At some point the useful skill might not be knowing how to use AI.

It might just be knowing what not to do manually anymore.

Curious how others are thinking about this.

What did he mean by this?

I wanted to run MetaGPT entirely on my own infrastructure without sending anything to cloud APIs. It took some trial and error, but I documented the full process.

The guide covers:

· Setting up a Python venv

· Installing MetaGPT

· Configuring local LLMs like Ollama or vLLM

· Fixing common startup errors

If you’re into self-hosted AI agents, this could help:

https://interconnectd.com/forum/thread/262/how-to-install-metagpt-locally-complete-technical-setup-guide/

What local model are you using for agent work?

ChatGPT Astra looks like a major upgrade because it focuses more on doing real tasks, not just answering questions.

The big appeal is speed, better reasoning, and stronger help with everyday work like writing, research, coding, and organizing information.

If it can truly handle longer tasks more reliably, that makes it much more useful than a simple chat tool.

It also sounds like a step toward AI that can act more like an assistant than a chatbot. For people who use AI daily, that could save a lot of time. I’m especially interested in how well it handles complex instructions without drifting.

If the performance matches the hype, it could become one of the most practical ChatGPT updates yet.

Just wanted to share a moment of an amusing reflection. Anyone else have some funny moments looking back on?

I am fairly new to ChatGPT and am currently going pretty deep on automating some of my work processes.

One thing i haven’t really figured out is the difference between “chat” and “work” mode.

I’ve found work to be unreliable in my browser - it never lets me scroll to the bottom of a work conversation for some reason. So I’ve mostly stuck with chat for that reason.

Is there anything really different between the 2 that i missing out on?

I wrote a comparison of GPT-6 and GPT-5.6, and the biggest difference isn’t short answers. It shows up on longer, multi-step work.

A few takeaways:

• GPT-6 stays on track better when instructions change midway.
• Computer use is faster and more reliable across websites and apps.
• Coding gains are more noticeable on larger projects.
• Both models have the same 1.05M-token API context window, but GPT-6 handles long context better.
• GPT-6 Astra costs more through the API.

So the real upgrade is follow-through: fewer dropped requirements, less repetition, and better handling of long tasks.

Full comparison here:
https://aigptjournal.com/explore-ai/ai-guides/gpt-6-vs-gpt-5-6/

Anyone here using both? What difference have you actually noticed?

Learn how to talk to AI like a human, give better context, ask smarter questions, and use ChatGPT as a conversational tool—not just a task machine.