runesleo

Today, ChatGPT’s “task continuity” problem hit me hard.
It wasn’t that the quality of a particular response declined, but something more troublesome:
Some conversations that had been running for a long time suddenly stopped responding;
Tools / MCP that had been working normally suddenly failed to connect in some sessions;
Although switching to a new conversation let me chat again, the task context, execution state, and progress were all very likely to be lost together.
I spent a large part of today not “getting things done,” but restoring “what AI had originally been helping me do.”
This sudde

Lately, I’ve been feeling this more and more strongly:
As the natural-language interfaces of AI Agents like Codex and Grok gradually mature, and connect to your own local workstation, browser, various tools, and the most powerful models in the cloud—
ordinary people are actually getting their own “AI operating system” for the first time.
You no longer need to open a dozen different apps, remember a bunch of commands, or manually string together workflows.
You only need to say:
“Take care of this.”
Then the Agent calls on your computer, code, browser, APIs, and models to complete the task step

Recently, I keep running into this when ChatGPT runs long tasks.
The most frustrating part is that the frontend says “streaming has been interrupted,” while the backend task is sometimes still running.
The more heavily I use agents, the more I realize that how smart the model is is only part of the equation.
Connections, reconnections, state recovery, and observability for long-running tasks are what truly affect the day-to-day user experience.

I’ve actually been using ChatGPT’s Remote Control for quite a while.
I used to think of it simply as a very convenient feature:
When I’m out, I can open my phone and continue taking control of Codex running on my Mac at home.
Send tasks from my phone → execute them locally on my Mac → check progress at any time → continue adding instructions.
But recently, as I’ve brought more and more work into AI, I’ve truly realized what this means.
Once AI can connect to computers, files, terminals, browsers, and various workflows, your phone is no longer really a “device for running AI.”
It’s more like a

I only discovered today that ChatGPT Pro actually comes with 100GB of file storage.
I’ve been using it for so long, yet I’ve only used 315MB.
This made me realize that I’ve always treated ChatGPT as “compute” and a “chat box,” but never truly as a long-term workspace.
So I reorganized my AI storage architecture:
Mac: execution
Local large drive: raw data / cold archives
Google Drive: official documents editable by humans
ChatGPT Library: a knowledge layer that AI can read, search, and reuse over the long term
The point isn’t to fill up the 100GB.
It’s to put the things that are truly worth hav

I’ve been working with AI and vibe coding for so long, but today was the first time I seriously looked at my Codex usage statistics:
Total tokens: 103.82 billion
Daily peak: 4.59 billion
Longest consecutive usage: 119 days
The numbers are much more staggering than I imagined.
But what’s really worth taking stock of isn’t how many tokens were burned, but what those tokens ultimately became.
Recently, I’m preparing to go through the AI automation systems I currently have up and running in full:
Research, monitoring, trading assistance, content production, remote execution across multiple devices

There’s a new model called Jev in the AI space these past few days, and I think it deserves a closer look.
The most counterintuitive thing about it isn’t how much higher its benchmark scores are, but that it doesn’t want to be an LLM that is simply “better at chatting.”
@typesafeai’s approach is:
Many software tasks don’t need the model to “generate a piece of text” at all.
A large number of AI workflows today actually look like this:
state → LLM → generate text / JSON → parse → validate → retry → the program decides what to do next
But what software really needs AI to answer is often just:
Is

Did you claim those $100 from the Grok bot?
I originally just wanted to connect the X paid API to Grok Bot for searches / timelines / mentions.
I added $25 to try it, but total showed approximately $123.
Only after breaking it down did I understand:
• prepaid ≈ $23.7 → the remainder of what I added
• free ≈ $99 → the starting credits granted when connecting (expires in about 1 year
Official statement on 8/29: Paid Grok Bot users get free X API credits to start
The official amount wasn’t fixed; the image above shows my actual test. Check free before connecting, and don’t be tricked by total int

Ever since I got MCPX, I’ve genuinely had this feeling lately:
It’s like I’ve unlocked “unlimited usage” for ChatGPT.
Of course, it isn’t actually unlimited.
The Chat mode itself already has a pretty generous quota, and after installing and configuring MCPX, many things that originally required me to switch software, find tools, or run commands myself can now be done directly in Chat.
And I think the best thing about MCPX isn’t that it “adds another new tool.”
It’s precisely that once it’s installed, you can even forget MCPX exists.
You can keep talking to ChatGPT normally, just like before:
H