runesleo

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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
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A few days before the Arc mainnet went live, I bought three coins:
$ARGUS , $TOLLY , $COOL .
My logic at the time was simple:
ARGUS = Launchpad / growth leader
TOLLY = platform value capture
COOL = Arc-native cultural Meme
Now, two days after the mainnet launch, all three coins have retraced sharply from their highs.
But after pulling the data again, I have not changed this view for now.
Instead, an interesting divergence has emerged:
Prices have fallen, but the fundamental data is clearer than when I bought.
Arc Mainnet Day 1 was indeed crazy.
There were approximately 7.76 million transactions
ARC-7.11%
ARGUS+1.28%
TOLLY-15.96%
UNI+1.64%
USDC-0.01%
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
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Podcast recommendation: Web3 101 E83, “What We Need to Know About the Quantum Threat Facing Bitcoin”
Simply put: Quantum computers may one day use public key information you have exposed to derive private keys and transfer your coins away. Mining is not the key issue; the type of wallet address is.
This is especially relevant for people who have used Inscriptions or Runes, or have a particularly large number of addresses. Addresses beginning with bc1p already have their key information on-chain; reusing an address can also expose that information. Relatively safer are unused bc1q addresses, an
BTC+1.07%
I’ve finally figured out that what I want to build is not an “on-chain new token monitor,” but a personal operating system for opportunities.
At first, I thought the problem was simply: why didn’t my monitoring catch a certain token in time when it later surged and entered the trending rankings?
But after taking apart the entire process, I realized that what really needs optimizing is not a particular token, a particular ranking, or even simply adding a few more data sources, but the entire system from discovery to outcome.
The process I want is:
Multi-chain activity, Launchpad activity, proje
September had just seen a rate hike, and the median dot still said “one more hike this year.”
October is the nearest window, with Polymarket pricing a 25bp hike at roughly 44–45%.
My read: slightly low; fair value is probably around 48–55%.
Thin edge, not a heavy-position signal.
So I only bought a very small position:
Yes / 25bp hike / @0.45 / approx. $40
What I didn’t do: 50bp hike, annual-line another-hike, or adding to the position without data.
Exit: close it when there’s no edge; admit I’m wrong if the data turns cold.
Learn in public, not investment advice.
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
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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
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Lately, everyone on X has been joining the Thin Muscle Club
I’m checking in too
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I recently ran into a very strange problem:
I was clearly using a regular Chat, and after connecting an MCP tool I had been using for a long time, the same conversation would somehow turn into Work midway through.
It wasn't a new Work session, nor was “Work” part of the name of one of my own background tasks. The original conversation really did gain a “Work” label in ChatGPT's sidebar.
At first, I suspected that mechanisms such as remote execution, asynchronous tasks, or long-running tasks had triggered some kind of automatic upgrade in ChatGPT. So I went ahead and broke the problem down, ope
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When a robot is about to fail, humans temporarily take over to correct a few steps. Are those few steps really worth teaching the model?
@axisrobotics A single-task simulation experiment report published on August 19 included the following results.
The task was to place a piece of tofu on a plate. The project team tested on the same batch of 480 new starting states:
• Original model: 40.0% success rate
• Directly learning the complete human correction process: 36.7%
• Training on filtered short correction segments: 48.3% (average across three training seeds)
At least in this comparison, direct
Today I used up the entire GPT-6 quota included with ChatGPT’s $200 Pro plan.
At first I thought there might be some kind of bug, but after checking OpenAI’s newly updated official documentation and digging through the quota status in my account, I realized:
Although GPT-6 Astra is called GPT-6 Pro in ChatGPT, the $200/month Pro subscription actually only includes 200 messages / week.
Not 200 messages per day—200 messages per week.
My account has now directly hit limit=200, GPT-6 Pro has been restricted, and ChatGPT has automatically fallen back to GPT-5.6 Thinking.
More interestingly, the $20
After AI makes execution cheaper, what a one-person company lacks most is not hands, but attention that hasn’t been consumed by operations.
Writing code, revising drafts, creating visuals, and running analyses are rapidly becoming general-purpose capabilities.
This can easily create an illusion: since AI can do everything, one person should do everything themselves.
But execution capacity has expanded, while attention has not expanded with it.
Building more systems, chasing more tools, and turning every step into something “enterprise-grade” may ultimately just give yourself more things to mai
I turned my X bookmarks into an Agent inbox that processes them automatically.
From now on, when I see something worth saving, all I need to do is bookmark it.
The Agent handles the rest:
1. Periodically read new bookmarks
2. Remove duplicates and determine whether they have value
3. Assign them to research, content, product, tasks, or no action needed
4. Have downstream workflows actually produce results
5. Record an acceptance receipt
6. Once explicitly enabled, automatically remove bookmarks that have been fully processed
7. Reopen the original post to confirm that the bookmark was actually