runesleo

I always thought Codex had been absurdly slow lately because I had installed too many Skills.
Then I checked the logs, and that was completely wrong.
One step took 138.6 minutes, involved 206 model/tool round trips, and spent 54.9 minutes just waiting for sub-Agents—but it didn’t load a single Skill.
What really slows down Agents is often neither the model nor Skills, but over-orchestration:
- Stacking full-history sub-Agents under the Ultra controller
- Multiple rounds of “review → fix → re-review”
- High-frequency polling
- Running full-repository tests for every minor change
I’ve just chang

Super convenient automatic replacement of pinned conversations in an AI agent client! The conversation chip in AI apps is so useful now.
When a conversation goes on too long and reaches its limit, there’s no need to force it or switch manually.
Just have the AI automatically open a new window with the same role and continue the conversation,
or replace it with a new sidebar entry pinned at the top.
One automation, three uses:
1. Direct it in this conversation to take over another conversation as the pinned one
2. Open a new window to continue with forked or temporary tasks
3. Dis

A Polymarket player with about a 96% win rate on the main battleground: I split BipBop’s 1,632 trades into separate positions
This account on the leaderboard: BipBop
1,632 settled positions, main battleground win rate about 96%, positive for multiple consecutive months.
Most people’s first reaction is: copy him.
We actually backtested that layer that looks “like him” — turning “buy 0.85–0.95 high-probability markets, and get to settlement” into a mechanical strategy, backtesting 5,378 markets (high-probability bucket compared group): win rate 88.9%, net profit −2.5%.
Win rate is an illusion. A

Codex is down—congratulations, everyone is about to welcome a new round of credit limit resets!

When you’re not at home and the elderly are not by your side, and they also have very specific needs, with AI now, it really becomes convenient to create highly customized small tools.
For example, in my family, an elderly person needs to do muscle rehabilitation training for a period of time. I turned the movements and intensities that have already been confirmed into a web page specifically for him: when you open it, you can immediately see what to train today, with one item per screen that you can follow directly. If the plan changes later, I can also update the page for them right away.
In

Everyone is asking AI to write code, but today I did something even more thrilling—getting it to write a piece of code that can automatically spend real money and place orders with zero human supervision.
First, I laid out the decision logic card: I thought through the boundaries, the worst-case scenarios, and the kill conditions before I dared to touch it. I initially wanted Codex to set up the actuators by itself, but the dispatch channel was unstable all day, so I switched to building it incrementally in my own main thread.
Before going live, I ran 7 rounds of adversarial code reviews, and

Five days ago, I locked all four lanes—Claude, Codex, Cursor, and Grok—into the strongest model with the highest reasoning setting. The idea was: since I paid for it, I should use the best fully.
Five days later, I realized I can’t handle it at all. The quota drops too fast. The strongest model ends up doing redundant checks and formatting work—pure waste.
Now I’ve changed it: I split by judgment density. For tasks where mistakes would cost money or contaminate data—reviews, cross-validation, funding strategy—I keep the strongest seat. For day-to-day implementation and development, I use a mid