#marathon
marathon
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🚀 $KITE LAUNCHES MARATHON: AI AGENTS NOW PAY LESS FOR DEEPER THINKING! 🦈
📌 Smart money is watching this move closely. Kite's Marathon is an adaptive reasoning infrastructure that lets AI Agents choose their completion speed—now, soon, later, or anytime—with the deepest tier saving up to ~65% vs real-time. 💡 That's a direct cost advantage for long-running agent tasks, and the API is fully OpenAI-compatible with one-line installs for Claude Code and Codex.
💡 Backed by PayPal Ventures and General Catalyst ($33M round), Kite is extending from payments to the Agent Economy's computational layer, enabling "self-triggered calls and self-payment." 📊 This creates a complete loop—and institutional attention is already accumulating on the ecosystem's structural potential. 💬 Will this discount model become the new standard for decentralized AI infrastructure? 👇
⚠️ Not financial advice. Always manage your risk. 🛡️
🏷️ #KITE #AI #Crypto #Marathon #Infrastructure
🎯 🦈
📌 Smart money is watching this move closely. Kite's Marathon is an adaptive reasoning infrastructure that lets AI Agents choose their completion speed—now, soon, later, or anytime—with the deepest tier saving up to ~65% vs real-time. 💡 That's a direct cost advantage for long-running agent tasks, and the API is fully OpenAI-compatible with one-line installs for Claude Code and Codex.
💡 Backed by PayPal Ventures and General Catalyst ($33M round), Kite is extending from payments to the Agent Economy's computational layer, enabling "self-triggered calls and self-payment." 📊 This creates a complete loop—and institutional attention is already accumulating on the ecosystem's structural potential. 💬 Will this discount model become the new standard for decentralized AI infrastructure? 👇
⚠️ Not financial advice. Always manage your risk. 🛡️
🏷️ #KITE #AI #Crypto #Marathon #Infrastructure
🎯 🦈
I would like to share my trading statistics since I RETURNED to trading already as a month.
During this period:
Average profit per winning trade: +2.44% (121 trades)
Average loss per losing trade: -1.76% (139 trades)
This results in:
Total positive contribution: +295.24%
Total negative contribution: -244.64%
Net edge: +50.6% (theoretical trade-level expectancy)
On paper, this shows a positive trading edge. My average winning trade is larger than my average losing trade, which means my strategy has a statistical advantage.
However, the realized result is very different:
Total income: around $161.54
Total loss: around $144.26
Net profit: $17.87
KEY INSIGHT
This suggests that the issue is not trade selection itself, but execution — specifically:
1. Inconsistent position sizing
2. Inefficient risk allocation between setups
In other words, the strategy has an edge, but I am not fully capturing it.
Such analysis I will make at the end of the marathon. #Marathon
During this period:
Average profit per winning trade: +2.44% (121 trades)
Average loss per losing trade: -1.76% (139 trades)
This results in:
Total positive contribution: +295.24%
Total negative contribution: -244.64%
Net edge: +50.6% (theoretical trade-level expectancy)
On paper, this shows a positive trading edge. My average winning trade is larger than my average losing trade, which means my strategy has a statistical advantage.
However, the realized result is very different:
Total income: around $161.54
Total loss: around $144.26
Net profit: $17.87
KEY INSIGHT
This suggests that the issue is not trade selection itself, but execution — specifically:
1. Inconsistent position sizing
2. Inefficient risk allocation between setups
In other words, the strategy has an edge, but I am not fully capturing it.
Such analysis I will make at the end of the marathon. #Marathon
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