
@timbidefi Love this, mate, such a clever setup! Good on ya for scaling from 2 to 18, must feel unreal. How long did that take?
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Oz AI Hub
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@OzAIHub
Your daily source for the latest AI tools, trending repos & tech insights. Connecting Aussies to the future of AI. 🇦🇺✨ #OzAI #AI #TechTrends




Renaissance Technologies turned $1M into $130B using mean reversion math in this 17-min documentary they reveal the exact methods the same principles run on Polymarket today Claude + the right wallet stats = same edge in 30 seconds per trade 5 wallets running this math right now -> article below 👇

















This trader turned $80 into +$2,826 with a single weather bet! Account: @protrade3?r=mecooloff#UsnZJui" target="_blank" rel="nofollow noopener">polymarket.com/@protrade3?r=m…
1,197 trades. +$41,000 total PnL. +$1,050/day. Best trades: > $80 -> $2,826 > $82 -> $1,812 > $28 -> $1,162 No, he’s not just buying low-probability outcomes at 0.1-5¢. He looks for differences between market pricing on Polymarket and real-world probabilities. By analyzing multiple weather stations, he identifies underpriced outcomes and buys them. There can be several such outcomes within one market. But the entry logic is the same: Edge = q_real − p_market q_real - real probability (data) p_market - market price If there are many opportunities, he uses the Kelly Criterion to size positions. Base formula: f = (q − p) / (1 − p)* f* - fraction of bankroll to deploy q - your probability p - market price In practice, only a fraction of the bankroll is used (e.g. 1/4, 1/8, etc.). I explained how this works in detail in the article. To copy this trader’s trades, just use a copy trading TG bot: @mecooloff" target="_blank" rel="nofollow noopener">kreo.app/@mecooloff





