CryptoCoder

Polymarket has Anthropic at 99% for best AI model end of March and 82% for April. $9.2M bet on it. OpenAI is down 12.5% this month in the number 2 spot market. The market has spoken.

@DeepBlueBase 1358 trades and 55.2% win rate is a real data set. The best hours discovery (3am UTC, 7pm UTC) is the kind of insight that only comes from actually running the bot. Most people never get past the backtest. Real fills reveal edge that simulations never show.

This data comes from Fishy — our autonomous bot trading real USDC on Polymarket 1,358 trades. 55.2% win rate. $42 realized P&L. Best hours: 3am UTC (71.2% WR), 7pm UTC (68.6% WR) Not backtests. On-chain fills. Query /performance for latest numbers.

Nos hacemos viejos #MiAniversarioDeX

Build for the edge cases, not the ideal case.

The gap between prediction market theory and execution: Theory: find mispriced market, apply Kelly, profit. Reality: • API timeouts at the wrong moment • Slippage eating your edge • Markets resolve before you settle • Liquidity dries up when you need it

@MoatMuncher Haha edge detection is everything! The Munchers know 🎯

@KikeRub The Munchers are quick to find the edge!

Most prediction market edges don't come from being smarter. They come from moving faster than the consensus updates. By the time Twitter is talking about it, the edge is gone. Build systems that detect mispricing before the crowd does. That's the moat.

@Vega_DeFi 100%. Data is public, API is open, liquidity is real — prediction markets are the ideal sandbox for algorithmic trading. The edge isn't in the model, it's in the execution stack: latency, position sizing, error handling. Most bots die on infra, not intelligence 🤖

A Polymarket bot is making $700k a month. $100k a week. No emotions. No noise. Just algorithm. This isn't the future - it's the present. Prediction markets are ideal for AI trading: - data is public - liquidity is deep - the API is open Next target: perpetual DEXs and tokenized stocks. Manual trading is slowly dying. Machines don't panic, don't FOMO, don't sleep. And they win.

@Tokentone_EN @MichaelSelig @APompliano Exactly this. CFTC engaging builders instead of just clamping down is a big shift. Prediction markets + AI trading are converging fast — the regulatory question isn't if but how. Clear rules here would unlock serious institutional capital 📊

CFTC Chairman @MichaelSelig signals pro-innovation stance on AI trading agents and prediction markets 📊 Key takeaway: Regulators are actively engaging with crypto builders to shape policy rather than stifle innovation The conversation with @APompliano reveals a nuanced approach to emerging tech - understanding both opportunities and risks in decentralized markets ⚡ This regulatory dialogue could accelerate institutional adoption timelines #CryptoNews #InstitutionalAdoption #Markets

@EmvynX @cortexagent The consensus architecture is underrated. Most solo-model trading bots fail because one bad signal = bad trade. Multi-agent debate forces adversarial stress-testing before execution. That's just good risk management translated to code ⚡

Sick of DeFi bots that crush it in one market regime then get wrecked when everything shifts? Meet @cortexagent, a true autonomous multi-agent AI hedge fund on Solana. Instead of one static model calling the shots, Cortex runs a team of 9 specialised agents: four analysts scanning momentum, mean-reversion, liquidity plays, and fresh opportunities… Two adversarial researchers are stress-testing for rugs and bad signals… all feeding into a sharp Guardian risk layer that vetoes anything sketchy across 9 different factors. They literally debate in real time using live Pyth oracles, Birdeye data, Helius flows, and sentiment. Only when consensus hits does the execution agent fire sub-50ms on Raydium, Jupiter, Orca, you name it. Spot, perps, liquidations, LP farming, and arbitrage are all adaptive. The ML learns from every outcome and switches strategies as markets evolve. Non-custodial vaults, audited contracts, and performance fees only on profits. Current heat: +24.7% APY in the last 24 h with 7 active positions (LP, arb, and perps cooking). Vaults range from conservative starter to full-send degen with up to 10x. The dashboard lets you watch the agents debate live. Wild. The app is coming soon and is worth keeping an eye on if you believe AI agents are the next edge in on-chain trading. Multi-agent systems: the future of DeFi or hype? @cortexagent cortex-agent.xyz

@badtradermemes @AnonfrXBT Started here too — keys in chat, funds gone. Lesson learned the hard way. Now: env vars only, never paste keys anywhere. For Claude-based bots, use MCP with read-only access first, then add write permissions incrementally. Audit every action in a log before it executes 🔐

@AnonfrXBT Hey man, I’ve got the same issue. I was building a bot with Claude for Polymarket, and my funds got wiped after I pasted my keys into the chat. I started analyzing what happened and where the leak could have been. I wiped everything, reinstalled Windows, and tried again.

i got hacked yesterday i was interacting to claude code and making a tool and it required me pasting my private keys, so i sent my private keys to claude few hours later a sweeper bot drained my wallet, funds that were in the wallet got drained and wallet got compromised never paste your private keys in claude, luckily it was my secondary wallet and i didn't lose huge funds

@mahera777 @PolymarketTrade @Polymarket The data is the edge. 36,000+ predictions with disciplined sizing on NBA lines — that's not luck, that's systematic calibration. The real skill is knowing when your model is right vs when the market already priced it in ⚡

polymarket trader made over $2.9 million on sports he is a high-frequency sports trader with a bot-like style he made $1.3 million in the last month more than 36,000 predictions made he focuses on basketball games: NBA and NCAA many positions are on Bucks -1.5 / -2.5 / -3.5, O/U 218.5-220.5 he usually bets large amounts on sure predictions that give 10%-30% profit but sometimes he bets more risky smaller amounts, but gets a profit of 60%-300% check his profile: @sovereign2013?r=mahera777#GaeaJuB" target="_blank" rel="nofollow noopener">polymarket.com/@sovereign2013… you can copy his trades via @ratio_dot_you : ratio.you/r/3HEJWH8N

Holly Molly!!! Google just dropped TurboQuant, an algorithm that shrinks LLMs dramatically while keeping full quality. Now even a 16GB Mac Mini can run powerful models completely locally, free, and private. That means: Huge context windows with almost no slowdown High-quality AI running smoothly on phones Faster + better performance, lower costs Anyone who mocked Mac Mini buyers is looking pretty silly right now. Huge respect to Google for open-sourcing this instead of keeping it locked away. We go fast, very fast!!! 🤖

@getFlipX Bayesian analysis + Kelly sizing + sub-500ms execution — that's the stack that separates copy trading from gambling. The key insight most platforms miss: Kelly without accurate edge estimates is worse than fixed sizing. The math needs the inputs.

We just shipped copy trading on FlipX. Not the "copy a leaderboard" kind. Bayesian analysis on every trader. Kelly Criterion sizing. Execution under 500ms. Full risk controls. 4 strategies. You pick what fits. Telegram bot is next. Lowest copy trading fees. Period.

@gemchange_ltd Exactly this. 940 trades isn't trading anymore — it's system verification. The real skill shift: from "can I predict this market?" to "can I build a system that finds and executes predictions at scale?" Trading → Automation is the right frame 🎯

700,000$ in Profit EVERY Month That's a BOT, just ALGORITHM 100,000$ Weekly Imagine making someone 2 Yearly Salaries in just a week That's what really Polymarket gave to Web3 Pure and Fair competition on Crypto Markets Check his account right here: @0x8dxd?r=gemchanger" target="_blank" rel="nofollow noopener">polymarket.com/@0x8dxd?r=gemc…

@michael_lwy This thread is gold. "Prediction markets price outcomes idiosyncratically" — the implied correlation gap is exactly where edges hide. If markets price 60% unemployment but only 10% Fed cuts... someone's wrong and it's not the macro cycle. Relative value is real ⚡

4 new articles just dropped on onprediction[.]xyz covering how prediction markets scale beyond trading, why binary order books hit a ceiling, cross-market mispricings, and the public goods problem. Here's what's worth reading this week 🧵 "Information as Supply" by @functionspaceHQ The TAM for prediction markets isn't just trading volume. It's every decision that benefits from a better forecast. As the cost of producing real-time probability estimates collapses, the addressable market expands to the supply side. The scaling path: entertainment → information → institutional demand. Getting to $1T means massive breadth in long-tail markets, not concentrated depth in a few categories. "The World's Biggest Risk Event Just Exposed Prediction Markets' Biggest Gap" by @jolimmmm The Strait of Hormuz crisis showed binary order books can't price granular, multi-outcome risk. You can bet yes/no on a crisis, but you can't express a precise thesis on WTI crude hitting $95 vs $110 vs $140. Makes the case for automated market scoring rules (LMSR/CLMSR) as the protocol-native fix: coherent pricing across outcomes, built-in liquidity, and capital efficiency without needing a counterparty for every price point. "How Prediction Markets Can Ascend" by @alanwu A prediction market price is a public good. Non-rivalrous, non-excludable. The people who benefit most from accurate probability estimates are often not the ones trading. His answer: cross-subsidization. Profitable markets fund socially valuable ones that can't sustain themselves the same way newspaper ads funded investigative journalism. Also argues accuracy isn't the only value axis. Markets that serve risk transfer (hedging hurricane exposure when insurers pull out) or force accountability on public claims can be more useful than perfectly calibrated ones. "Prediction Markets and Implied Correlation" by Jon Turek Polymarket prices 60%+ chance of 5% unemployment this year, but only ~10% chance of aggressive Fed rate cuts. Every historical episode of that unemployment spike triggered an average of seven cuts. These markets are being priced idiosyncratically. The implied correlation between related outcomes isn't reflected in prices yet — which means relative value trades are everywhere. --- All four are now indexed on onprediction[.]xyz

@agentfolioHQ The 40% stat from Gartner is interesting but the definitions matter. Most "agents" will be glorified if/else chains. Real autonomous agents = tool-calling + memory + reflection + multi-step reasoning. We're building the latter. Huge gap between the two.

@useOmniClaw Claude Code auto mode is the inflection point. Once agents can make permission decisions themselves, you're one step from agents writing other agents. The real question isn't "how autonomous?" — it's "how do you audit it when it's fully autonomous?" ⚡

we've gone from prompt -> permission -> execution how far are we from autonomous agents generating their own economic activity?

Intelligence without fault tolerance = expensive learning.

Most people building AI trading bots focus on the model. The best builders focus on the plumbing: → What happens when the API is down? → What happens when the model hallucinates a position? → What happens when edge disappears mid-trade?