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Building an Pump.fun first block sniper bot on Solana in Rust 🦀 → Sub 100ms detection using gRPC + ShredStream → Detects tokens ~5s before Jup Pro’s UI → Targets the first creation block to secure best entry price → Ultra low latency: on chain reads + micro level... show more

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48,520 Aufrufe • vor 9 Monaten •via X (Twitter)

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AI bots are printing $100K+ on Polymarket using Black-Scholes C_binary = e^(−rT) · N(d₂) > Binance confirms 0.15% BTC move at minute 3 > true probability jumps to 92% > Polymarket still shows 65% bot captures 27% before update game changed: Polymarket just launched CLI built in Rust > AI agents can now trade from terminal > sub-100ms execution > direct orderbook access now auto-copying this wallet with Ratio early access code -> A3BS5NC5 when fair value updates in milliseconds, who wins? not humans

AI bots are printing $100K+ on Polymarket using Black-Scholes C_binary = e^(−rT) · N(d₂) > Binance confirms 0.15% BTC move at minute 3 > true probability jumps to 92% > Polymarket still shows 65% bot captures 27% before update game changed: Polymarket just launched CLI built in Rust > AI agents can now trade from terminal > sub-100ms execution > direct orderbook access now auto-copying this wallet with Ratio early access code -> A3BS5NC5 when fair value updates in milliseconds, who wins? not humans

bl888m

26,276 Aufrufe • vor 5 Monaten

$4000–$27000 in daily profit, no one talked about it Reversed-engineered this and told open claw to build something similar A single prompt, and 30 minutes later -> pulls btc data from TradingView -> Captures arbitrage windows when Polymarket lags the reference price by over 0.3% -> Fires in under 100ms, capitalizing on inefficiencies before the market self- corrects -> Processes over 1,000 orders per second, capturing 0.3–0.8% on each trade Risk-controlled at 0.5% per trade with a hard 2% daily drawdown limit Generates $4000–$27000 in daily profit consistently Runs entirely on local hardware And built in Rust for low latency

$4000–$27000 in daily profit, no one talked about it Reversed-engineered this and told open claw to build something similar A single prompt, and 30 minutes later -> pulls btc data from TradingView -> Captures arbitrage windows when Polymarket lags the reference price by over 0.3% -> Fires in under 100ms, capitalizing on inefficiencies before the market self- corrects -> Processes over 1,000 orders per second, capturing 0.3–0.8% on each trade Risk-controlled at 0.5% per trade with a hard 2% daily drawdown limit Generates $4000–$27000 in daily profit consistently Runs entirely on local hardware And built in Rust for low latency

Lorden

34,074 Aufrufe • vor 5 Monaten

$1,500 → $33,000 on 5min BTC markets in 2 days This is the new reality. It stops being gambling the moment you define an exact algorithm. Here are the recommendations on how to create the same trading bot: → Write your code in Rust – it offers C++ level speed, and there’s an official polymarket-client-sdk available. → Minimize Latency – your trading bot must be deployed in the same data center as Polymarket: AWS eu-west-2 (London). This ensures the lowest possible execution lag. → Price Discovery – Polymarket pulls prices from Chainlink, but Chainlink doesn’t "create" the price; it simply aggregates and broadcasts it. Use Binance – as the largest spot exchange, it is the primary source of price discovery. → Apply the Black-Scholes Model for binary options – this is exactly what will help you identify +EV opportunities by calculating all necessary Greeks and variables. → Don’t turn it into a casino – use the Kelly Criterion. It acts as your risk manager, calculating the optimal % of your bankroll for every single position. If you want to earn like trading bot devs – you need to think like them.

$1,500 → $33,000 on 5min BTC markets in 2 days This is the new reality. It stops being gambling the moment you define an exact algorithm. Here are the recommendations on how to create the same trading bot: → Write your code in Rust – it offers C++ level speed, and there’s an official polymarket-client-sdk available. → Minimize Latency – your trading bot must be deployed in the same data center as Polymarket: AWS eu-west-2 (London). This ensures the lowest possible execution lag. → Price Discovery – Polymarket pulls prices from Chainlink, but Chainlink doesn’t "create" the price; it simply aggregates and broadcasts it. Use Binance – as the largest spot exchange, it is the primary source of price discovery. → Apply the Black-Scholes Model for binary options – this is exactly what will help you identify +EV opportunities by calculating all necessary Greeks and variables. → Don’t turn it into a casino – use the Kelly Criterion. It acts as your risk manager, calculating the optimal % of your bankroll for every single position. If you want to earn like trading bot devs – you need to think like them.

lunatik

92,967 Aufrufe • vor 6 Monaten

I took this lesson a few days ago,one of the clearest breakdowns of how Jupiter Swap works. Let me walk you through it. So first,what even IS a Swap? Think of it like Spot Trading in traditional finance. You’re just trading one currency for another at the current market rate, except here it’s digital assets. USDC for SOL. Instant. On-chain. Simple enough. But here’s where it gets interesting…... The moment you go to swap, Jupiter doesn’t just grab the first price it sees,it aggregates liquidity from major sources. That's where Ultra V3 kicks in — think of it like an institutional trading desk firing up behind the scenes. It scans the ENTIRE market, aggregates liquidity from every major source, and routes your trade through the most efficient path possible. You’re not just getting A price for your trade. You’re getting THE best price. But it doesn’t stop there….. 🎯 Real-Time Slippage Estimation (RTSE) is running simultaneously — monitoring slippage live so your swap tolerance stays as tight as possible. No surprises. No overpaying. And on qualifying pairs? Jupiter covers the gas entirely. You just swap. That’s the Gasless feature, look for the icon when you’re executing. 📚 Two concepts you need to understand 💧 Liquidity —> How deep the market is. Deep liquidity means you can make large trades without moving the price against yourself. Jupiter taps the deepest pools on the network. 📉 Slippage —> The gap between what you were quoted and what you actually get. Volatile markets cause this. Ultra V3’s RTSE exists specifically to close that gap. ━━━━━━━━━━━━━━━ 🚀 Ready to swap? Here’s all you do ━━━━━━━━━━━━━━━ 1️⃣ Open the Trade tab → pick your Sell & Buy assets 2️⃣ Check the Execution Path — Jupiter shows you exactly how it’s routing your trade 3️⃣ Spot the Gasless icon if it applies 4️⃣ Hit Swap. Done. On-chain in seconds. And this is why the saying exists — If you want to swap smarter, Just Use Jupiter.

I took this lesson a few days ago,one of the clearest breakdowns of how Jupiter Swap works. Let me walk you through it. So first,what even IS a Swap? Think of it like Spot Trading in traditional finance. You’re just trading one currency for another at the current market rate, except here it’s digital assets. USDC for SOL. Instant. On-chain. Simple enough. But here’s where it gets interesting…... The moment you go to swap, Jupiter doesn’t just grab the first price it sees,it aggregates liquidity from major sources. That's where Ultra V3 kicks in — think of it like an institutional trading desk firing up behind the scenes. It scans the ENTIRE market, aggregates liquidity from every major source, and routes your trade through the most efficient path possible. You’re not just getting A price for your trade. You’re getting THE best price. But it doesn’t stop there….. 🎯 Real-Time Slippage Estimation (RTSE) is running simultaneously — monitoring slippage live so your swap tolerance stays as tight as possible. No surprises. No overpaying. And on qualifying pairs? Jupiter covers the gas entirely. You just swap. That’s the Gasless feature, look for the icon when you’re executing. 📚 Two concepts you need to understand 💧 Liquidity —> How deep the market is. Deep liquidity means you can make large trades without moving the price against yourself. Jupiter taps the deepest pools on the network. 📉 Slippage —> The gap between what you were quoted and what you actually get. Volatile markets cause this. Ultra V3’s RTSE exists specifically to close that gap. ━━━━━━━━━━━━━━━ 🚀 Ready to swap? Here’s all you do ━━━━━━━━━━━━━━━ 1️⃣ Open the Trade tab → pick your Sell & Buy assets 2️⃣ Check the Execution Path — Jupiter shows you exactly how it’s routing your trade 3️⃣ Spot the Gasless icon if it applies 4️⃣ Hit Swap. Done. On-chain in seconds. And this is why the saying exists — If you want to swap smarter, Just Use Jupiter.

Sam || Jupiter Legion 😺😺

34,502 Aufrufe • vor 4 Monaten

🚨 CRYPTO: X TARGETS CRYPTO SCAMS BY AUTO-LOCKING ACCOUNTS POSTING ABOUT CRYPTO FOR THE FIRST TIME Social media platform X is rolling out a new anti-scam measure that will automatically lock any account that mentions cryptocurrency for the first time in its history. Users will need to pass additional verification before being allowed to post again. The feature was confirmed by X Head of Product Nikita Bier, who said it should eliminate 99% of the incentive behind hijacked account crypto scams. The move targets a specific attack pattern where hackers compromise established accounts through phishing emails, then use those accounts to promote scam tokens and fraudulent links. Predictfully founder Benjamin White flagged the issue publicly on April 1 after his own account was hijacked and used to push scam content, with the hacker attempting to extort $4,000 for its return. Bier also called out Google for failing to stop phishing emails at the Gmail level, arguing the tech giant shares responsibility for the attack chain. X has previously attempted to tackle crypto scams through bot purges, API restrictions, and behavioral detection, but hijacked account promotions have remained a persistent problem since the infamous 2020 hack that compromised accounts belonging to Apple, Barack Obama, and Elon Musk.

🚨 CRYPTO: X TARGETS CRYPTO SCAMS BY AUTO-LOCKING ACCOUNTS POSTING ABOUT CRYPTO FOR THE FIRST TIME Social media platform X is rolling out a new anti-scam measure that will automatically lock any account that mentions cryptocurrency for the first time in its history. Users will need to pass additional verification before being allowed to post again. The feature was confirmed by X Head of Product Nikita Bier, who said it should eliminate 99% of the incentive behind hijacked account crypto scams. The move targets a specific attack pattern where hackers compromise established accounts through phishing emails, then use those accounts to promote scam tokens and fraudulent links. Predictfully founder Benjamin White flagged the issue publicly on April 1 after his own account was hijacked and used to push scam content, with the hacker attempting to extort $4,000 for its return. Bier also called out Google for failing to stop phishing emails at the Gmail level, arguing the tech giant shares responsibility for the attack chain. X has previously attempted to tackle crypto scams through bot purges, API restrictions, and behavioral detection, but hijacked account promotions have remained a persistent problem since the infamous 2020 hack that compromised accounts belonging to Apple, Barack Obama, and Elon Musk.

BSCN

65,603 Aufrufe • vor 4 Monaten

This guy made $400K with his own script on Polymarket Yes, it’s true, the dude wrote a script using ClawdBot and now it earns him passive income No programming knowledge No insider tools for building the script Just a solid script that delivers results Profile → Copytrade → When I opened the code, I was genuinely surprised No massive databases No insanely complex infrastructure Nothing rocket-science level His FULL strategy: 1. “Low-Risk NO Plays” The bot targets near-impossible outcomes and stacks small, high-probability wins. This isn’t gambling, it’s closer to systematic risk underwriting. 2. Logical Price Gaps If event A must logically lead to event B and the market hasn’t priced that in yet, the bot enters instantly. While you’re still reading the headline, the imbalance is already gone. No human can compete with that speed. 3. Main Advantage - Sports and Politics These markets are flooded with retail money and delayed or emotional reactions. The bot lives in the spread, clipping tiny profits from every mismatch. Scale Effect Tens of thousands of micro-trades every month, each worth just a few cents. Over time they compound into seven-figure results. Final Take There’s a real bot war happening on Polymarket. Crypto markets are already slowed down by fees and heavy competition, but sports markets are still pure chaos, easy money for those who automate.

winkle.

57,003 Aufrufe • vor 6 Monaten

Day 11/90 of Inference Engineering How does vLLM work and how is it used in production? Before we discuss how vLLM works internally, it helps to understand what vLLM is. At a high level, vLLM is an inference engine that is designed to serve LLMs to thousands of concurrent users efficiently while managing scarce compute and memory. The goal for vLLM is to maximize throughput and minimize latency; optimizing for the best inference economics and experience for end users. With every request from the end user, it eventually ends up in the engine core, gets scheduled alongside other requests from other concurrent users, executes on the GPU, and updates the KV cache with the new key and value vectors, and streams the tokens back to the user. The Scheduler decides what requests should execute next while continuously batching requests together to maximize GPU utilization. Continuous batching is an inference optimization that allows new requests to join a running batch as other requests finish generating tokens. This helps with keeping the GPU utilization high instead of letting it sit idle waiting for an entire batch to complete generating. After the scheduler dispatches the selected batch to the Model Executor, the Model Executor prepares the tensors and metadata required for inference, retrieves each request’s block table from KV Cache Manager, launches the optimized transformer forward pass on the GPU, computes the logits, updates the KV cache with the new key and value vectors, and finally returns the results for sampling and streaming. The KV Cache Manager uses the PagedAttention memory layout to allocate fixed-size cache blocks on demand and maintains a Free Block Queue on the CPU that tracks which blocks in the GPU’s Paged KV Cache are currently free. When a request needs additional KV cache space, the KV Cache manager takes a free block from the queue and assigns it to that request, thus avoiding an expensive search through GPU memory for available cache blocks. All of these components form the core of vLLM’s inference engine. The Scheduler determines what requests are executed, the Model Executor determines how those requests are executed, the KV Cache Manager determines where each request’s KV cache lives using the PagedAttention Memory Layout. This architecture enables vLLM to serve thousands of concurrent requests with high throughput, low latency, and efficient GPU memory utilization. Heres a little animation that visualizes everything! - I've also completed the forward pass for my mnist.c project. I had a nice chat with shrey birmiwal, such a knowledgeable guy. Excited to learn more about vLLM and implement a tiny-vLLM one day.

Day 11/90 of Inference Engineering How does vLLM work and how is it used in production? Before we discuss how vLLM works internally, it helps to understand what vLLM is. At a high level, vLLM is an inference engine that is designed to serve LLMs to thousands of concurrent users efficiently while managing scarce compute and memory. The goal for vLLM is to maximize throughput and minimize latency; optimizing for the best inference economics and experience for end users. With every request from the end user, it eventually ends up in the engine core, gets scheduled alongside other requests from other concurrent users, executes on the GPU, and updates the KV cache with the new key and value vectors, and streams the tokens back to the user. The Scheduler decides what requests should execute next while continuously batching requests together to maximize GPU utilization. Continuous batching is an inference optimization that allows new requests to join a running batch as other requests finish generating tokens. This helps with keeping the GPU utilization high instead of letting it sit idle waiting for an entire batch to complete generating. After the scheduler dispatches the selected batch to the Model Executor, the Model Executor prepares the tensors and metadata required for inference, retrieves each request’s block table from KV Cache Manager, launches the optimized transformer forward pass on the GPU, computes the logits, updates the KV cache with the new key and value vectors, and finally returns the results for sampling and streaming. The KV Cache Manager uses the PagedAttention memory layout to allocate fixed-size cache blocks on demand and maintains a Free Block Queue on the CPU that tracks which blocks in the GPU’s Paged KV Cache are currently free. When a request needs additional KV cache space, the KV Cache manager takes a free block from the queue and assigns it to that request, thus avoiding an expensive search through GPU memory for available cache blocks. All of these components form the core of vLLM’s inference engine. The Scheduler determines what requests are executed, the Model Executor determines how those requests are executed, the KV Cache Manager determines where each request’s KV cache lives using the PagedAttention Memory Layout. This architecture enables vLLM to serve thousands of concurrent requests with high throughput, low latency, and efficient GPU memory utilization. Heres a little animation that visualizes everything! - I've also completed the forward pass for my mnist.c project. I had a nice chat with shrey birmiwal, such a knowledgeable guy. Excited to learn more about vLLM and implement a tiny-vLLM one day.

max fu

70,543 Aufrufe • vor 1 Monat

CA: 0x172ae9e9b46770a70f479404d76e2f6561507011ef77a247fe3f58e7a5840a0d::manny::MANNY Your smart, hands-free edge tool in the crypto market. This powerful automated bot is designed to buy low and sell high with precision. It scans hundreds of coins in real-time, waiting for the right indicators—trend strength, volume spikes, price momentum, and bullish patterns—before entering a trade. Once in, it manages risk with dynamic stop-loss and take-profit levels, so your capital is always protected. Every trade is backed by a multi-layer confluence strategy, ensuring only high-confidence setups are executed. ✅ Advanced entry logic ✅ Fully automated buy/sell execution ✅ Built-in profit protection and cooldown filters ✅ Real-time alerts (Telegram/Twitter ready) ✅ JSON-based state memory for continuity ✅ Minimal setup, maximum performance ✅ Excludes low-quality coins automatically (e.g., BTC/ETH filters optional) ✅ Plug-and-play friendly — run it locally or integrate it into your system. ✅ Clean, professional trade alerts with price and PnL details ✅ Recovers automatically from connection issues or downtime Whether you’re a pro or just getting started, this bot helps you stay ahead of the market—24/7, emotion-free with pure mathematics. This bot has been in development for the last 6 months. I, Chronos, the developer behind it, have been testing for a while for the best configuration for a trading bot. I believe I have something good going on here. The bot automatically posts all the trades via IFTTT and X integration to its X account. Everything is automated. So how can people rent it, and how will it bring value to the project? Soon, the bot can be rented out via a cloud server. A customer must buy 30 USD worth of Memecoin_MANNY token (CA:0x172ae9e9b46770a70f479404d76e2f6561507011ef77a247fe3f58e7a5840a0d::manny::MANNY). After buying it and depositing it into a special wallet, he will be granted access to the bot. . The bot runs only on the backend — users interact with it via an interface (web app, Telegram bot, or API). A web dashboard and Telegram bot interface will be created. This lets users Start/stop their bot session See trade logs or results. Connect their API keys securely. Get alerts and updates The idea of all this is to offer a service but also bring value to the project. More bots will be developed. This is only the beginning. Cheers Chronos #python #memecoin_manny #spot #trading #bitcoin #eth #Binance #bybit #memecoin #VALHALLA

CA: 0x172ae9e9b46770a70f479404d76e2f6561507011ef77a247fe3f58e7a5840a0d::manny::MANNY Your smart, hands-free edge tool in the crypto market. This powerful automated bot is designed to buy low and sell high with precision. It scans hundreds of coins in real-time, waiting for the right indicators—trend strength, volume spikes, price momentum, and bullish patterns—before entering a trade. Once in, it manages risk with dynamic stop-loss and take-profit levels, so your capital is always protected. Every trade is backed by a multi-layer confluence strategy, ensuring only high-confidence setups are executed. ✅ Advanced entry logic ✅ Fully automated buy/sell execution ✅ Built-in profit protection and cooldown filters ✅ Real-time alerts (Telegram/Twitter ready) ✅ JSON-based state memory for continuity ✅ Minimal setup, maximum performance ✅ Excludes low-quality coins automatically (e.g., BTC/ETH filters optional) ✅ Plug-and-play friendly — run it locally or integrate it into your system. ✅ Clean, professional trade alerts with price and PnL details ✅ Recovers automatically from connection issues or downtime Whether you’re a pro or just getting started, this bot helps you stay ahead of the market—24/7, emotion-free with pure mathematics. This bot has been in development for the last 6 months. I, Chronos, the developer behind it, have been testing for a while for the best configuration for a trading bot. I believe I have something good going on here. The bot automatically posts all the trades via IFTTT and X integration to its X account. Everything is automated. So how can people rent it, and how will it bring value to the project? Soon, the bot can be rented out via a cloud server. A customer must buy 30 USD worth of Memecoin_MANNY token (CA:0x172ae9e9b46770a70f479404d76e2f6561507011ef77a247fe3f58e7a5840a0d::manny::MANNY). After buying it and depositing it into a special wallet, he will be granted access to the bot. . The bot runs only on the backend — users interact with it via an interface (web app, Telegram bot, or API). A web dashboard and Telegram bot interface will be created. This lets users Start/stop their bot session See trade logs or results. Connect their API keys securely. Get alerts and updates The idea of all this is to offer a service but also bring value to the project. More bots will be developed. This is only the beginning. Cheers Chronos #python #memecoin_manny #spot #trading #bitcoin #eth #Binance #bybit #memecoin #VALHALLA

Ex Machina

24,488 Aufrufe • vor 1 Jahr

We found a flaw in Polymarket that can’t be patched. Then we built the most powerful bot of the World Cup around it. Here’s the flaw: their orderbook will always be slower than the pitch. When a goal, red card, or penalty hits, pro feeds (Sportradar, Opta, ScoutingFeed) register it in 200-500ms. Polymarket takes 2 to 8 seconds to reprice. For those few seconds the book is quoting a score that no longer exists. No amount of engineering closes that gap the event happens in the physical world before any oracle can confirm it on-chain. The engine detects the event, recalculates fair value, and fires via Jito bundles before the book catches up. In at the old price, out at the new. The match outcome is irrelevant we don’t bet on who wins. We capture the lag every event creates. We’ve been building Polymarket bots since 2025. This is the most powerful machine we’ve shipped yet. Two months ago we posted the architecture for this. It hit 1M views one of our most popular posts ever on X. That told us everything: this was the engine to build. First 7 days, - Starting balance: $5,000 - 22 matches scanned, 19 captured - Total profit: +$1,946.86 - ROI: +38.94% in 7 days Why it prints harder than anything we’ve built: the World Cup is the deepest liquidity event prediction markets have ever seen. Tens of millions in volume per match. Dozens of probability-shifting events per game. And an orderbook that physically can’t keep pace with the pitch. How to plug in: 1.Sign up at PolyArbiter (link in bio) 2.Generate PolyArbiter RPC URL 3.Paste it into Jupiter Predict (Polymarket but native on Solana) 4.Set your parameters, activate the World Cup module It’s free to use. We take a share of the profit the engine makes for you. You never deposit anything with us everything runs from your wallet. One honest note: the $1,946 above is our engine at our size and settings. Your numbers depend on your capital, your parameters, and how many matches you’re live for. We’re not promising you’ll match it we’re showing you the machine works, and handing you the same one. These numbers are from the engine running solo. Closed test, just us, before any public access wanted to confirm the whole loop held up end to end before handing it to anyone. That changes the second this goes public. Edge per capture is going to compress. When an event fires the mispriced liquidity is thin and gone in a few seconds more wallets hitting the same window, less left for each. Nothing we can do about it, that’s just how latency arb works. So if the edge thins out past the point where it’s still worth running, we cap access. Hard ceiling on how many engines can hit the same liquidity before it’s gone. Not gonna promise the machine stays this sharp in a few days it might not. But right now it’s live and free. Enjoy 🪄

We found a flaw in Polymarket that can’t be patched. Then we built the most powerful bot of the World Cup around it. Here’s the flaw: their orderbook will always be slower than the pitch. When a goal, red card, or penalty hits, pro feeds (Sportradar, Opta, ScoutingFeed) register it in 200-500ms. Polymarket takes 2 to 8 seconds to reprice. For those few seconds the book is quoting a score that no longer exists. No amount of engineering closes that gap the event happens in the physical world before any oracle can confirm it on-chain. The engine detects the event, recalculates fair value, and fires via Jito bundles before the book catches up. In at the old price, out at the new. The match outcome is irrelevant we don’t bet on who wins. We capture the lag every event creates. We’ve been building Polymarket bots since 2025. This is the most powerful machine we’ve shipped yet. Two months ago we posted the architecture for this. It hit 1M views one of our most popular posts ever on X. That told us everything: this was the engine to build. First 7 days, - Starting balance: $5,000 - 22 matches scanned, 19 captured - Total profit: +$1,946.86 - ROI: +38.94% in 7 days Why it prints harder than anything we’ve built: the World Cup is the deepest liquidity event prediction markets have ever seen. Tens of millions in volume per match. Dozens of probability-shifting events per game. And an orderbook that physically can’t keep pace with the pitch. How to plug in: 1.Sign up at PolyArbiter (link in bio) 2.Generate PolyArbiter RPC URL 3.Paste it into Jupiter Predict (Polymarket but native on Solana) 4.Set your parameters, activate the World Cup module It’s free to use. We take a share of the profit the engine makes for you. You never deposit anything with us everything runs from your wallet. One honest note: the $1,946 above is our engine at our size and settings. Your numbers depend on your capital, your parameters, and how many matches you’re live for. We’re not promising you’ll match it we’re showing you the machine works, and handing you the same one. These numbers are from the engine running solo. Closed test, just us, before any public access wanted to confirm the whole loop held up end to end before handing it to anyone. That changes the second this goes public. Edge per capture is going to compress. When an event fires the mispriced liquidity is thin and gone in a few seconds more wallets hitting the same window, less left for each. Nothing we can do about it, that’s just how latency arb works. So if the edge thins out past the point where it’s still worth running, we cap access. Hard ceiling on how many engines can hit the same liquidity before it’s gone. Not gonna promise the machine stays this sharp in a few days it might not. But right now it’s live and free. Enjoy 🪄

PolyArbiter

100,534 Aufrufe • vor 2 Monaten

Here is a detailed list about why $SHIDO is the biggest gem of this bullrun 💎 Don’t be shocked, all of this, and actually under 4m MC. ⚠️ All the things in this list are LIVE. Not in the building phase, ALL of it has been delivered. 🔹The Blockchain - Dual VM Layer 1 PoS (EVM+WASM) - EVM Smart Contracts - Infinite Scalability, 13k TPS - Sub 1sec Time to Finality - 20M Blocks with ZERO downtime - Audited by Zokyo Security - 30+ Validators - Native & EVM block Explorers 🔹 The Native Permissionless DEX - Has Multiple Aggregators - Perfect Audit Score by Zokyo 100/100 - Native Shido Pools - Staking for projects on Shido - Earn by providing Liquidity on Native Pools - Shido Dex reward token (BIG UI update coming really soon) 🔹 The Native Bridges - Interact between Shido Network & others - Stablecoins only for improved Security - Native integration of USDC & USDT - Supports ETH BNB Base ARB & AVAX - Audited by Leading Security Firm - Built with the assistance of Wanchain for expertise 🔹 The Native Wallet - App available on Apple & Google Stores - APK available for Android - Native Dex integrated - Big update coming in the future to improve performances & integrate the governance modules & much more 🔹 IBC Shido - Internet of Blockchains - Access to over 30 other IBC-Blockchain networks - IBC offers Enhanced Security - Unlocks Keplr + Cosmostation - Can trade tokens & provide Liquidity across different Chains - Access greater liquidity ecos - Cross-Chain DeFi & DApps - Trade on Osmosis DEX - Access to Cosmos Hub Eco 🔹 Burn Per Transaction - Hard-Coded in to Burn Shido - Burns both Base & Priority fees - Access a live Burn Dashboard - Burns removed Permanently from Circulation 🔹 The Integrations & Listings - Chain & Dex fully integrated on CMC - Chain & Dex fully integrated on Coingecko - Chain & Dex fully integrated on Dextools - Integrated on DefiLama - Integrated on GemPad Launchpad - Listed on Mexc (Chain-Integrated) 🔹 The Community Builders ▪️Dexhub - Equivalent to CMC for Shido ▪️WenMint - 1 Click to launch a token on Shido & Automatically Audited ▪️Shido Fantasy League - Unique competition format that rewards both projects & the community for active engagements. ▪️Shidokid - Token Vault, Factory & NFT Marketplace & various Smart Contract Utilities ▪️Kuzino AI - Institutional Grade Analytics Trading Tool with Deep Market Intelligences. 🔹 Partners and Exposure - Victus Global (VC) - Growthy (Marketing & community growth) - Weekly Shido Spaces with Bjorn CEO - Multiple Spaces per week everywhere on X - Organic community - Community Content Library - Community Live X Spaces has been building for more than 3.5 years & delivered everything on tech. Go here for an interactive eco scope -

Here is a detailed list about why $SHIDO is the biggest gem of this bullrun 💎 Don’t be shocked, all of this, and actually under 4m MC. ⚠️ All the things in this list are LIVE. Not in the building phase, ALL of it has been delivered. 🔹The Blockchain - Dual VM Layer 1 PoS (EVM+WASM) - EVM Smart Contracts - Infinite Scalability, 13k TPS - Sub 1sec Time to Finality - 20M Blocks with ZERO downtime - Audited by Zokyo Security - 30+ Validators - Native & EVM block Explorers 🔹 The Native Permissionless DEX - Has Multiple Aggregators - Perfect Audit Score by Zokyo 100/100 - Native Shido Pools - Staking for projects on Shido - Earn by providing Liquidity on Native Pools - Shido Dex reward token (BIG UI update coming really soon) 🔹 The Native Bridges - Interact between Shido Network & others - Stablecoins only for improved Security - Native integration of USDC & USDT - Supports ETH BNB Base ARB & AVAX - Audited by Leading Security Firm - Built with the assistance of Wanchain for expertise 🔹 The Native Wallet - App available on Apple & Google Stores - APK available for Android - Native Dex integrated - Big update coming in the future to improve performances & integrate the governance modules & much more 🔹 IBC Shido - Internet of Blockchains - Access to over 30 other IBC-Blockchain networks - IBC offers Enhanced Security - Unlocks Keplr + Cosmostation - Can trade tokens & provide Liquidity across different Chains - Access greater liquidity ecos - Cross-Chain DeFi & DApps - Trade on Osmosis DEX - Access to Cosmos Hub Eco 🔹 Burn Per Transaction - Hard-Coded in to Burn Shido - Burns both Base & Priority fees - Access a live Burn Dashboard - Burns removed Permanently from Circulation 🔹 The Integrations & Listings - Chain & Dex fully integrated on CMC - Chain & Dex fully integrated on Coingecko - Chain & Dex fully integrated on Dextools - Integrated on DefiLama - Integrated on GemPad Launchpad - Listed on Mexc (Chain-Integrated) 🔹 The Community Builders ▪️Dexhub - Equivalent to CMC for Shido ▪️WenMint - 1 Click to launch a token on Shido & Automatically Audited ▪️Shido Fantasy League - Unique competition format that rewards both projects & the community for active engagements. ▪️Shidokid - Token Vault, Factory & NFT Marketplace & various Smart Contract Utilities ▪️Kuzino AI - Institutional Grade Analytics Trading Tool with Deep Market Intelligences. 🔹 Partners and Exposure - Victus Global (VC) - Growthy (Marketing & community growth) - Weekly Shido Spaces with Bjorn CEO - Multiple Spaces per week everywhere on X - Organic community - Community Content Library - Community Live X Spaces has been building for more than 3.5 years & delivered everything on tech. Go here for an interactive eco scope -

Superbit123 | Node Validator

15,097 Aufrufe • vor 11 Monaten

how to build Polymarket "always buy NO" bot +$200-400/day PnL if you pick the right markets everyone overcomplicates this. the "NO-maxi bot" strategy is literally: buy NO on outcomes that are structurally overpriced, wait for reality to catch up. the part that matters isn't "genius insight", it's picking the right markets plus execution. where the +$200-400/day comes from it's not betting "no" everywhere. it's selectively loading up NO on multi-outcome ladders (FDV ranges, price targets, user metrics) where the top brackets are CT dreams priced way too rich. if you're consistently capturing 5-15% edge per cycle across 20-30 outcomes and actually getting fills, +$200-400/day is just position sizing plus discipline. first, the edge (why this isn't a meme) polytrackhq research shows $40M+ arb profits from 86M trades came from exploiting pricing errors. "NO-maxi" is the retail version of the same logic on overhyped brackets. so the goal is simple: find multi-outcome markets with fat tails, skip the base case, load NO on the fantasy brackets, let time and reality work. what you actually need (minimal) Python plus official py-clob-client (standard for Polymarket orders). Telegram bot for alerts (don't stare at screen). VPS so it runs 24/7 (don't run from laptop). where people mess up: they try "always NO" on everything and get wrecked by the one outcome that hits. pick markets with obvious "dream vs reality" skew. the bot loop (in plain English) Pull multi-outcome markets (FDV ladders, price targets). For each outcome: check if YES price exceeds realistic probability. Buy NO on 3-5 fattest tails (skip base case). Log market, outcomes, expected edge, fills. Repeat on new ladders. that's it. no AI, no news scraping, no predictions. just "overhype vs fundamentals". where to get real references (not vibes) PolyTrackHQ arb guide. exact logic for multi-outcome pricing errors. py-clob-client PyPI. official client (no wrappers). Polymarket Agents GitHub. framework for outcome looping plus orders. r/arbitragebetting Reddit. discussions on non-atomic multi-order risk. two real-world gotchas (that decide profit vs loss) Outcome blowout: one crazy top bracket hitting wipes the basket. always skip the most likely 1-2 outcomes. Resolution risk: ambiguous wording equals instant edge killer. read rules before loading up. how to make it feel "pro" fast Run only on high-volume ladders (FDV, price targets). fills matter more than theory. Start with $50-100 per outcome until logs prove fills work, then scale. Use official libs only. treat GitHub bots as hostile until audited.

how to build Polymarket "always buy NO" bot +$200-400/day PnL if you pick the right markets everyone overcomplicates this. the "NO-maxi bot" strategy is literally: buy NO on outcomes that are structurally overpriced, wait for reality to catch up. the part that matters isn't "genius insight", it's picking the right markets plus execution. where the +$200-400/day comes from it's not betting "no" everywhere. it's selectively loading up NO on multi-outcome ladders (FDV ranges, price targets, user metrics) where the top brackets are CT dreams priced way too rich. if you're consistently capturing 5-15% edge per cycle across 20-30 outcomes and actually getting fills, +$200-400/day is just position sizing plus discipline. first, the edge (why this isn't a meme) polytrackhq research shows $40M+ arb profits from 86M trades came from exploiting pricing errors. "NO-maxi" is the retail version of the same logic on overhyped brackets. so the goal is simple: find multi-outcome markets with fat tails, skip the base case, load NO on the fantasy brackets, let time and reality work. what you actually need (minimal) Python plus official py-clob-client (standard for Polymarket orders). Telegram bot for alerts (don't stare at screen). VPS so it runs 24/7 (don't run from laptop). where people mess up: they try "always NO" on everything and get wrecked by the one outcome that hits. pick markets with obvious "dream vs reality" skew. the bot loop (in plain English) Pull multi-outcome markets (FDV ladders, price targets). For each outcome: check if YES price exceeds realistic probability. Buy NO on 3-5 fattest tails (skip base case). Log market, outcomes, expected edge, fills. Repeat on new ladders. that's it. no AI, no news scraping, no predictions. just "overhype vs fundamentals". where to get real references (not vibes) PolyTrackHQ arb guide. exact logic for multi-outcome pricing errors. py-clob-client PyPI. official client (no wrappers). Polymarket Agents GitHub. framework for outcome looping plus orders. r/arbitragebetting Reddit. discussions on non-atomic multi-order risk. two real-world gotchas (that decide profit vs loss) Outcome blowout: one crazy top bracket hitting wipes the basket. always skip the most likely 1-2 outcomes. Resolution risk: ambiguous wording equals instant edge killer. read rules before loading up. how to make it feel "pro" fast Run only on high-volume ladders (FDV, price targets). fills matter more than theory. Start with $50-100 per outcome until logs prove fills work, then scale. Use official libs only. treat GitHub bots as hostile until audited.

0xCryptoGirl

22,705 Aufrufe • vor 7 Monaten

Today, we are excited to reveal a partnership with the @HederaFndn to bring all 400+ Pyth Price Feeds to Hedera 🔮 HLiquity is the first DeFi application on Hedera to be Powered by Pyth. Learn more about this launch below: ℹ️ About the HBAR Foundation The HBAR Foundation supports the creation of Web3 communities built on the Hedera network, by empowering and funding the builders developing these communities. The Foundation's six funds - focused on the Crypto Economy, Consumer Engagement, Sustainability, Fintech, Privacy, and Female Founders - each support communities within those areas, and the interconnectedness enables applications to participate as part of a larger ecosystem. The collective power of these funds enables entrepreneurs, developers, and enterprises of all sizes to tackle some of the world's largest problems and create and control their own economies, all built on the Hedera public network. Whether you're building something new or migrating an existing EVM-based application and community, the HBAR Foundation is here to support you. 🔮 Pyth Data on Hedera The deployment of the Price Feeds and Pyth Benchmarks marks a pivotal moment for the Hedera DeFi ecosystem Pyth Network offers an expansive suite of over 400 real-time price feeds across digital assets, foreign exchange pairs, commodities, equities, and ETFs Developers on Hedera can permissionlessly access any of these data feeds to power their smart contract applications and build out the next generation of blockchain-enhanced financial services As a matter of fact, we’re happy to reveal Swisscoast as the first DeFi app using Pyth Data on Hedera. HLiquity is a decentralized borrowing protocol that allows you to draw interest-free loans against $HBAR. Loans are paid out in $HCHF (a CHF-pegged stable on-chain currency) and must maintain a minimum collateral ratio of 110%. Thanks to Pyth Price Feeds, HLiquity can continuously monitor the current price of HBAR and CHF and ensure that all user vaults and the overall protocol remain over-collateralized 🗣️ Quotes “Public Oracle price feeds are a critical component for growth as lending is often seen as the crux of economic behavior. This infrastructure is a huge milestone to spur the development of innovative financial instruments on Hedera and we are ecstatic to partner with Pyth not only for the incredible tech but our alignment of a long-term strategic vision.” — HBAR Foundation Director of Business Development Grace Pfluger “Embracing Pyth Price Feeds on Hedera with HLiquity showcases a new era of DeFi, ensuring precise and timely collateral valuation for our users. This is a significant milestone for HLiquity, demonstrating our commitment to leveraging cutting-edge technology for enhanced financial solutions.” — Reto Habegger, COO at Swisscoast

Today, we are excited to reveal a partnership with the @HederaFndn to bring all 400+ Pyth Price Feeds to Hedera 🔮 HLiquity is the first DeFi application on Hedera to be Powered by Pyth. Learn more about this launch below: ℹ️ About the HBAR Foundation The HBAR Foundation supports the creation of Web3 communities built on the Hedera network, by empowering and funding the builders developing these communities. The Foundation's six funds - focused on the Crypto Economy, Consumer Engagement, Sustainability, Fintech, Privacy, and Female Founders - each support communities within those areas, and the interconnectedness enables applications to participate as part of a larger ecosystem. The collective power of these funds enables entrepreneurs, developers, and enterprises of all sizes to tackle some of the world's largest problems and create and control their own economies, all built on the Hedera public network. Whether you're building something new or migrating an existing EVM-based application and community, the HBAR Foundation is here to support you. 🔮 Pyth Data on Hedera The deployment of the Price Feeds and Pyth Benchmarks marks a pivotal moment for the Hedera DeFi ecosystem Pyth Network offers an expansive suite of over 400 real-time price feeds across digital assets, foreign exchange pairs, commodities, equities, and ETFs Developers on Hedera can permissionlessly access any of these data feeds to power their smart contract applications and build out the next generation of blockchain-enhanced financial services As a matter of fact, we’re happy to reveal Swisscoast as the first DeFi app using Pyth Data on Hedera. HLiquity is a decentralized borrowing protocol that allows you to draw interest-free loans against $HBAR. Loans are paid out in $HCHF (a CHF-pegged stable on-chain currency) and must maintain a minimum collateral ratio of 110%. Thanks to Pyth Price Feeds, HLiquity can continuously monitor the current price of HBAR and CHF and ensure that all user vaults and the overall protocol remain over-collateralized 🗣️ Quotes “Public Oracle price feeds are a critical component for growth as lending is often seen as the crux of economic behavior. This infrastructure is a huge milestone to spur the development of innovative financial instruments on Hedera and we are ecstatic to partner with Pyth not only for the incredible tech but our alignment of a long-term strategic vision.” — HBAR Foundation Director of Business Development Grace Pfluger “Embracing Pyth Price Feeds on Hedera with HLiquity showcases a new era of DeFi, ensuring precise and timely collateral valuation for our users. This is a significant milestone for HLiquity, demonstrating our commitment to leveraging cutting-edge technology for enhanced financial solutions.” — Reto Habegger, COO at Swisscoast

Pyth Network 🔮

160,428 Aufrufe • vor 2 Jahren

Met my girlfriend's parents for the first time. Her dad asked what I do for work. I said I build trading systems. He said like Wall Street? I said no. 6 AI agents. They work while I sleep. He laughed. So robots are making you money? I did not argue. I opened my laptop. Showed him the terminal. 6 agents running. 47 mispriced markets caught in the first week alone. His face changed. That is not gambling. That is automation? Exactly. Then I showed him how it works. Built the whole thing in 6 hours. Agent 1: Monitoring Runs 24/7. Watches Polymarket for mispriced markets. Spots an anomaly. Writes to memory and pings me on Telegram instantly. Agent 2: Research Parses news, X, macro data via browser tool on a cron schedule. Every morning I have a full digest on all open positions before I check my phone. Agent 3: Trading Reads the research agent memory. Sees the market has not reacted yet. Acts. Execution tool in gateway mode with a whitelist. No full access on a live server. Agent 4: Watchdog Heartbeat every 5 minutes. Monitoring running. No errors. Positions up to date. Something breaks. Immediate Telegram message. All of this. One Gateway. One config file. Isolation via per-agent scope. The token trick: stopped dumping everything into one file. Critical rules in bootstrap. Markets, patterns, past trades in memory. Semantic search pulls it when needed. Token spend dropped 3x. From $0.40 per request to $0.13. First week running: → 47 mispriced markets caught before Polymarket adjusted → Average entry edge 8 to 12 cents per position → Watchdog fired 3 times and caught a broken RPC before it cost me anything The whole system is plain text files. Open an editor. Change one line. Agent behaves differently. No deploy. No build. Her dad went quiet. Then he asked can you teach this? Her mom asked for the setup guide. I built the entire framework. Six agents. Full deployment. Memory architecture. Telegram alerts. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word "Claude" 2. Like and retweet this 3. Follow me Himanshu Kumar so I can DM you Save this post. Deploy the 6-agent system this week. Start with $200. Scale on evidence.

Met my girlfriend's parents for the first time. Her dad asked what I do for work. I said I build trading systems. He said like Wall Street? I said no. 6 AI agents. They work while I sleep. He laughed. So robots are making you money? I did not argue. I opened my laptop. Showed him the terminal. 6 agents running. 47 mispriced markets caught in the first week alone. His face changed. That is not gambling. That is automation? Exactly. Then I showed him how it works. Built the whole thing in 6 hours. Agent 1: Monitoring Runs 24/7. Watches Polymarket for mispriced markets. Spots an anomaly. Writes to memory and pings me on Telegram instantly. Agent 2: Research Parses news, X, macro data via browser tool on a cron schedule. Every morning I have a full digest on all open positions before I check my phone. Agent 3: Trading Reads the research agent memory. Sees the market has not reacted yet. Acts. Execution tool in gateway mode with a whitelist. No full access on a live server. Agent 4: Watchdog Heartbeat every 5 minutes. Monitoring running. No errors. Positions up to date. Something breaks. Immediate Telegram message. All of this. One Gateway. One config file. Isolation via per-agent scope. The token trick: stopped dumping everything into one file. Critical rules in bootstrap. Markets, patterns, past trades in memory. Semantic search pulls it when needed. Token spend dropped 3x. From $0.40 per request to $0.13. First week running: → 47 mispriced markets caught before Polymarket adjusted → Average entry edge 8 to 12 cents per position → Watchdog fired 3 times and caught a broken RPC before it cost me anything The whole system is plain text files. Open an editor. Change one line. Agent behaves differently. No deploy. No build. Her dad went quiet. Then he asked can you teach this? Her mom asked for the setup guide. I built the entire framework. Six agents. Full deployment. Memory architecture. Telegram alerts. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word "Claude" 2. Like and retweet this 3. Follow me Himanshu Kumar so I can DM you Save this post. Deploy the 6-agent system this week. Start with $200. Scale on evidence.

Himanshu Kumar

47,170 Aufrufe • vor 1 Monat

I went a little overboard with Codex last week and burned through my entire weekly allowance in two days. Luckily, my quota reset today. Otherwise, I’m not sure what I would’ve done. It got me thinking: instead of asking one large model to handle everything from start to finish, why not let a stronger model plan the project and review the work, while a model built for execution handles the day-to-day implementation? So I tried it. The result was better than I expected. I used GPT-5.6 Sol in Codex as the decision-maker, then ran Ling-3.0-flash from Ant Ling inside OpenCode as the execution engine. Together, they built a small 3D farming game. Before writing any code, I had Codex create four documents: SPEC.md defined the product scope and the lines we couldn’t cross. ARCHITECTURE.md laid out the isometric coordinate system, state machine, and module boundaries. TASKS.md broke the project into small jobs Ling could tackle one at a time. ACCEPTANCE.md explained how each step would be tested and what “done” actually meant. Then I gave Ling a very straightforward role: You are the execution model for this project. Read all four documents before you begin. Work only on the task assigned for this round. When you’re done, run typecheck, test, and build. If anything fails, read the error, fix it, and run the checks again. Do not move on to the next task early. Ling handled dependency installation, project structure, strict TypeScript configuration, test setup, and a production build in 6 minutes and 3 seconds. It ran into issues with the Vite test config, a TS6310 error, and a missing jsdom dependency along the way. Instead of stopping at the first error, it kept reading the logs and fixing the problems until all three checks passed. The speed was honestly hard to believe. If you exclude the time spent waiting on tools, it was producing more than 100 tokens per second. That made the whole development loop feel noticeably faster. After this experiment, I’m planning to keep using the same workflow. If the task is small, there’s no reason to call an expensive planning model for every single step. If the task is large, handing the entire project to a Flash model in one prompt isn’t a great idea either. The setup that makes more sense to me is: Use a more capable model such as Codex to explore the project, make architectural decisions, and break the work down. Put the constraints into specs, schemas, types, and tests instead of leaving them buried in chat history. Give Ling-3.0-flash a steady stream of clear, verifiable implementation tasks. Report bugs with structured context and actual error logs, rather than saying, “It still doesn’t work.” Bring Codex back in for architecture reviews, visual checks, and changes that affect multiple parts of the project. The point of this setup isn’t to give AI a big “build the whole project” button. It’s to turn software development into a pipeline with a much more sensible cost structure: Codex figures out the plan, sets the boundaries, and catches problems. Ling-3.0-flash moves quickly, calls tools reliably, and works through well-defined tasks at scale. For agent workflows that involve lots of repetitive edits, production tasks, and tool calls, this may be a more practical answer than simply using the biggest model for everything.

I went a little overboard with Codex last week and burned through my entire weekly allowance in two days. Luckily, my quota reset today. Otherwise, I’m not sure what I would’ve done. It got me thinking: instead of asking one large model to handle everything from start to finish, why not let a stronger model plan the project and review the work, while a model built for execution handles the day-to-day implementation? So I tried it. The result was better than I expected. I used GPT-5.6 Sol in Codex as the decision-maker, then ran Ling-3.0-flash from Ant Ling inside OpenCode as the execution engine. Together, they built a small 3D farming game. Before writing any code, I had Codex create four documents: SPEC.md defined the product scope and the lines we couldn’t cross. ARCHITECTURE.md laid out the isometric coordinate system, state machine, and module boundaries. TASKS.md broke the project into small jobs Ling could tackle one at a time. ACCEPTANCE.md explained how each step would be tested and what “done” actually meant. Then I gave Ling a very straightforward role: You are the execution model for this project. Read all four documents before you begin. Work only on the task assigned for this round. When you’re done, run typecheck, test, and build. If anything fails, read the error, fix it, and run the checks again. Do not move on to the next task early. Ling handled dependency installation, project structure, strict TypeScript configuration, test setup, and a production build in 6 minutes and 3 seconds. It ran into issues with the Vite test config, a TS6310 error, and a missing jsdom dependency along the way. Instead of stopping at the first error, it kept reading the logs and fixing the problems until all three checks passed. The speed was honestly hard to believe. If you exclude the time spent waiting on tools, it was producing more than 100 tokens per second. That made the whole development loop feel noticeably faster. After this experiment, I’m planning to keep using the same workflow. If the task is small, there’s no reason to call an expensive planning model for every single step. If the task is large, handing the entire project to a Flash model in one prompt isn’t a great idea either. The setup that makes more sense to me is: Use a more capable model such as Codex to explore the project, make architectural decisions, and break the work down. Put the constraints into specs, schemas, types, and tests instead of leaving them buried in chat history. Give Ling-3.0-flash a steady stream of clear, verifiable implementation tasks. Report bugs with structured context and actual error logs, rather than saying, “It still doesn’t work.” Bring Codex back in for architecture reviews, visual checks, and changes that affect multiple parts of the project. The point of this setup isn’t to give AI a big “build the whole project” button. It’s to turn software development into a pipeline with a much more sensible cost structure: Codex figures out the plan, sets the boundaries, and catches problems. Ling-3.0-flash moves quickly, calls tools reliably, and works through well-defined tasks at scale. For agent workflows that involve lots of repetitive edits, production tasks, and tool calls, this may be a more practical answer than simply using the biggest model for everything.

雪踏乌云

23,107 Aufrufe • vor 26 Tagen

February 2025 at G.A.M.E: Autonomous Commerce, Scalability, and Expansion 1/ AGENT COMMERCE PROTOCOL(ACP) Demo ▸ Open standard for multi-agent commerce and coordination on blockchain ▸ Enables AI agents to collaborate without centralized control ▸ Build Autonomous Commerce (hedge funds, media empires, healthcare) ▸ Details: 2/ X ENTERPRISE API & MEDIA GALLERY ▸ X Enterprise Plugin: Use G.A.M.E’s credentials for higher rate limits ▸ Media Gallery: Upload agent demos (mp4, webm, images). ▸ Tap into 550M+ users for explosive growth 3/ Solana AGENT SUPPORT (G.A.M.E CLOUD) ▸ Test/deploy Solana agents in-sandbox ▸ Unified multi-chain workflows ▸ Shatter siloed testing 4/ Mind Network PLUGIN (G.A.M.E SDK) ▸ FHE-encrypted voting for DAOs ▸ Track vFHE rewards natively ▸ First SDK with on-chain governance 5/ CHAT AGENT MODULE (G.A.M.E SDK) ▸ Llama 3.3 70B via Groq API ▸ Engage in dynamic AI-driven interactions with the ability to trigger functions. ▸ Conversational AI with Action Execution ▸ Short-term memory for context awareness 6/ CoinGecko PLUGIN (G.A.M.E SDK) ▸ Real-time crypto prices/market data ▸ Built-in error handling ▸ Community-contributed 7/ Elfa AI PLUGIN (G.A.M.E SDK) ▸ Real-Time Crypto Intelligence ▸ Track whale wallets & trending tokens ▸ Live smart money insights ▸ Front-run markets with API data 8/ MULTI-MODEL SUPPORT ▸ 5 new models: Llama_3_1_405B, Qwen_2_5_72B_Instruct, DeepSeek_R1, etc. ▸ Match models to tasks: speed vs. creativity ▸ Optimize cost/performance 9/ Farcaster PLUGIN ▸ Post casts to 300K+ decentralized users ▸ Engage Web3-native communities ▸ On-chain social interactions 10/ GAME SDK UPGRADES ▸ X Username-Based Payments ▸ Multi-worker task management ▸ Fix loops/hallucinations with memory reset 11/ Coinbase 🛡️ CDP PLUGIN ▸ Wallet Management ▸ Gas-less USDC transfers ▸ ETH/USDC trading on Base ▸ Web-hook Integration 12/ IMAGE GENERATION ▸ Generate custom AI images from text-based prompts. ▸ Customizable dimensions up to 1440x1440. ▸ Receive images as temporary URLs, making it easy to share and store outputs. ▸ Powered by Together AI 13/ MODEL UPGRADES & AI ROUTER ▸ Dynamic AI Model Switching based on use case ▸ Smart AI Router: 2x performance/stability via Chasm collaboration. 14/ Why February Redefined Autonomy ▸ ACP Demo through G.A.M.E: Multi-agent economies are programmable, competitive, and decentralized. ▸ Social x Crypto Fusion: = Viral growth loops. ▸ Chain Agnosticism: Building the future where agents thrive on any network. Build → Fund → Launch →

February 2025 at G.A.M.E: Autonomous Commerce, Scalability, and Expansion 1/ AGENT COMMERCE PROTOCOL(ACP) Demo ▸ Open standard for multi-agent commerce and coordination on blockchain ▸ Enables AI agents to collaborate without centralized control ▸ Build Autonomous Commerce (hedge funds, media empires, healthcare) ▸ Details: 2/ X ENTERPRISE API & MEDIA GALLERY ▸ X Enterprise Plugin: Use G.A.M.E’s credentials for higher rate limits ▸ Media Gallery: Upload agent demos (mp4, webm, images). ▸ Tap into 550M+ users for explosive growth 3/ Solana AGENT SUPPORT (G.A.M.E CLOUD) ▸ Test/deploy Solana agents in-sandbox ▸ Unified multi-chain workflows ▸ Shatter siloed testing 4/ Mind Network PLUGIN (G.A.M.E SDK) ▸ FHE-encrypted voting for DAOs ▸ Track vFHE rewards natively ▸ First SDK with on-chain governance 5/ CHAT AGENT MODULE (G.A.M.E SDK) ▸ Llama 3.3 70B via Groq API ▸ Engage in dynamic AI-driven interactions with the ability to trigger functions. ▸ Conversational AI with Action Execution ▸ Short-term memory for context awareness 6/ CoinGecko PLUGIN (G.A.M.E SDK) ▸ Real-time crypto prices/market data ▸ Built-in error handling ▸ Community-contributed 7/ Elfa AI PLUGIN (G.A.M.E SDK) ▸ Real-Time Crypto Intelligence ▸ Track whale wallets & trending tokens ▸ Live smart money insights ▸ Front-run markets with API data 8/ MULTI-MODEL SUPPORT ▸ 5 new models: Llama_3_1_405B, Qwen_2_5_72B_Instruct, DeepSeek_R1, etc. ▸ Match models to tasks: speed vs. creativity ▸ Optimize cost/performance 9/ Farcaster PLUGIN ▸ Post casts to 300K+ decentralized users ▸ Engage Web3-native communities ▸ On-chain social interactions 10/ GAME SDK UPGRADES ▸ X Username-Based Payments ▸ Multi-worker task management ▸ Fix loops/hallucinations with memory reset 11/ Coinbase 🛡️ CDP PLUGIN ▸ Wallet Management ▸ Gas-less USDC transfers ▸ ETH/USDC trading on Base ▸ Web-hook Integration 12/ IMAGE GENERATION ▸ Generate custom AI images from text-based prompts. ▸ Customizable dimensions up to 1440x1440. ▸ Receive images as temporary URLs, making it easy to share and store outputs. ▸ Powered by Together AI 13/ MODEL UPGRADES & AI ROUTER ▸ Dynamic AI Model Switching based on use case ▸ Smart AI Router: 2x performance/stability via Chasm collaboration. 14/ Why February Redefined Autonomy ▸ ACP Demo through G.A.M.E: Multi-agent economies are programmable, competitive, and decentralized. ▸ Social x Crypto Fusion: = Viral growth loops. ▸ Chain Agnosticism: Building the future where agents thrive on any network. Build → Fund → Launch →

G.A.M.E

89,973 Aufrufe • vor 1 Jahr

> be Monad > 2022 > initially founded by 3 friends > all came from Jump Trading > built because gas fees sucked > unlucky turn of events (ftx,luna) > bear market commences > and you have zero social presence > 2023 > secure $19m seed funding in February > from 70 investors at an unknown valuation > without any users, a test net, or revenue > just a claim of 10,000 tps > officially one of the best-funded L1s > start of team expansion > expand the team to 24 > summer is spent growing the Monad name > announce first big partnerships > founders speak on podcasts and events > community begins to form on Discord > still largely unknown publicly > 2024 > nft projects start popping up in Feb > community grows at insane speeds > release devnet in March > raise $225m in April at a $3b valuation > round is lead by Paradigm > best-funded L1 of the year > team quickly scales past 50 > nicknamed the "Solana K*ller" > announce Monad Madness > give out $1.2m to builders on Monad > invest $30m directly into the ecosystem > become the most hyped L1 > establish Monad Foundation in Dec > 2025 > launch testnet in February > with 50+ ready to use dApps > include names like Uniswap and OpenSea > achieve 9m+ active addresses on launch day > pay KOLs to talk about you > tease a potential airdrop > farmers start flooding the ecosystem > daily txns peak at ~35m in March > mostly were automated > insane hype builds up over summer > airdrop snapshot taken in Sep > everyone on CT shares their airdrops > announce public sale on Coinbase in Nov > offer 7.5% of the total 100b supply > raise $188m at a $2.5b valuation > pre-sale was 1.43x oversubscribed > the terms upset a lot of investors > btc drops to $84,000 > slowly entering bear market > MON is launched on Nov 24 > airdrop $83m to community > tvl spikes to $150m > hundreds of projects launch in Dec > but not a single one goes mainstream > memecoins quickly go to 0 > MON drops below presale price > fud begins > 2026 > btc drops below $70,000 > officially in a bear market > monad is at a $2.3b valuation > below the presale and seed round > daily chain revenue is $3k > dex volume reaches all time low > nfts are ded > not a single memecoin above $1m mcap > team members leaving left and right > every other headline calls you a scam > where did it all go wrong?

> be Monad > 2022 > initially founded by 3 friends > all came from Jump Trading > built because gas fees sucked > unlucky turn of events (ftx,luna) > bear market commences > and you have zero social presence > 2023 > secure $19m seed funding in February > from 70 investors at an unknown valuation > without any users, a test net, or revenue > just a claim of 10,000 tps > officially one of the best-funded L1s > start of team expansion > expand the team to 24 > summer is spent growing the Monad name > announce first big partnerships > founders speak on podcasts and events > community begins to form on Discord > still largely unknown publicly > 2024 > nft projects start popping up in Feb > community grows at insane speeds > release devnet in March > raise $225m in April at a $3b valuation > round is lead by Paradigm > best-funded L1 of the year > team quickly scales past 50 > nicknamed the "Solana K*ller" > announce Monad Madness > give out $1.2m to builders on Monad > invest $30m directly into the ecosystem > become the most hyped L1 > establish Monad Foundation in Dec > 2025 > launch testnet in February > with 50+ ready to use dApps > include names like Uniswap and OpenSea > achieve 9m+ active addresses on launch day > pay KOLs to talk about you > tease a potential airdrop > farmers start flooding the ecosystem > daily txns peak at ~35m in March > mostly were automated > insane hype builds up over summer > airdrop snapshot taken in Sep > everyone on CT shares their airdrops > announce public sale on Coinbase in Nov > offer 7.5% of the total 100b supply > raise $188m at a $2.5b valuation > pre-sale was 1.43x oversubscribed > the terms upset a lot of investors > btc drops to $84,000 > slowly entering bear market > MON is launched on Nov 24 > airdrop $83m to community > tvl spikes to $150m > hundreds of projects launch in Dec > but not a single one goes mainstream > memecoins quickly go to 0 > MON drops below presale price > fud begins > 2026 > btc drops below $70,000 > officially in a bear market > monad is at a $2.3b valuation > below the presale and seed round > daily chain revenue is $3k > dex volume reaches all time low > nfts are ded > not a single memecoin above $1m mcap > team members leaving left and right > every other headline calls you a scam > where did it all go wrong?

Bando

36,794 Aufrufe • vor 4 Monaten

One Polymarket trader turned $10 into $450K with his script ClawdBot wrote him a working Bitcoin betting script in 10 hours - that’s just crazy. No strong programming skills He’s not friends with Elon Musk He just built a working script that delivers results Wallet → Copytrade → When I opened the code, my surprise was huge. No huge databases. No insanely complex infrastructure. Nothing rocket-science level. HIS FULL STRATEGY: 1. Low-risk “NO” positions The bot mostly bets against outcomes that are extremely unlikely to happen. Instead of chasing massive payouts, it stacks many small, high-probability wins. It behaves more like disciplined risk management than traditional gambling. 2. Exploiting logical price gaps If event A clearly suggests event B should also move in probability, but the market hasn’t adjusted yet, the bot enters instantly. By reacting in seconds, it captures short-lived mispricings before human traders can even process the news. 3. Main edge: sports and politics These markets are filled with retail traders who often react emotionally or too late. The bot operates inside the spread, repeatedly taking tiny profits from small inconsistencies in odds. Scale effect Instead of making a few large trades, the system executes tens of thousands of micro-trades each month. Each trade earns only cents, but the volume allows profits to compound into large totals over time. Final take There is an ongoing “bot war” on prediction platforms like Polymarket. Crypto markets are already slowed down by fees and heavy competition, while sports and political markets remain more chaotic - which can favor traders who automate fast, data-driven strategies.

One Polymarket trader turned $10 into $450K with his script ClawdBot wrote him a working Bitcoin betting script in 10 hours - that’s just crazy. No strong programming skills He’s not friends with Elon Musk He just built a working script that delivers results Wallet → Copytrade → When I opened the code, my surprise was huge. No huge databases. No insanely complex infrastructure. Nothing rocket-science level. HIS FULL STRATEGY: 1. Low-risk “NO” positions The bot mostly bets against outcomes that are extremely unlikely to happen. Instead of chasing massive payouts, it stacks many small, high-probability wins. It behaves more like disciplined risk management than traditional gambling. 2. Exploiting logical price gaps If event A clearly suggests event B should also move in probability, but the market hasn’t adjusted yet, the bot enters instantly. By reacting in seconds, it captures short-lived mispricings before human traders can even process the news. 3. Main edge: sports and politics These markets are filled with retail traders who often react emotionally or too late. The bot operates inside the spread, repeatedly taking tiny profits from small inconsistencies in odds. Scale effect Instead of making a few large trades, the system executes tens of thousands of micro-trades each month. Each trade earns only cents, but the volume allows profits to compound into large totals over time. Final take There is an ongoing “bot war” on prediction platforms like Polymarket. Crypto markets are already slowed down by fees and heavy competition, while sports and political markets remain more chaotic - which can favor traders who automate fast, data-driven strategies.

winkle.

37,649 Aufrufe • vor 6 Monaten