Загрузка видео...

Не удалось загрузить видео

На главную

Most agentic wallets still need babysitting. Ours gives agents: • Natural-language execution • Gas-free trades on X Layer • Risk checks before signing See the docs:

wallet's profile picture

OKX Wallet

1,894,249 subscribers

17,459 просмотров • 1 месяц назад •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

Introducing OKX Agentic Wallet. Built for AI agents to execute across ~20 chains using natural language via CLI. TXs on X Layer are gas-free. Before signing, every transaction is simulated, screened, and validated. Execution halts if any risk appears. More info:
0:10

Introducing OKX Agentic Wallet. Built for AI agents to execute across ~20 chains using natural language via CLI. TXs on X Layer are gas-free. Before signing, every transaction is simulated, screened, and validated. Execution halts if any risk appears. More info:

OKX Wallet

98,508 просмотров • 5 месяцев назад

Zero human at the desk 🤖 GT AI Hedge Fund is no longer only about crypto. Now it trades STOCKS too. Powered by GT LLM, trained on 58M real trades, the fund runs through GT’s decision + execution layer - where 5 AI agents analyze, decide, and execute end to end. Everything is public: live equity, trade logs, and full deal history. 100% agentic. Zero human. Fully verifiable 📈
0:18

Zero human at the desk 🤖 GT AI Hedge Fund is no longer only about crypto. Now it trades STOCKS too. Powered by GT LLM, trained on 58M real trades, the fund runs through GT’s decision + execution layer - where 5 AI agents analyze, decide, and execute end to end. Everything is public: live equity, trade logs, and full deal history. 100% agentic. Zero human. Fully verifiable 📈

GT Protocol

36,715 просмотров • 1 месяц назад

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 execution → Built in risk checks to avoid blind execution Jupiter Rust Language
0:12

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 execution → Built in risk checks to avoid blind execution Jupiter Rust Language

anshuman Ꙫ

48,520 просмотров • 9 месяцев назад

Quack AI × Avalanche🔺 Q402 is officially expanding into the Avalanche ecosystem. We are pushing toward more scalable, execution-first environments for agents. With Avalanche's sub-second finality and builder-first stack, Q402 now provides a foundation for verifiable, policy-aware execution that scales with real-world usage. What’s new for the Avalanche community? The Q402 signature-based execution layer is now LIVE on the Avalanche C-Chain. Users and builders can now: • Zero Gas Barrier: Settle ERC-20 transfers (USDC, etc.) without holding AVAX for gas. • Sign-to-Pay: Decouple intent signing from transaction execution for a seamless UX. • Production-Ready: High-performance, secure, and auditable execution layer for the agent economy. Contract Deployed on C-Chain: Execution should feel native. Verification should be default. Welcome to the agent-native era on Avalanche.
0:12

Quack AI × Avalanche🔺 Q402 is officially expanding into the Avalanche ecosystem. We are pushing toward more scalable, execution-first environments for agents. With Avalanche's sub-second finality and builder-first stack, Q402 now provides a foundation for verifiable, policy-aware execution that scales with real-world usage. What’s new for the Avalanche community? The Q402 signature-based execution layer is now LIVE on the Avalanche C-Chain. Users and builders can now: • Zero Gas Barrier: Settle ERC-20 transfers (USDC, etc.) without holding AVAX for gas. • Sign-to-Pay: Decouple intent signing from transaction execution for a seamless UX. • Production-Ready: High-performance, secure, and auditable execution layer for the agent economy. Contract Deployed on C-Chain: Execution should feel native. Verification should be default. Welcome to the agent-native era on Avalanche.

Quack AI

23,322 просмотров • 5 месяцев назад

Day 15 of why you need to use Jupiter Mobile: GASLESS TRANSACTIONS Most wallets make you preload SOL for gas before you can do anything. On Jupiter Mobile, you trade gasless and without friction. Swap, trade and send to any wallet gas-free, even with 0 SOL in your wallet. Gasless swaps, 10x faster, 10x cheaper. Just Use Jupiter (Mobile).
0:18

Day 15 of why you need to use Jupiter Mobile: GASLESS TRANSACTIONS Most wallets make you preload SOL for gas before you can do anything. On Jupiter Mobile, you trade gasless and without friction. Swap, trade and send to any wallet gas-free, even with 0 SOL in your wallet. Gasless swaps, 10x faster, 10x cheaper. Just Use Jupiter (Mobile).

Jupiter

167,618 просмотров • 7 месяцев назад

We are now live! NoelClaw The Runtime layer for agentic AI Robinhood Chain. Your AI remembers, keeps working, and survives every session. Most AI assistants disappear when the conversation ends. Noelclaw gives them persistent state - memory that accumulates, agents that keep running, vaults that version knowledge, and workflows that continue after you close the chat. Instead of starting from scratch every conversation, your AI can remember, research, automate workflows, spawn agents, and keep working long after you close the chat. Connect MCP servers, build reusable skills, deploy persistent agents, and bring your AI into the real world. CA: 0x842245B92b3932Aa8E759A1dAc1eb5ce10cC4f0e website : app : npm : docs : github :
0:10

We are now live! NoelClaw The Runtime layer for agentic AI Robinhood Chain. Your AI remembers, keeps working, and survives every session. Most AI assistants disappear when the conversation ends. Noelclaw gives them persistent state - memory that accumulates, agents that keep running, vaults that version knowledge, and workflows that continue after you close the chat. Instead of starting from scratch every conversation, your AI can remember, research, automate workflows, spawn agents, and keep working long after you close the chat. Connect MCP servers, build reusable skills, deploy persistent agents, and bring your AI into the real world. CA: 0x842245B92b3932Aa8E759A1dAc1eb5ce10cC4f0e website : app : npm : docs : github :

Finch Agentic

13,363 просмотров • 1 месяц назад

The meme supercycle was fun, but the Agent Economy is the real revolution. We are witnessing the transition of AI from "tool" to "labor." It’s no longer just about chatbots, it’s about Economic Identity and Autonomous Execution. This is exactly where Bluwhale sits. We are building the Intelligence Layer that gives Agents the context (Identity) they need to actually work for you on-chain. The macro shift is here 🐋🚀
0:12

The meme supercycle was fun, but the Agent Economy is the real revolution. We are witnessing the transition of AI from "tool" to "labor." It’s no longer just about chatbots, it’s about Economic Identity and Autonomous Execution. This is exactly where Bluwhale sits. We are building the Intelligence Layer that gives Agents the context (Identity) they need to actually work for you on-chain. The macro shift is here 🐋🚀

Bluwhale

41,380 просмотров • 8 месяцев назад

DAPPOS is bringing Onchain OS Skills from X Layer OKX Wallet into the xBubble ecosystem, making OKX Wallet’s agent-ready wallet, trading, market data, and agentic payments protocol capabilities accessible across xBubble agents. Built on top of Onchain OS Skills, xBubble’s crypto-task SOPs help turn fragmented on-chain flows into a more seamless chat-native experience inside the xBubble app across mobile, desktop, and web. Users can monitor markets, prepare trades, manage wallet activity, and coordinate payment flows through a single conversation. Bubble Engine will continue to use Onchain OS Skills as the baseline for every future SOP iteration and upgrade. With Onchain OS Skills, DAPPOS is making agentic on-chain tasks more conversational, practical, and accessible.
0:12

DAPPOS is bringing Onchain OS Skills from X Layer OKX Wallet into the xBubble ecosystem, making OKX Wallet’s agent-ready wallet, trading, market data, and agentic payments protocol capabilities accessible across xBubble agents. Built on top of Onchain OS Skills, xBubble’s crypto-task SOPs help turn fragmented on-chain flows into a more seamless chat-native experience inside the xBubble app across mobile, desktop, and web. Users can monitor markets, prepare trades, manage wallet activity, and coordinate payment flows through a single conversation. Bubble Engine will continue to use Onchain OS Skills as the baseline for every future SOP iteration and upgrade. With Onchain OS Skills, DAPPOS is making agentic on-chain tasks more conversational, practical, and accessible.

DAPPOS

14,289 просмотров • 2 месяцев назад

My ex texted me out of nowhere. Asked what I've been up to. "Building trading systems." She laughed. "Like crypto?" "No. Open-source. Anyone can see how it works." She didn't believe me. She never did. So I sent her a screenshot. One wallet returned 76% in 2 months. Another moved $6.1M in volume. One more turned $900 into $7,400. She went quiet. "That's... from code?" Exactly. Then I sent her the repos. First: 86M+ trades. Every position ever taken on Polymarket. Free. Second: Market making bot. Both sides of the book. Google Sheets execution layer. Third: ML + heuristics. Flagged that $900 → $7,400 before anyone noticed. She replied the next day: "My brother wants to start." Profile: Most people think you need a finance degree or a fund. You don't. You just need to read the code and start.
0:16

My ex texted me out of nowhere. Asked what I've been up to. "Building trading systems." She laughed. "Like crypto?" "No. Open-source. Anyone can see how it works." She didn't believe me. She never did. So I sent her a screenshot. One wallet returned 76% in 2 months. Another moved $6.1M in volume. One more turned $900 into $7,400. She went quiet. "That's... from code?" Exactly. Then I sent her the repos. First: 86M+ trades. Every position ever taken on Polymarket. Free. Second: Market making bot. Both sides of the book. Google Sheets execution layer. Third: ML + heuristics. Flagged that $900 → $7,400 before anyone noticed. She replied the next day: "My brother wants to start." Profile: Most people think you need a finance degree or a fund. You don't. You just need to read the code and start.

Lunar

14,814 просмотров • 2 месяцев назад

Found a trader who turned $8 into $56,000 trading weather on Polymarket his strategy looks dumb until you see the math His profile: How he does it: -> only trades weather markets (temperature in London, Milan, Paris, Sao Paulo) -> waits until the day before resolution when the forecast is locked -> buys outcomes at 99c that resolve at $1 = +1c per trade -> 4,103 predictions, mostly micro-trades -> compounds tiny edges into 5 figures The part most people miss: his edge isn't predicting the weather -> it's noticing that on resolution day, the market still trades at 99c instead of $1 The math behind this is in the article below: the script from the article + free NASA data lets you run the same logic on any monthly weather, CPI or temperature market 👇 Copy him with me:
0:12

Found a trader who turned $8 into $56,000 trading weather on Polymarket his strategy looks dumb until you see the math His profile: How he does it: -> only trades weather markets (temperature in London, Milan, Paris, Sao Paulo) -> waits until the day before resolution when the forecast is locked -> buys outcomes at 99c that resolve at $1 = +1c per trade -> 4,103 predictions, mostly micro-trades -> compounds tiny edges into 5 figures The part most people miss: his edge isn't predicting the weather -> it's noticing that on resolution day, the market still trades at 99c instead of $1 The math behind this is in the article below: the script from the article + free NASA data lets you run the same logic on any monthly weather, CPI or temperature market 👇 Copy him with me:

Paone

10,650 просмотров • 3 месяцев назад

Our @Grammarly AI agents are here! Today, we’re launching eight new AI agents designed for students and professionals. We created many of these agents with students in mind because they’re the first generation entering a job market where employers expect both subject expertise AND AI fluency. These agents help with everything from finding credible sources to predicting reader reactions. One agent we’ve gotten great feedback on is AI Grader (I wish I had this in school), which you can see in the video below. It looks at your assignment rubric and gives you suggestions like your professor would, and a grade prediction before you submit your work. And these agents are available in docs, our new AI-native writing surface! I’m deeply proud of this launch—docs is powered by Coda (Superhuman Docs) technology and is a great integration moment between Grammarly and Coda. This is just the beginning of Grammarly’s journey to offering agents that work everywhere people work and collaborate. I’ve been loving using these agents, and I’m excited for our customers to get access. Try them for yourself here and let me know what you think:
0:17

Our @Grammarly AI agents are here! Today, we’re launching eight new AI agents designed for students and professionals. We created many of these agents with students in mind because they’re the first generation entering a job market where employers expect both subject expertise AND AI fluency. These agents help with everything from finding credible sources to predicting reader reactions. One agent we’ve gotten great feedback on is AI Grader (I wish I had this in school), which you can see in the video below. It looks at your assignment rubric and gives you suggestions like your professor would, and a grade prediction before you submit your work. And these agents are available in docs, our new AI-native writing surface! I’m deeply proud of this launch—docs is powered by Coda (Superhuman Docs) technology and is a great integration moment between Grammarly and Coda. This is just the beginning of Grammarly’s journey to offering agents that work everywhere people work and collaborate. I’ve been loving using these agents, and I’m excited for our customers to get access. Try them for yourself here and let me know what you think:

Shishir

13,564 просмотров • 1 год назад

In 2025, the AgentFlayer exploit highlighted a new category of risk in AI systems. It was not a traditional breach involving stolen credentials or broken encryption. Instead, it demonstrated how an autonomous AI agent could be manipulated into executing unintended actions by processing malicious instructions embedded inside content it automatically processes. The incident did not expose a flaw in one specific integration. It revealed a structural weakness in how many modern AI agents are built. Today’s agents are no longer passive language models. They read documents automatically, scan emails, connect to SaaS tools, access cloud storage, and execute actions across multiple systems. To be useful, they are granted meaningful permissions. That capability creates value, but it also expands the attack surface. Most agent environments operate in a trusted, plaintext execution model. Data is encrypted at rest and in transit, but it is typically decrypted during inference so the model can process it. That runtime visibility is where potential risk lies. In a zero-click scenario like AgentFlayer, an attacker can embed hidden instructions inside a document that the AI processes automatically. Because the agent may have access to connected systems such as Google Drive, Slack, or GitHub, it can potentially be influenced to retrieve sensitive information or perform unintended actions. The user does not need to click a malicious link or approve a suspicious request. Therefore, the core issue is that during execution, the system may have access to sensitive data and broad privileges, meaning whoever controls the execution environment ultimately controls access to that data. Now consider a different architectural approach. If a system is designed so that data remains protected during execution, the risk profile changes. On Nesa, privacy is enforced at the execution layer through Equivariant Encryption. Computation can occur on encrypted data, reducing the visibility surface during runtime. Sensitive inputs and models do not need to be exposed in plain text to infrastructure operators for inference to occur. This does not eliminate prompt injection, logic manipulation, or tool misuse. Encryption alone cannot prevent an agent from being instructed to take an unintended action if it has been granted that permission. What it does do is materially reduce confidentiality risk. By limiting access to readable sensitive data during execution and reducing unilateral visibility at the infrastructure layer, the potential blast radius of a successful manipulation attempt is constrained. As AI agents become more autonomous and embedded into enterprise workflows, security must move deeper into architecture. The goal is not to claim invulnerability. It is to reduce trust concentration and contain systemic exposure when failures occur. AgentFlayer was not simply a one-off exploit. It was a reminder that in autonomous systems, execution-layer design determines how risk propagates.
0:11

In 2025, the AgentFlayer exploit highlighted a new category of risk in AI systems. It was not a traditional breach involving stolen credentials or broken encryption. Instead, it demonstrated how an autonomous AI agent could be manipulated into executing unintended actions by processing malicious instructions embedded inside content it automatically processes. The incident did not expose a flaw in one specific integration. It revealed a structural weakness in how many modern AI agents are built. Today’s agents are no longer passive language models. They read documents automatically, scan emails, connect to SaaS tools, access cloud storage, and execute actions across multiple systems. To be useful, they are granted meaningful permissions. That capability creates value, but it also expands the attack surface. Most agent environments operate in a trusted, plaintext execution model. Data is encrypted at rest and in transit, but it is typically decrypted during inference so the model can process it. That runtime visibility is where potential risk lies. In a zero-click scenario like AgentFlayer, an attacker can embed hidden instructions inside a document that the AI processes automatically. Because the agent may have access to connected systems such as Google Drive, Slack, or GitHub, it can potentially be influenced to retrieve sensitive information or perform unintended actions. The user does not need to click a malicious link or approve a suspicious request. Therefore, the core issue is that during execution, the system may have access to sensitive data and broad privileges, meaning whoever controls the execution environment ultimately controls access to that data. Now consider a different architectural approach. If a system is designed so that data remains protected during execution, the risk profile changes. On Nesa, privacy is enforced at the execution layer through Equivariant Encryption. Computation can occur on encrypted data, reducing the visibility surface during runtime. Sensitive inputs and models do not need to be exposed in plain text to infrastructure operators for inference to occur. This does not eliminate prompt injection, logic manipulation, or tool misuse. Encryption alone cannot prevent an agent from being instructed to take an unintended action if it has been granted that permission. What it does do is materially reduce confidentiality risk. By limiting access to readable sensitive data during execution and reducing unilateral visibility at the infrastructure layer, the potential blast radius of a successful manipulation attempt is constrained. As AI agents become more autonomous and embedded into enterprise workflows, security must move deeper into architecture. The goal is not to claim invulnerability. It is to reduce trust concentration and contain systemic exposure when failures occur. AgentFlayer was not simply a one-off exploit. It was a reminder that in autonomous systems, execution-layer design determines how risk propagates.

Nesa

17,038 просмотров • 5 месяцев назад

We are excited to announce our $8.3M Series A to build the financial control layer for companies operating across stablecoin, crypto and fiat rails. Stablecoins settle in seconds. Yet too many treasury, risk, and compliance controls still run on business-day logic. Range closes that gap: a single real-time platform unifying your wallets, custodians, bank accounts, and exchanges, with risk and compliance controls that screen transactions before money moves and full visibility across rails. This round brings our total funding to $11M. Investors in the round include TX Ventures, Maven 11, SixThirty Ventures, Stellar, Onigiri Capital, and others. Range is already running at scale: - $30B+ in assets under management protected - 99.41% of all stablecoin payments tracked across 200+ networks - 10,000+ integrations across banks, custodians, and wallets See what total financial control looks like across stablecoins, crypto and fiat 👇
0:13

We are excited to announce our $8.3M Series A to build the financial control layer for companies operating across stablecoin, crypto and fiat rails. Stablecoins settle in seconds. Yet too many treasury, risk, and compliance controls still run on business-day logic. Range closes that gap: a single real-time platform unifying your wallets, custodians, bank accounts, and exchanges, with risk and compliance controls that screen transactions before money moves and full visibility across rails. This round brings our total funding to $11M. Investors in the round include TX Ventures, Maven 11, SixThirty Ventures, Stellar, Onigiri Capital, and others. Range is already running at scale: - $30B+ in assets under management protected - 99.41% of all stablecoin payments tracked across 200+ networks - 10,000+ integrations across banks, custodians, and wallets See what total financial control looks like across stablecoins, crypto and fiat 👇

Range

1,471,145 просмотров • 2 месяцев назад

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. It's open-source. Anyone can see how it works. He laughed. "So you're gambling with open-source code?" I didn't argue. I opened my laptop. One wallet I was tracking made $2.4M in March alone. Another traded $36M in volume. One more turned $35K into $442K. His face changed. "That's not gambling. That's... math?" Exactly. Then I showed him the repos. All public. All free. First one: Every trade ever made. 86M+ trades. Free to download. Snapshot saves 2+ days of research. Second one: Market making bot. Both sides of the book. Gas optimized. Google Sheets execution layer. Third one: ML + heuristics. Flagged that $35K → $442K wallet before anyone noticed. Her dad went quiet. Then he asked: "Can my son run this?" Her mom asked for the channel link. Profile: Most people think you need a finance degree or a hedge fund. You don't. You just need to read the code and start.
0:16

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. It's open-source. Anyone can see how it works. He laughed. "So you're gambling with open-source code?" I didn't argue. I opened my laptop. One wallet I was tracking made $2.4M in March alone. Another traded $36M in volume. One more turned $35K into $442K. His face changed. "That's not gambling. That's... math?" Exactly. Then I showed him the repos. All public. All free. First one: Every trade ever made. 86M+ trades. Free to download. Snapshot saves 2+ days of research. Second one: Market making bot. Both sides of the book. Gas optimized. Google Sheets execution layer. Third one: ML + heuristics. Flagged that $35K → $442K wallet before anyone noticed. Her dad went quiet. Then he asked: "Can my son run this?" Her mom asked for the channel link. Profile: Most people think you need a finance degree or a hedge fund. You don't. You just need to read the code and start.

Lunar

177,660 просмотров • 2 месяцев назад

My trainer at the gym asked what I do for work. "Trading systems. Open-source." He laughed. "Like day trading?" "No. Anyone can see how it works. All the code is public." He put down his weights. "So you just look at code all day?" I didn't argue. I opened my laptop. One wallet I was tracking returned 71% in a single month. Another processed $4.1M in volume. One more turned $500 into $8,200. His face changed. "That's... from some code?" Exactly. Then I showed him the repos. All public. All free. First one: Every trade ever made. 86M+ trades. Free to download. Snapshot saves 2+ days of research. Second one: Market making bot. Both sides of the book. Gas optimized. Google Sheets execution layer. Third one: ML + heuristics. Flagged that \$500 → \$8,200 wallet before anyone noticed. LLM auto market making. Open source from Polymarket team. My trainer went quiet. Then he asked: "Can my son run this? He's into computers." Profile: I said yes. Then I showed him how to start. Most people think you need a finance degree or a fund. You don't. You just need to read the code and start.
0:15

My trainer at the gym asked what I do for work. "Trading systems. Open-source." He laughed. "Like day trading?" "No. Anyone can see how it works. All the code is public." He put down his weights. "So you just look at code all day?" I didn't argue. I opened my laptop. One wallet I was tracking returned 71% in a single month. Another processed $4.1M in volume. One more turned $500 into $8,200. His face changed. "That's... from some code?" Exactly. Then I showed him the repos. All public. All free. First one: Every trade ever made. 86M+ trades. Free to download. Snapshot saves 2+ days of research. Second one: Market making bot. Both sides of the book. Gas optimized. Google Sheets execution layer. Third one: ML + heuristics. Flagged that \$500 → \$8,200 wallet before anyone noticed. LLM auto market making. Open source from Polymarket team. My trainer went quiet. Then he asked: "Can my son run this? He's into computers." Profile: I said yes. Then I showed him how to start. Most people think you need a finance degree or a fund. You don't. You just need to read the code and start.

Lunar

35,831 просмотров • 2 месяцев назад

An Anthropic paid for my espresso at Sightglass when he saw my screen. I was backtesting a Claude-built arbitrage system. Terminal open. Live trades firing. He glanced over. Stopped walking. That is not TradingView. What framework is that actually running. Claude Code. Three repos. One prompt. $20 per month. He sat down across from me without asking. I work on AlphaGo successor models. We test reinforcement agents for market simulation. You are running something similar but you let Claude write the strategy layer. Not just strategy. Detection. github/warproxxx/poly_data 86 million Polymarket trades. Every wallet. Every position. Every timestamp. You are feeding Claude transaction history and letting it identify asymmetric behavior patterns. Then cloning the profitable ones in real time. Exactly. One prompt: Scan every wallet with 150+ trades and ROI above 65%. Rank by consistency. Export top 40. Claude processed 18,600 wallets in 6 minutes. Returned 38. Top 15 wallets outperformed the bottom 18,000 combined. That is not analysis. That is alpha concentration. Precisely. And you did not write the ranking algorithm. Claude built it. I just connected it to execution logic. Then I opened the second repo. github/Polymarket/polymarket-cli Official Rust CLI. No auth required for reads. 600+ markets scanned in under 3 minutes. Claude scores: liquidity depth, pricing gap, resolution timeline. 512 markets reduced to 28 before capital moves. 94.5% filtered out before entry consideration. A notification hit. Position filled. +$127. How does it decide entry timing. Four agents. No shared state. Arbitrage detector, convergence scanner, whale mirror, volume surge tracker. 3 agents agree: full position. 2 agree: half size. Split vote: skip. Consensus filtering alone eliminated 46% of losses in backtest. And exit logic. The 38 top wallets almost never hold to settlement. 89% exit early. Average 71% of max profit captured. Immediate redeployment. My bot exits at 82% of projected move or 4x volume spike. Whichever hits first. You built a whale copy system that exits before the whales do. Correct. He set his coffee down slowly. How many trades per day. 12 average. Most rejected by filters before I see notifications. My setup: Claude API: $20/mo VPS Frankfurt: $6/mo poly_data: free polymarket-cli: free $300 seed capital. 34 days ago. $18,700 now. 318 trades. 76% win rate. Sharpe 2.61. I have not modified it in 34 days. He stared at the terminal without blinking. This is exactly what our adversarial testing team models. Market-adaptive agents with autonomous strategy evolution. Except you deployed it live. He messaged me the next day. Would you consider a conversation with our safety research lead. I told him this post is the conversation. Too late to contain. The edge is not predicting markets. It is identifying who already wins and mirroring them before the pattern shifts. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word "Money" 2. Like and retweet this post. 3. Follow me Himanshu Kumar so I can DM you Save this post. Build the whale mirror system this week. Start with $200. Scale on evidence.
0:19

An Anthropic paid for my espresso at Sightglass when he saw my screen. I was backtesting a Claude-built arbitrage system. Terminal open. Live trades firing. He glanced over. Stopped walking. That is not TradingView. What framework is that actually running. Claude Code. Three repos. One prompt. $20 per month. He sat down across from me without asking. I work on AlphaGo successor models. We test reinforcement agents for market simulation. You are running something similar but you let Claude write the strategy layer. Not just strategy. Detection. github/warproxxx/poly_data 86 million Polymarket trades. Every wallet. Every position. Every timestamp. You are feeding Claude transaction history and letting it identify asymmetric behavior patterns. Then cloning the profitable ones in real time. Exactly. One prompt: Scan every wallet with 150+ trades and ROI above 65%. Rank by consistency. Export top 40. Claude processed 18,600 wallets in 6 minutes. Returned 38. Top 15 wallets outperformed the bottom 18,000 combined. That is not analysis. That is alpha concentration. Precisely. And you did not write the ranking algorithm. Claude built it. I just connected it to execution logic. Then I opened the second repo. github/Polymarket/polymarket-cli Official Rust CLI. No auth required for reads. 600+ markets scanned in under 3 minutes. Claude scores: liquidity depth, pricing gap, resolution timeline. 512 markets reduced to 28 before capital moves. 94.5% filtered out before entry consideration. A notification hit. Position filled. +$127. How does it decide entry timing. Four agents. No shared state. Arbitrage detector, convergence scanner, whale mirror, volume surge tracker. 3 agents agree: full position. 2 agree: half size. Split vote: skip. Consensus filtering alone eliminated 46% of losses in backtest. And exit logic. The 38 top wallets almost never hold to settlement. 89% exit early. Average 71% of max profit captured. Immediate redeployment. My bot exits at 82% of projected move or 4x volume spike. Whichever hits first. You built a whale copy system that exits before the whales do. Correct. He set his coffee down slowly. How many trades per day. 12 average. Most rejected by filters before I see notifications. My setup: Claude API: $20/mo VPS Frankfurt: $6/mo poly_data: free polymarket-cli: free $300 seed capital. 34 days ago. $18,700 now. 318 trades. 76% win rate. Sharpe 2.61. I have not modified it in 34 days. He stared at the terminal without blinking. This is exactly what our adversarial testing team models. Market-adaptive agents with autonomous strategy evolution. Except you deployed it live. He messaged me the next day. Would you consider a conversation with our safety research lead. I told him this post is the conversation. Too late to contain. The edge is not predicting markets. It is identifying who already wins and mirroring them before the pattern shifts. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word "Money" 2. Like and retweet this post. 3. Follow me Himanshu Kumar so I can DM you Save this post. Build the whale mirror system this week. Start with $200. Scale on evidence.

Himanshu Kumar

15,985 просмотров • 1 месяц назад

Dune analytics MCP.. Claude becomes your on chain SQL analyst.. No dashboard has every query you'll ever need. dune does but writing SQL is a skill, and most of you would skip it.. i know this MCP fixes that. Claude writes the query, runs it on dune, and explains what the data actually means. with this MCP wired in, you don't need to know SQL. you describe what you want in plain english and Claude does the rest. like "Claude, which wallets bought $RAVE in last few weeks and still hold?" "Claude, show me the top 50 ETH wallets by stablecoin inflows last 7 days." "Claude, what's the median gas paid by ARB users in the last 24h?" Questions no dashboard can answer. one prompt away.. Setup (3 minutes) ▫️Step 1: grab a free dune API key → ▫️Step 2: add this to ~/.claude/settings.json or .mcp.json: { "mcpServers": { "dune": { "command": "npx", "args": ["-y", "dune-analytics-mcp"], "env": { "DUNE_API_KEY": "your-key-here" } } } } ▫️Step 3: restart claude. you'll see the dune tool load in your tool menu. that's it. you now have onchain SQL on tap. How to actually use it: 3 prompts i use: 1) Smart money watchlist: "claude, pull the top 20 wallets by realized pnl on $TOKEN in the last 30 days. show me which ones are still holding." gives you a clean leaderboard of who's actually winning on that token. add them to your etherscan watchlist. 2) accumulation vs distribution "claude, compare net inflows vs outflows for $TOKEN across all CEX wallets in the last 14 days." if whales are moving off exchanges → accumulation. onto exchanges → distribution. you see the rotation before the candle. 3) narrative heat check "claude, which 10 tokens saw the biggest % increase in unique new holders this week?" finds where fresh money is flowing. before this MCP i'd either, pay for a pro dune account + write queries manually, or look at someone else's dashboard and hope it answers my question… now claude writes it for me, in seconds, custom to my thesis. no dashboard in existence beats that. free tier covers most of what you need. upgrade if you're querying heavy. ( built a quick $RAVE post mortem dashboard using a prompt as shown in the video ) MORE SUCH USEFUL MCP for traders below.. 👇
0:19

Dune analytics MCP.. Claude becomes your on chain SQL analyst.. No dashboard has every query you'll ever need. dune does but writing SQL is a skill, and most of you would skip it.. i know this MCP fixes that. Claude writes the query, runs it on dune, and explains what the data actually means. with this MCP wired in, you don't need to know SQL. you describe what you want in plain english and Claude does the rest. like "Claude, which wallets bought $RAVE in last few weeks and still hold?" "Claude, show me the top 50 ETH wallets by stablecoin inflows last 7 days." "Claude, what's the median gas paid by ARB users in the last 24h?" Questions no dashboard can answer. one prompt away.. Setup (3 minutes) ▫️Step 1: grab a free dune API key → ▫️Step 2: add this to ~/.claude/settings.json or .mcp.json: { "mcpServers": { "dune": { "command": "npx", "args": ["-y", "dune-analytics-mcp"], "env": { "DUNE_API_KEY": "your-key-here" } } } } ▫️Step 3: restart claude. you'll see the dune tool load in your tool menu. that's it. you now have onchain SQL on tap. How to actually use it: 3 prompts i use: 1) Smart money watchlist: "claude, pull the top 20 wallets by realized pnl on $TOKEN in the last 30 days. show me which ones are still holding." gives you a clean leaderboard of who's actually winning on that token. add them to your etherscan watchlist. 2) accumulation vs distribution "claude, compare net inflows vs outflows for $TOKEN across all CEX wallets in the last 14 days." if whales are moving off exchanges → accumulation. onto exchanges → distribution. you see the rotation before the candle. 3) narrative heat check "claude, which 10 tokens saw the biggest % increase in unique new holders this week?" finds where fresh money is flowing. before this MCP i'd either, pay for a pro dune account + write queries manually, or look at someone else's dashboard and hope it answers my question… now claude writes it for me, in seconds, custom to my thesis. no dashboard in existence beats that. free tier covers most of what you need. upgrade if you're querying heavy. ( built a quick $RAVE post mortem dashboard using a prompt as shown in the video ) MORE SUCH USEFUL MCP for traders below.. 👇

Axel Bitblaze 🪓

32,016 просмотров • 4 месяцев назад

An Anthropic engineer caught my screen at a café in SF. “Are you seriously running Claude against live markets?” Yes. I walked him through it. 214 trades 74% WR +$9,437 / 19 days Copy it: Stack is simple: – historical market + wallet dataset – CLI for fast market access – execution layer with strict filters No predictions. First repo: The model ranks wallets by exit efficiency. Metric that matters: captured_value / expected_value > 0.70 If a wallet can’t realize its edge — it’s noise. Signals only trigger on repeated structure. Second repo: Position sizing = Kelly. f* = (p*b - q) / b Most cycles: idle. When conditions align: deploy capital. Three trades: +7c, +22c, +16c — all closed within hours He looked at the repos, paused: “We had internal experiments like this.” There’s no secret. Just discipline. Markets don’t pay for being right. They pay for exiting well.
0:17

An Anthropic engineer caught my screen at a café in SF. “Are you seriously running Claude against live markets?” Yes. I walked him through it. 214 trades 74% WR +$9,437 / 19 days Copy it: Stack is simple: – historical market + wallet dataset – CLI for fast market access – execution layer with strict filters No predictions. First repo: The model ranks wallets by exit efficiency. Metric that matters: captured_value / expected_value > 0.70 If a wallet can’t realize its edge — it’s noise. Signals only trigger on repeated structure. Second repo: Position sizing = Kelly. f* = (p*b - q) / b Most cycles: idle. When conditions align: deploy capital. Three trades: +7c, +22c, +16c — all closed within hours He looked at the repos, paused: “We had internal experiments like this.” There’s no secret. Just discipline. Markets don’t pay for being right. They pay for exiting well.

Discover

11,699 просмотров • 4 месяцев назад

Stripe Cards are now officially live on ClawVault If you're building agents that need to pay for things — APIs, SaaS, cloud, subscriptions, contractors — your agent can now get its own Visa card. Not a restricted virtual card on limited rails. A real Visa, powered by Stripe, accepted at every merchant on the internet. Spending limits. Merchant locks. Expiry controls. And that's just the defaults — the full rules engine goes deeper. Your agent spends freely within the rules — break one, and the charge gets flagged for your approval before it goes through. No other platform gives your agent a card and keeps you in control at the same time. Every AI agent will need a card. Every card will need a control layer. We built both. Now on Stripe. Your agent gets the card. You keep the control.🦞
0:10

Stripe Cards are now officially live on ClawVault If you're building agents that need to pay for things — APIs, SaaS, cloud, subscriptions, contractors — your agent can now get its own Visa card. Not a restricted virtual card on limited rails. A real Visa, powered by Stripe, accepted at every merchant on the internet. Spending limits. Merchant locks. Expiry controls. And that's just the defaults — the full rules engine goes deeper. Your agent spends freely within the rules — break one, and the charge gets flagged for your approval before it goes through. No other platform gives your agent a card and keeps you in control at the same time. Every AI agent will need a card. Every card will need a control layer. We built both. Now on Stripe. Your agent gets the card. You keep the control.🦞

ClawVault

32,631 просмотров • 5 месяцев назад

THIS 38,000-STAR GITHUB REPO TURNS ONE AI AGENT INTO A REAL TEAM THAT CAN BRANCH, VERIFY ITS WORK AND WAIT FOR YOUR APPROVAL most people still run agents as one long chain where every step waits, one failure kills the run and the full workflow starts again Task → Planner → 5 Researchers in Parallel → Skeptic → Writer → Human Gate LangGraph gives every node one job while a shared state carries the findings, decisions and context through the entire system the skeptic can reject an unsupported finding and route the work back before it contaminates the final report, while independent branches keep moving if the run crashes, durable execution resumes from the saved state instead of rebuilding everything, then human-in-the-loop pauses the graph before anything expensive gets sent or published bookmark this repo and watch one prompt turn into an actual org chart for AI agents
0:14

THIS 38,000-STAR GITHUB REPO TURNS ONE AI AGENT INTO A REAL TEAM THAT CAN BRANCH, VERIFY ITS WORK AND WAIT FOR YOUR APPROVAL most people still run agents as one long chain where every step waits, one failure kills the run and the full workflow starts again Task → Planner → 5 Researchers in Parallel → Skeptic → Writer → Human Gate LangGraph gives every node one job while a shared state carries the findings, decisions and context through the entire system the skeptic can reject an unsupported finding and route the work back before it contaminates the final report, while independent branches keep moving if the run crashes, durable execution resumes from the saved state instead of rebuilding everything, then human-in-the-loop pauses the graph before anything expensive gets sent or published bookmark this repo and watch one prompt turn into an actual org chart for AI agents

Gipp 🦅

11,524 просмотров • 27 дней назад