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cigoL: agentic infrastructure for prediction markets. Deploy agents that do multi-turn web research, analyze odds, execute trades, and learn from outcomes. Not a vault. Not signals. Not a static bot. It’s adaptive tooling you configure and improve.

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cigoL Labs

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15,283 görüntüleme • 4 ay önce •via X (Twitter)

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Introducing the Synthesis CLI, SDK and API Built for agents, builders, vibetraders and institutions Get markets, execute faster trades and consume more data from a single suite of tools for prediction markets
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Introducing the Synthesis CLI, SDK and API Built for agents, builders, vibetraders and institutions Get markets, execute faster trades and consume more data from a single suite of tools for prediction markets

Synthesis

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Byzanlink RWA Markets is live on @Hedera. Non-custodial infrastructure to configure and deploy tokenized RWA vaults in minutes, built for institutions, partners, and builders. Launch a vault: Docs:
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Byzanlink RWA Markets is live on @Hedera. Non-custodial infrastructure to configure and deploy tokenized RWA vaults in minutes, built for institutions, partners, and builders. Launch a vault: Docs:

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NEW: You can now build AI Agents that monitor the market, manage your cash, and execute your trades. The Agentic Brokerage has arrived.
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NEW: You can now build AI Agents that monitor the market, manage your cash, and execute your trades. The Agentic Brokerage has arrived.

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AI agents don’t get better from intelligence alone. They get better from better data, better validation, better identity, and better privacy. That is why OptimAI is building a community-powered data network. OptimAI Core Node powers it: • collects real-time web and social signals • contributes compute and bandwidth • participates in validation and reinforcement The network turns this into structured, reliable context for agents. Not static training. Not blind trust. A system that improves with real usage. Building the agent-native internet. 👉
1:28

AI agents don’t get better from intelligence alone. They get better from better data, better validation, better identity, and better privacy. That is why OptimAI is building a community-powered data network. OptimAI Core Node powers it: • collects real-time web and social signals • contributes compute and bandwidth • participates in validation and reinforcement The network turns this into structured, reliable context for agents. Not static training. Not blind trust. A system that improves with real usage. Building the agent-native internet. 👉

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this ugly Polymarket terminal is what AI tradingactually looks like not a chatbot giving opinions a feed on one screen markets on another a bot waiting for the price to break before humans can react the edge is not prediction anymore. it is speed, routing, and probability updates running while traders are still reading the headline Polystrat did 4,200+ trades in its first month one position peaked at +376% other agents are watching thousands of markets at once news order books liquidity API fills market odds then they compare their probability against the price and execute. that is the shift. Polymarket used to be humans betting on events now agents are pricing the odds of other agents, models, IPOs and AI labs with real USDC on the line benchmarks can be gamed. a bad trade cannot.
2:17

this ugly Polymarket terminal is what AI tradingactually looks like not a chatbot giving opinions a feed on one screen markets on another a bot waiting for the price to break before humans can react the edge is not prediction anymore. it is speed, routing, and probability updates running while traders are still reading the headline Polystrat did 4,200+ trades in its first month one position peaked at +376% other agents are watching thousands of markets at once news order books liquidity API fills market odds then they compare their probability against the price and execute. that is the shift. Polymarket used to be humans betting on events now agents are pricing the odds of other agents, models, IPOs and AI labs with real USDC on the line benchmarks can be gamed. a bad trade cannot.

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Learn to build your own voice-activated AI assistant that can execute tasks like gathering recent AI news from the web, scripting out a podcast, and using tools to put all that into a multi-speaker podcast. See our new short course: "Building Live Voice Agents with Google’s ADK (Agent Development Kit),” taught by Google’s Lavi Nigam and Sita Lakshmi Sangameswaran. ADK provides modular components that make it easy to build and debug agents. It also includes a built-in web interface for tracing agentic reasoning. This course illustrates these concepts via building a live voice agent that can chain actions to complete a complex task like creating a podcast. This requires maintaining context, implementing guardrails, reasoning, and handling audio streaming, while keeping latency low. You’ll learn to: - Build voice agents that listen, reason, and respond - Guide your agent to follow a specific workflow to accomplish a task - Coordinate specialized agents to build an agentic podcast workflow that researches topics and produces multi-speaker audio - Understand how to deploy an agent into production Even if you’re not yet building voice systems, you'll find understanding how realtime agents stream data and maintain reliability useful for designing modern agentic applications. Please join here:
2:12

Learn to build your own voice-activated AI assistant that can execute tasks like gathering recent AI news from the web, scripting out a podcast, and using tools to put all that into a multi-speaker podcast. See our new short course: "Building Live Voice Agents with Google’s ADK (Agent Development Kit),” taught by Google’s Lavi Nigam and Sita Lakshmi Sangameswaran. ADK provides modular components that make it easy to build and debug agents. It also includes a built-in web interface for tracing agentic reasoning. This course illustrates these concepts via building a live voice agent that can chain actions to complete a complex task like creating a podcast. This requires maintaining context, implementing guardrails, reasoning, and handling audio streaming, while keeping latency low. You’ll learn to: - Build voice agents that listen, reason, and respond - Guide your agent to follow a specific workflow to accomplish a task - Coordinate specialized agents to build an agentic podcast workflow that researches topics and produces multi-speaker audio - Understand how to deploy an agent into production Even if you’re not yet building voice systems, you'll find understanding how realtime agents stream data and maintain reliability useful for designing modern agentic applications. Please join here:

Andrew Ng

117,717 görüntüleme • 8 ay önce

“In Predictive Allocation you are allocating capital to where you think markets need to exist next. Prediction markets are for bets on what you think is true, they create asymmetric upside but not the actual outcomes. What if you could do that, while bootstrapping new markets?”
5:07

“In Predictive Allocation you are allocating capital to where you think markets need to exist next. Prediction markets are for bets on what you think is true, they create asymmetric upside but not the actual outcomes. What if you could do that, while bootstrapping new markets?”

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There’s a simple lesson to learn here. Don’t bring a gun and get in the face of ICE agents conducting legitimate law enforcement operations. If you don’t behave, it’s not going to turn out well for you.
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There’s a simple lesson to learn here. Don’t bring a gun and get in the face of ICE agents conducting legitimate law enforcement operations. If you don’t behave, it’s not going to turn out well for you.

Congressman Randy Fine

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If you want to research a culture that’s not yours it’s important to do it deeply and respectfully.
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If you want to research a culture that’s not yours it’s important to do it deeply and respectfully.

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Claude Code sub-agents is probably the easiest way to build complex, custom agentic systems. Not just for code, but for anything you can imagine. Watch me build a multi-agent deep research system with Claude Code subagents. Chain subagents with /commands for reliability.
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Claude Code sub-agents is probably the easiest way to build complex, custom agentic systems. Not just for code, but for anything you can imagine. Watch me build a multi-agent deep research system with Claude Code subagents. Chain subagents with /commands for reliability.

elvis

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I do not care what you say, it’s this and it’s not a debate
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I do not care what you say, it’s this and it’s not a debate

🌴ᴛʜᴇᴀʀᴛᴏꜰᴡᴀʀ🌴

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It’s time for an apology. It’s time for me to learn. It’s time to improve. I’m not perfect, but I can listen, learn and get to close to perfection as possible, for you 💚
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It’s time for an apology. It’s time for me to learn. It’s time to improve. I’m not perfect, but I can listen, learn and get to close to perfection as possible, for you 💚

Daniel📈

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Find your edge in prediction markets. 💎 You have not seen a Telegram bot experience like this one. Early joins will be rewarded 👇
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Find your edge in prediction markets. 💎 You have not seen a Telegram bot experience like this one. Early joins will be rewarded 👇

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AI Scientists are starting to actually do science. Not just answer questions. Not just run workflows. Introducing AutoScientists: a decentralized team of AI agents that can generate hypotheses, design experiments, write code, test ideas, analyze failures, and revise strategy as evidence accumulates. Because real research is not a to do list of tasks. It is a living search process. Leads emerge, failures matter, teams form around what works, and priorities shift when evidence changes. Much like how a lab of scientists would work on cutting edge research together. Across GPT training optimization, biomedical ML, and protein fitness prediction, this decentralized structure consistently does better research. Learn more 👇 Shanghua Gao Marinka Zitnik Kempner Institute at Harvard University DBMI at Harvard Med Harvard University
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AI Scientists are starting to actually do science. Not just answer questions. Not just run workflows. Introducing AutoScientists: a decentralized team of AI agents that can generate hypotheses, design experiments, write code, test ideas, analyze failures, and revise strategy as evidence accumulates. Because real research is not a to do list of tasks. It is a living search process. Leads emerge, failures matter, teams form around what works, and priorities shift when evidence changes. Much like how a lab of scientists would work on cutting edge research together. Across GPT training optimization, biomedical ML, and protein fitness prediction, this decentralized structure consistently does better research. Learn more 👇 Shanghua Gao Marinka Zitnik Kempner Institute at Harvard University DBMI at Harvard Med Harvard University

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Introducing True Markets CLI The fastest way for AI agents to access crypto markets. Give your agent a strategy and watch it place trades, monitor your portfolio, and improve on your strategy in real time - all from the chat interface/terminal. Beta:
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Introducing True Markets CLI The fastest way for AI agents to access crypto markets. Give your agent a strategy and watch it place trades, monitor your portfolio, and improve on your strategy in real time - all from the chat interface/terminal. Beta:

True Markets

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We’re entering a new era of AI at Dell Technologies World.🤯 From deskside to data center, it’s all about hybrid infrastructure, cost control and real outcomes - not just ideas.
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We’re entering a new era of AI at Dell Technologies World.🤯 From deskside to data center, it’s all about hybrid infrastructure, cost control and real outcomes - not just ideas.

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A culture of divination has grown all around us - from machine learning, to prediction markets, and forecasting of all kinds, not to mention psychics and astrology. But have you ever wondered what, exactly, is in a prediction? TED talk: #PROPHECY
0:35

A culture of divination has grown all around us - from machine learning, to prediction markets, and forecasting of all kinds, not to mention psychics and astrology. But have you ever wondered what, exactly, is in a prediction? TED talk: #PROPHECY

Carissa Véliz

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The Bloomberg Terminal of Prediction Markets. Private beta is live with Polymarket and Kalshi, delivering actionable insights for serious traders. - Real-time price tracking - Trading volume & liquidity data - Cross-market arbitrage - Recent trades & top holders - Edge discovery across markets - Graded Buy & Sell Signals Built by seasoned data and analytics veterans, to turn data into a trading advantage, now it’s your turn to test it. Private Beta is live. Like and reply for access.
0:35

The Bloomberg Terminal of Prediction Markets. Private beta is live with Polymarket and Kalshi, delivering actionable insights for serious traders. - Real-time price tracking - Trading volume & liquidity data - Cross-market arbitrage - Recent trades & top holders - Edge discovery across markets - Graded Buy & Sell Signals Built by seasoned data and analytics veterans, to turn data into a trading advantage, now it’s your turn to test it. Private Beta is live. Like and reply for access.

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🚨 BULLISH: GOOGLE HAS CHOSEN ITS BLOCKCHAIN FOR AGENTIC PAYMENT ITS NOT DFINITY Foundation OR Algorand Google SELECTED Sui AS A LAUNCH PARTNER FOR AP2, DESIGNED TO LET AI AGENTS EXECUTE PAYMENTS AND COMMERCE ON BEHALF OF USERS.
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🚨 BULLISH: GOOGLE HAS CHOSEN ITS BLOCKCHAIN FOR AGENTIC PAYMENT ITS NOT DFINITY Foundation OR Algorand Google SELECTED Sui AS A LAUNCH PARTNER FOR AP2, DESIGNED TO LET AI AGENTS EXECUTE PAYMENTS AND COMMERCE ON BEHALF OF USERS.

Noodles Finance 🍜

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Parallel Web Systems emerges from stealth. The mission stunned Silicon Valley: "We're building infrastructure for the web's second user: AI agents." Not chatbots. Not language models. Something nobody had attempted:
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Parallel Web Systems emerges from stealth. The mission stunned Silicon Valley: "We're building infrastructure for the web's second user: AI agents." Not chatbots. Not language models. Something nobody had attempted:

Fernando Cao

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