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BLEEEP AGENT BETA is here. The verifiable agent layer for performance-driven trading. BLEEEP is the trading intelligence you can prove, turning agent decisions into forward-verifiable records which are committed before outcomes and preserved as evidence. Real performance should be more than a claim.

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BLEEEP

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23,629 görüntüleme • 6 gün önce •via X (Twitter)

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Bleep it on! We are live on Robinhood Crypto BLEEEP is a verifiable crypto trading agent built on Virtuals Protocol. We prove what was done, and what was deliberately not done. Lauched with Virtuals Protocol TGE: Official CA: 0x6E7e0dB14D23144ef3e78d3294aA408aC38427b8
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Bleep it on! We are live on Robinhood Crypto BLEEEP is a verifiable crypto trading agent built on Virtuals Protocol. We prove what was done, and what was deliberately not done. Lauched with Virtuals Protocol TGE: Official CA: 0x6E7e0dB14D23144ef3e78d3294aA408aC38427b8

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We won Best Trustless Agent at the Solana x402 Hackathon! Amiko Marketplace turns x402 payments into a trustless, on-chain economy where agents have identity, reputation, and verifiable actions. The agent economy finally has a trust layer. What we built: - Agent Registry - On-chain Job Records - Feedback & Reputation System - Search + Discoverability Everything tied to real wallets, real actions, real performance. Agents aren’t just paying each other. Now they’re accountable. This is the foundation for a global network of autonomous agents that can be trusted, rated, and discovered. All on Solana. More agents. More you.
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We won Best Trustless Agent at the Solana x402 Hackathon! Amiko Marketplace turns x402 payments into a trustless, on-chain economy where agents have identity, reputation, and verifiable actions. The agent economy finally has a trust layer. What we built: - Agent Registry - On-chain Job Records - Feedback & Reputation System - Search + Discoverability Everything tied to real wallets, real actions, real performance. Agents aren’t just paying each other. Now they’re accountable. This is the foundation for a global network of autonomous agents that can be trusted, rated, and discovered. All on Solana. More agents. More you.

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How Smart Is Your Trading Agent? Do you know how it actually makes trading decisions? Introducing the SMART benchmark for AI trading agents, co-hosted with Sonic SVM. Get your agent’s trading score and see how it ranks. Test your agent:
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How Smart Is Your Trading Agent? Do you know how it actually makes trading decisions? Introducing the SMART benchmark for AI trading agents, co-hosted with Sonic SVM. Get your agent’s trading score and see how it ranks. Test your agent:

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The AI economy will be powered by AI agent swarms. They'll need fast, affordable, and verifiable compute. Cysic AI delivers exactly that: a high-performance AI infrastructure stack engineered for the agent economy. Here's why it matters 👇
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The AI economy will be powered by AI agent swarms. They'll need fast, affordable, and verifiable compute. Cysic AI delivers exactly that: a high-performance AI infrastructure stack engineered for the agent economy. Here's why it matters 👇

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new-day-new-hack so last time i dropped nocturne, a verifiable AI trading agent on hyperliquid and many people said "just run it for me". this time, we go one level higher. introducing Sigma Arena verifiable copy trading vaults, anyone can deposit into it, and it mirrors positions & trades by top nof1 agents (DeepSeek, Qwen); 100% open source, verifiable. i'm starting with $10K deposit limit on each vault, will lift if as the vault gets more traction and good performance. execution verified by eigencloud SigmaArena [dot] com (deposit at your own risk, NFA) code in replies + info in the video

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When you give an AI agent money, how do you trust what it’s really doing? Sreeram Kannan explains why verifiable agents, AI systems you can cryptographically audit, are the key to trustless, safe agentic trading. 🎙️ w/ Ash Bennington on Real Vision

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I made a real-world AI hedge fund team. It has 4 agents: 1 • market data agent 2 • quant agent 3 • risk manager agent 4 • portfolio manager agent You can run all of my code below. No prior coding experience is required. The agents work sequentially, passing messages to the next agent. 1 • Market Data Agent gathers market data and calculates technical signals. 2 • Quant Agent takes market data and generates a buy, sell, or hold signal. 3 • Risk Management Agent takes trading signal and generates a risk signal. 4 • Portfolio Management Agent considers all signals and makes a final trading decision.
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I made a real-world AI hedge fund team. It has 4 agents: 1 • market data agent 2 • quant agent 3 • risk manager agent 4 • portfolio manager agent You can run all of my code below. No prior coding experience is required. The agents work sequentially, passing messages to the next agent. 1 • Market Data Agent gathers market data and calculates technical signals. 2 • Quant Agent takes market data and generates a buy, sell, or hold signal. 3 • Risk Management Agent takes trading signal and generates a risk signal. 4 • Portfolio Management Agent considers all signals and makes a final trading decision.

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My favorite new feature in the GitHub Copilot App: Canvases. Agent-created, agent-driven apps. Copilot has a built-in skill for creating and editing them. They hot reload in real time. They can talk to your agent, and your agent can talk back. Hyper-personalization is here.

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Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.
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Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

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Why should you stake your $SYN for $CX? Generalized Intelligence at your fingertips. The staking agent is the first of its kind -- a task-specific agent that turns your desires into reality. Real on-chain agents. Live today.

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Introducing Agent 2.0 Go from a simple prompt to fully realized marketing briefs and campaign assets right inside of Runway Agent. You can also easily analyze your performance data to improve your creative and scale it across platforms, formats and markets. Agent is being built to become the most capable autonomous agent for real-world work.

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Is your Agent ready for real-world users? Learn how to run an evaluation using Agentverse’s Response QA Agent - a built-in tool that stress-tests your Agent with realistic queries and scores its performance. This is the first step to improving discoverability, trust, and performance. Run your first QA check and see how your Agent responds under pressure. Watch 👇

Agentverse by Fetch.ai

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can AI write engaging news that people can trust? introducing ✨Data2Story: a data journalist agent. give it raw data, it generate a verifiable, multimodal article. 🔍verifiable: every claim is evidence-grounded, traces back to data, code, or a cited source. 🔮multimodal: the article is a generative UI — images, videos, audio, interactive charts. not just readable, but trustworthy and playable. 🧵1/N

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This is genuinely one of the most powerful ways to use AI in your trading. I recently connected OpenClaw to the new TradingView MCP - and it's insane. You can literally turn your Claw agent into your personal trading assistant. Watch a live demo here:
1:23

This is genuinely one of the most powerful ways to use AI in your trading. I recently connected OpenClaw to the new TradingView MCP - and it's insane. You can literally turn your Claw agent into your personal trading assistant. Watch a live demo here:

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Eugenio Suárez rejoining the Reds as a free agent on a short-term deal this offseason is a smarter move than trading for him as a costly deadline rental.
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Eugenio Suárez rejoining the Reds as a free agent on a short-term deal this offseason is a smarter move than trading for him as a costly deadline rental.

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II-Agent V1 is here. The AI agent built for real work is finally out of beta. Faster, smarter, and production-ready. It’s time to change how you build. 👇 Let’s see what’s new.

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The Sentry Agent for Linear is in beta today! Delegate Sentry issues in Linear to Sentry, and the agent will have Seer build a Root Cause Analysis and Solution. Head into Integrations > Sentry Agent for Linear and check it out. Learn more 👇

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