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With the launch of GLM-5, introduces Agent Mode. - Agent Mode: Automatically breaks down tasks, orchestrates tools, drives execution, and delivers ready-to-use files. - Data Insights & Smart Writing: Upload data for instant visualizations. Go from outline to finished draft, all in one place. - AI Slides / Full-Stack...

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29,865 просмотров • 7 месяцев назад •via X (Twitter)

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Agentic Browser is now live! This feature allows your AI agent to access real-time internet information for tasks like answering questions or finding recommendations. With this update, you can now see your AI agent's thought process before it responds to your questions—offering greater transparency and insights. Additional updates: •The initial quota of 50 messages per agent has been significantly increased to enable more extensive experimentation •We’ve fine-tuned the AI agents, making their responses more engaging and interactive •We also pushed along crypto social insights tools sourced from Coindesk, Cointelegraph, Cryptocompare and many more, your AI agent now have access to real-time market data

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THIS GUY BUILT A COMMAND CENTER FOR MANAGING AI AGENT TEAMS he was running 20+ agents through openclaw. all from terminal tabs and Discord. 12 tabs open. still losing track of who's doing what. which cron failed. so he built a dashboard that gives you one place to see everything: > org map showing the full agent hierarchy, who reports to who and what tools each agent has > chat with any agent through a real UI instead of bouncing between terminals > kanban board for tracking tasks across all agents > cron monitor with pipeline visualizations so you can see which jobs are failing > memory browser to read through all agent knowledge files in one place you can even point it at your openclaw workspace and it auto-discovers every agent from their config files. zero setup. AND ITS OPEN SOURCE TOO.

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🚨NEW EPISODE: AI Is Changing How We Work with Crypto Data🚨 In today's episode, we're joined by Kofi to discuss how AI agents are fundamentally changing crypto data analysis, research, and engineering workflows. We discuss: - The evolution from manual coding to AI-assisted development - How MCPs unlock onchain data exploration - Using Herd MCP to analyze smart contracts - AI agents for protocol research and data engineering - Context windows, planning, and agent workflows - Cursor vs OpenCode vs Claude Code (Anthropic) - Managing multiple agents simultaneously - Model selection, costs, and productivity tradeoffs - Building internal AI knowledge bases - Agent orchestration and autonomous workflows - Training agents to replicate human expertise - The future of AI-powered crypto analytics - And much more—enjoy! — Timestamps: (00:00) Introduction (01:24) AI replaces frontends (03:24) Herd MCP demo (05:40) Exploring contracts with AI (08:06) Exploration, planning, execution (11:38) Investigating onchain activity (17:10) Managing agent workflows (20:20) Multi-agent productivity (23:01) Model selection tradeoffs (29:11) Trusting AI outputs (31:28) Missing links today (34:37) Agent orchestration future (38:02) Training agents from work (38:20) Outro

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YOUR AI AGENT CAN NOW COLLECT DATA FROM ALMOST ANY WEBSITE X, YouTube, Reddit, random websites, basically whatever you need for research, analytics or monitoring. Here are 3 GitHub tools that let your agent pull the data for you. 1. Agent-Reach Bundles tools for working with different platforms in one place. X, YouTube, Reddit, GitHub and more. 2. Patchright Enhanced Works through the browser with Playwright. You can listen to the requests a website makes and pull the data you need through a script. So if a website doesn’t have a proper API, you still have a way to get the data. 3. Scrapling A general purpose tool for pulling data from web pages. And the best part is that you can give all of this to your AI agent. For example: “Collect posts from 100 X accounts from the last month, find the most popular topics and analyze what’s working.” Or: “Collect reviews about this product from Reddit and YouTube and summarize the main complaints.” You tell the agent what data you need → it writes the code → collects the data → gives you the result. Basically, you’re giving your agent the ability to pull data from almost anywhere on the web. That opens up a whole different level of stuff you can automate.

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We're excited to introduce Box + Perplexity Computer – where your AI assistant becomes a true operator, working directly within your content hub. Empower your agents with: → A Real Filesystem for AI: Seamlessly connect Perplexity with your Box files → Intelligent Insights: Point Perplexity across multiple files to surface insights, patterns, and answers that drive strategic decisions. → Automated Multi-Step Workflows: Perplexity Computer handles complex tasks efficiently in the background. → Secure & Governed Operations: Leverage Box's security and permissions, ensuring your AI operates with the right access and governance. Watch how Box + Perplexity Computer transforms raw data into actionable intelligence, like building a comprehensive competitive brief from multiple documents.

Box

246,743 просмотров • 6 месяцев назад

Introducing NEO: The first Autonomous Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34.2% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on OpenAI's MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:
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Introducing NEO: The first Autonomous Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34.2% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on OpenAI's MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:

Neo AI

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