Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

SN68 METANOVA agentic drug discovery workflows. Micaela is using agents to search molecular similarities, identify possible applications, and accelerate research tasks that usually require large manual effort. #SN68 #Bittensor

15,449 Aufrufe • vor 3 Monaten •via X (Twitter)

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

🚨 BREAKING: The first-ever agentic browser is here — and it's shockingly good. Just tried Fellou, an AI browser that doesn’t assist you with browsing, it does the browsing for me. It's like Chrome but with a brain—AI agents handle deep research and workflows solo. Handles several projects in parallel. A top-tier AI intern — takes care of all the dirty and tedious work, so you don’t have to and its 100% Free 1️⃣Fellou’s not just another browser—it's an Agentic assistant that acts for you. 2️⃣It handles real tasks autonomously: research, cross-platform flows, and full automation. 3️⃣ Past browsing. Into real action. Fellou can automatically plan tasks, invoke tools, and execute actions to coordinate operations across multiple web interfaces, enabling various in-browser tasks. These include shopping, scheduling meetings, sending emails, and posting tweets based on webpage content. It’s the first Agentic Browser — with deep research, tab-level collaboration, and seamless automation. Deep Search acts like a smart intern: spins up five shadow browsers, digs across web and private platforms, and compiles richer insights fast. Highlights gaps and surfaces info you missed. Runs in parallel, won’t slow anything down. Automated workflows: Replaces manual clicking with invisible ops across pages. Reduces drag, frees up hours. Automation-aware browsing: Ask the page questions, reuse content in your drafts. 🧵 1/n

Rohan Paul

55,351 Aufrufe • vor 1 Jahr

For anyone trying to understand Bittensor from first principles, this lecture is a useful place to start. Presented by Bittensor co-founder const. Learn Bittensor > Start with Bitcoin, distributed systems, incentives, > How Bitcoin leads to Bittensor Subnets coordinating AI infrastructure. Topics: // Start - Bitcoin as more than a digital currency // Risks of AI centralization + closed systems // "The incentive computer" // How Bittensor subnets work (mining, validating) // How distributed AI infrastructure could scale globally // Impact on students, builders & future founders Recorded at the National University of Singapore Computer Science Club. NUS Computing Chapters - Bitcoin, AI, and Bittensor - Bitcoin history and decentralization - AI changes how engineers work - The danger of centralized AI power - Why most crypto visions fail - Bitcoin as the world’s largest compute network - Bitcoin as a market for compute - The idea of an “incentive computer” - Bitcoin compared to Bittensor - Classroom example of decentralized scoring - A simple subnet example - SN62 :: Ridges AI | SN62 SWE agents - SN3 templar :: Distributed AI Training - SN52 lium.io :: GPU rentals on Bittensor 128 subnets, some examples Why this matters for the future of work Q&A Subnet examples mentioned @ SN64 - Serverless + TEE Compute :: Chutes SN8 - Prop firm Vanta Trading SN52 - AutoML :: Gradients SN62 - SWE agents :: Ridges AI | SN62 SN51 - Compute / GPU rental lium.io SN4 - TEE compute for enterprise :: Targon SN3 - 72B Distributed Training run :: templar SN41 - Prediction markets :: @almanac_market SN44 - Computer Vision Score - Subnet 44 SN68 - Drug discovery :: METANOVA SN18 - Weather Forecasting Zeus | SN 18 SN50 - Bitcoin prediction data :: Synthdata SN61 - Quantum computing :: qBitTensor Labs SN14 - Bitcoin mining pool :: TaoHash SN34 - Perp Dex :: 0xMarkets SN17 - 3D model generation :: 404 SN33 - Data analytics :: ReadyAI SN19 - [Since relaunched] RPC infrastructure :

Openτensor Foundaτion

1,173,182 Aufrufe • vor 4 Monaten

New short course: Practical Multi AI Agents and Advanced Use Cases with crewAI. Learn to build and deploy advanced agent-based systems in real applications in this course, created with CrewAI and taught by its founder, João Moura! (Disclosure: I've made a small seed investment in CrewAI.) In this course, you’ll learn how to create advanced agent-based apps that use external tools, do performance testing, can be trained with human feedback, and perform multiple tasks with different large language models. You will build several practical agentic apps that provide real business value, such as an automated project planning system, lead scoring and engagement pipeline, customer support data analysis, and a robust content creation system. In detail, you will learn how to: - Create these multi-agent systems with the building blocks of tasks, agents, and crews, along with the different things that make them work, such as caching, memory, and guardrails. - Integrate your multi-agent application with internal and external systems. - Connect multiple agents in complex setups, including parallel, sequential, and hybrid configurations, and create flows involving multiple agentic applications working together. - Test your agentic workflow and train it using human feedback to optimize its performance for better and more consistent results. - Work with multiple LLMs in your multi-agent system, using the appropriate model sizes and providers to fit each agent’s specific task. - Start a project from scratch in your environment and prepare it for deployment. You’ll also learn from an interview between João and Jacob Wilson, the Commercial GenAI Principal at PwC , in which they discuss deploying agentic workflows in real industry use cases. By the end of this course, you will be equipped to start building custom multi-agentic systems for your work. Please sign up here!

Andrew Ng

341,204 Aufrufe • vor 1 Jahr