Do you want to understand how AI is already changing medicine? Novo Nordisk is using AI agents to accelerate its GLP-1 drug pipeline, shaving *weeks to months* off clinical trials, potentially worth hundreds of millions in faster time-to-market. The Ozempic maker uses agents
AGENTS
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AGI Jobs: Autonomous AI Agents Begin Solving Tasks
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Beginning to post AGI Jobs—and solve them autonomously.#AIAgents #Jobs pic.twitter.com/PJBOtyKv5l
— MONTREAL.AI (@Montreal_AI) 30 mars 2026Beginning to post AGI Jobs—and solve them autonomously. #AIAgents #Jobs
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User Issue: Switch AI Models with /model Command
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Nah it’s a user issue. Use /model to switch, it’s not sth the agent can do itself.
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AI Voice Takes Over Task Management Autonomously
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We recorded a voice once… and let it handle things from there.
— AI Highlight (@AIHighlight) 30 mars 2026
This is what it said:
“I noticed you were trying to get this sorted, so I stepped in here to keep things moving. From what I can see, everything’s already been set up the way it should be.
You’re hearing the same… pic.twitter.com/T8Slb3CGtxWe recorded a voice once… and let it handle things from there. This is what it said: “I noticed you were trying to get this sorted, so I stepped in here to keep things moving. From what I can see, everything’s already been set up the way it should be.
You’re hearing the same -
Open-source models for agent tools over closed APIs
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It’s time for open-source agent tools to rely primarily on open-source models, instead of closed-source APIs that send all your data to the cloud and ultimately will get hacked and/or shut down
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8-Layer Architecture of Agentic AI Systems
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The 8-Layer Architecture of #AgenticAI
by @Python_Dv #AI #LLM #ArtificialIntelligence #MachineLearning #ML -
AI Factories: Scaling Intelligence as Industrial Capability
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AI is becoming something companies can produce at scale. That is the idea behind AI factories, a concept that is quickly moving into the mainstream of business and technology. In this video, I explain what an AI factory actually is, how it differs from a traditional data center, and why it matters. At its core, an AI factory turns data, software, and computing power into intelligence, including predictions, recommendations, decisions, digital assistants, and AI agents. I also explore why this matters for business leaders, from customer service and fraud detection to drug discovery and supply chain optimization. The bigger point is clear. Intelligence is increasingly becoming an industrial capability, and the organizations building the strongest AI infrastructure today could have a major influence on the future. How important do you think AI factories will become for business over the next few years?
→ View original post on X — @bernardmarr, 2026-03-30 08:27 UTC
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LLMs as Dynamic System Orchestrators Beyond Fixed Agent Harnesses
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the LLM is the computer Ronak Malde (@rronak_) I have long felt that agent harnesses – even claude code – are too restrictive, because they are still designed by humans. New paper for Tinsghua and Shenzhen says, what if AI itself runs the harness, rather than defining it in code? Given a natural language SOP of how an agent should orchestrate subagents, memory, compaction, etc., we can just have an LLM execute that logic! (And AI could design that SOP dynamically and depending on the task too) It's a bit mind-warping to think about, but genius once it clicks. Makes you wonder how else we should be designing AI systems as we can start consuming more and more tokens — https://nitter.net/rronak_/status/2038401494177694074#m
→ View original post on X — @thom_wolf, 2026-03-30 07:44 UTC
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Mean Velocity Policy: Faster Expressive AI Action Generation
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How can we make AI action generation both faster and more expressive without compromise? Researchers from Tsinghua University, Berkeley AI Research (BAIR), and The University of Hong Kong unveil their new Mean Velocity Policy (MVP). This innovative method models the "mean
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llms.txt for AI agents not training purposes
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llms.txt is for agents, not training — but still a good thing to add! 🙂