Agent 4 didn't replace what worked. It rethought it. The building experience is fundamentally better.
The Design Canvas now works across every artifact type.
Collaboration happens in real time—no forking required.
And planning no longer pauses your build.
SYSTEMS
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Agent 4 rethinks building experience with improved canvas and real-time collaboration
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AGIJobManager and OpenClaw: Formal AGI Architecture Framework
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Formal doctrine: 1. AGIJobManager defines the searchable, inspectable, publicly verifiable environment space. 2. OpenClaw is the bounded solver-and-transfer engine that turns those environments into solutions, evidence bundles, and reusable stepping stones. That is a serious
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EU Commission Allocates €200 Million for Submarine Cable Infrastructure
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Commission makes available €200 million for submarine cable and digital infrastructure projects | Shaping Europe’s digital future https://
digital-strategy.ec.europa.eu/en/news/commis
sion-makes-available-eu200-million-submarine-cable-and-digital-infrastructure-projects
…
#digitaleu #infrastructure #innovation #technology @digitaleu @ArturHabant @elaniazito @CurieuxExplorer @Shi4Tech -
NVIDIA Unveils ProRL Agent for Reinforcement Learning of LLM Agents
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NVIDIA AI Unveils ProRL Agent: A Service Infrastructure for Reinforcement Learning of Multi Turn LLM Agents at Scale! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang
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5G IoT Agentic AI Convergence Transforms Telecom Networks Into Intelligent Systems
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As 5G, IoT, and Agentic AI converge, telecom networks are no longer passive infrastructure, they’re becoming adaptive, self-optimizing systems that sense, decide, and act in real time. How connectivity is evolving from moving data to orchestrating intelligence ⬇️ Read: ➡️ linkedin.com/pulse/when-conn… #AI #MWC26
→ View original post on X — @haroldsinnott, 2026-03-30 14:06 UTC
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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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The AI Engineering Bible: Complete Guide to Production Ready AI Systems
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"The AI Engineering Bible: The Complete and Up-to-Date Guide to Build, Develop and Scale Production Ready AI Systems"
— Kirk Borne (@KirkDBorne) 30 mars 2026
Stay Ahead. Become Irreplaceable.
…with this book: https://t.co/r1zzLWqgfX
The AI Engineering Bible takes a full-stack engineering perspective—helping you… pic.twitter.com/2UuvslEjtV"The AI Engineering Bible: The Complete and Up-to-Date Guide to Build, Develop and Scale Production Ready AI Systems" Stay Ahead. Become Irreplaceable. …with this book: http://
amzn.to/4jOVbXG The AI Engineering Bible takes a full-stack engineering perspective—helping you -
AI-Designed Agent Harnesses Replace Human-Coded Constraints
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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
→ View original post on X — @debashis_dutta, 2026-03-29 23:42 UTC