Check out my latest article: Leading a #MultiCloud #Data & #AI #AIPlatform Transformation Across a Regulated #Enterprise linkedin.com/pulse/leading-m… via @LinkedIn
→ View original post on X — @craigbrownphd, 2026-04-05 13:38 UTC
AI
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DiscoveryPrime First Live Premium Procurement Public Guide Released
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Now live: the public guide to DiscoveryPrime’s first live premium procurement. This is how intelligence becomes infrastructure: discovered, tested, trusted, and directed into work at scale. montrealai.github.io/discove… #AGIALPHA #AIAgents #AGIJobs
→ View original post on X — @montreal_ai, 2026-04-05 13:33 UTC
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Agent Frameworks for Coding Converge Toward Fully Autonomous AI Systems
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Agent frameworks for coding are evolving fast, giving you the ability to build and control full applications with minimal input. What’s happening now is convergence, where major players are racing toward the same goal of fully autonomous AI systems. pic.twitter.com/aiDeh6ycQ5
— Satya Mallick (@LearnOpenCV) 5 avril 2026Agent frameworks for coding are evolving fast, giving you the ability to build and control full applications with minimal input. What’s happening now is convergence, where major players are racing toward the same goal of fully autonomous AI systems.
→ View original post on X — @learnopencv, 2026-04-05 13:32 UTC
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Coding Agents: Markdown Skills and Registry Extensions
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Good suggestion. A coding agent can already read markdown files from the main repo, but you could add an explicit skills extension with a dedicated folder and registry.
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Top AI Papers of the Week: March 30 – April 5
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The Top AI Papers of the Week (March 30 – April 5) – Meta-Harness – AI Agent Traps – Emotion Concepts in LLMs – Self-Organizing LLM Agents – The Price Reversal Phenomenon – Asynchronous Software Engineering Agents – Coding Agents are Effective Long-Context Processors Read on for more: DAIR.AI (@dair_ai) x.com/i/article/204012254193… — https://nitter.net/dair_ai/status/2040783675609030789#m
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Learning Path: LLM Architecture, Reasoning Models, and Production Systems
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I would probably start with 1. my Build A Large Language Model (From Scratch) book to understand the basic architecture and basic pipeline. Then maybe 2. Build A Reasoning Model (From Scratch) for inference scaling and reinforcement learning
3. Maybe one of the "production" -
DiscoveryPrime: First Live Premium AI Talent Procurement Platform
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🌌 DiscoveryPrime — the first live premium procurement, fully revealed. montrealai.github.io/discove… Where intelligence is discovered, proven, and deployed. Not just selecting talent. Allocating the world’s most powerful capabilities. #AIAgents #AGIJobs #Jobs
→ View original post on X — @ceobillionaire, 2026-04-05 13:23 UTC
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Cosine Similarity: Elegant Geometric Approach to Document Comparison
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“Cosine Similarity” is everywhere in machine learning, but it’s often treated as a black box. At its core, it’s just a normalized measure in a vector space, comparing two document representations. It doesn’t really understand meaning, it’s a purely geometric view based on the angle between vectors, yet it works surprisingly well at capturing how similar two documents are, almost as if it understood their content. Simple idea, but the intuition behind it is genuinely elegant. This is one of the most read pages on Algebrica. algebrica.org/cosine-similar…
→ View original post on X — @deeplearn007, 2026-04-05 13:22 UTC
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Exoskeleton Suit Restores Mobility for People with Disabilities
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Exoskeleton Suit Giving Mobility to the Handicapped
— Ronald van Loon (@Ronald_vanLoon) 5 avril 2026
by @MarchUnofficial
#Engineering #ArtificialIntelligence #Innovation #Technology pic.twitter.com/RGDaajP1vFExoskeleton Suit Giving Mobility to the Handicapped
by @MarchUnofficial #Engineering #ArtificialIntelligence #Innovation #Technology