#LLM vs. #RAG vs. #AIAgent vs. #AgenticAI
by @PythonPr #GenerativeAI #ArtificialIntelligence #MachineLearning
MACHINE LEARNING
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Comparison of LLM, RAG, AI Agent, and Agentic AI Technologies
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AI Tool Maintains Identity Across Context Changes
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the part that actually got me is that changing the background doesn't change who it is. every other tool loses the person the second the context shifts. this one doesn't and that's a completely different thing
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The Ultimate Roadmap to Learn AI Agents
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The Ultimate Roadmap to Learn #AIAgents
— Ronald van Loon (@Ronald_vanLoon) 9 avril 2026
by @Python_Dv#GenerativeAI #ArtificialIntelligence #MachineLearning pic.twitter.com/tE5SacFm9oThe Ultimate Roadmap to Learn #AIAgents by @Python_Dv #GenerativeAI #ArtificialIntelligence #MachineLearning
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Optimize Market Analysis Using Big Data Techniques
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How to Optimize Market #Analysis Using #BigData
— Ronald van Loon (@Ronald_vanLoon) 9 avril 2026
by @antgrasso
#DataScience #Analytics #Data pic.twitter.com/DVwHW09ZBIHow to Optimize Market #Analysis Using #BigData by @antgrasso #DataScience #Analytics #Data
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OpenAI AI Model Solves Five Erdős Mathematical Problems
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Holy: OpenAI researchers report solving five (!) additional Erdős problems using an internal model, showcasing AI’s growing strength in deep mathematical reasoning.
— Chubby♨️ (@kimmonismus) 9 avril 2026
Excitement for spud increases day by day. https://t.co/xss1rxAm6ZHoly: OpenAI researchers report solving five (!) additional Erdős problems using an internal model, showcasing AI’s growing strength in deep mathematical reasoning. Excitement for spud increases day by day. Mehtaab Sawhney (@mehtaab_sawhney) We’ve just released another paper solving five further Erdős problems with an internal model at OpenAI: arxiv.org/abs/2604.06609. Several of the proofs were especially enjoyable to digest while writing the paper. My personal favorite was the solution to Erdős Problem 1091. The question asks: if a graph G has chromatic number 4, while every small subgraph has chromatic number at most 3, must it contain an odd cycle with many diagonals? The internal model gives a very enlightening counterexample to this conjecture, and the proof was a pleasure to understand. For those so inclined, a really fun exercise is to try to reconstruct the proof from Figure 5 of the paper, which was of course produced by Codex. — https://nitter.net/mehtaab_sawhney/status/2042072817395757467#m
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Artificial Intelligence Engineering Roadmap by Dhanian
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#ArtificialIntelligence Engineering Roadmap
— Ronald van Loon (@Ronald_vanLoon) 9 avril 2026
by @e_opore
#AI #MachineLearning #MI #ML pic.twitter.com/HrUrtSJEfL#ArtificialIntelligence Engineering Roadmap by @e_opore #AI #MachineLearning #MI #ML
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RAGEN-2: Reasoning Collapse in Agentic Reinforcement Learning
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RAGEN-2: Reasoning Collapse in Agentic RL
— 机器之心 JIQIZHIXIN (@jiqizhixin) 9 avril 2026
Paper: https://t.co/w4mCiZzCOp
Project: https://t.co/LFS5PpioMF
Code: https://t.co/f5bO13kGrn pic.twitter.com/dMzR3eZMwcRAGEN-2: Reasoning Collapse in Agentic RL Paper: huggingface.co/papers/2604.0… Project: ragen-ai.github.io/v2/ Code: github.com/mll-lab-nu/RAGEN
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RAGEN-2 Framework Tackles Template Collapse in LLM Agents
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Are your LLM agents truly reasoning, or just stuck repeating the same patterns?
— 机器之心 JIQIZHIXIN (@jiqizhixin) 9 avril 2026
Zihan Wang @wzenus and a stellar team from Northwestern, Stanford, Microsoft, Oxford, and Imperial College London have uncovered "template collapse", a hidden flaw where LLM agents appear diverse… pic.twitter.com/0XRXFuc7rxAre your LLM agents truly reasoning, or just stuck repeating the same patterns? Zihan Wang @wzenus and a stellar team from Northwestern, Stanford, Microsoft, Oxford, and Imperial College London have uncovered "template collapse", a hidden flaw where LLM agents appear diverse but fail to adapt to new inputs. Their RAGEN-2 framework introduces Mutual Information to accurately measure true "cross-input distinguishability" and proposes SNR-Aware Filtering to select high-signal training prompts. This new metric and method vastly outperform current approaches, boosting LLM agent performance and input dependence across critical tasks like planning, math reasoning, web navigation, and code execution! And this paper is also #1 Paper of the day on Hugging Face!
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Shenzhen Emerges as the World’s Robotics Hub
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This Video Proves It: Shenzhen Is the World’s Robotics Hub
— Ronald van Loon (@Ronald_vanLoon) 9 avril 2026
by @Robo_Tuo
#Robotics #MachineLearning #ArtificialIntelligence #Innovation #Technology pic.twitter.com/TgVQIetO7RThis Proves It: Shenzhen Is the World’s Robotics Hub by @Robo_Tuo #Robotics #MachineLearning #ArtificialIntelligence #Innovation #Technology
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50 Steps to Master AI and Machine Learning
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50 Steps to Master #AI
— Ronald van Loon (@Ronald_vanLoon) 9 avril 2026
by @PythonPr#ArtificialIntelligence #MachineLearning #ML #DL pic.twitter.com/2hCL0D6nAL50 Steps to Master #AI by @PythonPr #ArtificialIntelligence #MachineLearning #ML #DL