Github Repo: github.com/rasbt/LLMs-from-s…
→ View original post on X — @sumanth_077, 2026-04-01 13:21 UTC
MACHINE LEARNING
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Build Large Language Models from Scratch: Complete Book Repository
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Build a Large Language Model from scratch! This repository contains the code examples for developing, pretraining, and finetuning a LLM from scratch. It is the official codebase for the book Build a Large Language Model (From Scratch). Notebook examples are included for each chapter: Chapter 1: Understanding Large Language Models Chapter 2: Working with Text Data Chapter 3: Coding Attention Mechanisms Chapter 4: Implementing a GPT Model from Scratch Chapter 5: Pretraining on Unlabeled Data Chapter 6: Finetuning for Text Classification Chapter 7: Finetuning to Follow Instructions Link to the repo in the comments!
→ View original post on X — @sumanth_077, 2026-04-01 13:21 UTC
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LaPha: AI Agents Think Exponentially Better in Poincaré Space
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How can we give AI agents exponentially more room to think and solve complex problems? Researchers from Shanghai Academy of AI for Science, CMU, and others unveil LaPha. This new method trains AlphaZero-like LLM agents in a unique "Poincaré latent space." It leverages negative
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Axelera AI Demonstrates Advanced Edge AI with Metis Cards at ISC West
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5 models. 48 AIPU cores. Two 8K streams. Real time. At ISC West last week, we ran person-of-interest identification and weapon detection simultaneously using the Voyager SDK and three 4-chip Metis cards. 1,440+ model inferences/sec at 30-58W per card. The pilot phase of edge AI is over. Here's how we built it: eu1.hubs.ly/H0t6H2D0 #EdgeAI #ComputerVision #ISCWest
→ View original post on X — @axeleraai, 2026-04-01 13:00 UTC
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Computer Age Statistical Inference: Algorithms, Evidence, Data Science Book
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Computer Age Statistical Inference — Algorithms, Evidence, and Data Science: https://
amzn.to/47zAhar -
5 Production Scaling Challenges for Agentic AI in 2026
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5 Production Scaling Challenges for Agentic AI in 2026 machinelearningmastery.com/5…
→ View original post on X — @craigbrownphd, 2026-04-01 12:44 UTC
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AI Efficiency in Radiology and Its Impact on Radiologist Demand
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I just read this news, and it makes sense. Up until now, it's always been said that more AI in application (using radiology as an example) leads to more people being able to see radiologists due to increased efficiency, and therefore more human radiologists would be needed
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Linker Hand06: Precision Robotics at Human Scale
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Next-Level #Robotics: Linker Hand06 Brings Human-Scale Precision
— Ronald van Loon (@Ronald_vanLoon) 1 avril 2026
by @CyberRobooo
#Robots #ArtificialIntelligence #Innovation #Technology #Tech pic.twitter.com/BtTD0I1H72Next-Level #Robotics: Linker Hand06 Brings Human-Scale Precision
by @CyberRobooo #Robots #ArtificialIntelligence #Innovation #Technology #Tech -
Prompting vs RAG vs Training: Understanding Model Adaptation Methods
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When you paste your company data into ChatGPT, you are not retraining the model. You are giving it temporary context for that conversation.
— Louis-François Bouchard 🎥🤖 (@Whats_AI) 1 avril 2026
Same with RAG and embeddings: you are not injecting knowledge into the model’s brain, you are giving it access to external memory it can… pic.twitter.com/KllVuKY7kTWhen you paste your company data into ChatGPT, you are not retraining the model. You are giving it temporary context for that conversation. Same with RAG and embeddings: you are not injecting knowledge into the model’s brain, you are giving it access to external memory it can search when needed. Training is the part that actually changes the model’s internal weights and reshapes how it behaves. That is why prompting is great for context, RAG is great for controllable memory, and training is best when you need the model to truly adapt to a new style, skill, or domain. Huge difference, and it matters a lot when you are deciding where to spend time and money. Curious where fine-tuning fits in this stack? I’m Louis-François, PhD dropout, now CTO & co-founder at Towards AI. Follow me for tomorrow’s no-BS AI roundup 🚀
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Human And Machine: AI’s Future In Collaboration Not Replacement
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Human And Machine: The Future Of #AI Lies In Collaboration, Not Replacement
by Sylvio Lindenberg @Forbes Learn more: https://
bit.ly/3PBZYB1 #ArtificialIntelligence #MachineLearning #ML #DL