Github Repo: github.com/rasbt/LLMs-from-s…
→ View original post on X — @sumanth_077, 2026-04-01 13:21 UTC
RESEARCH
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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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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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Preparing Alpha-AGI Ascension: Full Execution Plan
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Preparing to solve and execute the full [ α-AGI Ascension ]. #AGIALPHA #AGIFirst #Ascension
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Perfect Weight Prediction as AGI Indicator
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If it can give ALL weights accurately we would definitely be past AGI already
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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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Frontier Data and Evaluation: Essential Infrastructure for Creative AI
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Frontier data and evaluation is the infrastructure the creative AI space desperately needed.
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Contra Labs: Frontier Data and Evaluation Lab for Creative AI
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Models do not improve without better judgment.
— AI Highlight (@AIHighlight) 1 avril 2026
Everyone is training models.
Almost no one has access to real creative taste. @Contra Labs is where that judgment comes from. https://t.co/5sjl04bsIeModels do not improve without better judgment. Everyone is training models. Almost no one has access to real creative taste. @Contra Labs is where that judgment comes from. ben (@contraben) Introducing Contra Labs. The first frontier data and evaluation lab for Creative AI. — https://nitter.net/contraben/status/2039021014244262000#m
→ View original post on X — @aihighlight, 2026-04-01 11:46 UTC