If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/BAb4J28FUKAvi Chawla (@_avichawla):There's a new learning paradigm for AI agents.It learns the way humans do.Think about how you learned to drive. Nobody memorizes every route turn by turn. You develop instincts like maintaining a safe distance, anticipating what other drivers will do, and braking early in the https://t.co/2MWE7WXEgw ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/BFN1giRtPMAvi Chawla (@_avichawla):OpenClaw meets RL!OpenClaw Agents adapt through memory files and skills, but the base model weights never actually change.OpenClaw-RL solves this!It wraps a self-hosted model as an OpenAI-compatible API, intercepts live conversations from OpenClaw, and trains the policy in https://t.co/ddj08qfDAX ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/2WBPXbLvYXAvi Chawla (@_avichawla):build agents that never forget.(100% open-source, self-evolving AI memory)most agents have no real memory. every conversation starts fresh with no recall of yesterday and no understanding of how information connects.and here's where most people go wrong when trying to fix https://t.co/NSF68QBtpt ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):You can watch this ML course with your grandma.Making Friends with ML is the best non-technical intros to ML I’ve ever seen.A 6.5-hour course that covers:- Intro to ML- ML in practice- The 12 steps of AI- Intro to ML algorithmsRequires zero technical background. https://t.co/8J8cDhNeBh ...

Technology & AI - The report highlights the possibility of building a personalized ChatGPT from scratch [1] - It references Karpathy's nanochat as a minimal codebase for building modern LLMs [1] - The setup process involves learning to train a tokenizer [2] - The setup process involves mastering next-word prediction through pre-training [2] Learning Objectives - The report focuses on learning how to train a tokenizer from the ground up [2] - The report focuses on pre-training to master next-word prediction [2]

Technology & Development - Unsloth enables fine-tuning and local deployment of LLMs on iOS/Android devices [1] - LLMs can be deployed and run directly on phones [1] - Qwen3 was run on an iPhone 17 Pro at approximately 25 tokens per second [1]

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/np057bqlC3Avi Chawla (@_avichawla):AWS did it again!They have introduced a novel way for developers to build Agents.Today, when you build an Agent, you start with a simple goal, then end up juggling prompts, routing logic, error handling, tool orchestration, and fallback flows.One unexpected user input and https://t.co/KPS3aKAer9 ...

Research Methodology - Randomly splitting data can lead to significant errors in research papers [1] - Andrew Ng's team made a mistake in a research paper due to random data splitting [1] Insights & Resources - Tutorials and insights on DS (Data Science), ML (Machine Learning), LLMs (Large Language Models), and RAGs (Retrieval-Augmented Generation) are shared daily [1]

Performance Improvement - Suggests speeding up native Python code by over 50x [2] - Identifies Python's default interpreter (CPython) as slow due to its dynamicity [1] Programming Insights - Highlights the ability to change a variable's type after definition as a reason for slowness [1] - Mentions a 4-step process to achieve the speed improvement [2]

AI Engineering & RAG - The document discusses building a unified query engine over data spread across several sources using vector DB and RAG (Retrieval-Augmented Generation) [1] - It presents a scenario of an AI engineer interview at Google, focusing on querying data from sources like Gmail and Drive [1] - The author shares tutorials and insights on DS (Data Science), ML (Machine Learning), LLMs (Large Language Models), and RAGs [1]