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]

Industry Focus - The industry emphasizes sharing insights and tutorials on Data Science (DS), Machine Learning (ML), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAGs) [1] - The industry encourages users to reshare insightful content within their networks [1]

Open-Source Platform - Sim is presented as a 100% open-source alternative to n8n for building and deploying Agentic workflows [1] - Sim is a drag-and-drop platform [1] Application and Functionality - The platform was used to build a finance assistance app and connected to Telegram [2] - The workflow is simple and runs 100% locally [2] - It works with any local LLM [2]

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. ...

LLM Evaluation Tools - DeepEval transforms LLM evaluations into a two-line test suite [1] - DeepEval helps identify the best models, prompts, and architecture for AI workflows, including MCPs (Multi-Choice Preference) [1] - DeepEval is 100% open-source with 11 thousand stars [1] Framework Compatibility - DeepEval works with all frameworks like LlamaIndex, CrewAI, etc [1] Community Engagement - The author encourages readers to reshare the information [1] - The author shares tutorials and insights on DS (Data Science), ML (Machine Learning), LLMs (Large Language Models), and RAGs (Retrieval-Augmented Generation) daily [1]