@LightningAI 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/55NJmdW6tVAvi Chawla (@_avichawla):I built my own ChatGPT from scratch, and you can too.Karpathy's nanochat is a single, clean, minimal, and hackable codebase to build a modern LLM.By setting this up, you'll learn how to:> train a tokenizer from the ground up> pre-training: master next-word prediction> https://t.co/6sYZj4q0AI ...

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):Deploy and run LLMs directly on your phone!Unsloth now lets you fine-tune LLMs and deploy them 100% locally on iOS/Android devices.The video shows this in action, where I ran Qwen3 on an iPhone 17 Pro at ~25 tokens/s.I have shared a guide in the replies. https://t.co/p4NqLj0jRE ...

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 ...

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/ZI7b5wIQ8kAvi Chawla (@_avichawla):Andrew Ng's team once made a big mistake in a research paper.And it happened due to randomly splitting the data.Here's exactly what happened (with solution): ...

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/daRwW5JFjhAvi Chawla (@_avichawla):Speed up your native Python code by over 50x!And it takes just 4 simple steps.Python’s default interpreter (CPython) is slow primarily because of its dynamicity.For instance, after defining a variable of a specific type, it can be changed to some other type.But these https://t.co/QtcuC4C8rL ...

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]

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. ...

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]