Code-first AI for production
Learn to build and deploy machine learning, RAG, and AI agent systems through practical, step-by-step projects.
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Learners
IIT KGPIIT Kharagpur
Alumnus
4.8★
Instructor rating
Guided Roadmaps
Follow a sequenced roadmap instead of piecing together disconnected tutorials.
A structured, progressive roadmap for developers seeking to master AI agents, covering Python foundations, LangChain, LangGraph, and private multi-agent RAG.
A comprehensive curriculum taking you from zero programming knowledge to professional data manipulation, mathematical visualization, and core ML model building.
Latest from the library
Start with recent code-first guides, or filter the library by the skill you are building.
We read the config files and weight maps of Qwen3.8-Flash-Next and Qwen 3.8 27B to find the four changes that let a 180B model run only 6B of its weights per token.
IBM dropped the Mamba hybrid in Granite 4.2. We measured what that costs against Gemma 4, Qwen 3.8 and Ornith 1.5 on one RTX 5090.
We benchmarked Ornith 1.5 9B and 35B-A3B against Gemma 4-26B-A4B and Qwen 3.8 27B on one RTX 5090, and found the two mixtures are nearly identical on hardware and separated entirely by training.
Meet your instructor
I'm Laxmi Kant Tiwari, IIT Kharagpur alumnus, founder with a successful startup exit, and an engineer with 10+ years across industry and academia. Everything here is taught the way real production systems are built.
Read my storyFree Video Tutorials
Full video walkthroughs, free: new tutorials every week.
Qwen 3.8 Flash Next (Qwen 4) vs 27B: Qwen 4 Architecture Teardown
Qwen 3.8 27B vs Qwen 3.6: Why Qwen 3.8 Wins
Qwen 3.8 27B vs Muse Glimmer vs Gemma 4 - Tested Locally on RTX5090 and Ollama
In-Depth Courses
Go deeper with complete projects, private repositories, and certificates.
Master Langchain v1, Local LLM Projects, Ollama, DeepSeek, LLAMA 3.2, Complete Integration Guide.
Agentic RAG and Chatbot, AI Agent, DeepSeek, LLAMA 3.2 Agent, FAISS Vector Database.
Build MCP servers & clients with Python, Streamlit, ChromaDB, LangChain, LangGraph agents, and Ollama integrations.
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What Students Say About Us
“The instructor explained clearly and its examples were easy to implement. I recommend it for introducing yourself into the latest advancements in transformers.”
“This is a nice hands-on video. The contents enhance the knowledge of fine tuning the model. I have also found that the 'theory' parts are very worth to learn.”
“The lecture is well-organized and delivered clearly, and easy to learn.”
