Over the next few weeks, we're launching a video series that takes you end-to-end from "I just installed Ollama" to "I can design and ship serious, graph-based AI agents using LangChain and LangGraph, entirely on my own machine." Who This Is For This journey is designed for: - Developers and data practitioners who want hands-on experience building agentic AI systems - Startups prototyping AI features before committing to expensive API costs - Enterprise teams with data privacy requirements who need local-first solutions - Tech leaders and researchers who want to deeply understand agent architectures without being locked into hosted models Prerequisites Before starting, you should have: - Basic Python knowledge: functions, classes, pip, virtual environments - API/JSON familiarity: understanding request/response patterns - Terminal comfort: running commands, navigating directories - Hardware: 16GB+ RAM recommended; GPU optional but helpful for larger models What This Journey Covers Across the series, we'll walk through five major phases. Each phase ends with tangible projects you can run locally and adapt to your own use cases. --------------------------------------------------------------- Phase 0 – Local Stack: Ollama + Python Estimated time: 1-2 hours We begin by setting up a local AI sandbox: - Installing and configuring Ollama - Pulling and running popular open models (e.g., Llama, Mistral) - Understanding hardware requirements and model selection for different tasks - Creating a clean Python environment and wiring up a minimal script to talk to a local LLM By the end, you'll have a lightweight local playground where you can experiment without API keys or usage limits. Troubleshooting covered: Model selection guide, memory optimization, when local models shine vs. their limitations --------------------------------------------------------------- Phase 1 – LangChain Fundamentals with Local Models Estimated time: 3-4 hours Next, we introduce LangChain as the "capabilities layer" on top of your local model:

What type of problems is AI better at than traditional rule-based software?

What can I do to make sure we have more participation in this community. I am open for ideas. One idea was to build a real world project based on requests. But again keep it small enough so we all understand. If you have any other requests please do let me know - I want to bring some more participation. I think more daily videos will be coming too where I would love to teach new concepts. Once you add the requests here - after 24 hours I will add a poll for this. The comments will be closed after 24 hours for this question.

Most professionals have a scaling problem. Your knowledge is valuable. Your time is limited. So every time you teach, present, onboard, or create content, you start from scratch. That’s exactly what this course fixes. I just launched Build Your Digital Twin — a hands-on course where you learn how to turn your expertise into an AI Digital Twin that can: • Teach • Present • Create slides • Generate videos • Scale your knowledge without scaling your time You’ll build this using ChatGPT, Gamma, and HeyGen—step by step. 💡 For a limited time, the entire course is available for $1. Yes. One dollar. 👉 Enroll here: https://maven.com/the-school-of-ai/build-your-digital-twin?promoCode=DEAL If you’ve been thinking about: • Creating courses • Scaling your content • Using AI beyond chat • Building leverage with your expertise This is the easiest place to start.

What’s the first technical problem you’d try to automate with AI in your current workflow?