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Over the past 12 months we’ve built 95+ AI agents — from research helpers, content bots, data automators, to code-assistants. Today We’re sharing the 10 GitHub repositories that helped us level up: everything from foundational theory to production-grade workflows. These are the building blocks we wish we had from Day One.
🔟 The 10 Repos + Why each one matters
1. Hands‑On Large Language Models
🔗 Link FREE here.
What it is: The official code repo for the O’Reilly book by Jay Alammar & Maarten Grootendorst.
Key insights:
Starts from tokens & embeddings, moves through transformers, RAG, multimodal LLMs.
Highly visual, with ~300 custom figures & heavy focus on intuition + code.
Why it matters: This gives you the conceptual foundation before you build agents. If you skip this, you’ll miss what makes the “engine” tick.
2. AI Agents for Beginners
🔗 Link FREE here
What it is: A free 10–15 lesson course from Microsoft that walks you through AI-agent basics: what they are, what they can do, how to build one all the way to browser use.
Key insights:
Covers types of agents, use-cases, building blocks.
Includes working code samples and supports Azure AI Foundations.
Why it matters: For anyone new to “agents” (rather than just “LLMs”), this bridges the gap — you’ll learn what an agent is, why you’d build one, and how to get started.
3. GenAI Agents
🔗 Link FREE here
What it is: A GitHub repo packed with tutorials & code for generative-AI agents, from simple conversational bots to multi-agent architectures.
Key insights:
Shows workflow for building, sharing, experimenting with agents.
Suitable for both beginner and more advanced practitioner.
Why it matters: After you’ve learned the basics, you’ll need concrete architectures — this repo gives you replication-ready examples for next-level builds.
4. Made With ML
🔗 Link Free here
What it is: A highly-practical repo by Goku Mohandas focused on end-to-end ML apps: design → build → deploy → iterate.
Key insights:
Emphasises software-engineering best-practices (CI/CD, MLOps, tracking).
Targets “ML in production” rather than just toy models.
Why it matters: Building an agent is one thing; deploying it, maintaining it, versioning it is what separates hobby from professional. This repo shows you how.
5. Prompt Engineering Guide
🔗 Link FREE here
What it is: A curated hub of prompt-engineering resources: papers, tutorials, best practices.
Key insights:
Teaches you how to craft, debug, refine prompts — a key skill for reliable agent behaviour.
Covers failures, guardrails, robust prompting.
Why it matters: A poorly designed prompt = unstable agent. Getting this right boosts output quality, trust and safety.
6. Hands‑On AI Engineering
🔗 Link here
What it is: Showcases of LLM-powered apps and agent solutions you can replicate/adapt.
Key insights:
Real-world agent use-cases, code you can fork and build on.
Focus is on application & adaptation, not just theory.
Why it matters: As you move into building your 2nd/3rd/10th agent, you need inspiration + scaffold. This gives both.
7. Awesome Generative AI Guide
🔗 Link here
What it is: A curated list of learning materials, tools, research for generative AI.
Key insights:
Not just for agents — wide-angle on generative workflows, architectures, tools.
Good for staying up to date + exploring adjacent possibilities.
Why it matters: Agents don’t exist in a vacuum — you’ll want to tap into the broader genAI ecosystem and fuel innovation. This guide helps you stay ahead.
8. Designing Machine Learning Systems
🔗 Link here
What it is: Material (summaries + examples) from the classic book on scalable ML system design.
Key insights:
Focuses on architecture, scalability, system-level thinking.
Helps you build agents not just that work, but that scale reliably.
Why it matters: Building one agent is fine; building a platform for many agents with shared infra & reliability is where you’ll differentiate.
9. Machine Learning for Beginners (Microsoft)
🔗 Link here
What it is: A free, beginner-friendly Microsoft course that introduces essential ML concepts with hands-on practice.
Key insights:
Covers classic ML before you dive into LLM/agents.
Good refresher or stepping-stone if you’re not yet confident in general ML.
Why it matters: Agents often assume you “already know ML.” If you’re still solidifying fundamentals, this gem fills the gap.
10. LLM Course
🔗 Link here
What it is: A free, detailed roadmap + notebooks on building, deploying LLM-powered apps and agents.
Key insights:
From design to deployment: you’ll walk the full path.
Gives structure to your “agent learning journey” rather than jumping from repo to repo.
Why it matters: With all the above pieces, this course helps you tie them together — turn bits into coherent end-to-end builds.
🎯 Ready for the next step?
Over the coming weeks we’ll send deep dives into:
Architecture patterns behind high-performing agents
Which repos are best for beginners vs. builders vs. engineers
Workflows we used in our 90 + agent builds
Hands-on guides so you can build your own agent
Stay tuned for those.
If you’re serious about learning AI start here.
