Principal Engineer and Google Developer Expert (GDE) for Google Cloud and AI. I build AI agents, RAG systems and LLM apps on Google Cloud with ADK, Vertex AI, Gemini, and MCP, and write about what I learn along the way.
Things I build and run.
aiml.sh: free AI engineering courses on LLMs, RAG and AI agents, plus MCP, inference and evals, with diagrams, runnable Python code and tests.
superPDF AI: 68 free PDF tools that run in your browser, plus Chat with PDF with page citations and PDF agents you approve step by step. Files stay on your device.- @superpdf/cli: superPDF's open-source reader as a CLI, MCP server and local REST API (Apache-2.0). It turns PDFs into Markdown, HTML or JSON with headings, tables, charts as data and OCR in 15 languages, without a language model. Try it:
npx @superpdf/cli export report.pdf --output report.md 
Rivul AI: search 300M+ research papers, draft with sources attached, ask your own PDFs, scans and recordings with cited answers, and run research agents. Includes an MCP server and a REST API.
whatisai: learn AI five minutes at a time, with a 29-chapter course, a glossary of about 800 terms and a curated AI news edition every day at 7 PM ET.
openalex-semantic-search: semantic search over 300M+ OpenAlex papers without a vector database, served from a single CPU server. It powers the paper search in Rivul AI.
17 free courses and 224 chapters, with diagrams, runnable Python code and tests.
| Track | Course | What you build | Chapters |
|---|---|---|---|
| Foundations |  AI Fundamentals |
Build the parts of a docs assistant: model requests, context, structured output, document ranking, validated tool calls and evaluations. | 11 |
 Build a Small Language Model From Scratch |
Build and inspect a small one-block transformer and its complete local training and generation path. | 11 | |
 LLM Inference Engineering |
Implement and inspect inference request handling, cache ownership, scheduling, measurement and capacity decisions using real local runs and clearly bounded experiments. | 20 | |
 Building AI Systems |
Choose, measure and improve an AI system against explicit quality, latency, cost and failure requirements. | 21 | |
 Building MLOps |
Take one user-return prediction application from a manual baseline through reproducible training, local release controls and outcome monitoring. | 21 | |
| Google ADK 2.0 |  Building Agents with Google ADK 2.0 |
Build agents, tools and multi-step workflows with Google ADK 2.0, then evaluate them. | 17 |
| Connecting Agents to Tools and Services with Google ADK 2.0 |
Connect agents to BigQuery, databases through MCP Toolbox, Agent Search, Google Search and code execution, then add Skills and A2A. | 6 | |
| Running and Deploying Agents with Google ADK 2.0 |
Serve, observe, evaluate and deploy ADK agents with runtime controls, context caching, traces, simulated users, Cloud Run and Agent Runtime. | 20 | |
| Building Live and Voice Agents with Google ADK 2.0 |
Stream audio and screen frames with ADK live agents, handle tools and sessions, test voice callbacks, and build a custom WebSocket bridge. | 7 | |
 Building, Evaluating, and Deploying Agents with Google Agents CLI |
Scaffold, enhance, upgrade, evaluate, deploy, publish and observe ADK agents across the full Google Agents CLI lifecycle. | 6 | |
| Agents |  Building Agents with LangGraph |
Build and compare self-contained code-review agents and workflows with LangGraph: tools, stop conditions, context and token budgets, timeouts, checkpoints, idempotent retries, verified outcomes, human approval and memory. | 9 |
 Agent Design, Optimization and Harnesses |
Choose a control policy, improve it on separated task data and independently verify bounded local actions. | 9 | |
 Context and Memory Engineering for AI Agents |
Assemble bounded model requests, compare context policies and maintain scoped memory with explicit provenance, retention and deletion rules. | 10 | |
| RAG, MCP and evals |  Retrieval-Augmented Generation (RAG) |
Build developer-document search with LangChain and pgvector, then answer from retrieved text and charts with checked source references. | 6 |
 Building Agentic RAG Applications |
Build a documentation assistant that answers from permitted, versioned sources. | 37 | |
 Model Context Protocol (MCP) |
Implement, test and secure MCP servers and clients, from stdio to a remote OAuth-protected service with scoped database access. | 7 | |
 LLM Evals |
Test LLM features with pytest and DeepEval, measure RAG and tool-call quality per stage, and gate prompt, model and dataset changes on regressions in CI. | 6 |
➡️ Browse all courses · 📬 Subscribe to the aiml.sh newsletter · 📰 Latest AI news
- google-adk-masterclass: 20 hands-on modules on ADK 1.x, from basics to advanced multi-agent topics. For ADK 2.0, see the courses on aiml.sh.
- adk-vertex-ai-rag-engine: a RAG agent built on Google ADK and Vertex AI RAG Engine, with semantic search over your documents in a couple of minutes.
- mcp-gemini-search and mcp-flight-search: MCP servers that pair Gemini function calling with real-time flight search.
- How I Built Semantic Search Over 300 Million Plus Academic Papers
- Building a Local, Multimodal AI Terminal Agent with Gemma 4
- Building a RAG Agent with Google ADK, Antigravity SDK, Agent Skills, A2UI, and Gemini Enterprise…
- Four Layers of Agent Engineering
- Top 30 AI/ML Terms Every Developer Should Know
➡️ More on arjunprabhulal.com/blog · Medium
- Google DevFest Atlanta 2026 (Oct 30): From Spec to Shipping: Building AI Agents with Google ADK and Antigravity
- ATLDevCon 2026 (Oct 17): From Spec to Shipping: Building AI Agents with Google ADK and Antigravity
- AICamp Atlanta (Aug 24): Building Multi-Agent Systems using Google ADK 2.0 and MCP
➡️ All talks