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Dear reader, let me introduce you…

The Decoding AI Magazine

Your weekly magazine on shipping AI products that just work.

I founded this magazine to solve the problem I faced for the first five years of my career: escaping the “PoC purgatory.” I realized that finding a team that knows how to ship AI software is rare. Too many AI projects get stuck at Jupyter Notebooks or fancy demos that never see a real user.
Decoding AI is the solution. It’s your weekly hub for learning how to design, build, and ship production-grade AI systems. End-to-end. From idea to production. From data collection to deploying, monitoring and evaluation. With a focus on AI principles, software patterns and infrastructure systems that will thrive in a future dominated by AI coding tools.
Stop building prototypes. Start shipping AI that works.

Real-world guides taking you from the PoC purgatory to shipping AI products that work.
Every Tuesday morning, you'll get one free actionable tip to design, build, and deploy production-grade AI systems in less than 8 minutes.

Join 40,000+ engineers for content on designing, building, and shipping AI software. Learn what you need to take an idea to production. New issues every Tuesday.

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Editor’s Picks

Flowchart comparing offline and online machine learning pipelines, including data collection, filtering, model training, inference, and evaluation steps.

Build your Second Brain AI assistant

Diagram of Context Engineering Guide 101 showing flow from user input, through short-term and long-term memory, agent processing, action tools, and output to user, with database and server components.

Context Engineering: 2025’s #1 Skill in AI

Diagram of architecture for agentic RAG systems for video games, illustrating data flow between memory, policy, inference pipelines, data sources, and evaluation datasets with logos of MongoDB, Opik, Groq, and Wabbit.

From 0 to Pro AI Agents Roadmap

Maxime Labonne

“Working with Paul on the LLM Engineer's Handbook showed me his unique ability to bridge the gap between theoretical AI and modern AI Engineering best practices.”
Louis-Francois Bouchard

“Paul is one of the rare people who truly understands both the technical depth of AI and how to teach it in a way that’s engaging and actually clicks.”
Paolo Perrone

“Paul is one of the sharpest minds I know in AI and ML. Working with Paul is a masterclass in building scalable, maintainable AI. His technical insight and clarity of thought are unmatched.”
Maria Vechtomova

“Paul doesn’t just talk about AI - he builds it, and when he talks, it’s worth listening.”
Eduardo Ordax

“If you are serious about building with AI and leave the hype behind, Paul is one of those folks you should follow.”
Pau Labarta Bajo

"If it's decodable, Paul will decode it for you.”
Miguel Otero Pedrido

“In a world where most just talk about AI, Paul actually builds it. A true AI engineer, who shows how to create AI systems that actually work.”
Hugo Bowne-Anderson

"Decoding AI is an invaluable resource for technical builders to stay up to date in such a rapidly moving space: bookmark it!”
Shaw Talebi

"I've learned a lot from Paul. He's my go-to guy when it comes to MLOps.”