Hamel Husain’s Blog

Notes on applied AI engineering, machine learning, and data science.

I am a machine learning engineer with over 20 years of experience. I have worked at companies such as Airbnb and GitHub, which included early LLM research used by OpenAI for code understanding. I have also led and contributed to numerous popular open-source machine-learning tools. I am currently an independent consultant helping companies build AI products.

💼 Work With Me

I co-teach a popular course, AI Evals for Engineers & PMs. We have taught over 3,000 students from 500+ companies including OpenAI, Anthropic, and Google. I encourage you to check it out if you are interested in systematically improving your AI products.

If you need more hands-on help, I’m open to new consulting work and other forms of advisory. If you need help with your project, you can find more information here.

📮 Blog

I often share my experience building AI products. Below is a selected assortment of my longer-form writing.

Date	Title
11/18/25	LLM Evals: Everything You Need to Know
10/1/25	Selecting The Right AI Evals Tool
7/11/25	Stop Saying RAG Is Dead
6/23/25	Inspect AI, An OSS Python Library For LLM Evals
3/24/25	A Field Guide to Rapidly Improving AI Products
1/19/25	Thoughts On A Month With Devin
12/13/24	nbsanity - Share Notebooks as Polished Web Pages in Seconds
11/30/24	Building an Audience Through Technical Writing: Strategies and Mistakes
10/29/24	Using LLM-as-a-Judge For Evaluation: A Complete Guide
10/10/24	Concurrency Foundations For FastHTML
7/29/24	An Open Course on LLMs, Led by Practitioners
6/1/24	What We’ve Learned From A Year of Building with LLMs
4/12/24	Debugging AI With Adversarial Validation
3/29/24	Your AI Product Needs Evals
3/27/24	Is Fine-Tuning Still Valuable?
2/14/24	Fuck You, Show Me The Prompt.
1/11/24	How To Debug Axolotl
1/9/24	Dokku: my favorite personal serverless platform
12/17/23	Tokenization Gotchas
11/15/23	Tools for curating LLM Data
10/28/23	vLLM & Large Models
10/15/23	Optimizing LLM latency
1/16/23	Why Should ML Engineers Learn Kubernetes?
7/28/22	nbdev + Quarto: A new secret weapon for productivity
2/9/22	Notebooks in production with Metaflow
12/18/20	ghapi, a new third-party Python client for the GitHub API
11/20/20	Nbdev: A literate programming environment that democratizes software engineering best practices
9/1/20	fastcore: An Underrated Python Library
9/1/20	Data Science Meets Devops: MLOps with Jupyter, Git, & Kubernetes
3/6/20	GitHub Actions: Providing Data Scientists With New Superpowers.
2/21/20	Introducing fastpages, An easy to use blogging platform with extra features for Jupyter Notebooks.
2/5/20	Python Concurrency: The Tricky Bits
9/20/19	CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
4/10/19	How to Automate Tasks on GitHub With Machine Learning for Fun and Profit
5/29/18	How To Create Natural Language Semantic Search for Arbitrary Objects With Deep Learning
1/18/18	How To Create Magical Data Products Using Sequence-to-Sequence Models
12/16/17	How Docker Can Help You Become A More Effective Data Scientist
5/10/17	Automated Machine Learning — A Paradigm Shift That Accelerates Data Scientist Productivity @ Airbnb

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