{ "@context": "https://schema.org", "@id": "https://aggr.aymericbeaumet.com/items/hamel-dev/2024-12-13-nbsanity-share-notebooks-as-polished-web-pages-in-seconds/#webpage", "@type": [ "WebPage", "ArchiveComponent" ], "articleBody": "![Hamel Husain](https://hamel.dev/hamel_transparent.png)\n\nI’m a machine learning engineer with 20+ years of experience. I’ve worked at Airbnb and GitHub, including early LLM [research](https://arxiv.org/abs/1909.09436) used by OpenAI for [code understanding](https://openai.com/index/introducing-text-and-code-embeddings). I’ve also led and contributed to popular [open-source](https://hamel.dev/oss/opensource.html) machine-learning tools.\n\nI’m currently working to bring data science back to AI: helping teams debug, analyze, and measure their systems. I call this “evals,” and it’s the focus of my teaching and writing.\n\n## 🎓 Learn With Me\n\nI co-teach **[AI Evals for Engineers and PMs](https://maven.com/parlance-labs/evals)**, with over 5,000 students from 500+ companies (including OpenAI, Anthropic, and Google). Check the page for upcoming cohorts.\n\nFor answers to common questions about evals, read the [AI Evals Guide](https://hamel.dev/blog/posts/evals-faq/).\n\n* * *\n\nI often share my experience building AI products. Below is a selection of my long-form writing.\n\n| Date | Title |\n| -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 9/30/26 | [Claude’s new auto eval tool](https://hamel.dev/blog/posts/claude-auto-evals/index.html) |\n| 9/18/26 | [AI Evals: Everything You Need to Know](https://hamel.dev/blog/posts/evals-faq/index.html) |\n| 8/12/26 | [AI Product Engineering Notes](https://hamel.dev/notes/llm/ai-product-engineering/index.html) |\n| 7/11/26 | [Do Automated Evals Work?](https://parlance-labs.com/blog/posts/auto-evals.html) |\n| 6/29/26 | [“It’s Hard to Eval” Is a Product Smell](https://hamel.dev/blog/posts/eval-smell/index.html) |\n| 3/26/26 | [The Revenge of the Data Scientist](https://hamel.dev/blog/posts/revenge/index.html) |\n| 3/2/26 | [Evals Skills for Coding Agents](https://hamel.dev/blog/posts/evals-skills/index.html) |\n| 1/18/26 | [Why I Stopped Using nbdev](https://hamel.dev/blog/posts/ai-stack/index.html) |\n| 10/1/25 | [Selecting The Right AI Evals Tool](https://hamel.dev/blog/posts/eval-tools/index.html) |\n| 7/11/25 | [Stop Saying RAG Is Dead](https://hamel.dev/notes/llm/rag/not_dead.html) |\n| 6/23/25 | [Inspect AI, An OSS Python Library For LLM Evals](https://hamel.dev/notes/llm/evals/inspect.html) |\n| 3/24/25 | [A Field Guide to Rapidly Improving AI Products](https://hamel.dev/blog/posts/field-guide/index.html) |\n| 1/19/25 | [Thoughts On A Month With Devin](https://www.answer.ai/posts/2025-01-08-devin.html) |\n| 12/13/24 | [nbsanity - Share Notebooks as Polished Web Pages in Seconds](https://www.answer.ai/posts/2024-12-13-nbsanity.html) |\n| 11/30/24 | [Building an Audience Through Technical Writing: Strategies and Mistakes](https://hamel.dev/blog/posts/audience/index.html) |\n| 10/29/24 | [Using LLM-as-a-Judge For Evaluation: A Complete Guide](https://hamel.dev/blog/posts/llm-judge/index.html) |\n| 10/10/24 | [Concurrency Foundations For FastHTML](https://hamel.dev/notes/fasthtml/concurrency.html) |\n| 7/29/24 | [An Open Course on LLMs, Led by Practitioners](https://hamel.dev/blog/posts/course/index.html) |\n| 6/1/24 | [What We’ve Learned From A Year of Building with LLMs](https://applied-llms.org/) |\n| 4/12/24 | [Debugging AI With Adversarial Validation](https://hamel.dev/blog/posts/drift/index.html) |\n| 3/29/24 | [Your AI Product Needs Evals](https://hamel.dev/blog/posts/evals/index.html) |\n| 3/27/24 | [Is Fine-Tuning Still Valuable?](https://hamel.dev/blog/posts/fine_tuning_valuable.html) |\n| 2/14/24 | [Fuck You, Show Me The Prompt.](https://hamel.dev/blog/posts/prompt/index.html) |\n| 1/11/24 | [How To Debug Axolotl](https://hamel.dev/blog/posts/axolotl/index.html) |\n| 1/9/24 | [Dokku: my favorite personal serverless platform](https://hamel.dev/blog/posts/dokku/index.html) |\n| 12/17/23 | [Tokenization Gotchas](https://hamel.dev/notes/llm/finetuning/tokenizer_gotchas.html) |\n| 11/15/23 | [Tools for curating LLM Data](https://hamel.dev/notes/llm/finetuning/data_cleaning.html) |\n| 10/28/23 | [vLLM & Large Models](https://hamel.dev/notes/llm/inference/big_inference.html) |\n| 10/15/23 | [Optimizing LLM latency](https://hamel.dev/notes/llm/inference/inference.html) |\n| 5/30/23 | [On commercializing nbdev](https://hamel.dev/blog/posts/nbdev/index.html) |\n| 1/16/23 | [Why Should ML Engineers Learn Kubernetes?](https://hamel.dev/blog/posts/k8s/index.html) |\n| 7/28/22 | [nbdev + Quarto: A new secret weapon for productivity](https://www.fast.ai/2022/07/28/nbdev-v2/) |\n| 2/9/22 | [Notebooks in production with Metaflow](https://outerbounds.com/blog/notebooks-in-production-with-metaflow/) |\n| 12/18/20 | [ghapi, a new third-party Python client for the GitHub API](https://github.blog/2020-12-18-learn-about-ghapi-a-new-third-party-python-client-for-the-github-api/) |\n| 11/20/20 | [Nbdev: A literate programming environment that democratizes software engineering best practices](https://github.blog/2020-11-20-nbdev-a-literate-programming-environment-that-democratizes-software-engineering-best-practices/) |\n| 9/1/20 | [fastcore: An Underrated Python Library](https://fastpages.fast.ai/fastcore) |\n| 9/1/20 | [Data Science Meets Devops: MLOps with Jupyter, Git, & Kubernetes](https://blog.kubeflow.org/mlops/) |\n| 3/6/20 | [GitHub Actions: Providing Data Scientists With New Superpowers.](https://fastpages.fast.ai/actions/markdown/2020/03/06/fastpages-actions.html) |\n| 2/21/20 | [Introducing fastpages, An easy to use blogging platform with extra features for Jupyter Notebooks.](https://fastpages.fast.ai/fastpages/jupyter/2020/02/21/introducing-fastpages.html) |\n| 2/5/20 | [Python Concurrency: The Tricky Bits](https://python.hamel.dev/concurrency/) |\n| 9/20/19 | [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://arxiv.org/abs/1909.09436) |\n| 4/10/19 | [How to Automate Tasks on GitHub With Machine Learning for Fun and Profit](https://medium.com/%40hamelhusain/mlapp-419f90e8f007?sk=760e18e8f007) |\n| 5/29/18 | [How To Create Natural Language Semantic Search for Arbitrary Objects With Deep Learning](https://medium.com/@hamelhusain/semantic-code-search-3cd6d244a39c) |\n| 1/18/18 | [How To Create Magical Data Products Using Sequence-to-Sequence Models](https://medium.com/data-science/how-to-create-data-products-that-are-magical-using-sequence-to-sequence-models-703f86a231f8) |\n| 12/16/17 | [How Docker Can Help You Become A More Effective Data Scientist](https://medium.com/towards-data-science/how-docker-can-help-you-become-a-more-effective-data-scientist-7fc048ef91d5) |\n| 5/10/17 | [Automated Machine Learning — A Paradigm Shift That Accelerates Data Scientist Productivity @ Airbnb](https://medium.com/airbnb-engineering/automated-machine-learning-a-paradigm-shift-that-accelerates-data-scientist-productivity-airbnb-f1f8a10d61f8) |\n\nNo matching items\n\n## 📬 Follow Me\n\nThe best way to follow my activity is [X (Twitter)](https://twitter.com/HamelHusain).\n", "authors": [], "captured_at": "2026-09-27T19:29:15.293681927+00:00", "category": "programming", "content_html": "

\"Hamel

\n

I’m a machine learning engineer with 20+ years of experience. I’ve worked at Airbnb and GitHub, including early LLM research used by OpenAI for code understanding. I’ve also led and contributed to popular open-source machine-learning tools.

\n

I’m currently working to bring data science back to AI: helping teams debug, analyze, and measure their systems. I call this “evals,” and it’s the focus of my teaching and writing.

\n

🎓 Learn With Me

\n

I co-teach AI Evals for Engineers and PMs, with over 5,000 students from 500+ companies (including OpenAI, Anthropic, and Google). Check the page for upcoming cohorts.

\n

For answers to common questions about evals, read the AI Evals Guide.

\n
\n

I often share my experience building AI products. Below is a selection of my long-form writing.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
DateTitle
9/30/26Claude’s new auto eval tool
9/18/26AI Evals: Everything You Need to Know
8/12/26AI Product Engineering Notes
7/11/26Do Automated Evals Work?
6/29/26“It’s Hard to Eval” Is a Product Smell
3/26/26The Revenge of the Data Scientist
3/2/26Evals Skills for Coding Agents
1/18/26Why I Stopped Using nbdev
10/1/25Selecting The Right AI Evals Tool
7/11/25Stop Saying RAG Is Dead
6/23/25Inspect AI, An OSS Python Library For LLM Evals
3/24/25A Field Guide to Rapidly Improving AI Products
1/19/25Thoughts On A Month With Devin
12/13/24nbsanity - Share Notebooks as Polished Web Pages in Seconds
11/30/24Building an Audience Through Technical Writing: Strategies and Mistakes
10/29/24Using LLM-as-a-Judge For Evaluation: A Complete Guide
10/10/24Concurrency Foundations For FastHTML
7/29/24An Open Course on LLMs, Led by Practitioners
6/1/24What We’ve Learned From A Year of Building with LLMs
4/12/24Debugging AI With Adversarial Validation
3/29/24Your AI Product Needs Evals
3/27/24Is Fine-Tuning Still Valuable?
2/14/24Fuck You, Show Me The Prompt.
1/11/24How To Debug Axolotl
1/9/24Dokku: my favorite personal serverless platform
12/17/23Tokenization Gotchas
11/15/23Tools for curating LLM Data
10/28/23vLLM & Large Models
10/15/23Optimizing LLM latency
5/30/23On commercializing nbdev
1/16/23Why Should ML Engineers Learn Kubernetes?
7/28/22nbdev + Quarto: A new secret weapon for productivity
2/9/22Notebooks in production with Metaflow
12/18/20ghapi, a new third-party Python client for the GitHub API
11/20/20Nbdev: A literate programming environment that democratizes software engineering best practices
9/1/20fastcore: An Underrated Python Library
9/1/20Data Science Meets Devops: MLOps with Jupyter, Git, & Kubernetes
3/6/20GitHub Actions: Providing Data Scientists With New Superpowers.
2/21/20Introducing fastpages, An easy to use blogging platform with extra features for Jupyter Notebooks.
2/5/20Python Concurrency: The Tricky Bits
9/20/19CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
4/10/19How to Automate Tasks on GitHub With Machine Learning for Fun and Profit
5/29/18How To Create Natural Language Semantic Search for Arbitrary Objects With Deep Learning
1/18/18How To Create Magical Data Products Using Sequence-to-Sequence Models
12/16/17How Docker Can Help You Become A More Effective Data Scientist
5/10/17Automated Machine Learning — A Paradigm Shift That Accelerates Data Scientist Productivity @ Airbnb
\n

No matching items

\n

📬 Follow Me

\n

The best way to follow my activity is X (Twitter).

\n", "content_markdown": "![Hamel Husain](https://hamel.dev/hamel_transparent.png)\n\nI’m a machine learning engineer with 20+ years of experience. I’ve worked at Airbnb and GitHub, including early LLM [research](https://arxiv.org/abs/1909.09436) used by OpenAI for [code understanding](https://openai.com/index/introducing-text-and-code-embeddings). I’ve also led and contributed to popular [open-source](https://hamel.dev/oss/opensource.html) machine-learning tools.\n\nI’m currently working to bring data science back to AI: helping teams debug, analyze, and measure their systems. I call this “evals,” and it’s the focus of my teaching and writing.\n\n## 🎓 Learn With Me\n\nI co-teach **[AI Evals for Engineers and PMs](https://maven.com/parlance-labs/evals)**, with over 5,000 students from 500+ companies (including OpenAI, Anthropic, and Google). Check the page for upcoming cohorts.\n\nFor answers to common questions about evals, read the [AI Evals Guide](https://hamel.dev/blog/posts/evals-faq/).\n\n* * *\n\nI often share my experience building AI products. Below is a selection of my long-form writing.\n\n| Date | Title |\n| -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 9/30/26 | [Claude’s new auto eval tool](https://hamel.dev/blog/posts/claude-auto-evals/index.html) |\n| 9/18/26 | [AI Evals: Everything You Need to Know](https://hamel.dev/blog/posts/evals-faq/index.html) |\n| 8/12/26 | [AI Product Engineering Notes](https://hamel.dev/notes/llm/ai-product-engineering/index.html) |\n| 7/11/26 | [Do Automated Evals Work?](https://parlance-labs.com/blog/posts/auto-evals.html) |\n| 6/29/26 | [“It’s Hard to Eval” Is a Product Smell](https://hamel.dev/blog/posts/eval-smell/index.html) |\n| 3/26/26 | [The Revenge of the Data Scientist](https://hamel.dev/blog/posts/revenge/index.html) |\n| 3/2/26 | [Evals Skills for Coding Agents](https://hamel.dev/blog/posts/evals-skills/index.html) |\n| 1/18/26 | [Why I Stopped Using nbdev](https://hamel.dev/blog/posts/ai-stack/index.html) |\n| 10/1/25 | [Selecting The Right AI Evals Tool](https://hamel.dev/blog/posts/eval-tools/index.html) |\n| 7/11/25 | [Stop Saying RAG Is Dead](https://hamel.dev/notes/llm/rag/not_dead.html) |\n| 6/23/25 | [Inspect AI, An OSS Python Library For LLM Evals](https://hamel.dev/notes/llm/evals/inspect.html) |\n| 3/24/25 | [A Field Guide to Rapidly Improving AI Products](https://hamel.dev/blog/posts/field-guide/index.html) |\n| 1/19/25 | [Thoughts On A Month With Devin](https://www.answer.ai/posts/2025-01-08-devin.html) |\n| 12/13/24 | [nbsanity - Share Notebooks as Polished Web Pages in Seconds](https://www.answer.ai/posts/2024-12-13-nbsanity.html) |\n| 11/30/24 | [Building an Audience Through Technical Writing: Strategies and Mistakes](https://hamel.dev/blog/posts/audience/index.html) |\n| 10/29/24 | [Using LLM-as-a-Judge For Evaluation: A Complete Guide](https://hamel.dev/blog/posts/llm-judge/index.html) |\n| 10/10/24 | [Concurrency Foundations For FastHTML](https://hamel.dev/notes/fasthtml/concurrency.html) |\n| 7/29/24 | [An Open Course on LLMs, Led by Practitioners](https://hamel.dev/blog/posts/course/index.html) |\n| 6/1/24 | [What We’ve Learned From A Year of Building with LLMs](https://applied-llms.org/) |\n| 4/12/24 | [Debugging AI With Adversarial Validation](https://hamel.dev/blog/posts/drift/index.html) |\n| 3/29/24 | [Your AI Product Needs Evals](https://hamel.dev/blog/posts/evals/index.html) |\n| 3/27/24 | [Is Fine-Tuning Still Valuable?](https://hamel.dev/blog/posts/fine_tuning_valuable.html) |\n| 2/14/24 | [Fuck You, Show Me The Prompt.](https://hamel.dev/blog/posts/prompt/index.html) |\n| 1/11/24 | [How To Debug Axolotl](https://hamel.dev/blog/posts/axolotl/index.html) |\n| 1/9/24 | [Dokku: my favorite personal serverless platform](https://hamel.dev/blog/posts/dokku/index.html) |\n| 12/17/23 | [Tokenization Gotchas](https://hamel.dev/notes/llm/finetuning/tokenizer_gotchas.html) |\n| 11/15/23 | [Tools for curating LLM Data](https://hamel.dev/notes/llm/finetuning/data_cleaning.html) |\n| 10/28/23 | [vLLM & Large Models](https://hamel.dev/notes/llm/inference/big_inference.html) |\n| 10/15/23 | [Optimizing LLM latency](https://hamel.dev/notes/llm/inference/inference.html) |\n| 5/30/23 | [On commercializing nbdev](https://hamel.dev/blog/posts/nbdev/index.html) |\n| 1/16/23 | [Why Should ML Engineers Learn Kubernetes?](https://hamel.dev/blog/posts/k8s/index.html) |\n| 7/28/22 | [nbdev + Quarto: A new secret weapon for productivity](https://www.fast.ai/2022/07/28/nbdev-v2/) |\n| 2/9/22 | [Notebooks in production with Metaflow](https://outerbounds.com/blog/notebooks-in-production-with-metaflow/) |\n| 12/18/20 | [ghapi, a new third-party Python client for the GitHub API](https://github.blog/2020-12-18-learn-about-ghapi-a-new-third-party-python-client-for-the-github-api/) |\n| 11/20/20 | [Nbdev: A literate programming environment that democratizes software engineering best practices](https://github.blog/2020-11-20-nbdev-a-literate-programming-environment-that-democratizes-software-engineering-best-practices/) |\n| 9/1/20 | [fastcore: An Underrated Python Library](https://fastpages.fast.ai/fastcore) |\n| 9/1/20 | [Data Science Meets Devops: MLOps with Jupyter, Git, & Kubernetes](https://blog.kubeflow.org/mlops/) |\n| 3/6/20 | [GitHub Actions: Providing Data Scientists With New Superpowers.](https://fastpages.fast.ai/actions/markdown/2020/03/06/fastpages-actions.html) |\n| 2/21/20 | [Introducing fastpages, An easy to use blogging platform with extra features for Jupyter Notebooks.](https://fastpages.fast.ai/fastpages/jupyter/2020/02/21/introducing-fastpages.html) |\n| 2/5/20 | [Python Concurrency: The Tricky Bits](https://python.hamel.dev/concurrency/) |\n| 9/20/19 | [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://arxiv.org/abs/1909.09436) |\n| 4/10/19 | [How to Automate Tasks on GitHub With Machine Learning for Fun and Profit](https://medium.com/%40hamelhusain/mlapp-419f90e8f007?sk=760e18e8f007) |\n| 5/29/18 | [How To Create Natural Language Semantic Search for Arbitrary Objects With Deep Learning](https://medium.com/@hamelhusain/semantic-code-search-3cd6d244a39c) |\n| 1/18/18 | [How To Create Magical Data Products Using Sequence-to-Sequence Models](https://medium.com/data-science/how-to-create-data-products-that-are-magical-using-sequence-to-sequence-models-703f86a231f8) |\n| 12/16/17 | [How Docker Can Help You Become A More Effective Data Scientist](https://medium.com/towards-data-science/how-docker-can-help-you-become-a-more-effective-data-scientist-7fc048ef91d5) |\n| 5/10/17 | [Automated Machine Learning — A Paradigm Shift That Accelerates Data Scientist Productivity @ Airbnb](https://medium.com/airbnb-engineering/automated-machine-learning-a-paradigm-shift-that-accelerates-data-scientist-productivity-airbnb-f1f8a10d61f8) |\n\nNo matching items\n\n## 📬 Follow Me\n\nThe best way to follow my activity is [X (Twitter)](https://twitter.com/HamelHusain).\n", "dateCreated": "2026-10-18T23:27:08.867657739+00:00", "date_created": "2024-12-13T08:00:00+00:00", "date_modified": "2024-12-13T08:00:00+00:00", "date_published": "2024-12-13T08:00:00+00:00", "description": "Transform your GitHub Jupyter notebooks into beautiful, readable web pages with a single URL change. 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No setup required.", "tags": [ "jupyter" ], "temporalCoverage": "2026-09-27T19:29:15.293681927+00:00", "title": "nbsanity - Share Notebooks as Polished Web Pages in Seconds", "url": "https://aggr.aymericbeaumet.com/items/hamel-dev/2024-12-13-nbsanity-share-notebooks-as-polished-web-pages-in-seconds/", "word_count": 609 }