It is said that the two greatest problems of history are: how to account for the rise of Rome, and how to account for her fall. If so, then the volcanic ashes spewed by Mount Vesuvius in 79 AD - which entomb the cities of Pompeii and Herculaneum in South Italy - hold history’s greatest prize. For beneath those ashes lies the only salvageable library from the classical world.

Nat Friedman was the CEO of Github form 2018 to 2021. Before that, he started and sold two companies - Ximian and Xamarin. He is also the founder of AI Grant and California YIMBY.

And most recently, he has created and funded the Vesuvius Challenge - a million dollar prize for reading an unopened Herculaneum scroll for the very first time. If we can decipher these scrolls, we may be able to recover lost gospels, forgotten epics, and even missing works of Aristotle.

We also discuss the future of open source and AI, running Github and building Copilot, and why EMH is a lie.

Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform. Read the full transcript here. Follow me on Twitter for updates on future episodes.

Timestamps

(0:00:00) - Vesuvius Challenge

(0:30:00) - Finding points of leverage

(0:37:39) - Open Source in AI

(0:40:32) - Github Acquisition

(0:50:18) - Copilot origin Story

(1:11:47) - Nat.org

(1:32:56) - Questions from Twitter

I went over to the OpenAI offices in San Fransisco to ask the Chief Scientist and cofounder of OpenAI, Ilya Sutskever, about:

* time to AGI

* leaks and spies

* what's after generative models

* post AGI futures

* working with Microsoft and competing with Google

* difficulty of aligning superhuman AI

Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform. Read the full transcript here. Follow me on Twitter for updates on future episodes.

Timestamps

(00:00) - Time to AGI

(05:57) - What’s after generative models?

(10:57) - Data, models, and research

(15:27) - Alignment

(20:53) - Post AGI Future

(26:56) - New ideas are overrated

(36:22) - Is progress inevitable?

(41:27) - Future Breakthroughs

For 4 hours, I tried to come up reasons for why AI might not kill us all, and Eliezer Yudkowsky explained why I was wrong.

We also discuss his call to halt AI, why LLMs make alignment harder, what it would take to save humanity, his millions of words of sci-fi, and much more.

If you want to get to the crux of the conversation, fast forward to 2:35:00 through 3:43:54. Here we go through and debate the main reasons I still think doom is unlikely.

Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform. Read the full transcript here. Follow me on Twitter for updates on future episodes.

Timestamps

(0:00:00) - TIME article

(0:09:06) - Are humans aligned?

(0:37:35) - Large language models

(1:07:15) - Can AIs help with alignment?

(1:30:17) - Society’s response to AI

(1:44:42) - Predictions (or lack thereof)

(1:56:55) - Being Eliezer

(2:13:06) - Othogonality

(2:35:00) - Could alignment be easier than we think?

(3:02:15) - What will AIs want?

(3:43:54) - Writing fiction & whether rationality helps you win

A few weeks ago, I sat beside Andy Matuschak to record how he reads a textbook.

Even though my own job is to learn things, I was shocked with how much more intense, painstaking, and effective his learning process was.

So I asked if we could record a conversation about how he learns and a bunch of other topics:

* How he identifies and interrogates his confusion (much harder than it seems, and requires an extremely effortful and slow pace)

* Why memorization is essential to understanding and decision-making

* How come some people (like Tyler Cowen) can integrate so much information without an explicit note taking or spaced repetition system.

* How LLMs and video games will change education

* How independent researchers and writers can make money

* The balance of freedom and discipline in education

* Why we produce fewer von Neumann-like prodigies nowadays

* How multi-trillion dollar companies like Apple (where he was previously responsible for bedrock iOS features) manage to coordinate millions of different considerations (from the cost of different components to the needs of users, etc) into new products designed by 10s of 1000s of people.

Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform. Read the full transcript here. Follow me on Twitter for updates on future episodes.

To see Andy’s process in action, check out the video where we record him studying a quantum physics textbook, talking aloud about his thought process, and using his memory system prototype to internalize the material.

You can check out his website and personal notes, and follow him on Twitter.

Cometeer

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If you want to sponsor an episode, contact me at [email protected].

Timestamps

(00:00:52) - Skillful reading

(00:02:30) - Do people care about understanding?

(00:06:52) - Structuring effective self-teaching

(00:16:37) - Memory and forgetting

(00:33:10) - Andy’s memory practice

(00:40:07) - Intellectual stamina

(00:44:27) - New media for learning (video, games, streaming)

(00:58:51) - Schools are designed for the median student

(01:05:12) - Is learning inherently miserable?

(01:11:57) - How Andy would structure his kids’ education

(01:30:00) - The usefulness of hypertext

(01:41:22) - How computer tools enable iteration

(01:50:44) - Monetizing public work

(02:08:36) - Spaced repetition

(02:10:16) - Andy’s personal website and notes

(02:12:44) - Working at Apple

(02:19:25) - Spaced repetition 2

Here is my conversation with Dario Amodei, CEO of Anthropic.

Dario is hilarious and has fascinating takes on what these models are doing, why they scale so well, and what it will take to align them.

Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform. Read the full transcript here. Follow me on Twitter for updates on future episodes.

Timestamps

(00:00:00) - Introduction

(00:01:00) - Scaling

(00:15:46) - Language

(00:22:58) - Economic Usefulness

(00:38:05) - Bioterrorism

(00:43:35) - Cybersecurity

(00:47:19) - Alignment & mechanistic interpretability

(00:57:43) - Does alignment research require scale?

(01:05:30) - Misuse vs misalignment

(01:09:06) - What if AI goes well?

(01:11:05) - China

(01:15:11) - How to think about alignment

(01:31:31) - Is modern security good enough?

(01:36:09) - Inefficiencies in training

(01:45:53) - Anthropic’s Long Term Benefit Trust

(01:51:18) - Is Claude conscious?

(01:56:14) - Keeping a low profile

Have you heard of FastAPI? An application programming interface is vital to make your software accessible to users across the internet. FastAPI is an excellent option for quickly creating a web API that implements best practices. This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

We share an introduction to FastAPI written by the framework’s author, Sebastián Ramírez. The goal behind the article is to get you started creating production-ready APIs.

David covers an article about the Python import system and how it remains a mystery for many Python developers. We share some additional Real Python resources on the import system and statements.

We cover several other articles and projects from the Python community including, a buffet of specialized data types with Python’s collections module, maps with Django using GeoDjango, PostGIS, and Leaflet, moving SciPy to the Meson build system, what’s new in Python 3.11, a community-maintained Python framework for creating mathematical animations, and easily make PDFs with pdfme.

Course Spotlight: Python Modules and Packages: An Introduction

In this course, you’ll explore Python modules and Python packages, two mechanisms that facilitate modular programming. See how to write and import modules so you can optimize the structure of your own programs and make them more maintainable.

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Python’s collections: A Buffet of Specialized Data Types – Python has a number of useful data types beyond the built-in lists, tuples, dicts, and sets. In this tutorial, you’ll learn all about the series of specialized container data types in the collections module from the Python standard library. Learning the collections module is a great way to level up your Python programming knowledge!

Maps With Django: GeoDjango, PostGIS, and Leaflet – This quickstart guide shows you how to create a web map using Django’s GeoDjango module. Data for the map is stored in a PostgreSQL database using the PostGIS extension, and Leaflet, a lightweight JavaScript library for interactive maps, is used on the front-end. You’ll not only learn how to set up the Django application and display the map but also add markers to the map and automatically center the map on the application user’s location.

Moving SciPy to the Meson Build System – In accordance with PEP 632, distutils will be deprecated in Python 3.10 and in Python 3.12 it will be removed. This posed a big problem for SciPy, since it’s build system depends on NumPy’s distutils module — an extension of Python’s built-in distutils. The SciPy maintainers set out to find a new build system and settled on Meson, which solves a number of build issues and even scores a 4x speed-up on build times!

What’s New In Python 3.11 – Python 3.10 is still in beta, but work on Python 3.11 has already begun. Big changes include some major improvements to tracebacks as well as a new cube root function in the math module.

Behind the Scenes: How the Python Import System Works – Importing a Python module is probably one of the most used language features. But Python’s import system remains a mystery to many Python developers, even folks with years of experience. This in-depth article explores how the import system works from the top down. You’ll learn everything from the difference is between absolute and relative imports to how Python searches for modules and packages and resolves naming conflicts.

Using FastAPI to Build Python Web APIs – In this guide, written by FastAPI creator Sebastián Ramírez, you’ll learn the main concepts of FastAPI and how to use it to quickly create web APIs that implement best practices by default. By the end of it, you will be able to start creating production-ready web APIs.

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Support the podcast & join our community of Pythonistas

Have you wanted to get your Python code to consume data from web-based APIs? Maybe you’ve dabbled with the requests package, but you don’t know what steps to take next. This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

We discuss an article titled, “Python’s APIs: A Winning Combo for Reading Public Data”. David shares another Real Python article about creating microservices using Google Remote Procedure Calls (gRPC).

We also cover several other articles and projects from the Python community including, making a difficult data analysis question easy with pandas, efficiently cleaning text with pandas, the tricky bits of Python concurrency, building rich terminal dashboards, making better assertions for Python tests, and building and managing real-life data science projects with metaflow.

Course Spotlight: Making HTTP Requests With Python

The “requests” library is the de facto standard for making HTTP requests in Python. It abstracts the complexities of making requests behind a beautiful, simple API so that you can focus on interacting with services and consuming data in your application. This course shows you how to work effectively with “requests”, from start to finish.

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Python Microservices With gRPC – Learn how to build a robust and developer-friendly Python microservices infrastructure using gRPC and Kubernetes. You’ll also explore advanced topics such as interceptors and integration testing.

Python’s APIs: A Winning Combo for Reading Public Data – Learn what APIs are and how to consume them using Python. You’ll also learn some core concepts for working with APIs, such as status codes, HTTP methods, using the requests library, and much more.

Making a Difficult Data Analysis Question Easy With Pandas – A great strategy to use when faced with a tricky data analysis problem is to reshape the dataset into a format that turns it into an easy problem. In this article, you’ll look at an example involving a simple calculation and extensive reshaping in pandas.

Efficiently Cleaning Text With Pandas – In this article, you’ll see some examples of cleaning text fields in a large data file and learn several strategies for efficiently cleaning unstructured text fields using Python and pandas.

Python Concurrency: The Tricky Bits – An exploration of threads, processes, and coroutines in Python, with interesting examples that illuminate the differences between each.

Building Rich Terminal Dashboards – Learn how to use the Rich CLI library’s new terminal dashboard feature.

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Support the podcast & join our community of Pythonistas

Are you interested in building REST APIs with Flask and SQLAlchemy? This week we have Doug Farrell on the show. We talk about his four-part Real Python article series on Python REST APIs.

We discuss the various Python tools and libraries used in the series. Doug also shares his practices for continuous learning. Doug has worked in process control, embedded systems, and has a long background in software development.

He’s currently a developer at ShutterFly, and discusses developing tools for his internal customers. He also teaches Python to kids at a STEM school near where he lives.

Doug is writing a book for Manning Publications, “The Well-Grounded Python Developer”. The book is currently available in an early access state. And as always please check out all the additional resources and tools that Doug discusses, they are all gathered for you in the show notes.

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What are the core lessons you’ve learned along your Python development journey? What are key takeaways you would share with new users of the language? This week on the show, Duarte Oliveira e Carmo is here to discuss his recent talk, “Four Years of Python.”

Duarte works at the crossroads of machine learning, data science, and software engineering. He began using Python in his graduate studies and never looked back. In 2021, he wrote a blog post about some of the valuable lessons he’s learned. Then he decided the lessons and concepts in the post might make a good conference talk.

We cover the steps in his process of crafting the presentation, practicing it at a smaller conference, and finally presenting it at PyCon Italia last year.

Course Spotlight: Building a URL Shortener With FastAPI and Python

In this video course, you’ll build an app to create and manage shortened URLs. Your Python URL shortener can receive a full target URL and return a shortened URL. You’ll also use the automatically created documentation of FastAPI to try out your API endpoints.

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Level up your Python skills with our expert-led courses:

Support the podcast & join our community of Pythonistas