Your guide to Mostly Harmless Ideas
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Your guide to Mostly Harmless Ideas
An up-to-date index of everything you can read in this Substack. Start here if you are new, or if you want to review past articles you may have missed.
Welcome!
This is an updated list of the most relevant content I’ve posted as of November 2023. These are mostly long-format, evergreen, educational posts on many topics in Computer Science written for a broad audience. This summary doesn’t include discussions, insider posts, or other casual articles. You can always see everything in the archive.
If you’re new here, this is a guide to exploring the topics I’ve written about. If you’re a regular reader, this may be a chance to find an interesting older post you might have missed or to fill any gaps in previous articles you’ve read.
I’ll keep this post updated with links to all articles organized in a way that’s easy to browse, depending on your interests and background, and I will send bi-monthly updates via email to you.
This post is rather long and will probably get cut in your email. You can read it online to make sure you see everything.
I hope you find something interesting to read!
The Mostly Harmless Newsletter
The Mostly Harmless Newsletter is the only subscriber-exclusive content I write regularly. It’s a bi-weekly article discussing some relevant, recent AI and Computer Science topics.
These articles are more opinionated and less evergreen than the rest. I write them for an audience interested in staying up-to-date with current trends but looking for deeper discussions that bypass the prevalent hype in the field. If you think this would interest you, here’s a special discount that is also the best way to support my writing and allow me to spend time on educational content.
Here are the latest issues:
There are many more benefits to a paid subscription, but if you can’t or don’t want to get one, you can always buy individual articles using this Telegram bot.
Introduction to Computer Science
Articles in the Intro to CS section are meant to work as introductory topics on Computer Science, suitable for all audiences. I write them mainly thinking about people coming from outside to the world of Computer Science: students, professionals from other domains, journalists, or just laypeople interested in knowing what this is about.
You can start reading the following post that summarizes the whole field:
Foundations of Machine Learning
Once you read that big post, you can explore the series on the Foundations of Machine Learning.
The next two articles are already in the making. They will discuss several modeling paradigms and techniques to evaluate and compare different models.
(This is an ongoing series. I will link to all posts here as they are published.)
Tech Tuesdays
Tech Tuesdays are a regular section I’m doing on short, intuitive explanations for complex Computer Science topics. So far, they cover topics from computability theory, complexity, and machine learning.
Individual articles
I also write individual articles on specific algorithms or concepts. These need not be read in any specific order.
Technical Guides
In the Guides section, you will find how-to articles related to technical writing. The first article in the series details a loose process you can use for writing technical articles:
The second article explains the why-what-how framework to structure technical articles to ensure your reader is engaged.
Essays on Science and Education
Essays are opinionated on the subjects I care about most, mostly science and education. I wrote a long rant on improving peer review by adopting an open-source ethos.
And I’ve written a few essays on teaching principles and education in general. These two are about changing the mindset from individual quantitative evaluation to a more team-based and project-based framework.
This one is about how to structure educational content, from theory to applications or the other way around, depending on the nature of the material.
This essay discusses the benefits and drawbacks of nurturing a competitive mindset in computer science education.
The following one is about why writing online is actually good for you, regardless of whether you get readers or not.
And the last one is a rather emotional rant that I wrote during a very scary medical issue for my youngest daughter.
Great Ideas in Computer Science
The Great Ideas in Computer Science section is a long-term project of mapping out the history of Computer Science from the point of view of the most relevant ideas in the field. It will be divided into arcs or series of 5 or so posts.
The first series is the Origin Arc, a story that spans from ancient computing devices to Alan Turing building the first general-purpose computer and establishing the foundational theory of the field.
The first entry in the series focuses on Leibniz’s dream of a computational language.
(This is an ongoing series. I will link to all posts here as they are published.)
Philosophy of Computer Science
The Philosophy section is where I explore the philosophical implications of Computer Science. The first article is about the nature of thinking, a non-traditional reading of TUring’s seminal paper where he defines the Turing Test.
This second post is a detailed exploration of the notion of Truth, and how it can (or cannot) be defined objectively for any domain.
Other stuff
These are two written exchanges I had with incredible thinkers about various technical and philosophical topics.
This is a short index to some pretty cool Twitter threads I made back in the days when Twitter was cool.
And this is a rather silly post I wrote for my 34th birthday with some of the most important lessons I’ve learned about life.
Final words
This is not a comprehensive list of everything I’ve written but an overview of the most relevant and, in my opinion, exciting posts. You can check everything else in the archive.
I hope these links provide you with some insightful or at least intriguing topics to read about. Let me know if you want me to dig deeper into these topics. The opinion of readers like you fuels my desire to write more.