The philosophy of computational complexity (2018) - there’s a lot of nice links from this one. The article itself deals with philosophy and what it should absorb from computational complexity. And the undercurrent is that both philosophy, and science has a whole, hasn’t really absorbed all that much from the field. Even though it offers a lot of insights and computational limits crop up in a lot of places - from biology, to economics, quantum physics etc. Also includes a nice discussion on various level of “truthyness” of the P=?NP problem, and what that would mean for cryptography and the world in general.

AI winter is well on it’s way (2018) - what is it with AI and overpromising and underdelivering? This would be the second AI winter and third fall of neural networks since the field was invented 50+ years ago. The general trend is that deep learning is not going to solve AGI. Just like SVMs didn’t, or graphical models, or shallow neural networks, or logical models etc. Seems it’s a tough nut to crack.

In defense of skepticism about deep learning (2018) - another “skepticism” camp article about the current deep learning dominance of machine learning and AI research. This one is a more pop-sci answer to a series of questions posted by the author in a scientific publishing setting. The focus is on the flaws of deep learning as a path to AGI mostly.

After 5 years and \$3M, here’s everything we learned from building Ghost (2018) - Ghost is an open source CMS, which competes with the likes of WordPress, Medium etc. It’s pretty well liked in the community, and I’m glad they’re doing alright. I’m also glad to learn that the project is altruistically organized as a charity with very social goals, rather than strictly profit-seeking ones. Other than that, the main takeaway is that building a large OSS project is hard.