I often advocate people surrounding me to build their own side projects. I believe they can fulfill you in so many ways: for your career, your relationships, or your independence. Buffer, the company I currently work for, was itself a side project.
I’ve built myself multiple side projects. The major ones being PartyInBeijing (inactive), Nodablock (inactive), Citymayor (need to fix), and now TravelHustlers. I believe each of them contributed and lead me to my current situation: being satisfied in my career, financially, and socially.

The best designers employ specific habits, learned practices, and observed principles when they work. Here are a few of them.

1. Experts involve the user
2. Experts design elegant abstractions
3. Experts focus on the essence
4. Experts simulate continually
5. Experts look around
6. Experts reshape the problem space
7. Experts see error as opportunity
8. Experts think about what they are not designing

(read the article: nice pictures to visualize the different concepts :-))

AI research is making great strides toward its long-term goal of human-level or superhuman intelligent machines. If it succeeds in its current form, however, that could well be catastrophic for the human race. The reason is that the “standard model” of AI requires machines to pursue a fixed objective specified by humans.

We are unable to specify the objective completely and correctly, nor can we anticipate or prevent the harms that machines pursuing an incorrect objective will create when operating on a global scale with superhuman capabilities. Already, we see examples such as social-media algorithms that learn to optimize click-through by manipulating human preferences, with disastrous consequences for democratic systems.

Somewhere around 2014 I found an /etc/passwd file in some dumps of the BSD 3 source tree, containing passwords of all the old timers such as Dennis Ritchie, Ken Thompson, Brian W. Kernighan, Steve Bourne and Bill Joy.

Keyword extraction (also known as keyword detection or keyword analysis) is a text analysis technique that consists of automatically extracting the most important words and expressions in a text.

It helps summarize the content of a text and recognize the main topics which are being discussed.

Calculate digits of π. From scratch, that is, using only addition, subtraction, multiplication, and division.

Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps.

Bayes Theorem provides a principled way for calculating a conditional probability. It is a deceptively simple calculation, although it can be used to easily calculate the conditional probability of events where intuition often fails. Bayes Theorem also provides a way for thinking about the evaluation and selection of different models for a given dataset in …

Finally, after many people have asked for it: here is part two of the mutt-series: this time we talk about actually using and configuring mutt, and some other smaller tools needed for a nice email-workflow.

The link to part one is reported in the beginning of the article.

GitHub announces the CodeSearchNet Challenge and releasing a large dataset for natural language processing and machine learning.

Publishing a paper in academia is challenging, stimulating, and a bit baffling. Challenging because the research might fail. Stimulating because research may start assuming one outcome and finish with a totally different one. Baffling because after the paper is written and ready, I have to find it a home for

A new release from OpenAI shows how complex behavior emerges.

This week, leading AI lab OpenAI released their latest project: an AI that can play hide-and-seek. It’s the latest example of how, with current machine learning techniques, a very simple setup can produce shockingly sophisticated results.

A set of useful links ti time series labeling tools.