A video editor with motion smoothing.

Super cool post on how to use electric grid frequency variations to match them into recorded sound clips, and basically allowing to timestamp the clip.

Codon is a high-performance Python compiler that compiles Python code to native machine code without any runtime overhead. Typical speedups over Python are on the order of 100x or more, on a single thread. Codon supports native multithreading which can lead to speedups many times higher still.

Twenty five years ago, The Big Lebowski blew into theaters like a tumbleweed on an empty street.

Domestic audiences barely showed up, with the comedic detective tale only earning $18 million. Audiences gave it a B CinemaScore. Critics sniffed that it wasn’t as good as Joel and Ethan Coen’s last release, the Oscar-winning Fargo.

But that was just, like, their opinion, man.

“I thought it was going to be a big hit,” star Jeff Bridges tells THR, along with sharing some of his personal behind-the-scenes photos from the film’s set, many of which appeared in his 2003 book, Pictures. “I was surprised when it didn’t get much recognition. People didn’t get it, or something.”

Comprehensions are a fantastic language feature in Python. They are an elegant alternative to manually constructing and populating data structures. Comprehensions are declarative – they just say what they are, as opposed to the implicit logic of manual looping. When it comes to simple object creation, comprehension should be used whenever possible. This goes not just for lists, but also for dictionaries and sets.

However, a widely perceived drawback to comprehensions is that they are harder to debug. When something goes wrong with a manual loop, the first thing to do is to print out the iterated values as they turn up. But the values of a list comprehension can’t be accessed, so print-debugging isn’t possible. To deal with this, it’s common to unravel the comprehension into a manual loop. Manual loops are uglier and more complicated and more error-prone than comprehensions, but that’s the price that must be paid for debuggability.

Cycling is one of the most sustainable modes of transportation. Increased ridership reduces fossil fuel consumption and pollution, saves space, and improves public health and safety. However, the bicycle itself has managed to elude environmental critique. Studies that calculate the environmental impact of cycling almost always compare it to driving, with predictable results: the bicycle is more sustainable than the car. Such research may encourage people to cycle more often but doesn't encourage manufacturers to make their bicycles as sustainable as possible.

Over many centuries, man’s obsession with time has led to increasing accurate ways to measure it. As the precision of techniques and instruments has increased over the years, so the units used to define time have become ever smaller.

The minute and the second have given way to the SI units of the millisecond, the microsecond, the nanosecond, the picosecond, and the femtosecond, which is the the length of time that light takes to travel the diameter of a virus.

Large Language Models (LLM) are on fire, capturing public attention by their ability to provide seemingly impressive completions to user prompts (NYT coverage). They are a delicate combination of a radically simplistic algorithm with massive amounts of data and computing power. They are trained by playing a guess-the-next-word game with itself over and over again. Each time, the model looks at a partial sentence and guesses the following word. If it makes it correctly, it will update its parameters to reinforce its confidence; otherwise, it will learn from the error and give a better guess next time.

Projects are fraught with uncertainty, so it is no surprise that the language and tools of probability are making their way into project management practice. A good example of this is the use of Monte Carlo methods to estimate project variables. Such tools enable the project manager to present estimates in terms of probabilities (e.g. there’s a 90% chance that a project will finish on time) rather than illusory certainties. Now, it often happens that we want to find the probability of an event occurring given that another event has occurred. For example, one might want to find the probability that a project will finish on time given that a major scope change has already occurred. Such conditional probabilities, as they are referred to in statistics, can be evaluated using Bayes Theorem. This post is a discussion of Bayes Theorem using an example from project management.