For the past few months, I’ve been building a home NAS for my family, mainly to store the terabytes of photos we have lying around in different hard drives all over the place. I’ve always wanted a place to store/backup all my university and work-related files too, and setting up this home server solved all my needs. However, while everything works fine when I’m under the same local network, it would be nice if I have access to my network drives when I’m outside at work, and even better when I can backup my photos while I’m on vacation somewhere else.

The other day I was working on generating a synthetic dataset using an LLM to evaluate another model that my company is currently developing. The workflow involves asking the LLM to read an article and extract some useful keywords from what it reads. I was using Google’s Gemini 1.0 Pro for the task and it understood the question fairly well, the only problem left is to find a way to format the model’s response in a structured way (like a JSON) for the response to be used later.

Sometimes I find myself having to navigate between multiple projects. And since my main working laptop is on Windows, naturally I tried to customize my command line tool (I use PowerShell because WSL is just way too slow) to jump between directories faster. My folder organization looks something like this: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 home │ ├───Desktop │ │ file011.

Continuing from last week’s discussion on the volume of a hypersphere, we’re going to take a look at how to uniformly sample points on a 3D sphere (also applies to N-dimensional Hyperspheres), and the common pitfall that comes with it. Let’s say you are building an algorithm that spawns Pokemons at random locations all around the globe for the game Pokemon Go. The first thing you might do is to uniformly sample locations on Earth to spawn your Pokemons.

This week I was watching Netflix’s latest sci-fi adaptation of Three Body Problem (SPOILER ALERT). The story revealed how the San-Ti alien race built the Sophon, a supercomputer the size and mass of a single proton, to sabotage the scientific advancement on Earth. While the TV series didn’t dive too deep into the methodology behind it, the novel (strongly recommend!) described how the San-Ti engineers were able to take advantage of the dimensions of these particles.

Normalization has been a standard technique for vision-related tasks for a while, and there are dozens of different strategies out there. It can be overwhelming to try to understand each of them. Recall that no matter what strategy we pick, the goal of normalization is to “shift” the target samples into a certain distribution. This is usually good for stabilizing training, as normalization standardizes the input. Let’s look at how the top 4 most used normalization strategies work, and why you might choose one over another.