A Java geek

There is an evergreen debate in the Java world: should you always use BigDecimal for money? The short answer is no. The real answer is: it depends on your computational context: the precision you need, the rounding rules you must follow, and the performance budget you have. The problem is that this conversation is often driven by dogma rather than engineering.

In the first post of this series, I focused on the project foundations: what should I do to create a video from photos taken from the same position year after year? I dedicated the second part to aligning images. It wasn’t as easy as I expected. I stumbled upon new concepts, such as ORB and RANSAC. In this third and final post, I want to tackle the video creation itself, explain some 'artistic' decisions, and leave the door open to future work.

Before working for 2 years on the Apache APISIX API gateway, I was mainly oblivious to API gateways. It’s only by working with them that I understood their value. Decoupling the client and the server unlocks a lot of options: moving authentication to the API Gateway, securing APIs, deduplicating API requests, etc. In this post, I want to describe how the same pattern applies to AI. AI gateways AI gateways work in a similar way.

In the previous post, I described the Seasons project: a time-lapse of hundreds of pictures taken from nearly the same viewpoint over the years. The hardest challenge wasn’t taking the pictures or assembling them, but aligning them. You might have noticed the nearly part about viewpoint in the above paragraph. Indeed, it’s an approximation. I’m a human being, not a tripod. The position changes ever so slightly, and so does the exact angle.

I live close to nature. I regularly go for a run in the countryside. Over several years, during my runs, I’ve taken pictures from the same position, always roughly the same angle. I had a vague idea in the back of my mind, as an 'artistic' project. One day, I’d turn those photos into a time-lapse video, one that would show the passage of seasons across a single place. Spoiler, here’s the work in progress: However, I knew that this project would take ages.

Good engineers make decisions based on data. Most businesses assumed that the more data, the better the decision. Then, several factors put a halt to the hoarding of ever more data. GDPR and its localized counterparts, and the cost of storage. However, before the GDPR came into effect, the Datensparsamkeit approach already existed. Datensparsamkeit is a German word that’s difficult to translate properly into English.