Scalable Machine Learning Service

Machine learned models can be really big, like the multi-billion weight GPT models, there is a chance they contain sensitive data and their output need to be sanitized or something third. This post is on the fundamentals of a scalable ML architecture.

Keep reading

Fraud and Networks

The Case: As a financial institution we want to avoid fraud. Fraud is a broad term, so in this case, we focus specifically on identity theft. Ie. We want to be able to quickly detect oddities signaling that someone's identity is being used illegally.

Keep reading

The MVP Engineer

An MVP engineer helps scope a product, build the product to verify its business potential, set up a team and lastly scope a road map for further product development.

Keep reading

Objects and State

Object oriented programming is inherently stateful. This fits well for some applications and can confuse and reduce reliability for others.

Keep reading

Introducing Second Brains

Introducing Second Brains

Seconds brains have reached quite the hype. Both with tools like RoamResearch, Obsidian and friends. Needless to say, it is a good tool. Since I started building my second brain, I have been journalizing more often than not.

Keep reading

Building a Scraper

Scrapers are integral to data intensive applications. They span real development projects, and, as such, there are key architectural decisions to make.

Keep reading