Hi, I'm Dennis. I'm a software engineer and here I write about things I dig into - mostly AI/ML, Wasm, and Java. I share what I learned to help others and my future self. You can read more about me on the About page.
5 practical use-cases for an ML-powered RSS digest
Beside creating an ML model for an RSS digest the other fun way is to actually use it. Here I take a look at the different possible use-cases.
Optimizing a model by using metrics
This post shows how to use metrics for optimizing a model and how it helped to improve the AUC from 0.59 to 0.84.
Selected reading
- AI/ML Implementing Retrieval-augmented generation (RAG) with an own LLM
- AI/ML Using LLMs for test data generation
- AI/ML Generating recommendations for blog posts with machine learning
- Wasm Wasm Component Model By Example
- Wasm Under the surface of WASI in Rust
- Wasm Creating an own WASI function
- Software Engineering Hexagonal Architecture
- Software Engineering Imperative vs. declarative programming
- Java How Spring MVC handles requests
- Java Java CRaC
- Java Preventing TOCTOU attacks
Defining, tracking and training a machine learning model
Defining, tracking and training a machine learning model enables us to predict the articles
Building an RSS digest: All posts
This aims to give a better structure to read the posts about the RSS digest implementation.
Note taking in 2026
A look at my note taking process in 2026
Dataset preparations for training a machine learning model
When the features are collected, they can be used for training a model. This post takes look at the preparation of the training pipeline.
Training two models for better results in recommendation systems
Training one model for two different intents can lead to issues in recommendation systems. Two models trained on different signals can help to omit this issue.