A follow-up on building a generative art system in Go, showcasing new engines like Perlin Pearls, Swirl, and Cells, along with expanded color palettes to create richer, more varied generative artworks.

Took on the challenge of shrinking my site as much as possible. From trimming scripts to rethinking images, I learned a lot about where the real weight comes from, and how far optimization can go.

Generative art shifts the focus from drawing images to designing systems. Instead of sketching directly, you define algorithms, randomness, and rules, then let the system produce the output. For me what makes it interesting is the fact that you don’t just create one piece, but a machine capable of generating infinite variations.

A detailed comparison of gzip, Snappy, LZ4, and zstd compression algorithms, analyzing their impact on storage savings, performance, and cost based on real-world data benchmarks.

A deep dive into common compression algorithms like gzip, Snappy, LZ4, and zstd, explaining their trade-offs in speed, compression ratio, and ideal use cases for data engineering pipelines.

Explore DuckDB, an in-process SQL OLAP database, as a powerful alternative to Spark for local data analytics. Learn how it achieves high performance, manages large datasets, and its ideal use cases for data engineering.

Introducing kafka-replay-cli, a lightweight Python tool for Kafka message replay and debugging. Learn about its features for dumping, replaying, and querying Kafka data, and the architectural decisions behind its development.

Dive into Kafka producers, exploring how they handle message partitioning, serialization, and batching for optimal throughput. Understand delivery guarantees, idempotent producers, and transactional writes for reliable and exactly-once message delivery.

Understand how Kafka consumers work, from their pull-based data fetching and offset management to how consumer groups enable parallel processing and scalability. Explore different message delivery semantics.

Discover the core architectural decisions and OS-level optimizations that enable Apache Kafka’s exceptional high throughput, including append-only logs, sequential I/O, zero-copy transfers, and message batching.