Typosquatting on PyPI: Malicious Package Mimics Popular 'bro...
Socket detected a malicious Python package impersonating a popular browser cookie library to steal passwords, screenshots, webcam images, and Discord ...
Securing gRPC Services with JWT Authentication in Go
gRPC provides several mechanisms to enhance the security of your services. In this blog post, we'll explore how to implement authentication in gRPC, focusing on interceptors, metadata, and JWTs (JSON Web Tokens) as the authentication mechanism.
Besides retries, circuit breakers1 are probably one of the most commonly employed resilience patterns in distributed systems. While writing a retry routine is pretty simple, implementing a circuit breaker needs a little bit of work. I realized that I usually just go for off-the-shelf libraries for circuit breaking and haven’t written one from scratch before. So, this is an attempt to create a sloppy one in Go. I picked Go instead of Python because I didn’t want to deal with sync-async idiosyncrasies or abstract things away under a soup of decorators.
Optimizing Postgres table layout for maximum efficiency
Introduction When modeling a Postgres database, you probably don’t give much thought to the order of columns in your tables. After all, it seems like the kind of thing that wouldn’t affect storage or performance. But what if I told you that simply reordering your columns could reduce the size of your tables and indexes by 20%? This isn’t some obscure database trick — it’s a direct result of how Postgres aligns data on disk.\n
bjesus/pipet: a swiss-army tool for scraping and extracting data from online assets, made for hackers
a swiss-army tool for scraping and extracting data from online assets, made for hackers - GitHub - bjesus/pipet: a swiss-army tool for scraping and extracting data from online assets, made for hac...
Building a Single-Page App with htmx | jakelazaroff.com
People talk about htmx as though it's saving the web from single-page apps. Well, I guess I missed the memo, because I used htmx to build a single-page app.
This blog post debunks six common myths surrounding pg_vector, a popular Postgres extension for storing and querying vectors, which is often misunderstood. It clarifies that while vector indexes can enhance performance, they are not always necessary, and different types of vector indexes serve distinct purposes. The post also addresses misconceptions about the limitations on vector dimensions, the evolving nature of pg_vector, and its compatibility with sparse vectors like BM25. Through practical examples, the post highlights how pg_vector offers flexibility in vector storage and retrieval, making it a powerful tool beyond just RAG applications.