Insights
Ideas for building
better software.
Engineering perspectives on architecture, product development, AI, and the everyday decisions behind useful software.
From the engineering desk
Browse practical articles or follow the latest technology updates.
Subscribe via RSS →Curated links from external sources — not 360Softy original articles.
Run Kimi K3 using 29 GB of RAM at 0.50 tok/s
Comments
The physics of Docker build caching
Comments
Dispatches from O'Reilly: The best risk mitigation strategy in data? A single source of truth
Your semantic layer is a risk mitigation strategy. Not risk in the abstract, compliance-framework sense, but the practical, operational risk that quietly drains organizations every day..
What’s !important #16: sibling-index() Animations, Use Cases for the infinity Keyword, Container Stuck Queries, and More
The soon-Baseline sibling-index() function for animations, CSS and the 2026 FIFA World Cup, use cases for the infinity keyword, container "stuck" queries, and more. What’s !important #16: sibling-index() Animations, Use Cases for the infinity Keyword, Container Stuck Queries, and More originally handwritten and published with love on CSS-Tricks. You should really get the newsletter as well.
AI scammers outperform humans when it comes to building trust
The AI chatbot was more effective at creating “exploitable trust” than the humans.
ESET tracks rise in malicious AI skills and adaptable malware
Attackers are adapting established techniques to AI platforms, emerging technologies, and changing user behavior. ESET's new threat report examines the rise of malicious AI skills, AI-assisted malware, ClickFix attacks, record quishing activity, and ransomware tools designed to disable security software. [...]
Spark Performance Deep Dive on Databricks: Shuffle Tuning, Skew Handling, and Z-Ordering With Delta Lake + Unity Catalog
The Problem With "Just Add More Workers" Most Spark performance issues on Databricks aren't solved by scaling the cluster — they're caused by shuffle and skew, and no amount of extra nodes fixes a badly partitioned join. This post builds a realistic pipeline (order events joined against a small dimension table, aggregated, and written to Delta Lake) from the ground up, and uses it to work through: How Spark's shuffle actually behaves during a wide transformation Diagnosing and fixing data skew w
Let’s start with a conversation
Tell us what you’re working on.
An idea, a challenge, or a system that needs to work better. We’ll help you understand the next step.
Prefer email? [email protected]