We optimized for code-generation speed while the real bottleneck — cognitive overhead and knowing where to make changes — remained completely untouched.
LLMs can quickly generate web application code, but AI-written code may contain security vulnerabilities. This article reviews testing methods for LLM systems.
This post traces that journey using triangular number computation as a practical example of intentional fall-through and connects the technique to Duff's Device.
Learn how to generate documentation using an LLM with mdship, and how to ensure that the prompts, which are now the source documentation, do not get lost.
Three structural shifts enterprise data security teams should make in 2026, based on verifiable data and a decade of experience building protection products.
If you’re a backend engineer working with AWS and curious about how we went from autocomplete-style AI to agentic, this one breaks down the architecture shifts.
Blockchain and data streaming are bringing unprecedented levels of security, transparency, and real-time mechanisms to move data across the digital world.
A walk-through of the new JDWP-based on-device debugging pipeline for ParparVM iOS apps and Android apps, with a step-by-step IntelliJ tutorial for each.
Finding bugs is what testing produces; understanding quality is why it exists. QA's future belongs to those who understand products, customers, and risks, not just bugs.
RAG helps AI retrieve relevant data. GraphRAG connects entities and relationships. Context engineering turns both into accurate, safe, production-ready AI systems.
Use workflows for control, agents for flexibility, and multi-agent systems only when complexity truly demands it. Add intelligence only where it makes a real difference.