Modify URI-based API versioning to use date-based versions, easing operations, ensuring immutability, and also separating core logic from API responses.
The blog introduces you to the four pillars of observability, AWS and Azure cloud-native services, and ROI to help in architects and engineer's quest for system clarity.
Agile teams often produce more reports than they need. This article explains how reporting overload happens and provides steps to build a high-value reporting system.
This article examines how integrating AI into the software development lifecycle (SDLC) is enabling teams to move from MVPs to large, resilient systems.
HAIP 1.0 mandates signed requests (JAR), encrypted responses, and certificate-based verifier identity for VP flows. Here's how to approach it with Spring and Android.
AI Agents perceive, reason, plan, and act autonomously using LLMs. This article breaks down the core components that power every agent and shows you how to build one.
AI-enhanced code review systems use embeddings and LLMs in Git hooks to catch repetitive issues, freeing human reviewers to focus on higher-level architectural decisions.
The A3 Framework helps teams decide when to Assist, Automate, or Avoid AI by categorizing work before prompting, reducing risk, and safeguarding trust.
This article provides a practical guide to building a fault-tolerant Google Cloud data pipeline architecture with Firestore, Pub/Sub, Dataflow, and BigQuery.
Compare planning and execution times for similarity searches using trigram matching, case-insensitive regex and wildcard patterns, with and without GiST or GIN indexing.
Citizen application development is enabling every creator to convert ideas into strategic, enterprise-level innovation with unmatched speed, clarity, and control.
AI can’t transform logistics without a standard protocol. LCP lets carriers and shippers share a common digital language, enabling large-scale, intelligent supply chains.
ML systems introduce security risks most teams aren’t prepared for. The piece explores emerging ML-specific threats and what effective MLSecOps looks like in practice.