Fullstack Developer
Building innovative digital solutions at AvXion
Most AI agents fail not because models are weak, but because context is treated as text instead of as a managed system resource. This article breaks down how long-running agents should engineer memory, context, and tool selection to remain stable over time.
Most AI systems don’t fail because large language models are weak or prompts are poorly written. They fail because context is treated as text instead of infrastructure. This article explores why hallucinations, memory loss, and tool misuse are usually context failures, not model failures—and why context engineering is emerging as a core systems discipline for building reliable, scalable AI applications.
As systems grow, tightly coupled services start to fail under load. This article explains why Pub/Sub becomes inevitable in scalable architectures, drawing from real production problems, engineering lessons, and how experienced teams use event-driven design to build resilient, high-growth systems.
Netflix operates one of the most complex and resilient distributed systems in the world. This article explores how Netflix scaled its architecture to serve hundreds of millions of users globally—covering cloud migration, microservices, data distribution, content delivery, chaos engineering, and the real-world challenges Netflix faced while building a fault-tolerant streaming platform.
Discover practical best practices for integrating AI ethically into your workplace. Learn how to boost productivity while addressing bias, privacy, and fairness in 2025's AI landscape.
Many developers wonder why Model Context Protocol (MCP) is needed when AI tools already work. This article explains what MCP solves, when to use it, and how it enables scalable, reliable, and secure AI agent systems in real-world applications.
AI coding agents are reshaping how software is built. Meta’s acquisition of Manus signals a shift toward agent-driven development, where AI plans, codes, tests, and iterates alongside developers. This article explores what it means for modern software engineers and how to adopt AI agents responsibly.
Modern systems demand **speed, scalability, and strong contracts** between services. REST APIs have served us well, but as systems evolve into **microservices**, REST begins to show limitations in performance, type safety, and real-time communication.
