The AI industry has rapidly shifted from standalone LLM chatbots to autonomous agents that take real actions – and a whole new vocabulary has emerged around MCP (Model Context Protocol)…
Every backend engineer knows the feeling. You’re deep in flow state, the architecture is crystal clear in your head, and the code is coming out clean. Then you spot it…
The AI industry is increasingly deploying small language models for production workloads. This shift reflects a change from prioritizing the biggest model to selecting the right-sized model for business requirements.…
Fragmented AI tooling creates unnecessary complexity and duplicated work. Before MCP, developers often had to build separate integrations for each AI platform, creating unnecessary complexity, duplicated work, and platform lock-in.…
Using AI tools is becoming very expensive. Instead of broader usage, there are pushbacks from companies to limit their AI spending. Uber burned through its entire 2026 AI coding tools…
Building your first AI agent in 2026 is not the same as calling an LLM API. While a standard LLM call is a static request-response cycle, an agent has the…
In the realm of enterprise AI systems, one often-overlooked yet critical component is AI agent permissions. These permissions are the backbone of operational correctness and security, ensuring that autonomous AI…
AI-ready backends require three capabilities: streaming response support, vector storage access, and async concurrency. These capabilities work together to create a robust and efficient backend that can handle the demands…
The next major AI wave is shifting from public chatbots toward internal enterprise AI tools. For too long, businesses have been experimenting with public-facing chatbots, often with limited success. However,…
