Older posts
A growing collection of previous posts on artificial intelligence and emerging tech trends.
DESIGN.md: The Missing Contract Between Designers and AI
DESIGN.md is Google Labs' new open specification that gives AI coding agents a machine-readable source of truth for your design system.
Development
From UX to AX: Why Designing for AI Agents Changes Everything
Agent protocols like MCP and A2A are settled, but compliance is not reliability. Here is what changes for design teams, research and metrics in 2026.
UX
What Building an MCP Server Teaches You About AI Integration
Learn how to design, secure and ship Python MCP servers with FastMCP in 2026, covering scope, tools versus resources, transport, auth and production readiness.
Development
Designing MCP Tools an AI Agent Can Actually Use
MCP tool design has become the hidden discipline behind reliable AI agents, and most teams are still writing tool metadata for humans instead of LLMs.
Content design
Inside MCP: Understanding the Client-Server Architecture
MCP defines how AI apps connect to external systems through hosts, clients, servers, JSON-RPC 2.0, and flexible stdio or HTTP transports.
Development
The M × N Problem: Why AI Tool Integration Doesn't Scale
MCP has replaced the M×N integration problem with a single standard protocol, transforming how AI models and tools connect through 2026.
Artificial Intelligence
Model Context Protocol Explained: What MCP Is and What It Replaces
MCP is the open standard connecting AI models to tools and data. Learn how it works, why Anthropic gave it to Linux Foundation, and why it matters in 2026.
Tech
From Prompt to Prototype: How AI Tools Are Changing Product Design and Development
Prompt-to-prototype tools like Lovable, Uizard and V0 have matured in 2026, but export quality and upstream framing decide whether they deliver.
UX
How to Prototype and Test AI Agents Without Building a Production Stack
Reliable AI agents in 2026 depend on the harness around them. Learn how n8n, Langflow, Make and Gumloop support eight essential reliability layers.
Development
Confluence as an AI Knowledge Layer: Grounding Custom GPTs in Enterprise Content
Confluence has become the grounding layer for enterprise AI. Learn how Rovo, MCP, and content design determine whether your GPTs give trustworthy answers.
Knowledge Architecture
How to Debug AI Prompts: From Guesswork to Systematic Prompt Testing
Prompt failures usually come from mismatches between task, context, and evaluation. Here is the disciplined 2026 playbook teams use to fix them for good.
Natural Language Processing
Why Prompts Fail: A Practical Framework for Diagnosing and Fixing LLM Instructions
Most AI agent failures in 2026 originate in the harness, not the model. Learn how to diagnose, classify, and repair prompt failures the right way.
Artificial Intelligence