12 articles exploring ai
A platform-agnostic how-to for building a disciplined personal content system with voice definition, pillar tracking, research libraries, and AI discoverability built in from day one.
Most AI coding tool comparisons still reward the wrong things. A workflow-first breakdown of Claude Code, Cursor, Copilot, Windsurf, and Antigravity through the lens that actually matters: how teams ship under real constraints.
AI-generated code feels fast, but the maintenance cost appears later. Why AI creates locally correct but globally fragile systems, and the engineering standards that fix it.
Why one-off prompting does not compound, and how to move from isolated prompts to repeatable AI workflows using playbooks, MCP data sources, and action layers.
A practical blueprint for structuring Claude Code projects so they stay predictable as they grow. From folder layout and .claudeignore to prompts, skills, and AI-friendly component patterns.
The MCP servers that matter most for real AI leverage: analytics, email, calendar, GitHub, databases, observability, SEO, social, docs, and file storage. Plus practical playbooks for turning them into repeatable workflows.
The 10 Claude Code skills that now separate developers who merely generate from those who ship differentiated products. From UI taste and frontend structure to brand systems and skill creation.
How six companies are building the future of geospatial systems—from real-time Earth monitoring to predictive intelligence. The stack replacing static maps with decision engines.
The 15 AI researchers, builders, and thinkers worth following on X in 2026. Cut through hype with voices from OpenAI, Meta, Stanford, and the venture ecosystem.
16 concrete strategies to reduce token consumption by 60–90% while keeping Opus and Sonnet actively predicting. From .claudeignore to multi-agent architectures.
AI fundamentally changed the unit economics of software development. Discover how the most successful Series A founders are architecting for this shift to win at better valuations.
After auditing dozens of AI programmes, the pattern is identical: companies optimise for technical metrics that boards don't care about. Here's how to fix the framing.