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10 posts
MCP standardizes how AI agents talk to tools and data. What it actually solves, how the pieces fit together, and where it fits next to n8n-style automation.
AI features moved from developer tools into photo, video, and banking apps in 2026. Here is what that shift means for building consumer-facing products.
AI-assisted development lets one developer credibly own product, design, frontend, and content. Here is what that actually requires versus what it removes.
App Router file-based conventions that make Next.js predictable for humans also make it easy for AI agents to navigate. Here is why that is not a coincidence.
AI meeting-notes tools promise to remove note-taking entirely. Here is what that automation gets right, where it fails quietly, and how I would build it in n8n.
AI coding assistants stopped being autocomplete once they could read a codebase and plan multi-file changes. Here is what actually changed in how I work.
Local LLMs via Ollama trade raw capability for privacy, cost, and offline access. Here is how I actually decide between local and API-based models.
AI writes implementations faster, but that raises the value of tests instead of lowering it. Here is the testing discipline I apply to AI-generated changes.
Vibe coding gets code running fast, but running and correct are different claims. Here's the gap I actually check before shipping AI-generated code.
Reusable n8n + Groq automation patterns — prompt chaining, structured output, and rate-limit handling — from workflows I actually run.