Transformers: Impressive, but Really the Future?

Why the Transformer architecture hits its limits with simple arithmetic and why a paradigm shift is needed.

Best of: Claude Code – Agents, Hooks & Git Magic

Key learnings from community projects and docs for a robust, reproducible AI dev pipeline with Claude Code.

Common Crawl: Gold for the Data World

What Common Crawl is, what it contains, and why this open web archive is essential for AI training, NLP, and data …

Excel Is Not AI Food – It Is the Packaging

Why an MCP server around Excel data is the most effective way to let AI work precisely and cost-efficiently with …

MoE ≠ Less RAM -- But More Speed ⚡️

Why Mixture-of-Experts doesn't reduce memory on end devices, but primarily boosts throughput -- and what that means for …

🔍 Transformer Explainer: Understand LLMs -- Without Mystifying Them

How the Transformer Explainer interactively shows what really happens inside large language models -- and why that leads …

🔥 Claude Code in Practice: Hooks, Subagents & Multi-Agent Power

Claude Code delivers real workflow features for dev teams: hooks, subagents, multi-agent orchestration, and repo context …

🚀 Lightweight, Powerful and Versatile: The New Gemma 3 270M Model

Google's Gemma 3 270M shows that AI can be capable with just 270 million parameters -- ideal for local applications, …