AI & LLMs

The numbers tell the story: in November 2024, Model Context Protocol server downloads hovered around 100,000. By April 2025, that figure exploded to over 8 million. By early 2026, researchers documented 3,238 MCP-related GitHub repositories, while the broader AI ecosystem saw 4.3 million AI-related repositories—a 178% year-over-year jump. MCP didn’t just grow; it became infrastructure. What started as Anthropic’s solution to a specific problem—how to connect Claude to external data sources without building custom integrations for every system—has evolved into something far more significant. MCP is now the de facto standard for AI-tool integration, the “USB-C for AI” that the industry didn’t know it needed until it arrived. ...

Every time you increase a Transformer’s context window from 4K to 128K tokens, you’re asking the attention mechanism to compute a matrix 1,024 times larger. The O(n²) complexity isn’t a bug—it’s fundamental to how self-attention works. Every token must attend to every other token, creating a quadratic relationship that makes long-context models prohibitively expensive. Mamba, introduced by Albert Gu and Tri Dao in December 2023, doesn’t just optimize around this constraint. It eliminates it entirely, replacing attention with selective state space models that scale linearly O(n) while matching Transformer quality. A Mamba-3B model outperforms Transformers of the same size and matches Transformers twice its size. The key insight? Making the model’s memory mechanism input-dependent—letting it choose what to remember and what to forget. ...