Boris Cherny of Anthropic outlines nine ways Claude squanders 73% of your tokens before processing your prompt, including base model overhead, re-reading history, and forgotten hooks. He debunks “Claude got dumber” complaints and shows how to spot and fix these token drains.

This article breaks down the core concepts behind LLMs—from next-token prediction training to tokens, vectors and attention layers—to show how they generate text. It also covers context windows, parameters and why model scale affects performance.

This article explores the overwhelming failure rate of crypto tokens, revealing that over 99.99% have effectively failed. It discusses the concentration of value in a few top tokens and the ease of creating new tokens, which contributes to the noise in the market. The author emphasizes the importance of focusing on established assets like Bitcoin and Ethereum.

As the new year begins, the author reflects on a more active approach to cryptocurrency trading while maintaining a lean portfolio. Highlighting the AI Agent sector and upcoming protocols like x402 and ERC-8004, they present four low-cap tokens worth researching, emphasizing the potential for gains despite the overall market's risks.