CoolDeep AI Digest

The article argues that many users treat Claude merely as a conversational search box, repeatedly starting each session from scratch and thus missing most of its capabilities. The author realized this after seeing a list of 100 Claude tips that revealed powerful features he had never used, despite running an AI newsletter. He describes his old workflow—opening a new chat, pasting context, asking a question, copying the answer, and closing—as inefficient and costly, because Claude had no memory of his voice, projects, or past work. The solution is to build infrastructure around Claude so that context, workflows, and tools persist across interactions. Key tools and features highlighted include: Projects (to store brand voice, guidelines, and reference documents so every chat inherits that context); Skills (saved workflows that encode repetitive instructions like tone, length, or formatting, eliminating the need to re‑explain them); MCP Connectors (which let Claude access real‑time data from Google Drive, Gmail, GitHub, Slack, or the web via the Claude in Chrome extension); Memory (a setting that lets Claude learn from chat history to build a personal profile over time); Claude Code (a terminal‑based tool for serious development work, superior to the chat interface for coding); Prompt Caching (reduces API costs by caching repeated system prompts or reference documents, saving up to 90%); and the Batch API (offers 50% cheaper processing for non‑urgent jobs with a 12‑24 hour turnaround). The author’s practical takeaways are to start small: turn on Memory immediately, then create a Project for your work, followed by building a Skill for a repeated task, then gradually add connectors, explore Claude Code if you develop, and finally leverage Prompt Caching and the Batch API to cut API expenses. By layering these features, users shift from using Claude as a disposable search engine to leveraging it as a compounding AI platform that saves time, improves consistency, and reduces costs.