My Claude Journey

How do Pro subscription limits work, and why do long chats drain your allowance so quickly? This episode demystifies Claude's resource management, covering 5-hour session limits, weekly usage caps, and pay-as-you-go usage credits. We break down per-token API pricing—explaining why output tokens cost five times as much as input tokens and how prompt caching saves money. Plus, gain actionable habits to split long tasks and stretch your compute budget.

How do you choose the right model tier for a complex job? In this episode, we compare Claude's model family—from lightweight Haiku to Sonnet, Opus, and Fable. We explore adaptive thinking, showing how forcing the model to write out its internal step-by-step reasoning catches multi-step traps and logic errors. Discover how to adjust effort levels (from Low to Max) in both the app and Claude Code to strike the perfect balance between reasoning depth, speed, and token usage.

• How does Claude step outside its fixed training data to interact with the world? In this episode, we trace the step-by-step tool loop—the ask, act, return cycle that lets Claude search the live web, read pages, and manipulate local files. We examine the fixed knowledge cutoff date and explain why stale training facts sound just as confident as fresh ones. Finally, learn how to enforce exact quotes, verify citations, and write standing rules that ensure online searches remain accurate.

When you type a simple message, Claude reads far more than just your text. This episode dissects the hidden prompt stack built into every turn—from Anthropic's system prompts and tool descriptions to account preferences, project instructions, memory, and tool results. Learn how prompt caching significantly cuts processing costs for unchanged setup layers. We also explore instruction decay, explaining why certain guidelines fade over time and which layers automatically reload after a long chat is summarised.

How do AI models measure text, and why do long chats start losing quality? In this episode, we unpack the mechanics of tokens and context windows—the foundational units behind AI memory, platform limits, and usage costs. We explore why Claude forgets everything between messages and must re-read the entire conversation history on every single turn. Discover why long conversations suffer from "context rot", how automatic compaction works when limits are reached, and how handoff notes can preserve your usage allowance.

What is the difference between "Claude" the AI model and the software products built around it? In this debut episode, we break down how the same underlying model runs across various surfaces—from web and desktop apps to Claude Code, browser extensions, and Microsoft Office add-ins. We examine the three levers that define every AI surface: tools, reach, and context. You'll also learn how model tiers work, how subscription allowances function across surfaces, and where each topic sits on our 23-lesson course map.