Claude Code: What Power Users Actually Do

SUMMARY

Artem Zhutov, a Claude Code power user with 800 hours of experience, reveals advanced techniques for self-improving AI agents through feedback loops, skill curation, observability, and life context integration to boost productivity.

STATEMENTS

Most Claude Code users accumulate tools without real power; only the top 1% build self-improving systems by analyzing conversations and feeding insights back to agents.
All Claude Code conversations are stored as JSON files locally, enabling analysis to identify friction patterns and improve agent memory automatically.
Session analysis skills allow agents to review past interactions, extract user frustrations via keywords, and propose CLAUDE.md enhancements to reduce repetitions.
Voice-to-text apps like Whisper Flow provide leverage by dictating over a million words, with Claude Code skills analyzing dictation patterns across apps for optimization.
Verbose output in Claude Code reveals internal processes, such as prompts, tool calls, and context reading, helping users master the tool's limitations and capabilities.
Claude Code's internal architecture includes a system prompt, tool definitions (read, edit, bash, skills), conversation history, and memory files like CLAUDE.md for personalized behavior.
Context management is crucial due to finite token limits; tools show remaining space (e.g., 160,000 tokens) to prevent performance degradation from overload.
Git reviews with USER-COMMENT patterns enable precise feedback on agent changes, allowing quick iterations without hardcoding by referencing external data like JSON files.
Obsidian serves as a local dashboard for skills, using databases to track status (active, archived), filter usage, and embed comments for agent-driven improvements.
Skills are simple Markdown files; patterns include targeted tools (e.g., diagram creation), routers for workflows (morning routines), and on-demand CLI integrations replacing inefficient MCP services.

IDEAS

Accumulating 35 skills but using only five highlights that tool quantity creates noise, not productivity; curation via analysis separates elite users from novices.
Feeding personal frustration data from conversations back into CLAUDE.md creates autonomous agent evolution, turning raw interactions into self-refining memory.
Local JSON storage of all chats democratizes data access, allowing users to build custom analytics skills that uncover hidden behavioral patterns in AI interactions.
Voice dictation's million-word scale, analyzed by hour and app, reveals peak productivity windows, transforming casual input into structured insights for workflow tweaks.
Verbose mode demystifies AI as a "black box," showing exact prompt flows and tool executions side-by-side, empowering users to debug and extend capabilities intuitively.
Claude Code's lean system prompt, combined with dynamic tool loading, keeps context efficient, avoiding the 14% token waste from always-on MCP integrations.
Progressive disclosure loads only relevant skill details on demand, mimicking human learning to scale agent intelligence without overwhelming finite context windows.
English as a "programming language" redefines notes from static artifacts to executable code, where AI acts on brain dumps to generate tasks and goals instantly.
Handoff prompts with Obsidian tagging preserve session context across limits, turning ephemeral chats into persistent, reusable knowledge bases for long-term projects.
Curating followed sources like Obsidian's CEO and tool creators fosters rapid learning through vicarious experimentation, accelerating personal AI system mastery.
CLI tools supplant MCP by integrating services like Gmail on-demand, reducing overhead and enabling seamless life-tool fusion without constant context bloat.
Brain dumps from walks, transcribed via Gemini Flash, become actionable via Claude Code's access to personal vaults, bridging unstructured thought to executable plans.
Weekly session analysis extracts high-leverage insights from personal data, making users the ultimate architects of their AI's growth trajectory.

INSIGHTS

Self-improving agents emerge not from more tools but from recursive feedback loops that mine user frustrations to refine memory, elevating AI from reactive to proactive partners.
Local data sovereignty in Claude Code transforms passive chat logs into active intelligence sources, enabling personalized evolution that outpaces generic tutorials.
Observability through verbose outputs and diagrams reveals AI's inner workings, shifting users from tool consumers to system designers who anticipate and mitigate limitations.
Skill simplicity as Markdown files democratizes advanced automation, where routing patterns turn singular tools into interconnected hubs for holistic life management.
Progressive CLI integrations replace bloated MCPs, optimizing token budgets to prioritize interaction over overhead, thus sustaining peak AI performance longer.
Integrating life context via Obsidian vaults makes English an executable medium, converting scattered notes into a unified, AI-orchestrated ecosystem for human flourishing.

QUOTES

"Most Claude Code users don't get it. The 1% build systems that improve themselves."
"English is a programming language."
"All of your cloud code conversations are stored as a JSON files on your computer."
"You might have 50 skills and yeah you feel like okay I'm very productive now I have like 50 skills but in reality you just use a couple of them and all of the rest is just noise."
"Your context uh how do you store your context how do to feed it to your agent. That's the highest leverage you can imagine."

HABITS

Analyze personal conversations weekly using session skills to identify friction and update CLAUDE.md for better agent behavior.
Use verbose output in every new Claude Code session to monitor internal processes and understand tool interactions.
Review Git changes after agent tasks with USER-COMMENT patterns to provide precise feedback and iterate on outputs.
Maintain an Obsidian dashboard for skills, updating statuses like active or archived to track and curate usage patterns.
Transcribe brain dumps from walks using voice tools, then feed them into Claude Code for task extraction and execution.

FACTS

Claude Code users can store all conversations as local JSON files, facilitating custom analysis without external dependencies.
Verbose output reveals that Claude Code's system prompt and tools consume about 15,000 tokens initially, leaving room for dynamic loading.
MCP integrations like Notion and Linear occupy 14% of the context window, reducing available space for productive interactions.
The speaker has dictated over 1 million words using Whisper Flow, analyzed by day, hour, and app for productivity insights.
Claude Code's context window allows up to 160,000 free tokens for conversations before performance starts degrading around 50% usage.

REFERENCES

Whisper Flow: Voice-to-text app for dictation analysis and frustration pattern detection.
Obsidian: Local note-taking tool used as dashboard for skills, tasks, goals, and session handoffs.
CLAUDE.md: Personal memory file for agent instructions, values, and behavioral rules.
MCP (Model Context Protocol): Outdated service for tool integrations like Notion and Linear, replaced by CLI.
Gemini Flash: Transcription tool for converting walk recordings into text for Claude Code processing.
Git: Version control for reviewing agent changes with USER-COMMENT patterns.

HOW TO APPLY

Start by enabling verbose output in Claude Code settings to observe prompt flows, tool calls, and context usage during interactions, building intuition for the agent's internals.
Analyze past conversations weekly using a session analysis skill: locate JSON files in project folders, extract user messages and friction keywords, then generate reports with proposed CLAUDE.md improvements.
Curate skills in Obsidian by creating a database view with columns for name, description, status (active/archived), and comments; filter by usage to eliminate noise and embed agent-readable feedback.
Integrate life context by consolidating notes, tasks, and goals into a single Obsidian vault folder accessible to Claude Code, then test by querying daily tasks or reviewing morning goals via natural language.
Implement handoff prompts for long sessions: when context nears limits, instruct the agent to summarize progress into a tagged Obsidian file, then reference it in new sessions to resume seamlessly.

ONE-SENTENCE TAKEAWAY

Master Claude Code by building self-improving feedback loops and curating life context for executable AI-driven productivity.

RECOMMENDATIONS

Prioritize feedback loops over tool accumulation to evolve your agent autonomously from conversation data.
Replace MCP with on-demand CLI skills to optimize context efficiency and integrate services like Gmail seamlessly.
Use Obsidian as a central hub for all notes and tasks, making your personal knowledge executable through Claude Code.
Conduct weekly analyses of dictation and chats to uncover patterns and refine CLAUDE.md for reduced frustrations.
Experiment with brain dumps from daily activities, transcribing them to generate actionable tasks within your AI ecosystem.

MEMO

In the fast-evolving world of AI-assisted coding, Artem Zhutov stands out as a pioneer who has logged 800 hours in Claude Code, Anthropic's command-line interface for the Claude model. Far from the novice rush to download endless skills and services, Zhutov argues that true power lies in self-improving systems. "Most users don't get it," he says. "The 1% build systems that improve themselves." His approach begins with feedback loops: analyzing JSON-stored conversations to spot frustrations—like ignored instructions or hardcoded values—and feeding them back to refine the agent's CLAUDE.md memory file. This recursive process turns raw interactions into a smarter, more intuitive assistant, avoiding the repetition that plagues casual users.

Zhutov's toolkit emphasizes observability to demystify the AI's black box. By toggling verbose output, users see the exact prompts, tool calls, and context reads in real time, contrasting clean responses with the underlying machinery. A detailed diagram of Claude Code's internals reveals a lean system prompt, tool definitions for reading, editing, and bashing files, plus dynamic skill loading. Context management becomes paramount here; with a finite 200,000-token window (often starting at 160,000 free), bloating it with always-on integrations like MCP for Notion or Linear wastes 14% of space, degrading performance past 50% usage. Instead, Zhutov champions CLI tools loaded on demand—a "progressive disclosure" that keeps the agent nimble.

Skills, reduced to simple Markdown files in folders, form the backbone of his workflow, but curation is key. Out of 35 skills, only five see regular use; the rest is noise. Patterns vary: targeted tools for specifics like diagram generation with Excalidraw, or router hubs directing morning routines and weekly reviews through sub-workflows. Git reviews amplify control, where USER-COMMENT patterns let users annotate changes—flagging hardcodes, for instance—and prompt the agent to iterate programmatically. Obsidian, a local alternative to Notion, dashboards this ecosystem with databases tracking skill statuses and embedding comments for AI resolution, blending note-taking with execution.

Beyond coding, Zhutov integrates life itself into Claude Code, echoing Obsidian CEO Kevin Roose's tweet that "English is a programming language." Scattered apps give way to a unified vault: years of notes, tasks, and goals in one folder, all executable via natural queries. A walk's brain dump, transcribed with Gemini Flash and a wireless mic, yields action items tied to personal objectives—Claude pulls today's tasks or morning goals effortlessly. Handoff prompts save sessions to tagged Obsidian files when contexts fill, preserving progress for immigration analyses or long projects.

For newcomers, Zhutov advises starting small: enable verbose mode, review Git changes, backup data, and build core skills while analyzing sessions weekly. His free starter kit and upcoming workshops underscore a community ethos, where experimentation with curated sources like Obsidian influencers accelerates mastery. In an era of AI hype, Zhutov's method isn't about more tools—it's about making your context the highest-leverage asset, transforming AI from helper to co-architect of a flourishing life.