Field guide / primary domain

Claude

217

sources in this field

Updated July 16, 2026

Current thesis

The shortest path to orientation.

Claude Code is now a $2.5B run-rate product powering 4% of all GitHub commits, anchoring Anthropic's decisive move into Platform through Claude Managed Agents (PaaS at $2.58 fulfillment cost on $1k service, Linear SDK integration), the Advisor Strategy (Opus planning with Sonnet/Haiku execution as first-class pattern), the Monitor tool (event-driven background scripts), /ultraplan (cloud planning with browser review and local CLI back-teleport), Claude Design (Opus 4.7 vision rendering HyperFrames videos in 2 prompts), and official setup/plugin flows. The workflow has matured into a general work operating system: CEOs use it as AI Chief of Staff; Marcus Moretti runs Spiral at Every as one-person PM/code/support/marketing with strategy.md + /ce:product-pulse cron replacing 60% of the old PM week; sales teams run 11-API pipelines through Skills files. Role specialization ships as packages (gstack installs 6-specialist team in 30 seconds) and multi-model loops enable Opus 4.7 planning, GPT-5.5 review, Playwright validation, and @conductor_build model-switching at ~$400/month. Plan files, /handover, structured /goal prompts, premortem flipping ("it's 6 months from now and this is dead"), /ss screenshot skill, and specialized harnesses (designer, marketer, sales, motion, bookkeeper) operate alongside agent-view research (consolidating cross-project coding sessions) and one-command skill promotion (turning personal PM OS into team OS without leaking context). Architecture: plain-text markdown vault + Claude Code engine. Three-file configuration for articulate agents: SOUL.md (constitution—voice/values, brutally specific or reverts to ChatGPT), USER.md (~4000-word user model), AGENTS.md (operational playbook). Cost hierarchy matters: 80% of agent tasks are janitorial, making hierarchical model routing (10x cost reduction) essential. Scratchpad/napkin patterns provide distinct memory compounding across sessions. Obsidian + Claude Code is the community stack; vault pattern extends to git-as-version-history (Tolaria) and scales to teams—four independent implementations (DoorDash, Pendo, Google, solo) converged on the same three-layer team-knowledge architecture, confirming compounding data is the moat, not technology. Design extends from 3-layer harness (Skills + Canvas + Inspiration) to Claude Design + HyperFrames, Codex image generation replacing UI prototyping, Refero's 2,000 DESIGN.md training files, and Lamina Labs' whiteboard animation SDK. Ecosystem: Anthropic investments (11 open-source plugins, free course, 14-min agent guide, 33-page Skills guide, Managed Agents, official setup plugin) + community tooling (gstack, self-improving skills: 32/50 → 47/50 overnight, claude-smart, Evo, GitHub plugin marketplace auto-sync, CodexBar, Decode, FieldTheory, Excalidraw, ByteRover, Tolaria MCPs, NVIDIA 80-model free API, LibreChat wrappers, 1,200+ hour research workflows, voice cloning at 94% accuracy, Codex overflow). Six extension mechanisms (Plugins, Skills, MCPs, Commands, Subagents, Hooks) across three layers: environment (skip-permissions flags, game sound, performance recovery), context architecture (CLAUDE.md <200 lines, tiered manifests), and skill design (state machines, self-improving eval loops). System engineering beats prompt engineering.

Evidence board

Claims worth carrying forward
01

Knowledge systems fail because they optimize for input, not output. A vault without a feedback loop is 'a graveyard with good folders.' The fix is three mechanisms: frictionless capture (<10 seconds), a connection layer (Claude reading across notes), and a daily push of insights back to you.

02

Four-layer architecture: (1) Capture tools—Readwise, Airr, Whisper, Telegram bot; (2) N8N pipeline routing content into vault; (3) Obsidian as permanent markdown storage; (4) Claude as intelligence layer reading across everything. Each layer has one job; nothing overlaps.

03

Telegram-to-Obsidian quick capture: N8N workflow—Node 1: Telegram Trigger (message, bot_id); Node 2: Code node formats markdown (inbox/{{date}}-quick-capture.md); Node 3: Write File to vault /inbox/. Takes 30 minutes to build with Claude Code and N8N. Handles all phone captures permanently after that.

04

CLAUDE.md is the root-level context file Claude reads at session start. Required sections: Who I Am (name, work, focus, goals), Current Projects (active, stuck, next milestone), vault folder map, behavioral instructions (surface connections, challenge assumptions, flag contradictions), and current reading/obsessions. Update Current Projects and Reading sections weekly.

05

Daily brief prompt runs via N8N at 6am on a schedule: reads /inbox (last 24h) and /notes (last 7 days), outputs three things—(1) 3 specific connections between recent and older notes with quoted passages; (2) one pattern across the week's reading; (3) one question worth sitting with today. Saves to /inbox/brief-{{date}}.md automatically.

06

Weekly synthesis prompt asks Claude to identify: (1) emerging thesis forming in your thinking; (2) contradictions between recent saves and prior beliefs (showing both from your own notes); (3) knowledge gaps—what perspective is missing; (4) single highest-leverage action. Framed as 15-minute Monday session. This is where compounding actually occurs.

07

Five-folder vault structure designed to resist collapse: Inbox (raw captures), Notes (processed sources), Ideas (your own thinking), Projects (active work), CLAUDE.md (root). Rule: when in doubt, put it in inbox. Complex folder structures fail because categorization ambiguity raises capture friction until the system breaks.

08

Compound effect timeline: Month 1—useful tool. Month 3—Claude connects notes from 8 weeks ago to current problems you've forgotten. Month 6—full record of beliefs held and changed, patterns recognized before you consciously named them. Six-month head start is not closeable by working harder—only by starting earlier.

Adjacent fields

Key voices

Latest evidence

Recent additions

All synthesized insights →

CyrilXBT

How to Build an Obsidian Knowledge Vault That Gets Smarter Every Day Without You Doing Anything

A detailed 6-step system for building an Obsidian vault that auto-captures content via Readwise, Airr, Whisper, and a Telegram bot, routes everything through N8N pipelines, and uses Claude to generate daily briefings and weekly syntheses. The core thesis: knowledge systems fail because they optimize for input, not output—feedback loops (daily briefs, weekly synthesis) are what turn a dead archive into a thinking partner.

Charlie Hills

Garry Tan's gstack: 23-Skill AI Engineering Team for Claude Code

gstack is Garry Tan's open-source Claude Code setup that transforms a solo AI assistant into a structured 6-role virtual engineering team (CEO, Eng Manager, Designer, QA, Release Engineer, Doc Engineer) via 23 slash-command skills. Tan claims ~810× his 2013 productivity rate measured in normalized logical lines of code, shipping 3 production services and 40+ features in 60 days part-time. The system enforces a think→plan→build→review→test→ship→reflect sprint discipline where each skill feeds context into the next, and supports 10-15 parallel sprints via Conductor.

GREG ISENBERG

Design.md + AI Skills: Consistent Startup Branding in One Hour

Google's open-source Design.md format captures typography, colors, and spacing in a single markdown file that agents reference to produce consistent outputs. Combined with reusable skill files (landing page, mobile, pitch deck), it creates a design system any AI agent can apply uniformly across all surfaces—replacing the common pattern of polishing one screen while everything else looks generic.

Charly Wargnier

Tolaria: Open-Source Mac/Linux Desktop App for Human-AI Shared Knowledge Vaults

Tolaria is a free, open-source (AGPL-3.0) desktop app for Mac and Linux that implements Karpathy's LLM wiki concept — a shared knowledge environment for humans and AI agents. It uses plain markdown, Git-backed vaults, and includes a built-in MCP server for Claude Code integration. Built with Tauri, React, and Rust; 100K+ lines, 85% test coverage, 9.9/10 code health.

Aakash Gupta

Team OS in Claude Code: Shared Repo Architecture for Institutional Knowledge

Hannah Stulberg (DoorDash PM) built a shared git repo where every team function checks in context—call summaries, decision logs, analytics queries—queryable in natural language via Claude Code. Four independent implementations (DoorDash, Pendo, Google, solo) converged on the same 3-layer architecture, suggesting a generalizable pattern for solving institutional knowledge loss.

elvis

LLM Wikis + HTML Artifacts as Agent-Connected Work OS

A workflow pattern combining LLM Wikis (structured knowledge stores) with interactive HTML Artifacts creates a bidirectional agent-UI layer. Artifacts are built on top of wikis, can invoke agents, and agents can update artifacts—enabling inbox-zero automation, research scheduling, topic discovery, and live figure generation from a single HTML file.

Shann³

How to Become a Hermes Agent Operator

Comprehensive operator guide for Hermes Agent by Nous Research—an open-source autonomous agent framework with 150K GitHub stars and #1 OpenRouter usage. Covers architecture (brain/personality/skillset), four-level deployment progression (laptop to full VPS fleet), multi-agent control room patterns, a 21-step SEO pipeline, and the prototype→production methodology for marketing automation.

klöss

/goal Command Structure for Codex, Claude Code, and Hermes

A structured /goal prompt template for AI coding agents (Codex, Claude Code, Hermes) that prevents scope creep, ranks uncertainties before acting, and closes completion loops. Key sections: GOAL (single measurable outcome), CONSTRAINTS, PLAN (understand-first), DONE WHEN (verifiable state), VERIFY (tests + rollback), STOP RULES (halt on ambiguity, surface ranked proposals not open questions).

George from 🕹prodmgmt.world

The /goal Command and What It Demands from Product Managers

Claude Code's /goal and Codex's /goal commands implement a completion-contract pattern: a working agent loops until a separate evaluator confirms a stated condition is met, judged only from evidence surfaced in the conversation. The post argues this punishes vague PM requirements faster than traditional development, raising the bar from 'write enough detail an engineer understands' to 'define done clearly enough an agent can keep trying and a harness can inspect proof.' Includes a practical goal template and anti-patterns.