My team has booked 58 sales calls from LinkedIn DMs in 2026 (so far). Here’s the dm system we use. For free. Most people treat LinkedIn DMs like spam: sending generic messages, guessing what to say after comments, and getting ignored. I treated it like a conversion system. And built systems that actually worked: • Comment → DM delivery templates that reliably turn post engagement into conversations • Signal-based DM sequences triggered by profile visits, follows, funding, events, and content engagement •Competitor-commenter scripts that start conversations with people already engaging in your category • Follow-up + voice note frameworks most engineers never systemise or reuse The result? Just short of $45K in revenue. I’m giving away everything: → 28 ready-to-send LinkedIn DM templates → Comment-to-DM delivery flows (connected + not connected) → Competitor commenter capture sequences → Warm trigger + profile-visit DM scripts → Follow-up timing logic and straight-sell voice note templates The actual playbook. If you want it: Drop “DM” in the comments and I’ll send you the link.

𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲 𝗶𝘀𝗻'𝘁 𝗮 𝗳𝗮𝗻𝗰𝗶𝗲𝗿 𝗮𝘂𝘁𝗼𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲. It's a different category of tool entirely. I spent the weekend mapping the 15 patterns that actually change how I ship code — not the surface-level tricks, but the primitives that collapse hours of work into a single prompt. Here's what the mental model looks like once it clicks: 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗹𝗮𝘆𝗲𝗿 → CLAUDE. md for persistent project memory → /init to bootstrap it from an existing repo → Agentic search instead of blind grep 𝗖𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝗹𝗮𝘆𝗲𝗿 → Custom slash commands for team-wide prompts → Skills (SKILL. md) for on-demand domain expertise → Subagents for parallel specialist work → Plugins to install curated bundles with one line 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗹𝗮𝘆𝗲𝗿 → Plan Mode (Shift+Tab) — review before execute → Hooks for PreToolUse / PostToolUse guardrails → Headless (-p flag) for CI, cron, scripts 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗹𝗮𝘆𝗲𝗿 → MCP servers for Jira, Postgres, GitHub, Linear → Image input for Figma-to-code translation → Git workflows in natural language → Test-driven refactor loops that actually close 𝗧𝗵𝗲 𝗳𝗶𝗻𝗮𝗹𝗲 → The full agentic loop: plan → code → test → commit → PR, from one prompt The pattern I see engineers miss most often: They treat Claude Code like a chatbot with file access. One prompt, one answer, repeat. The teams getting leverage treat it like a runtime — with memory (CLAUDE. md), policies (Hooks), reusable capabilities (Skills, Slash Commands), and delegated specialists (Subagents). That shift is what turns 500 lines of boilerplate into one instruction. Dropping the full infographic in the comments. 𝗪𝗵𝗶𝗰𝗵 𝗹𝗮𝘆𝗲𝗿 — 𝗰𝗼𝗻𝘁𝗲𝘅𝘁, 𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻, 𝗰𝗼𝗻𝘁𝗿𝗼𝗹, 𝗼𝗿 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 — 𝗶𝘀 𝘆𝗼𝘂𝗿 𝘁𝗲𝗮𝗺 𝘂𝗻𝗱𝗲𝗿𝗶𝗻𝘃𝗲𝘀𝘁𝗶𝗻𝗴 𝗶𝗻 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄?