Software engineering

Post LinkedIn lead magnet · Software engineering

Your Clay table has 50+ columns, and you’re suddenly asked “What do all of these do?”… Good luck explaining that! Spend only a week in Clay and you’ll realize it’s incredible for building, but terrible for documenting. Complex Clay tables become black boxes fast. Formulas hidden in dropdowns, AI prompts scattered across columns, dependencies that only make sense to whoever built it, etc. So I built the Clay Table Parser Tool in Datagen.dev First, I reverse-engineered Clay’s internal API. Then, I wrapped it into a single endpoint that extracts everything: → Every formula and waterfall logic → Every AI prompt and JSON schema → Every field dependency (what feeds into what) → All in a structured markdown report via API plus a shareable URL One API call. Full documentation. Done. Plug it into Claude Code, use it as an MCP server in the Claude app, or (if you wanna get meta) call the API from inside a Clay table itself. 🤯 Perfect for client handoffs, audits, or debugging your own mess from 3 months ago. DM me or comment if you want to try this API! Not trying to gate it, just want real feedback so I can make it better for everyone.

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DM me or comment if you want to try this API!

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Software engineering

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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?

Dropping the full infographic in the comments.

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Software engineering

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Commente "BUDGET" et on fait le calcul sur ton cas précis.

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