We’re looking for a sharp automation specialist who can build clean, reliable systems that help sales teams move faster and operate better. ◽Budget : $500 USD | 15 hrs/week commitment. You’ll work across Make.com, GoHighLevel, Google Sheets, webhooks, and AI tools to build automations, dashboards, and scalable sales workflows for our business and client accounts. ◽Skills & Experience Required •Strong experience with Make •Very proficient with GoHighLevel •Confident with webhooks, routers, filters, and logic-based workflows •Good with Google Sheets, dashboards, and reporting •Able to connect CRMs, forms, webhooks, and spreadsheets •Organized with naming conventions, folders, and documentation •Comfortable using AI tools like Claude, ChatGPT or Gemini •Proactive, detail-oriented, and accountable •Able to take a Loom, Miro board, or rough idea and turn it into a working system. ◽What You’ll Do >Build automations in Make.com and GoHighLevel >Create KPI Dashboards and sales tracking systems >Improve existing workflows and fix bottlenecks >Keep systems clean, organized, and easy to duplicate >Test your work before launch >Suggest better ways to build, not just follow instructions >Help create scalable systems that can be used across multiple client accounts ∆ Bonus Experience : Sales operations systems CRM reporting Speed-to-lead tracking Setter/closer dashboards Client account duplication | system licensing We’re looking for someone who enjoys ownership, solves problems, and takes pride in building systems that are clean, reliable, and scalable.

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While the AI world has been buzzing about Claude Code and the emerging AI OS narrative, the n8n team quietly shipped something that deserves a lot more attention: the official n8n MCP server. What this means in plain terms: any AI agent that can call tools can now connect directly to your n8n instance and build workflows from scratch. Claude Code, Codex, Gemini, Cursor. If it speaks MCP, it can now author, test, and deploy n8n automations without a human touching the canvas. The server comes with full knowledge of every n8n node, the official documentation, and built-in error correction and test execution before it ever hands the workflow back to you. That's not a small thing. That's the authoring layer becoming agentic. Where this gets interesting: autonomous workflow factories Here's an architecture that's now within reach for any serious automation builder: Imagine a queue — a simple database or even a spreadsheet — tracking n8n workflow jobs your clients need built. A scheduled trigger (a recurring n8n workflow, or better yet, a Claude Routine running on an hourly cadence) pulls the next job off the queue, spins up an AI agent armed with the n8n MCP server, and hands it the spec. The agent builds the workflow, tests it, resolves any errors, and posts a ready-to-review draft to your n8n account. Then it marks the job complete and moves to the next. That's a fully autonomous workflow factory. No human in the loop until the review stage. Going deeper: multi-agent pipeline orchestration This doesn't have to stop at a single build agent. With frameworks like OpenClaw or Claude's own multi-agent primitives, you can extend this into a proper pipeline: - A Planner agent breaks down a client's high-level requirement into discrete workflow specs - A Builder agent uses the n8n MCP server to construct each workflow - A QA agent reviews the output against the original spec, flags issues, and loops back - A Delivery agent notifies the client (via email or Slack), posts documentation, and closes the ticket

24 seconds, motion graphics, vertical. First pass. Shipped below. Pattern that worked: Paste-ready prompt block(Hyperframes SKILL + my design system + brief in one paste) Animation templateMature design system pre-attached First-pass output was already brand-true.No fix-prompt iterations. Capture step is a headless Playwright + ffmpeg pipeline(wait for fonts.ready, snap to t=0, record, crop) Reusable for any future Animation export. Cheers @Nate Herk Genuine credit.Your video flipped my “wait and see” into “test it now.”

I built Alice with local self-learning memory. Alice is my outbound research, lead gen and email drafting agent. She can crawl and enrich leads, store memory and RAG data locally in SQLite, generate drafts with a local LLM, create reports, and expose MCP tools for other AI clients. But the real feature is this: Alice does not write one email and forget. When I accept, reject, send, or get a reply from a draft, she can store that outcome as memory. The next draft can use those lessons. Every campaign makes the next campaign smarter. Her sales voice is direct, evidence-led, and consultative. No generic “hope you’re well” outreach. Alice studies the business, finds a real angle, and frames the message around value creation. She combines: - lead research - audit evidence - brand voice rules - accepted/rejected feedback - outreach outcomes - sales playbooks - knowledge RAG Every draft has intent: warm first contact, value creation, objection follow-up, or closing. The best part is how she angles things. Alice can turn a small business weakness, unclear offer, or missed opportunity into a useful sales conversation without sounding pushy. No cloud memory. No black-box training. Just local feedback loops, RAG, and measurable outcomes. Less like a prompt. More like a local operating system for outbound.