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‼️ We’re Always Hiring — But Not Just Anyone.
If you’re in this community, you already know we don’t operate like a normal company. We move fast. We execute hard. We don’t just learn about AI — we build with it daily. If you’re looking for “slow, steady, and safe,” this isn’t for you. We’re building the future — and that takes a different kind of operator. We don’t care about degrees or titles. We care about execution, attitude, and how fast you can learn. More importantly, we’re looking for people who: - Don’t need constant hand-holding - Hate mediocrity - Actually want to make an impact So if you’re hungry, sharp, and ready to move — This is your shot. Check our open roles and apply here: Morningside: https://bit.ly/ms-skool AAA Accelerator: https://bit.ly/aaa-skool We’ll keep this post pinned — we’re always looking for A-players. Let’s see what you’ve got.
‼️ We’re Always Hiring — But Not Just Anyone.
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Must Read for Anyone Starting an AI Business
Hey everyone 👋 I want to make sure you’re truly using what’s inside the Classroom here on Skool — because it isn’t just theoretical content. It’s the proven starting point for building an AI service business in 2026, based on everything I've learned scaling my own 7-figure AI agency. When I first launched my business back in 2022, I was figuring things out in the dark — long days, trial and error, and a lot of mistakes before the real patterns finally emerged. Since then, I’ve worked with 7, 8, and 9-figure clients, helped thousands of people start AI agencies, and studied what separates the people who succeed from the ones who stall. And now the data is clear: There are TWO proven paths people are using to break into AI. All you have to do is choose the path that fits how you think and work. That’s why the very first thing you should do here is go to the Start Here module inside the Classroom. Inside it, you’ll find: - A clear breakdown of the two proven paths - Clarity on how to pick the one that fits you best - Then you'll find your playbook to land your first paid client fast Everything I wish I had when I started — the frameworks, playbooks, lessons, and action plans — is inside this Classroom. And I continue to update it based on what’s working right now. It’s all here for you, step by step. Don’t let this sit in your dashboard like another course. This is the stuff I lived to be where I am right now. If you aren't already, make sure you're following/subscribed to me for my latest content to help you on your journey: → Main Channel: https://www.youtube.com/@LiamOttley → VLOG Channel: https://www.youtube.com/@LiamOttleyVLOGs → Instagram: https://www.instagram.com/liamottley/ → X: https://twitter.com/liamottley_
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Hello, everyone.😀 Have a good day! I am a fullstack and AI developer with extensive experience. I design intelligent solutions that combine AI and full-stack development. From building applications with accessibility as a top priority to automating complex workflows, I possess broad experience in turning ideas into effective digital experiences. I am looking for a capable partner to collaborate with me on a long-term basis. To briefly mention the role, you will be performing agency duties. If you are interested, please feel free to contact me at any time. Please contact me via DM, Telegram, or WhatsApp.💬 Telegram ID: jito_400 WhatsApp: +81 90-5721-3304
Built a RAG-powered intake chatbot for PI law firms. Here's the full breakdown 👇
What it does for the firm: Visitor lands on the site, describes their injury, bot qualifies them, sends them a confirmation email + SMS, pings the attorney on Slack, and logs the lead to Google Sheets. Zero human involvement until the attorney shows up for the call. How it's built (n8n): Workflow 1 — Ingestion Schedule Trigger → DELETE old vectors → scrape website → extract HTML → chunk with Character Text Splitter → embed with Mistral → store in Supabase Workflow 2 — Chat Chat Webhook → AI Agent → Respond to Webhook Agent tools: Supabase Knowledge Base, Google Sheets, Gmail, SMS, Slack Memory: Window Buffer Memory (keeps conversation context) A fix worth mentioning: First version was stacking duplicate vectors every time the ingestion ran. Added a DELETE call before re-ingestion — wipes the old data, rebuilds clean every time. Stack: n8n · Mistral · Supabase · Google Sheets · Gmail · Twilio · Slack This is a demo but the whole thing is deployable as-is. Would love feedback — especially if anyone's built something similar for legal.
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Built a RAG-powered intake chatbot for PI law firms. Here's the full breakdown 👇
Looking for experience AI automation and agents enginners to work on client projects
Am loooking for AI einginners to deploy n8n flows and AI agents build , letme know who has experience and willing to do free lancer project works
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