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🚀New Video: Higgsfield Just Turned Claude Into a Creative Agency
Higgsfield gives you access to the best AI image and video models, and Claude can talk to it directly through an MCP or CLI. In this video I show you how to turn Claude and Claude Code into a full creative agency that researches your market, builds a brand, generates product photos and ads, tracks every output in a Google Sheet, and runs on routines while you sleep. You'll see how to use Marketing Studio for hyper-motion launch videos, build reusable skills for consistent outputs, and scale up to hundreds of ad variations a week without being the bottleneck on creativity or production.
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🚀New Video: I Tried 100+ Claude Code Skills. These 6 Are The Best.
After 400 hours in Claude Code, I noticed that businesses keep paying for the same six types of skills. In this video, I break down each one, what it does, and why these simple, boring skills are the ones that actually sell. Whether you're brand new to AI automations or already building for clients, these are the skills worth learning first.
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🏆 Community Wins Recap | Apr 25 – May 1
From AI roles and first clients to live receptionist systems and enterprise training deals - this week inside AIS+ showed what happens when builders stop watching and start executing. 🚀 Standout Wins of the Week inside AIS+ 👉 @Griffin Maklansky went from being laid off to landing an AI Workflow Builder role in just 1 month. 👉 @Ahmed Bin Faisal landed another $2,000 USD client — an interior design firm — and broke down exactly what led to the close 👉 @Narsis Amin built a working AI restaurant receptionist handling bookings, availability, and CRM logging end-to-end. 👉 @Josh Holladay closed a $4.5K (+$1K) client with half up front today — and dropped his top 10 lessons from the close 👉 @Dion Wang received his first official testimonial, validating real client impact and around 40 hours/month saved. 🎥 Super Win Spotlight | @Duy Nguyen Duy started as an engineer who was curious about AI — but unsure how to turn that curiosity into something real. After joining AIS+, he went from learning passively to building his own AI-operated business, Sharper Automations. Since then, he has: • Built a 24-agent AI business operating system • Landed 2 local paying clients through word-of-mouth • Created a system that improves itself weekly through feedback loops • Started moving toward his goal of leaving his corporate job His biggest shift? From “Can I really do this?” → to building a real business around AI automation.
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DeepSeek vs Perplexity 🤖⚡
DeepSeek vs Perplexity 🤖⚡ DeepSeek = 🧠 Smart thinker Perplexity = 🔍 Smart researcher DeepSeek dominates in coding, math & reasoning Perplexity wins in real-time search + accurate sources 👉 Want raw AI power? Go DeepSeek 👉 Want fast, fact-checked answers? Go Perplexity Best combo? Use both. 🚀
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I Manage My Entire IT Infrastructure with AI and I will NEVER go back!
3 months ago I embarked on an experiment. As a 30 year network engineer I wanted to see how much infrastructure AI could actually manage. I built a Claude Code agent to run my entire stack. Gateway to workstations, everything in between. It's been working amazing! I don't think I can ever go back to managing it myself anymore, it saves me HOURS of manual configurations when deploying anything!. It's stood up containers and applications, configured my VLANs, configured my Netbird mesh, manages the dual Pi-hole pair, the SIEM, the secrets vault, the workflow runtime, the backup server. Anything I need to do in my stack, I just talk to it. Natural language conversation with Claude in terminal. Done. Plus a proactive layer on top: daily Slack alerts the agent acts on autonomously. New CVE drops on a package I'm running? Triage and a patch sequence land in Slack before I'm awake. Pi-hole drift across the resolver pair? Auto-corrected, journaled, summarized. I read the digest, not the dashboards. The cluster it manages: - 4 Proxmox nodes - 18 LXCs + 1 VM - 3 months continuous, weekly kernel updates on cron - Only manual step: I loaded Proxmox onto the host boxes. The agents did everything else. Today I documented it all. Public runbook on my website. Open-source GitHub repo for anyone who wants to run the same experiment. And today I'm also breaking that single agent into 11 specialists — mesh, vault, substrate, edge, LAN, telemetry, workflow, docs, plus three cyber peers (security, threat detection, vuln management). Each one 80–150 lines instead of one big 432-line spec. Each one runs on the smallest model that handles its work. Haiku on the read paths, Sonnet on change-prone domains, Opus reserved for incidents only. The split should drop my API cost to roughly 1/13 of what a typical agent call runs today. Faster responses, less wasted context, cleaner reasoning, same coverage. → Repo: https://github.com/Mfrostbutter/Infra-AI-IT-Team-Runbook
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