From new client wins to digital twins and foundation-level breakthroughs - another week of real progress inside AIS+ Here are this week’s highlights inside AIS+ 👇 👉 Kevin Hardin built a job-sorting automation using Apify + n8n to instantly organize Upwork leads - a solid real-world workflow in action. 👉 @Elijah Williams restructured his entire agency model after completing the One-Person Agency course — turning insights into a roadmap. 👉 @Rakshith Gowda solved a Veo 3.1 API error after hours of debugging - a reminder that persistence pays off. 👉 @Simon Cousineau is now featured weekly on Canadian radio as their official AI expert - turning consistency into credibility. 👉 Malek Kilani built an AI-powered language coach in under an hour, shared the PRD, and inspired others to ship faster. 🎥 Super Win Spotlight of the Week: @Lutfiya Miller | From Toxicologist to AI Consultant Lutfiya went from learning AI workflows to landing two new clients at a scientific conference - proving what happens when confidence meets consistency. AIS+ helped her go from fear and uncertainty to building powerful automations and speaking at multiple events in 2025. 🎥 Watch her story 👇 Lutfiya’s journey is proof that when you replace fear with action, everything starts to click. ✨ Want to see more wins like these every week? Join the builders inside AI Automation Society Plus - where learning turns into confidence and results 🚀

From first clients to viral builds and automation breakthroughs - this week inside AIS+ was loaded with momentum 👇 Here are this week’s highlights inside AIS+ 👇 👉 @Lutfiya Miller landed two new clients at a scientific conference and is now invited to speak at multiple events in 2024 - turning conversations into opportunities. 👉 Shuyi Li wrapped her first $255 client project and shared three powerful lessons to avoid beginner mistakes. 👉 @Rakshith Gowda dropped two advanced automations - an AI News Digest and an Inbox Manager with auto-replies. 👉 Georg Lauenstein automated a daily ransomware threat briefing customized for Germany’s security teams - a smart use of AI for real-world impact. 👉 @Mohd Hisyamudin Bolhassan (Sam) closed a $600 chatbot build even before joining AIS+ - proof that free knowledge can open real doors when paired with action. ✨ Want to see more wins like these every week? Join the builders inside AI Automation Society Plus - where consistency turns learning into real results 🚀

Built a daily probate scraper for a real estate agent who needed to monitor new probate cases but was tired of manually checking the county website every morning. The system automatically grabs the latest probate filings, processes the CSV data, and sends a clean summary email with key stats like total records, cases with/without attorneys, and filing dates. The beauty is in the summary - instead of downloading random CSV files and cluttering up their computer, they get a quick overview email that lets them decide if anything's worth pursuing. If there's nothing interesting, they just delete the email. If something catches their eye, they can download the full CSV directly from the email. Here's the full walkthrough: https://www.loom.com/share/826fc02dfa14430995ef470f404e1ed1 You can download the automation here: https://ronjabouin.gumroad.com/l/itcpcu Anyone else automating daily data monitoring for their business?

Curious to see when everyone does their best building, when do you usually find yourself deep in automation mode?

Microsoft released Agent Lightning, an open-source Python framework that lets you train and improve AI agents without rewriting your existing logic. It works with existing setups such as LangChain, AutoGen, and the OpenAI Agents SDK, adding a training layer that helps agents learn from experience and feedback. Key Highlights: - Compatible with LangChain, AutoGen, and other agent frameworks - Adds a training loop with minimal code changes - Supports reinforcement learning, prompt tuning, and supervised fine-tuning - Automatically captures prompts, actions, and rewards for optimization - Allows defining custom reward functions for domain-specific tuning It’s 100% open source. 🙌🏻