Hey everyone, After watching Nate's trading video, I was inspired to build my own solution. I’ve spent the last few months developing a custom trading bot for Hyperliquid, and the results have been incredible: I’ve achieved a 45% profit over the last two months, with backtests showing a reliable 100% annual return. The strategy is built around Momentum Breakouts. Here are the three pillars that made this bot significantly more effective and efficient: 1. Smarter Architecture (Saving Credits) Instead of relying on expensive automation platforms for every execution, I used Cursor and Claude to program the logic once. The bot runs as a monitor/scanner via a Cronjob. This setup executes the trading code independently, saving a massive amount of API credits while maintaining high performance. 2. Momentum & Market Selection The core of the strategy is a Momentum Breakout approach. The most important lesson I’ve learned: Don’t trade in sideways markets. Everything depends on the "Universe" you select and the filters you apply. By using specific filters to identify the current trend direction, the bot only enters when there is real movement, avoiding the "chop" of horizontal ranges. 3. Walk-Forward Testing (Validating the Edge) To ensure the strategy is actually profitable and not just "curve-fitted" to past data, I use Walk-Forward/Out-of-Sample testing. - The algorithm is trained on a specific time interval. - It is then applied to a completely unknown set of data where the future is "hidden." This rigorous testing gives me the confidence that the strategy is robust and ready for real market conditions. I'm curious—is anyone else here automating their Hyperliquid strategies? If you have questions about setting up Cronjobs, using Cursor for trading code, or my filtering process for trend direction, let’s discuss i n the comments! 👇

Hey AI Society, I've recently found myself launching my own AI agency, LOVIZ AI (https://loviz-ai.de/), largely driven by the increasing number of inquiries I was getting. It's been an interesting journey diving deeper into this space. I'd love to get your thoughts and feedback on the website. I built it entirely through Telegram and OpenClaw / Cursor CLI on my VPS, which was a unique experience in itself. Any suggestions for improvement would be greatly appreciated! Looking forward to your insights. Best, Lorenz

Most people use n8n to connect Tool A to Tool B. I use it as the entire backend for a SaaS platform. Inboundy (inboundy.app) doesn't have a traditional backend. It runs on n8n as an API Gateway — 8 webhook routes, auth handling, auto onboarding, feature delegation, state management via Supabase, and rate-limiting. All in workflows. The architecture: Frontend → n8n Webhooks → Services → Supabase No Express server. No Flask. No microservices. Just workflows. But the real killer feature isn't the architecture — it's observability. When a request fails, I see exactly which node broke and why. Instantly. No digging through logs, no reproducing bugs locally. The error is right there in the workflow editor. With traditional code — especially AI-generated code — you end up with functions calling functions calling functions. Good luck tracing that when something breaks. I've been there. Hours of debugging code I barely understand, written by an LLM that's already forgotten it wrote it. n8n forces you into a visual structure. You can't hide complexity behind layers of abstraction. Every step is visible. Every failure is traceable. Is it the "right" way to build a SaaS? Probably not. But it shipped in weeks instead of months, it handles real users every day, and when something breaks — I know exactly where. Sometimes the best architecture is the one you don't have to maintain. https://inboundy.app

Hey AI Society! I made this because I was honestly tired of the manual LinkedIn grind. Constant manual outreach is a repetitive process that just drains time and resources. So, I built Inboundy (formerly InLinked). It’s not just another bot; it’s a LinkedIn automation platform that uses AI Agents and n8n orchestration to handle the outreach for you. What it does: Auto-Connecting: It automatically finds and connects with prospects based on specific keywords and locations. Smart Scraping: Extracts profile information from your connections to build qualified lists. AI Messaging: Generates hyper-personalized messages by analyzing both your profile and the recipient's data. Safety First: It uses Puppeteer to simulate human behavior and stay within LinkedIn's rules. I’m currently looking for beta testers! Your feedback is absolutely crucial to help me refine the workflows and make this tool even better for everyone. Try it out here: https://inboundy.app/ Let me know what you think! Would you like me to help you draft your first AI-powered outreach sequence?

Hello everyone, I am looking for beta testers for my platform, Inlinked (https://inlinked.de/). Inlinked is a tool designed to automate LinkedIn networking, specifically focusing on connection requests and messaging workflows. For those interested in the technical implementation, the backend is fully powered by n8n. I am currently offering a 7-day trial for new users. I would greatly appreciate it if some members of this community could test the platform and provide feedback on its functionality and user experience. If you are interested in trying it out, you can sign up on the website or message me directly if you have any questions. Best regards, Lorenz