Today I completed my first workflow on the N8 automation platform , after installing docker container on my modest computer and using the Ngrok program. I installed a version of Llama 3.2 on my computer. Then I set up some simple chat node and simple memory node.. The result was fantastic.

Level up your n8n AI agents effortlessly! In this video, discover how to utilize OpenAI's Responses API as a powerful chat model to enhance your agents. I show you the quicker, easier method to bake in essential tools like web search and file search directly into your agents, bypassing the need for connecting other tools, messing with prompts, and building data pipelines for you knowledge base. Learn exactly why this new approach is a significant upgrade and how to set it up in n8n without writing any code, unlocking a new level of power and capability for your AI agents.

Recruitment workflow template is fantastic. Job posting automation, candidate communication, interview scheduling - all smooth. Resume screening though? Still manual. HR reviewing 100+ resumes per job posting. The template couldn't help with that part. Until I extended it with intelligent resume processing. THE SCREENING PROBLEM: 100 resumes for open position. HR needs to find: 5+ years experience, specific skills, education requirements, salary expectations alignment. Manual screening: 8-12 hours per job posting. Recruitment template automated everything EXCEPT the actual resume evaluation. The bottleneck everyone dreads. Worse: After resume 40, HR fatigue sets in. Qualified candidates at position 67 might get skipped. Great talent missed because humans get tired. THE EXTENSION: Added resume parsing and scoring to workflow. Resume uploaded → Extract experience, skills, education → Score against job requirements → Rank candidates → Present top matches to HR. Removes human fatigue factor. Every resume gets same quality analysis. SCORING CRITERIA: Years experience (weighted 30%) Required skills present (weighted 40%) Education match (weighted 15%) Location/salary alignment (weighted 15%) Automatic scoring on 0-100 scale. Configurable weights per job type. NOW THE WORKFLOW: Job posted → Resumes arrive → Automatically parsed → Scored against criteria → Top 15 candidates flagged → HR reviews only qualified matches → Interviews scheduled. THE NUMBERS: Before: 100 resumes, manual screening, 8-12 hours After: 100 resumes, automated scoring, HR reviews top 15, 2-3 hours Time saved: 6-9 hours per posting Better outcomes too. Scoring catches qualifications HR might miss when fatigued after resume 47. Hidden benefit: Bias reduction. Consistent evaluation criteria applied to every candidate. WHAT GETS EXTRACTED: Work history with dates and companies. Skills mentioned anywhere in resume. Education degrees and institutions. Certifications and licenses. Contact information. Salary expectations when mentioned. Key projects and achievements.

Huge shoutout to the Top 5 Members of AI Automation Society for November! These are the people leading the way, sharing knowledge, and helping spread the AIS culture: 1️⃣ @Muskan Ahlawat 2️⃣ @Kevin troy Lumandas 3️⃣ @Frank van Bokhorst 4️⃣ @Christian Rivadeneira 5️⃣ @Noel Doe Massive congrats to all of you for your consistency and energy this month. You help set the tone for the whole community. I also want to congratulate every single member of AIS! Because of your support, wins, questions, posts, and energy, AI Automation Society is officially the number two Skool community out of more than 200,000 communities, and number one in the tech category. That is insane. And it's because of YOU. Thank you for showing up, helping others, sharing resources, and building with us. This community keeps getting stronger every month, and that momentum is what makes AIS special. Let’s keep learning, growing, and pushing the future of AI automation together. 🚀 Cheers, Nate