Hey community! I wanted to share my "post-mortem" after implementing the local Long-form to Short-form clipping workflow. If you aren't running a NASA-level workstation, this breakdown is for you. 💻 The Testing Stack (Low-Spec Challenge) - OS: Windows 11 - CPU: Intel Core i5 10th Gen - GPU: NVIDIA MX (Legacy architecture / Limited CUDA support) - RAM: 8 GB (The real bottleneck) ⚠️ Main Technical Friction Points 1. VRAM & RAM Exhaustion: With only 8GB, the system chokes quickly. The legacy GPU couldn't handle heavy models, forcing the execution to fall back to the CPU. 2. n8n Node Versioning: The Read Binary Files (v2) node presented several compatibility conflicts. I had to manually adjust the buffer logic to prevent the workflow from crashing. 3. Docker & Local Networking: It’s not "plug & play." To get n8n communicating correctly with your video processing containers, you need a solid grasp of Docker volumes and networking. 💡 Key Takeaways & Optimizations - Model Selection (Whisper): My machine couldn't handle the large model. The Fix: I downgraded to the tiny/base model. - FFMPEG is King: I learned more about codecs and video manipulation this week than in the last year. You will have to get your hands dirty with custom FFMPEG commands to adjust resolutions and bitrates. - Beyond Copy-Paste: The template is just a foundation. The segmentation logic requires manual tweaking depending on how the AI detects "silences" or "points of interest." - AI-Assisted Debugging: Don't just use AI for code; use it to find lightweight library alternatives when you hit a MemoryError. 🛠️ Recommendations for the Community 1. Resource Monitoring: Keep your Task Manager open. If Docker hits 95% RAM usage, n8n will likely disconnect. 2. Think Critically: Question every node. Do you really need to process the full video, or can you extract the audio first to save resources? Conclusion: This was a massive learning experience regarding containers and media processing. Don't let hardware limitations stop you—just get creative with optimization.

You can try out Gemini 3.0 Pro for free at https://aistudio.google.com/ Gemini 2.5 Pro was still my go-to model for full fledged vibe coding and I had been hearing rumors of 3.0 so I'm excited to see if finally drop. I'll be testing it out later this week. But on top of that, Google also released their new IDE, Antigravity. Similar to Cursor (and maybe a repurposed Windsurf) it is an AI based coding platform, but right now you can use everything in it for free so worth downloading and practicing some vibe coding for no cost at all. Let me know if you try them out or have any vibe coding questions!

This is a quick tutorial for automatically scheduling YouTube uploads using Google Sheets, Google Drive, and n8n. Once you've created a Google client that connects your YouTube and Google Drive accounts, you can easily setup a scheduling system in Google Sheets to upload Videos to YouTube on whatever schedule you want. This could also be expanded to upload to TikTok, Instagram or LinkedIn as well!