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!

With all the crazy AI models that dropped a few weeks ago: GPT 5.1 Pro, Gemini 3 Pro, Nano Banana Pro, Grok 4.1, Claude Opus 4.5... OpenAI apparently issued a "Code Red" email that they need to get GPT back to the top of the list. Rumors are there's 4 more models, possible "GPT 5.2" getting ready to release soon to put OpenAI back on top in the AI race. In the meantime, Gemini 3 Pro, Grok 4.1, and Claude Opus 4.5 are trading to be at the top in terms of best performing models for text and development. Google is leading in basically every AI use case now: text, images, video and audio. When in doubt, can't go wrong with using a Google model for your AI needs. But what have you been using lately?

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.