Copyright 2026 © Nabil Samari
Why I Automated Social Media Posts
As an entrepreneur, time is the most expensive resource. For one client (Luigi's, an Italian restaurant at Helsinki Airport), weekly social media posting took roughly 2 hours:
- Browse the image library and pick photos (15 min)
- Write English posts (30 min)
- Resize images for Instagram (15 min)
- Log into Buffer and create drafts (20 min)
- Double-check hashtags, times, both locations (10 min)
This repeated every single week. I decided to automate the entire chain.
The Architecture
Here's the pipeline I built:
Nextcloud (images) → Ollama/qwen3-vl (GPU vision) → DeepSeek V4 Pro (text) → Buffer (publishing)
Each step in detail:
1. Images in Nextcloud — Customer photos sync automatically to Nextcloud. The Hermes agent reads the folder contents via WebDAV.
2. Local GPU Vision — A Qwen3-VL model (8.8B parameters, Q8_0 quantization) analyzes images locally on an RTX 4070 Ti SUPER GPU. Each image takes ~15 seconds. The model correctly identifies food, mood, colors, and details — even from Finnish-language advertising images.
3. Text Generation — DeepSeek V4 Pro writes a complete social media post from the vision output. The text follows the brand's style (in Luigi's case: casual, energetic, English). Posts are 100-130 words, include both restaurant locations, opening hours, and hashtags.
4. Buffer Drafts — Hermes creates a Nextcloud public share link for the image and sends it with the text to Buffer's GraphQL API. Both a Facebook draft (text only) and an Instagram draft (text + image) are created automatically. Posts are scheduled Monday through Friday at different times.
5. Reporting — Once complete, Hermes sends a report to Discord: how many posts created, for which days, and a link to Buffer. If anything fails, you get an immediate notification.
The entire pipeline runs as a cron job every Monday at 9:00 AM.
