How I Turn One Idea Into a Repeatable Product Pipeline (No GPU)

Description and use

An automated Python pipeline of four scripts eliminates duplicate uploads, ensures compliant metadata, converts Markdown to styled PDFs, and packages digital products, running on a single vCPU with 2 GB RAM and no GPU.

Problem

Duplicate uploads and metadata errors caused account flags and rejected batches when uploading vector assets to Adobe, Vecteezy, and Dreamstime.

Outcome

Automated pipeline eliminated duplicate uploads, ensured metadata compliance, produced styled PDFs, and packaged products efficiently on minimal hardware.

Workflow

• Create folder layout with assets/, output/, ready‑to‑submit/, packs/, research/, scripts/
• Run make_metadata.py to generate platform‑specific CSV headers
• Use upload_tracker.py to record and prevent duplicate uploads
• Convert Markdown docs to styled PDFs with md2pdf.py
• Package assets, cover, and thumbnail into a zip with pack_product.py
• Track upload status and repeat for each product

Evidence

Adobe, Vecteezy and Dreamstime each reject a wrong CSV header instantly. Not the file, the header.

Source

I once uploaded the same 12 vectors twice in one afternoon. Same folder, same batch script, one coffee too many. The metadata had matching titles, so the platform flagged the account for duplicate submission.

Source

All four scripts fit in one idea of a pipeline. I used them to package 39 products, 856 MB of output, on a single vCPU with 2 GB of RAM and no GPU.

Source