Recortes
youtube-shorts-pipeline
Verticals: one command turns a topic into a researched, voiced, captioned, and uploaded YouTube Short.
★ 2,3 mil estrelasMIT
Ver o guia →MoneyPrinter é um projeto de recortes de código aberto de FujiwaraChoki (FujiwaraChoki/MoneyPrinter no GitHub). Tem 14.063 estrelas no GitHub e 1.828 forks, é publicado sob a licença MIT e foi atualizado pela última vez no GitHub em 26 de março de 2026.
MoneyPrinter — automate YouTube Shorts from a single topic: local Ollama writes the script and metadata, stock footage and voiceover are assembled into a finished short, MIT
Clone the repo and run the interactive setup script (setup.sh), or follow docs/quickstart.md — the docs are centralised in the docs folder.
Point it at your local Ollama models for script/metadata generation and fill in the .env values the setup asks for (footage sources and any services you use).
Give it a topic; the queue (API + worker) generates the script, assembles footage and voiceover, and renders the finished Short.
The whole pipeline — script, metadata, footage, voiceover, edit, render — from a single topic prompt.
Scripts and metadata come from local Ollama models: no per-video LLM API bill and prompts stay on your machine.
The DB-backed generation queue (API + worker + Postgres in Docker) makes long renders reliable and resumable.
An interactive setup script and centralised docs (quickstart, Docker, troubleshooting) instead of scattered README fragments.
Then use the setup script or the quickstart in docs/.
git clone https://github.com/FujiwaraChoki/MoneyPrinter.git
cd MoneyPrinterThe documented guided route from the docs index.
./setup.shQuickstart, Docker deployment and troubleshooting live here.
docs/README.md (in the repo)Also on this shelf: videocutterAI — web-based highlight finder that auto-generates clips from your uploads. Nearby on the same shelf, LinguaClip turns any video or podcast into language listening practice with Anki cards. Readers working through the Clipping shelf also stop at video-clipforge, 9-stage AI video factory: idea to finished short, with self-improving scores.
FujiwaraChoki (Sami) and contributors — FujiwaraChoki/MoneyPrinter on GitHub.
Yes — MIT licensed. You may still need accounts/keys for footage sources or services you configure, and a machine that can run Ollama and render video.
The current version is Ollama-first: script generation and metadata are powered by local Ollama models, per the README.
Against hosted AI Shorts generators, MoneyPrinter is free, local and fully inspectable — you own the pipeline and the queue — at the cost of running Ollama, Postgres and rendering yourself. Against manual editing, it trades craft for volume and speed.
Clone the repo, run ./setup.sh (or follow docs/quickstart.md), configure Ollama and your .env, then queue your first topic.
Verticals: one command turns a topic into a researched, voiced, captioned, and uploaded YouTube Short.
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