This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine
I initially designed this application with the intention of entering the Week 3 challenge. However, since I found out about the event late and missed the deadline, I adapted it to perfectly align with the Week 4 theme: Ghost in the Machine.
The application is called Policy Phantom, an open-source AI-based early detection tool that scans YouTube video scripts or transcripts for potential monetization policy violations before the video is uploaded. I interpreted this week's theme by analogizing the YouTube algorithm as a "phantom" that silently judges content, and this application acts as a medium to read the curse so creators can save their videos from the "demonetization graveyard."
Note: The local computer I used has fairly slow specifications for running LLM inference, so I intentionally fast-forwarded some of the parts in this demo video for the sake of the viewers' time efficiency.
Policy Phantom adalah aplikasi compliance checker berbasis open-source AI yang dirancang untuk memindai transkrip atau naskah video YouTube secara lokal. Aplikasi ini mendeteksi potensi pelanggaran pedoman monetisasi (seperti Reused Content, Synthetic/AI Content, hak cipta, dan sensitivitas bahasa) sebelum video diunggah, guna mencegah risiko demonetisasi (Demonetization Graveyard).
Proyek ini dibangun khusus untuk Hacktoberfest Challenge Week 4: "Ghost in the Machine".
Pada pembuatan konten digital, isu privasi, keamanan naskah, dan biaya operasional menjadi tantangan utama:
This application is built on a foundation of open-source AI to ensure entirely local execution:
policy.json file. Once this data sync is complete, the API feature is ignored, and the
response = requests.post('http://localhost:11434/api/generate', json={
"model": "gemma2:2b",
"prompt": f"{system_prompt}\n\nTranscript/Script:\n{transkrip_ringkas}",
"options": {"temperature": 0.2},
"stream": False
})
For a content creator, unpublished scripts and video drafts are highly sensitive intellectual property. If using closed APIs, creators must send that raw data to third-party servers.
The open-weight approach I used (Gemma 2 and Whisper) allows this application to operate with an offline-first approach. This provides absolute assurance of content privacy, consumes zero monthly API quota costs, and frees the application from the risks of closed API services that could suddenly change, be restricted, or shut down.
The most memorable moment while developing this application was when I ran tests using old videos from several YouTube channels that had actually been hit by demonetization sanctions. When the Risk Meter indicator lit up bright red and the Gemma model accurately highlighted the exact phrases that caused it, I felt a genuine sense of horror. It felt like the application was summoning back the dark memories and anxiety creators feel when facing the mysterious YouTube strike algorithm.