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Applied AI · Case studyCode reviewed

GameplayCollector V2A library that checks what it keeps.

A collector driven by a local AI that researches, selects and archives videos to Drive with hash checks.

PythonOllamaFFmpegrcloneGoogle Drive
Illustration: video clips sorted into a private archive

A library that checks what it keeps.

A collector driven by a local AI that researches, selects and archives videos to Drive with hash checks.

My role

Product definition and AI-assisted development, connecting requirements, implementation and review.

Local pilot with 73 tests passing. The first delivery archived 18 videos (7.16 GB) with size and MD5 verified; re-running the same 18 IDs produced 0 new downloads.

Building an editing library without broken, duplicate or off-topic files.

You type a subject; a local model (Ollama, CPU only) plans the searches and ranks real results. The program validates schema, paths, duration and limits before downloading, runs FFmpeg QC and only sends to Drive once size and hash match.

Ask — Plain-language request.

An explanatory diagram of the running workflow. This is not live system monitoring.

Searches planned by a local AI

Implemented¹

FFmpeg QC and hash checks

Implemented¹

Private Drive upload through rclone

Implemented¹

¹ Identified in code or implementation documentation; not equivalent to a new production test.

PythonOllamaFFmpegrcloneGoogle Drive

When a source asks for a bot check, the batch pauses and reports it: no cookies, proxies or workarounds. The AI gets no secrets, runs no commands and cannot invent IDs.

A small model is useful when the program around it validates everything it returns.

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