Gemini AutoCut

An opinionated, automatic video editor that cuts out pauses and errors on the fly using Gemini AI.

Does the project demonstrate quality application development?

Yes, a clean, intuitive user interface is featured by the project, abstracting the complexity of AI video processing. Robust error handling for file uploads and API states was implemented, ensuring a smooth user experience even when large video files are processed. Best practices in modern web app development are demonstrated by the application architecture, which separates frontend interaction from the heavy-lifting of the Gemini API.

Does the project leverage Google Gemini 3?

Absolutely. Gemini 3's native multimodal capabilities are utilized to process video and audio directly, without the need for separate transcription or frame-extraction pipelines.

Long Context Window: The massive context window is leveraged to feed entire raw video files (or large segments) into the model at once.

Multimodal Reasoning: The content is understood by Gemini 3, allowing for semantic edits (e.g., "Cut out the parts where I stumble" or "Keep only the highlights related to X") to be performed.

Is the code of good quality and is it functional?

The codebase has been structured to be modular and well-documented. Readability and maintainability were prioritized, utilizing clear variable naming and structured function calls to the Gemini API. The core "AutoCut" functionality is fully operational: a video can be uploaded, cutting criteria can be defined (via prompt or presets), and a processed list of timestamps or a modified edit decision list (EDL) ready for export is received by the user.

How big of an impact could the project have in the real world?

A significant impact could be realized for the creator economy and enterprise media. Video editing is known to be time-consuming, often taking hours for just minutes of final footage. High-quality editing is democratized by "Gemini AutoCut," reducing the "rough cut" phase from hours to seconds. The barrier to entry for new creators is lowered, and throughput for professional editors and marketing teams is drastically increased.

How useful is the project to a broad market of users?

A massive market ranging from social media influencers (YouTube, TikTok) to corporate educators and podcasters is served. The same bottleneck is faced by anyone who produces video content: editing. By the most tedious parts of the workflow being automated (removing silence, mistakes, or irrelevant tangents), immediate utility is provided to millions of content creators worldwide.

How significant is the problem the project addresses, and does it efficiently solve it?

The problem of "editing fatigue" is addressed, a major pain point that leads to creator burnout and slower content cycles. While decibel levels (silence detection) are mostly relied upon by current tools, this is efficiently solved by "Gemini AutoCut" through the introduction of semantic intelligence—understanding context, not just volume—providing a much more refined result with minimal user effort.

How novel and original is the idea?

While silence removers exist, semantic auto-cutting is presented as a novel application enabled specifically by Gemini 3. The ability for an AI to be asked to "Make this video more fast-paced and exciting" and for clips to be intelligently trimmed based on visual and audio cues is considered a leap forward from standard, rule-based editing scripts.

Does it address a significant problem or create a unique solution?

A unique solution is created by generative AI reasoning being combined with timeline editing. Instead of text or images just being generated, Gemini's reasoning is applied to the temporal structure of media. Video editing is turned from a manual labor task into a supervisory task.

Is the problem clearly defined, and is the solution effectively presented through a demo and documentation?

The problem (editing takes too long) is clearly defined in the submission. A real-world example is walked through in the demo video: a raw, unpolished recording is taken and transformed into a tight, publish-ready clip using the tool. Step-by-step instructions for reproduction are provided in the accompanying README.

Have they explained how they used Gemini 3 and any relevant tools?

Yes. It is detailed how the Gemini 3 model is initialized, how multimodal file uploads to the File API are managed, and how system prompts are structured to output machine-readable timestamps (JSON/EDL) rather than just chat text. The integration with [mention other tools, e.g., FFMPEG, Python, Streamlit, etc.] to perform the actual video slicing is also explained.

Have they included documentation or an architectural diagram?

A README.md file has been included that outlines the project structure, setup instructions, and an architectural flow diagram showing how video data flows from the user -> Google AI Studio/Gemini API -> Processed Metadata -> Final Video Output.

Built With

• gemini
• google-ai-studio
• react
• typescript