Written with ChatGPT 4o.

I recently ran into a workflow challenge while recording video content: I wanted to capture both my camera feed and my screen display, but instead of overlaying them in OBS (as is typical for live streaming), I wanted to record them separately so I could later compose them freely in DaVinci Resolve. My goal was to keep both sources clean and flexible for editing—ideal for adding effects, cuts, or repositioning in post.

This post is the result of a conversation I had with ChatGPT 4o, and it really helped me streamline this process.

Here's what I wanted to do.

• Record both camera and screen in OBS.
• Avoid overlaying them live.
• Import both into DaVinci Resolve as separate video sources.
• Compose freely in the timeline (resize, reposition, etc).

And here's the solution proposed by ChatGPT.

There are two main ways to approach this, depending on how much control you want:

Option 1: Record Side-by-Side in OBS, Split Later with FFmpeg

1. Set up OBS canvas with camera and screen next to each other (e.g. camera on the right, display on the left).
2. Record the session as a single video file.
3. Use ffmpeg to split that file into two separate videos.

Example FFmpeg command:

ffmpeg -i input.mp4 -filter_complex "[0:v]crop=960:1080:0:0[left]; [0:v]crop=960:1080:960:0[right]" -map "[left]" display.mp4 -map "[right]" camera.mp4

Adjust the resolution and crop values depending on your canvas layout.

Option 2: Record Each Source Separately Using OBS Plugin

Use the Source Record plugin for OBS:

1. Install Source Record.
2. Add it to your camera source and configure it to record to a separate file.
3. Do the same for your display capture.
4. You can still record a full composite if you want, but each input gets its own clean file.

This approach is cleaner and better if you plan to edit often.

🛠 Bonus: Compose in DaVinci Resolve

1. Import both resulting video files.
2. Drop them into your timeline on separate tracks.
3. Resize, crop, and position however you like.

That’s it! This workflow gives me much more flexibility when editing content—especially for tutorials, interviews, or any scenario where I want to fine-tune the composition in post. Hope this helps!

I enjoyed learning about Disney's sodium vapor background removal process, which is used in movies such as Mary Poppins (1964), Bedknobs and Broomsticks (1971), and Pete’s Dragon (1977).

This method works much better than green and blue chroma keys, but as the video experiment shows, it's much more challenging to achieve.

How to run Google Gemma 2B- and 7B-parameter instruct models locally on the CPU and the GPU on Apple Silicon Macs.

See transcript ›

An overview of esbuild, a JS bundler up to 100x faster than its competitors.

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Creating grids with native HTML5 and the "display: flex" CSS property.

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How to encode an image dataset to reduce its dimensionality and visualize it in the 2D space.

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Where are voice memos stored on the Mac?

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How to use TensorFlow inside of a Docker container.

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How to sort a Vue.js view by different attributes and toggle different view modes.

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How to hide un-compiled Vue templates while loading.

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How to build a website with the Next.js React framework and TypeScript.

# TL;DR
npx create-next-app@latest --ts
cd my-app
npm run build
npm start
# ready - started server on 0.0.0.0:3000, url: http://localhost:3000

See transcript ›

Load Vue JS and Lodash from CDNs.

See transcript ›

Here's a video in which I test if OpenAI's DALL-E can generate usable texture maps from an uploaded image.

This texture comes with one of Apple's project examples and the idea of generating textures with DALL-E came from Adam Watters on Discord.

My video on how to use the YouTube Data API v3 to upload videos to your channel from Python scripts and the command line is now on YouTube.

The video walks through how to create a project in the Google API Console, register an application, generate credentials, and use them in a Python script that can be called from the command-line interface to upload videos to your YouTube account with a web browser.

Types of authorization credentials

• OAuth 2.0. The application first sends a client ID and client secret to obtain a token. (This is the method used in the video.)
• API keys. The key identifies your project and provides API access, quota, and reports.
• Service account. Server-to-server, app-level authentication using robot accounts.

Google Account, Project, and Application

• You need a Google Account to access the Google API Console, request an API key, and register your application.
• Create a project in the Google Developers Console and obtain authorization credentials so your application can submit API requests.
  • Go to the API Console and select the project that you just registered.
  • Visit the Enabled APIs page. In the list of APIs, make sure the status is ON for the YouTube Data API v3.
• Go to the Credentials page and create one.
  • Project name
  • Organization (optional)
  • Click Create
• Your project should now be showing in the dropdown next to Google Cloud Platform.

Create OAuth 2 credentials

• Open the Credentials page again.
• Set up the login screen info
• Add scopes (e.g, https://www.googleapis.com/auth/youtube.upload)
• Add test users

Set up a Python environment with the Google API Client Library with Anaconda

conda create -n yt python=3.8 -y && conda activate yt
pip install google-api-python-client
pip install google_auth_oauthlib
python -c "from apiclient.discovery import build;print(build)"

Upgrading the Python upload video script to Python 3

As the upload_video.py sample is written in Python 2, there are minor edits that need to be done to upgrade to Python 3. (Here's the Python 3 version.)

• Add from http import client
• Replace appearances of httplib. with client.
• Update the syntax of except and print statements from Python 2 to 3.

Dispose the created conda environment

You can delete the previously created Python environment when you're done as follows.

conda remove -n yt --all -y

Watch Upload Videos to YouTube with the Data API in Python