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The aspiration of infrastructure-as-code tools is that you use them to manage everything. You create all your resources by writing a file that defines them, and any time you change something, you update the definition in that file. It’s a nice idea, but easier said than done. Creating the resources with infrastructure-as-code isn’t too bad; the tricky part is updating them later. If you have a large or thorny codebase, it may not be obvious where a particular resource is defined – when you want to make changes, which file should you update? If you’re in a hurry, it’s tempting to make a manual change now, and tell yourself you’ll come back to update the code “later” – when you have more time to find the file – but “later” rarely comes. To make this easier, I recommend tagging all your resources with a link to the file where the resource is defined. At work, we’re managing AWS resources defined in Terraform. The Terraform AWS Provider supports setting default tags – you write them once, and then they get applied to every resource that can be tagged. This is what that looks like for us: provider "aws" { default_tags { tags = { TerraformConfigurationURL = "https://github.com/wellcomecollection/aws-account-infrastructure/tree/main/accounts/storage" } } } The TerraformConfigurationURL points to a specific subfolder of a GitHub repository, which is where this particular set of Terraform configuration files are stored. If we’re looking at a resource in the AWS console, we can look for the TerraformConfigurationURL tag. If it’s there, we can follow the URL to find the Terraform where the resource is defined. This is particularly simple with Terraform and AWS, because of the support for default tags. It might be more cumbersome if you’re using a different tool or managing different types of resources, but I still think it’s worth the benefits. I originally created these tags to solve the “where is this thing defined” problem. I’ve found something in the...
18th Aug 2023

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More from alexwlchan

Abusing ID3 chapters to turn videos into glanceable podcasts

I listen to a lot of podcasts, and I like how they fit around other tasks. I press play, lock my phone, and put it down. I’m free to wash the dishes, fold the laundry, or shop for groceries. Unfortunately, more and more information is only published as a video. Technical talks, conference sessions, video essays – they don’t work in an audio-only podcast app. I could convert these videos to MP3 files, but that breaks down the moment a video isn’t pure spoken word. If a speaker says, “Look at this slide” or holds up a diagram, an audio-only file leaves me stranded. I don’t want to give up the podcast player I like, nor stare at a screen for an hour – but I do want the information in these videos. To solve this, I’m abusing my podcast player’s chapter support. This gives me the best of both worlds: I can listen to a video as audio-first, and glance at my lock screen if I need a moment of visual context. The idea: Chapters every few seconds MP3 files can have ID3 metadata, and ID3 metadata can include chapters. A chapter covers a particular time range, and it can have an associated title, description, and cover art. My podcast app of choice is Overcast, which can’t play videos, but it does have robust chapter support. I can jump between chapters, navigate a table of contents, and see per-chapter cover art. To get videos into Overcast, I’m creating MP3 files with a new chapter every few seconds, and the per-chapter cover art is a corresponding frame from the video. As I play the file, I get a slow, stop-motion-like rendition of the original video. If my phone is locked, I can glance at my lock screen and see the current frame in the Now Playing screen. Overcast is developed by Marco Arment, and I got this idea from Forecast, his app for adding chapters to podcasts. In particular, I was struck by its ability to create chapters that don’t display in the chapter list – ideal if I don’t want a table of contents with hundreds of entries. As I was developing my script, I compared my output to the output from Forecast to ensure I was creating the chapters correctly. The code: FFmpeg and Mutagen There are three steps in this process: Convert a video file to an MP3 Extract images from the video at a fixed interval Insert the images as hidden chapters in the MP3 file Let’s go through each in turn. 1. Convert a video file to an MP3 Converting a video file to an MP3 is a single FFmpeg command: ffmpeg -i video.mp4 audio.mp3 This is consistently the slowest step of the process, and I do wonder if I could use different settings or an alternative encoder to make it go faster – but it’s not slow enough to be worth further investigation. 2. Extract images from the video at a fixed interval Extracting images from a video needs a more complicated FFmpeg command: ffmpeg -i video.mp4 \ -vf 'fps=1/5,scale=iw*sar:ih,scale=min(iw\,945):min(ih\,945):force_original_aspect_ratio=decrease' \ thumbnail_%04d.jpg This extracts an image every 5 seconds, downscales any image larger than 945 pixels square (while preserving the original aspect ratio), and saves the results as sequentially numbered JPEG images (thumbnail_0001.png, thumbnail_0002.png, and so on). The key is the -vf flag, which defines two FFmpeg filters: The fps filter selects one frame every 5 seconds (fps=1/5). The first scale filter scales the width based on the sample aspect ratio (scale=iw*sar:ih). Without this filter, frames can be stretched and distorted. The second scale filter scales the input video, preserving the original aspect ratio (force_original_aspect_ratio=decrease), and ensuring the output images fit within 945×945px or the size of the input video, whichever is smaller. My limit is 945 pixels because that’s the largest size that cover art is shown on my iPhone. This filter still isn’t completely correct – it sometimes creates images from portrait videos that are smaller than I’m expecting – but it’s good enough. These are only thumbnails for glancing at, and if I want to change it later, I can always do the image resizing outside FFmpeg. 3. Insert the images as hidden chapters in the MP3 file Inserting the chapters into the MP3 file is more complicated. Although FFmpeg has basic support for ID3 metadata, as far as I know, it can’t insert chapters with per-chapter artwork. Instead, I’m going to reach for Python and the Mutagen library. Here’s the code to add a chapter to an MP3 file: from mutagen.id3 import APIC, CHAP, ID3, PictureType audio = ID3("audio.mp3") with open("thumbnail_0001.jpg", "rb") as f: img_data = f.read() image_frame = APIC(mime="image/jpeg", type=PictureType.OTHER, data=img_data) chapter_frame = CHAP( element_id="chp1", start_time=0, end_time=5 * 1000, sub_frames=[image_frame] ) audio.add(chapter_frame) audio.save() This creates a single chapter that lasts the first 5 seconds (0 to 5000 milliseconds), and the per-chapter cover art is thumbnail_0001.jpg. If we ran this in a loop, we could add images for every 5 second slice of the original video. This code is inserting two frames into the ID3 metadata: The CHAP (chapter) frame contains the timing information, and it can have subframes for metadata like title, chapter art, or associated URL. The APIC (attached picture) subframe contains information about a picture, which can either be a blob of image data or a URL to an image on the web. Normally, you’d also insert a CTOC frame which defines a table of contents, but I don’t want a TOC with hundreds of 5-second chapters, so I’m deliberately not doing this here. This is allowed by the ID3 spec – you’re not required to insert a CTOC frame if you’re using chapters, and you can have chapters that aren’t listed in your table of contents. To work out which frames I needed, I used Forecast to create some chapters by hand, and I inspected their frames. In particular, loading an MP3 and calling Mutagen’s pprint() method shows a human-readable list of frames, and then I could drill into the individual fields: from mutagen.id3 import ID3 audio = ID3("audio.mp3") print(audio.pprint()) I wrapped all this code in a project called glancecast, which allows you to convert a video file with a single command, with optional flags to set the frame length and chapter art size: $ python3 glancecast.py interesting_talk.mp4 interesting_talk.mp3 The process takes a minute or so to complete, most of which is spent transcoding the video file to MP3. The resulting MP3s are usually 40 to 50 MB in size, which is very reasonable. The outcome: How it looks in practice Here’s what one of these “glanceable” podcasts looks like in Overcast and on my lock screen: Maggie Appleton presented this talk over two years ago and it’s been on my “talks to watch” list ever since. Once I put it in Overcast? I listened to it in less than a day. It’s not a lot of extra information, but enough that I can quickly glance down and get the gist of what a speaker is saying. Both views update with a new frame every few seconds, or I can put my phone in my pocket and ignore the screen. I’ve used this approach for half a dozen videos so far, and I’m happy with the results. I expect to keep using it, because I have a long queue of videos I’ve been meaning to watch. If you’d like to try this, check out glancecast for the full code and instructions. [If the formatting of this post looks odd in your feed reader, visit the original article]

11 hours ago 1 votes
How Tailscale tracked down a 16-year-old SQLite bug →

I wrote a post for the Tailscale blog about a long-running series of corruption incidents, and how they eventually led us to find an SQLite bug that predates my entire programming career. I’m incredibly proud of this, both the work and the blog post. Before Tailscale, I was coming from smaller teams where I didn’t get to tackle problems of this scale or complexity. This was exactly the sort of tricky, deep technical challenge I wanted to be part of (though I’d rather it hadn’t been quite so stressful)! I’m glad I got to play a small part in these incidents, and I learnt so much from the more experienced engineers I worked with. I never want to hear the words “SQLite corruption” again, but if I do, I’d want to have Tailscalars at my side. Writing the blog post has a blast, too. The piece transformed from a rough draft into a solid, engaging piece of writing, thanks to thoughtful feedback from many people at Tailscale. Most of my writing is self-edited, and it’s always a pleasure to work with a dedicated editor. Please check out the blog post if you haven’t read it already – I think it’s a fascinating technical story, and one readers of this site are bound to enjoy. [If the formatting of this post looks odd in your feed reader, visit the original article]

13th Aug 2026 2 votes
I don’t want to repeat repeat myself

Yesterday at work, a customer spotted a typo in our UI: “you can use the use the Tailscale CLI”. After the typo was fixed, I wanted to find other cases of accidentally repeated words or phrases. I used two regular expressions to search every codebase for unnecessary repetition. The first regex finds repeated words: \b([A-Za-z]+) \1\b Backfill product data from from Stripe Learn more about about inviting users Argument must be be one of host name, IP set name, IP prefix, or IP There’s a capturing group for a single word made up of letters ([A-Za-z]+), a space, then a backreference to the group. That expression is surrounded by word boundary assertions \b, which check that I’m at the start/end of a word – this avoids finding repeated character sequyences that within longer words, like “with the reason”. The second regex finds repeated phrases: \b([A-Za-z]+ [A-Za-z]+) \1\b Follow the steps in the in the "How to" section Log in to in to your account To configure federated identities federated identities using the Go SDK I’ve changed the capturing group, so now it looks for two words separated by a space. Sometimes repetition is useful, like when I really really went to emphasise a point, but often it’s just a typo. Cleaning up these mistakes has been a fun Friday cleanup task. [If the formatting of this post looks odd in your feed reader, visit the original article]

3rd Jul 2026 1 votes
Using Pytester to test my Playwright fixtures

A month ago, I wrote about my Playwright fixture for testing static websites in a browser. I’ve been copying that fixture from project-to-project, but recently I decided to add it to chives, the utility library I use for all my static websites (or tiny archives). One of my rules for chives is that everything in it has to be tested – but how do you test a pytest fixture? Test code is just code, and it isn’t immune to bugs. Who tests the tests? Enter Pytester, a tool designed for testing pytest plugins. Pytester allows you to run isolated test suites, make assertions about the outcomes, and verify the behaviour of custom fixtures. In your top-level test suite, you always want everything to be passing, but with Pytester you can write a mixture of passing and failing tests, and check the results are what you expect. Pytester is disabled by default, so you first enable it in your top-level conftest.py file (the pytest configuration file where you configure plugins and fixtures): # conftest.py pytest_plugins = ["pytester"] Here’s an example of using Pytester where we create a test suite with two tests and check that one passes, one fails: from pytest import Pytester def test_with_pytester(pytester: Pytester): """ Run an isolated test suite with pytester. """ # Make a temporary pytest test file pytester.makepyfile( """ def test_arithmetic(): assert 2 + 2 == 4 def test_list_inclusion(): assert "yellow" in ["red", "green", "blue"] """ ) # Run the isolated test suite with pytest result = pytester.runpytest() # Check that one test passed, one failed result.assert_outcomes(passed=1, failed=1) I can imagine creating something similar with some complicated collection of nested functions, exec() and pytest.raises, but using Pytester is a cleaner interface than what I’d build. Under the hood, Pytester creates a temporary directory, writes specified files into it, then runs a fresh pytest subprocess against it. It has helper functions for writing files, including Python files (makepyfile), a conftest.py file (makeconftest), and plain text files (maketxtfile). When we’re testing a fixture, we can create a conftest.py file that imports that fixture, then reference it in the tests. Here’s a more complicated example, where we import one of my Playwright fixtures in my conftest.py, write an HTML file into the temporary directory, then use them both in the test: from pytest import Pytester def test_browser_fixture(pytester: Pytester): """ Try testing the browser fixture with pytester. """ # Make a conftest.py file pytester.makeconftest(""" from chives.browser_fixtures import browser """) # Make an HTML file (pytester.path / "greeting.html").write_text(""" <p>Hello world!</p> """) # Make a temporary pytest test file pytester.makepyfile( """ from chives.browser_fixtures import file_uri from playwright.sync_api import Browser, expect def test_browser_fixture(browser: Browser) -> None: uri = file_uri("greeting.html") p = browser.new_page() p.goto(uri) expect(p.get_by_text("Hello world!")).to_be_visible() """ ) # Run the isolated test suite with pytest result = pytester.runpytest() # Check that one test passed result.assert_outcomes(passed=1) This pattern is sufficient for many fixtures, but it doesn’t work for Playwright – if you run this test, the isolated test suite gives an error rather than a passing test. Playwright needs you to install a web browser to work (for example, playwright install webkit), and Pytester runs in a sufficiently isolated environment that Playwright can’t find the browsers you already have installed. We could run the install command inside the temporary directory, but that would be slow and inefficient – it would be better if we could tell Playwright to look for the already-installed browsers elsewhere. If we set the PLAYWRIGHT_BROWSERS_PATH environment variable inside our isolated test suite, Playwright will look there for browsers. First, we need to work out where browsers are installed – we could hard-code the location, or we could inspect the executable_path property property on a browser: from pathlib import Path from playwright.sync_api import sync_playwright import pytest @pytest.fixture(scope="session") def playwright_browsers_path() -> str: """ Return the cache directory where Playwright browsers are installed. """ with sync_playwright() as p: # In my local builds, this returns a path like: # # ~/Library/Caches/ms-playwright/webkit-2272/pw_run.sh # # Unwrap two levels to get to the `ms-playwright` folder. return str(Path(p.webkit.executable_path).parent.parent) Then we need to set this as an environment variable inside the Pytester test suite. I couldn’t find an easy way to set an environment variable; the best approach I came up with was to modify os.environ inside the conftest.py file. (Perhaps we could access the MonkeyPatch object and set more environment variables, but using private attributes is icky.) Here’s how the new test starts: def test_browser_fixture(pytester: Pytester, playwright_browsers_path: str): """ Test the browser fixture with pytester. """ # Make a conftest.py file pytester.makeconftest(f""" from chives.browser_fixtures import browser import os os.environ["PLAYWRIGHT_BROWSERS_PATH"] = {playwright_browsers_path!r} """) ... and now the overall test passes. Here’s the complete code for the new test: test_browser_fixture.py from pathlib import Path from playwright.sync_api import sync_playwright import pytest from pytest import Pytester @pytest.fixture(scope="session") def playwright_browsers_path() -> str: """ Return the cache directory where Playwright browsers are installed. """ with sync_playwright() as p: # In my local builds, this returns a path like: # # ~/Library/Caches/ms-playwright/webkit-2272/pw_run.sh # # Unwrap two levels to get to the `ms-playwright` folder. return str(Path(p.webkit.executable_path).parent.parent) def test_browser_fixture(pytester: Pytester, playwright_browsers_path: str): """ Test the browser fixture with pytester. """ # Make a conftest.py file pytester.makeconftest(f""" from chives.browser_fixtures import browser import os os.environ["PLAYWRIGHT_BROWSERS_PATH"] = {playwright_browsers_path!r} """) # Make an HTML file (pytester.path / "greeting.html").write_text(""" <p>Hello world!</p> """) # Make a temporary pytest test file pytester.makepyfile( """ from chives.browser_fixtures import file_uri from playwright.sync_api import Browser, expect def test_browser_fixture(browser: Browser) -> None: uri = file_uri("greeting.html") p = browser.new_page() p.goto(uri) expect(p.get_by_text("Hello world!")).to_be_visible() """ ) # Run the isolated test suite with pytest result = pytester.runpytest() # Check that one test passed result.assert_outcomes(passed=1) The full test suite is more extensive, and checks that certain scenarios fail or error – will the fixtures spot the mistakes I expect them to? For example, my Page fixture is meant to load a page and fail the test if there are any console warnings or errors; does it actually fail the test correctly? I don’t expect to use Pytester very often, because it’s rare for me to write fixtures complex enough to need their own test suite – but sometimes I do, and it’s good to know how to create another layer of safety net. [If the formatting of this post looks odd in your feed reader, visit the original article]

5th Jun 2026 1 votes
Using Playwright to test my static sites

I build a lot of static websites – including this site and all of my local media archives – and I want to test them. Most of my pages are static HTML and I can write automated tests that analyse the HTML, but for more complex sites I have JavaScript that runs in the browser and modifies the page. The only way to test that functionality is to open the page in a browser, click around, and see what happens. I could do that manually, but it quickly gets tedious. To automate this process, I’ve been using a testing framework called Playwright, which is designed for this sort of end-to-end testing. It’s a tool that allows you to programatically control a web browser, look at the contents of a page, and make assertions about what’s there. Playwright can be used to test or script any kind of web app; I’m using it for static sites because those are the only web apps I have. Playwright is available as a CLI, or there are libraries to use it with TypeScript, Python, .NET, and Java. All my other tests are written in Python, so that’s what I’m using. Writing a basic test with Playwright To set up Playwright with Python, you install the playwright library using pip or uv, then install a web browser for Playwright to control. (You can’t use Playwright with the browser you use day-to-day; you need special binaries with control hooks.) I use Safari as my main browser, and Safari is based on WebKit, so let’s install that: $ uv pip install playwright $ python3 -m playwright install webkit Then we can start writing tests. Here’s a basic test in which Playwright launches WebKit, opens example.com, and checks the text Example domain is visible on the page: from playwright.sync_api import expect, sync_playwright def test_basic_playwright() -> None: """ Run a basic test with Playwright: load a web page and check it contains the expected text. """ with sync_playwright() as p: browser = p.webkit.launch() page = browser.new_page() page.goto("https://example.com/") expect(page.get_by_text("Example domain")).to_be_visible() browser.close() For a larger app, you might run your tests with multiple browsers to check compatibility – Playwright supports lots of other browsers, including Chromium, Firefox, and Mobile Safari in emulation. I’m just testing private sites where I’m the only user, so a single browser is fine. This test passes in about half a second on my computer. That’s fine for a single test, but it would add up if I had lots of tests, each starting and stopping the browser every time. It would be nice to make that process faster, and to reduce some of the boilerplate as well. A pair of Playwright fixtures To reduce the repetition and reuse the browser instance, I have a couple of pytest fixtures to simplify things. The first is a session-scoped fixture that starts the browser at the start of the test run, and closes it when I’m done: from collections.abc import Iterator from playwright.sync_api import Browser, sync_playwright import pytest @pytest.fixture(scope="session") def browser() -> Iterator[Browser]: """ Launch an instance of WebKit to interact with in tests. """ with sync_playwright() as p: webkit = p.webkit.launch() yield webkit webkit.close() Because this is a session-scoped fixture, it only runs once per test suite – that means the browser is only started once, then the same instance is reused for all the tests. This makes a large test suite significantly faster. My other fixture is a bit more complicated – it gives you a page to interact with, and at the end of the test it checks the page didn’t have any warnings or errors. This is a strict approach, which helps me spot errors in areas I wasn’t explicitly testing. Here’s the fixture: from collections.abc import Iterator from playwright.sync_api import Browser, Page import pytest @pytest.fixture(scope="function") def page(browser: Browser) -> Iterator[Page]: """ Open a new page in the browser. If there are any errors or warnings when loading the page, the test will fail when this fixture is cleaned up. """ p = browser.new_page() # Capture anything that gets logged to the console. console_messages = [] p.on("console", lambda msg: console_messages.append(msg)) # Capture any page errors page_errors = [] p.on("pageerror", lambda err: page_errors.append(err)) yield p # Check there weren't any console errors logged to the page. console_errors = [ msg.text for msg in console_messages if msg.type == "error" or msg.type == "warning" ] assert console_errors == [] # Check there weren't any page errors assert page_errors == [] These two fixtures allow for tighter, faster tests, focusing on what the test is actually checking. Here’s the example test, rewritten to use this fixture: def test_playwright_with_fixture(page: Page) -> None: """ Run a test using my Playwright fixture: load a web page, check it contains the expected test, and check it loads without errors. """ page.goto("https://example.com/") expect(page.get_by_text("Example domain")).to_be_visible() I use the page fixture for most tests, where I want to spot any unexpected errors or warnings. If I’m testing error handling specifically, I use the browser fixture and create a new page which isn’t treated as strictly. Getting file:/// URIs for Playwright Normally Playwright is used with http: and https: URLs, but my static websites are stored as HTML files on my local disk, and I often open them with file: URLs. I could spin up a web server in my tests, but that’s extra overhead and might affect the results – there are subtle differences between how browsers handle pages opened with file: vs http:. To convert file paths to file: URLs, I use the pathname2url function from the urllib.request module. I combine this with os.path.abspath to get a full URL I can pass to Playwright: >>> from os.path import abspath >>> from urllib.request import pathname2url >>> path = "index.html" >>> pathname2url(abspath(path), add_scheme=True) 'file:///Users/alexwlchan/repos/alexwlchan.net/index.html' Assertions in Playwright Playwright has a different set of assertion helpers to regular Python tests, and it takes some getting used to – I still have to consult the documentation when I write new tests. Here are examples of assertions I’ve written using Playwright: Testing that a redirect is working: resp = page.goto("https://alexwlchan.net/projects/chives/files/doesnotexist.txt") assert resp is not None assert resp.status == 200 assert resp.url == "https://alexwlchan.net/projects/chives/files/?missing=doesnotexist.txt" Test that text does or does not appear on a page: from playwright.sync_api import expect page.goto("https://www.example.com") expect(page.get_by_text("Example Domain")).to_be_visible() expect(page.get_by_text("Alex Chan")).not_to_be_visible() or: assert "Example Domain" in page.content() assert "Alex Chan" not in page.content() Locate an element with a CSS selector, and check it does or doesn’t appear on a page: page.goto("https://www.example.com") expect(page.locator("h1")).to_be_visible() expect(page.locator("h2.title")).not_to_be_visible() Locate an element, and make assertions about its attributes: page.goto("https://www.example.com") href = page.locator("a").first.get_attribute("href") assert href == "https://iana.org/domains/example" Locate an element, and make assertions about the text it contains: page.goto("https://www.example.com") assert page.locator("a").inner_text() == "Learn more" Check that an element with particular inner text is visible on the page: page.goto("https://www.example.com/") expect(page.locator('//h1[text()="Example Domain"]')).to_be_visible() Locate an element immediately following a different element. I’ve used this a couple of times when I have tables or definition lists with a label in one element, and a value in another: dt_locator = page.locator('//dt[text()="Profile page:"]') next_dd = dt_locator.locator("xpath=following-sibling::*") assert ( next_dd.inner_html().strip() == '<a href="https://www.flickr.com/photos/nasahqphoto/">NASA HQ PHOTO</a>' ) Check the number of matching elements on a page; for example, the length of a list: page.goto("https://alexwlchan.net/articles/") assert page.locator("#list_of_posts li").count() >= 10 Check the title of the page: page.goto("https://www.example.com/") assert page.title() == "Example Domain" Check the behaviour of the page when JavaScript is disabled: context = browser.new_context(java_script_enabled=False) page = context.new_page() expect(page.locator("noscript .error")).to_be_visible() noscript_elem = page.locator("noscript .error") assert noscript_elem.inner_text() == "You must enable JavaScript to use this page." This is just a fraction of what Playwright can do; it can be used to build far more complicated tests that walk through a web app and test multi-step user flows. I’m only using it to make assertions about snippets of JavaScript, but it’s still useful. For a long time, I told myself that my static sites were simple enough not to need testing, but that didn’t prevent bugs from slipping in, and it limited what I could build. Now I can write proper tests for my sites, I can be more confident I haven’t broken anything, I can experiment faster, and I can try more ambitious ideas. [If the formatting of this post looks odd in your feed reader, visit the original article]

2nd May 2026 1 votes

More in programming

Abusing ID3 chapters to turn videos into glanceable podcasts

I listen to a lot of podcasts, and I like how they fit around other tasks. I press play, lock my phone, and put it down. I’m free to wash the dishes, fold the laundry, or shop for groceries. Unfortunately, more and more information is only published as a video. Technical talks, conference sessions, video essays – they don’t work in an audio-only podcast app. I could convert these videos to MP3 files, but that breaks down the moment a video isn’t pure spoken word. If a speaker says, “Look at this slide” or holds up a diagram, an audio-only file leaves me stranded. I don’t want to give up the podcast player I like, nor stare at a screen for an hour – but I do want the information in these videos. To solve this, I’m abusing my podcast player’s chapter support. This gives me the best of both worlds: I can listen to a video as audio-first, and glance at my lock screen if I need a moment of visual context. The idea: Chapters every few seconds MP3 files can have ID3 metadata, and ID3 metadata can include chapters. A chapter covers a particular time range, and it can have an associated title, description, and cover art. My podcast app of choice is Overcast, which can’t play videos, but it does have robust chapter support. I can jump between chapters, navigate a table of contents, and see per-chapter cover art. To get videos into Overcast, I’m creating MP3 files with a new chapter every few seconds, and the per-chapter cover art is a corresponding frame from the video. As I play the file, I get a slow, stop-motion-like rendition of the original video. If my phone is locked, I can glance at my lock screen and see the current frame in the Now Playing screen. Overcast is developed by Marco Arment, and I got this idea from Forecast, his app for adding chapters to podcasts. In particular, I was struck by its ability to create chapters that don’t display in the chapter list – ideal if I don’t want a table of contents with hundreds of entries. As I was developing my script, I compared my output to the output from Forecast to ensure I was creating the chapters correctly. The code: FFmpeg and Mutagen There are three steps in this process: Convert a video file to an MP3 Extract images from the video at a fixed interval Insert the images as hidden chapters in the MP3 file Let’s go through each in turn. 1. Convert a video file to an MP3 Converting a video file to an MP3 is a single FFmpeg command: ffmpeg -i video.mp4 audio.mp3 This is consistently the slowest step of the process, and I do wonder if I could use different settings or an alternative encoder to make it go faster – but it’s not slow enough to be worth further investigation. 2. Extract images from the video at a fixed interval Extracting images from a video needs a more complicated FFmpeg command: ffmpeg -i video.mp4 \ -vf 'fps=1/5,scale=iw*sar:ih,scale=min(iw\,945):min(ih\,945):force_original_aspect_ratio=decrease' \ thumbnail_%04d.jpg This extracts an image every 5 seconds, downscales any image larger than 945 pixels square (while preserving the original aspect ratio), and saves the results as sequentially numbered JPEG images (thumbnail_0001.png, thumbnail_0002.png, and so on). The key is the -vf flag, which defines two FFmpeg filters: The fps filter selects one frame every 5 seconds (fps=1/5). The first scale filter scales the width based on the sample aspect ratio (scale=iw*sar:ih). Without this filter, frames can be stretched and distorted. The second scale filter scales the input video, preserving the original aspect ratio (force_original_aspect_ratio=decrease), and ensuring the output images fit within 945×945px or the size of the input video, whichever is smaller. My limit is 945 pixels because that’s the largest size that cover art is shown on my iPhone. This filter still isn’t completely correct – it sometimes creates images from portrait videos that are smaller than I’m expecting – but it’s good enough. These are only thumbnails for glancing at, and if I want to change it later, I can always do the image resizing outside FFmpeg. 3. Insert the images as hidden chapters in the MP3 file Inserting the chapters into the MP3 file is more complicated. Although FFmpeg has basic support for ID3 metadata, as far as I know, it can’t insert chapters with per-chapter artwork. Instead, I’m going to reach for Python and the Mutagen library. Here’s the code to add a chapter to an MP3 file: from mutagen.id3 import APIC, CHAP, ID3, PictureType audio = ID3("audio.mp3") with open("thumbnail_0001.jpg", "rb") as f: img_data = f.read() image_frame = APIC(mime="image/jpeg", type=PictureType.OTHER, data=img_data) chapter_frame = CHAP( element_id="chp1", start_time=0, end_time=5 * 1000, sub_frames=[image_frame] ) audio.add(chapter_frame) audio.save() This creates a single chapter that lasts the first 5 seconds (0 to 5000 milliseconds), and the per-chapter cover art is thumbnail_0001.jpg. If we ran this in a loop, we could add images for every 5 second slice of the original video. This code is inserting two frames into the ID3 metadata: The CHAP (chapter) frame contains the timing information, and it can have subframes for metadata like title, chapter art, or associated URL. The APIC (attached picture) subframe contains information about a picture, which can either be a blob of image data or a URL to an image on the web. Normally, you’d also insert a CTOC frame which defines a table of contents, but I don’t want a TOC with hundreds of 5-second chapters, so I’m deliberately not doing this here. This is allowed by the ID3 spec – you’re not required to insert a CTOC frame if you’re using chapters, and you can have chapters that aren’t listed in your table of contents. To work out which frames I needed, I used Forecast to create some chapters by hand, and I inspected their frames. In particular, loading an MP3 and calling Mutagen’s pprint() method shows a human-readable list of frames, and then I could drill into the individual fields: from mutagen.id3 import ID3 audio = ID3("audio.mp3") print(audio.pprint()) I wrapped all this code in a project called glancecast, which allows you to convert a video file with a single command, with optional flags to set the frame length and chapter art size: $ python3 glancecast.py interesting_talk.mp4 interesting_talk.mp3 The process takes a minute or so to complete, most of which is spent transcoding the video file to MP3. The resulting MP3s are usually 40 to 50 MB in size, which is very reasonable. The outcome: How it looks in practice Here’s what one of these “glanceable” podcasts looks like in Overcast and on my lock screen: Maggie Appleton presented this talk over two years ago and it’s been on my “talks to watch” list ever since. Once I put it in Overcast? I listened to it in less than a day. It’s not a lot of extra information, but enough that I can quickly glance down and get the gist of what a speaker is saying. Both views update with a new frame every few seconds, or I can put my phone in my pocket and ignore the screen. I’ve used this approach for half a dozen videos so far, and I’m happy with the results. I expect to keep using it, because I have a long queue of videos I’ve been meaning to watch. If you’d like to try this, check out glancecast for the full code and instructions. [If the formatting of this post looks odd in your feed reader, visit the original article]

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