Brought to you by Meta. In addition to remaining active in the open source community and conference circuit, this podcast offers another channel that allows us ...
Introducing a new Android UI Framework like Jetpack Compose into an existing app is easy right? Import some AARs and code away. But what if your app has specific performance goals to meet, has existing design components, integrations with navigation and logging frameworks? That is where Summer and her team come in who handle large-scale migrations for Instagram. They aim to provide developers with the best possible experience when working on our code bases, even if that requires some temporary pain on the side of infrastructure teams that have to maintain multiple implementations at once. Why Summer thinks it is worth it, how they approach the rollout of a new framework and so much more is all discussed in episode 70. Got feedback? Send it to us on Threads (https://threads.net/@metatechpod), Instagram (https://instagram.com/metatechpod) and don’t forget to follow our host Pascal (https://mastodon.social/@passy, https://threads.net/@passy_). Fancy working with us? Check out https://www.metacareers.com/. Links Jetpack Compose: https://developer.android.com/compose Litho: https://fblitho.com/ Google Showcase: Meta built threads in only 5 months using Jetpack Compose: https://android-developers.googleblog.com/2023/10/meta-built-threads-in-only-5-months-using-jetpack-compose.html Flipper: https://fbflipper.com/ Timestamps Intro 0:06 Intro Summer 1:29 Notable differences moving from FB to IG 2:26 The Instagram Data & UI Architecture team 2:58 Why modernise? 3:44 Where has the risk paid off? 6:08 What does Compose look like? 7:49 Compose v Litho 11:15 Where does Litho still have the upper hand? 14:53 Meta contributions to Compose 16:38 Compose pitfalls 19:10 Rolling Compose out across the company 20:13 Design systems 22:12 Downsides of establishing another UI framework? 24:22 Rollout stages 28:43 Experimentation stage 32:32 Closed enrollment phase 38:15 Graduation criteria 39:38 Outro 42:20 Bants 44:04
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69: To type or not to type — measuring productivity impact with DAT
Do types actually make you more productive or is it just more typing for you to do on the keyboard? That's just one of the questions we managed to answer at least on a small scale with Diff Authoring Time or DAT, here at Meta. Want to know how we leverage metrics to run experiments on productivity in our internal codebase? Tune in to episode 69. Got feedback? Send it to us on Threads (https://threads.net/@metatechpod), Instagram (https://instagram.com/metatechpod) and don’t forget to follow our host @passy (https://mastodon.social/@passy, and https://threads.net/@passy_). Fancy working with us? Check out https://www.metacareers.com/. Links Hack language: https://hacklang.org/ Timestamps Intro 0:06 Henri Intro 1:45 Ian Intro 3:13 Moritz Re-Intro 3:28 DAT Recap 3:48 What is Hack? 4:20 Inner and outer loop 14:13 Experimenting with language features 17:47 Code sharing frameworks at Meta 27:43 Measuring framework productivity 29:01 Will we see more experiments? 34:23 Time savings from code sharing 37:28 Outro 39:03 Blooper 39:52
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68: How to Build a Mixed Reality Headset
How do you build your own mixed reality headset from sketch to scale? That's exactly what Alfred Jones, VP of hardware engineering at Meta Reality Labs, discussed with host Pascal. From choosing the right display technology, battery, thermal budget and of course hitting the right price point. How he manages to not fall victim to choice paralysis and so much more in episode 68. Got feedback? Send it to us on Threads (https://threads.net/@metatechpod) or Instagram (https://instagram.com/metatechpod) and don’t forget to follow our host Pascal (https://threads.net/@passy_). Fancy working with us? Check out https://www.metacareers.com/. Links Caddy: https://engineering.fb.com/2024/07/18/virtual-reality/caddy-cad-mixed-reality-mr-meta/ Timestamps Intro 0:06 Alfred Introduction 1:40 Who do you work with? 3:23 Decision making frameworks 5:20 Is MR the final destination? 7:19 What makes good passthrough such a challenge? 10:18 How to build your own MR headset 13:51 Hardware design constraints 19:00 Prototype phases 22:34 Durability testing 26:23 Dogfooding at Meta 28:55 Magic wand for technical limitations 31:56 Outro 34:26
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67: Measuring Developer Productivity with Diff Authoring Time
At Meta, engineers are our biggest asset which is why we have an entire org tasked with making them as productive as possible. But how do you know if your projects for improving developer experience are actually successful? For any other product, you would run an A/B test but that requires metrics and how do you measure developer productivity? Sarita and Moritz have been working on exactly that with Diff Authoring Time which measures how long it took to submit a change to our codebase. Host Pascal talks to them about the way this is implemented, the challenges and abilities this unlocks. Got feedback? Send it to us on Threads (https://threads.net/@metatechpod), Twitter (https://twitter.com/metatechpod), Instagram (https://instagram.com/metatechpod) and don’t forget to follow our host @passy (https://twitter.com/passy, https://mastodon.social/@passy, and https://threads.net/@passy_). Fancy working with us? Check out https://www.metacareers.com/. You can follow our guest Moritz on X (https://x.com/Inventitech) or check out his website on inventitech.com. Links Meta Connect 2024: https://www.meta.com/en-gb/connect/ Timestamps Episode intro 0:05 Sarita Intro 2:33 Moritz Intro 3:44 DevInfra as an Engineer 4:25 DevInfra as a Data Scientist 5:12 Why DevEx Metrics? 6:04 Average Diff Authoring Time at Meta 9:55 Events for calculating DAT 10:55 Edge cases 13:15 DAT for Performance Evaluation? 20:29 Analyses on DAT data 22:29 Onboarding to DAT 23:23 Stat-sig data 25:06 Validating the metric 26:34 Versioning metrics 28:09 Detecting and handling biases 29:19 Diff coverage 30:30 Do we need DevX metrics in an AI software engineering world? 31:23 Measuring the impact of AI tools 32:23 What's next for DAT? 33:40 Outtakes 36:22
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66: Inside Bento - Serverless Jupyter Notebooks at Meta
Bento is Meta’s internal distribution of Jupyter Notebooks, an open-source web-based computing platform. Host Pascal is joined by Steve who worked with his team on building many features on top of Jupyter, including scheduled notebooks, sharing with colleagues and running notebooks without a remote server component by leveraging Webassembly in the browser. Got feedback? Send it to us on Threads (https://threads.net/@metatechpod), Twitter (https://twitter.com/metatechpod), Instagram (https://instagram.com/metatechpod) and don’t forget to follow our host @passy (https://twitter.com/passy, https://mastodon.social/@passy, and https://threads.net/@passy_). Fancy working with us? Check out https://www.metacareers.com/. Links Scheduling Jupyter Notebooks at Meta: https://engineering.fb.com/2023/08/29/security/scheduling-jupyter-notebooks-meta/ Serverless Jupyter Notebooks at Meta: https://engineering.fb.com/2024/06/10/data-infrastructure/serverless-jupyter-notebooks-bento-meta/ Jupyter Notebooks: https://jupyter.org/ Timestamps Intro 0:06 Who is Steve? 1:49 What are Jupyter and Bento? 2:48 Who is Bento for? 3:40 Internal-only Bento features 4:42 Scheduled notebooks 11:39 Integrating with existing batch jobs 17:10 The case for serverless notebooks 20:59 Enter wasm 24:29 Upgrade paths from serverless to server 26:29 Bringing more Python libraries to the browser 30:21 Adding magick(s) 31:52 DataFrame magic and AI 36:41 What's next? 38:29 Outro 43:17
Brought to you by Meta. In addition to remaining active in the open source community and conference circuit, this podcast offers another channel that allows us to highlight the technical work of our engineers who will discuss everything from low-level frameworks to end-user features. Throughout the podcast, Meta engineer Pascal Hartig (@passy) will interview developers in the company.
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