Language Models Transform Scientific Literature Reviews

Elicit uses language models to accelerate the scientific literature review process.
Models replace academics’ manual work of reading thousands of papers and extracting data into spreadsheets.

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Brought to You By:

• turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable.

• O'Reilly Early Release: Scaling AI Adoption in Engineering – a free book on how to adopt and scale AI in a pragmatic way inside of engineering orgs. Complimentary, thanks to Antithesis.

• Entire – every agent prompt, tool call, stored in your repo, and mirrored.

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What can everyone else learn from designers and design engineers? As it turns out, there’s plenty, as I discovered when one of the best design engineers in the industry, Maggie Appleton, came onto the Pragmatic Engineer Podcast. She’s a staff research engineer at GitHub Next, where she builds prototypes to explore how software engineers might collaborate with AI in new ways. Maggie is at the intersection of design, anthropology, and web development, and was the first designer hired by AI startup Elicit, and Lead Design engineer at AI startup, Normally.

Today’s episode is more visual than usual because Maggie brought her notebook along, so there are peeks inside its pages of prototypes and more:

We got into designers’ work and how their design processes are adapting to and changing with AI. We explore why Maggie starts projects with pens and notebooks, what distinguishes design engineers from other designers, and why understanding engineering constraints leads to better collaboration with engineers.

We also discuss how Maggie uses jigs to gain more control over AI agents, why human judgment and style still matter when models can generate designs, and how inconsistent AI capabilities can mislead us.

Timestamps

00:00 Intro

03:24 From anthropology to tech

10:18 What does a designer do?

18:23 How Maggie works

24:55 The case for planning with physical tools

31:53 Why Maggie is learning woodworking

33:13 Design engineers and engineering constraints

38:49 How Maggie uses Figma

40:30 Design at GitHub Next

45:12 How has AI changed design

50:37 When models design and why humans are still needed