# case study Published articles for case study. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Presentation: Teaching Engineers, Trusting AI: How Education Enabled Autonomous Code Review DevFeed: [Presentation: Teaching Engineers, Trusting AI: How Education Enabled Autonomous Code Review]() Original publisher: [Read original article]() Author: Sarah Deitke Published: 2026-09-16T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Artificial Intelligence](), [Code review](), [Pull Request](), [Tooling](), [engineering-culture](), [observability]() Tags: [adoption](), [agile](), [ai](), [ai-ml-data-engineering](), [architecture-design](), [artificial-intelligence](), [automated](), [automation](), [autonomous](), [case-study](), [code-review](), [code-reviews](), [culture](), [culture-methods](), [developer](), [developer-experience](), [development](), [duolingo-ai-literacy-code-review](), [infoq](), [metrics](), [ml-data-engineering](), [observability](), [pairing](), [presentation](), [qcon-london-2026](), [qcon-software-development-conference](), [transcripts]() ### AI overview Sarah Deitke presents Duolingo's approach to cultural AI adoption through internal AI literacy workshops, observability dashboards, and safe AI guardrails. The presentation includes a case study on redesigning code review with an automated PR risk-assessment bot and reports faster delivery without increased defect rates. ### Source excerpt Sarah Deitke discusses how Duolingo drives cultural AI adoption beyond tooling access. She explains their internal AI literacy workshops and observability dashboards, then shares a case study on redesigning code review using an automated PR risk-assessment bot. Deitke demonstrates how pairing targeted developer education with safe AI guardrails speeds up delivery without increasing defect rates. By Sarah Deitke ## Building Depth: Designing and Developing a 3D Renderer Inside Figma DevFeed: [Building Depth: Designing and Developing a 3D Renderer Inside Figma]() Original publisher: [Read original article]() Author: Aleksei Kipin Published: 2026-09-13T11:34:39Z Content type: article Language: en Sources: [Codrops]() Topics: [Figma 3D renderer](), [Figma]() Tags: [3d](), [3d-design](), [3d-modeling](), [3d-renderer-for-figma](), [3d-rendering](), [3d-visualization](), [3d-workflow](), [articles](), [case-study](), [creative-development](), [depth-3d-renderer](), [depth-figma](), [depth-figma-plugin](), [design-engineering](), [design-tools](), [figma](), [figma-3d](), [figma-3d-renderer](), [figma-plugin](), [figma-plugin-design](), [interactive-3d](), [plugin-development](), [product-design](), [product-design-case-study](), [real-time-rendering](), [three-js](), [ui-design](), [ux-design](), [visualization](), [webgl](), [workflow]() ### AI overview An article about building Depth, a Figma plugin for quickly rendering 3D models during design work. It describes the workflow, plugin architecture, rendering behavior, and export approach. ### Source excerpt A look at how Depth came together, from building a 3D renderer inside Figma to designing a website that explores what it can do. ## Yestalgia: Bringing Decathlon's '90s Spirit to Life Through a Playful Digital Experience DevFeed: [Yestalgia: Bringing Decathlon's '90s Spirit to Life Through a Playful Digital Experience]() Original publisher: [Read original article]() Author: Quentin Hocdé and Jonathan Da Costa Published: 2026-09-12T08:40:31Z Content type: article Language: en Sources: [Codrops]() Topics: [micro-interactions for websites]() Tags: [90s-inspired-design](), [90s-web-design](), [animation](), [articles](), [case-study](), [creative-development](), [creative-technology](), [creative-web-design](), [decathlon-digital-experience](), [decathlon-website](), [decathlon-yestalgia](), [design](), [development](), [editorial-web-design](), [experimental-web-design](), [fashion-web-design](), [gsap](), [gsap-animation](), [illustration](), [immersive-web-experience](), [interactive-storytelling](), [interactive-website](), [lenis-smooth-scroll](), [motion-design](), [nostalgic-web-design](), [performance](), [piecesjs](), [product](), [product-focused-design](), [product-launch](), [rive](), [rive-animation](), [typography](), [walkman-navigation](), [web](), [web-animation](), [wordpress](), [wordpress-custom-theme](), [yestalgia]() ### AI overview This article presents Yestalgia, Decathlon's '90s-inspired capsule collection website. It explains how editorial product presentation, vibrant illustration, expressive typography, animation, micro-interactions, and a cassette-player navigation concept turn the campaign's nostalgic art direction into a playful digital experience. ### Source excerpt A closer look at how Yestalgia brings Decathlon's '90s-inspired vision to the web through art direction, interaction, animation, and creative development. ## Still: From Akira to Ink Wash, Building a Generative Garden in WebGPU DevFeed: [Still: From Akira to Ink Wash, Building a Generative Garden in WebGPU]() Original publisher: [Read original article]() Author: Ming Jyun Hung Published: 2026-09-09T14:11:57Z Content type: article Language: en Sources: [Codrops]() Topics: [webgpu](), [procedural flowers]() Tags: [3d](), [articles](), [case-study](), [creative-coding](), [deep-dive](), [flower-animation](), [generative](), [generative-art](), [generative-garden](), [generative-tendrils](), [gpu-instancing](), [ink-wash-effect](), [interactive-3d](), [japanese-art](), [japanese-print-style](), [procedural](), [procedural-animation](), [procedural-art](), [procedural-flowers](), [real-time](), [real-time-3d](), [three-js](), [three-js-case-study](), [three-js-shading-language](), [toon-shading](), [tsl](), [tsl-shaders](), [vat](), [vertex-animation-textures](), [webgpu](), [webgpu-shaders]() ### AI overview A technical deep dive into a WebGPU-based, real-time 3D generative garden. It describes combining toon shading, ink-wash shadows, silk-like grain, and procedural flowers and tendrils to translate Japanese print-inspired visual principles into an interactive web scene. ### Source excerpt A technical deep dive into Still, exploring how WebGPU, procedural systems, and Japanese art influences come together to create a living generative garden. ## China Merchants Bank Wins CNCF End User Case Study Contest for Unifying AI Training and Inference on Kubernetes DevFeed: [China Merchants Bank Wins CNCF End User Case Study Contest for Unifying AI Training and Inference on Kubernetes]() Original publisher: [Read original article]() Author: Haley White Published: 2026-09-08T01:54:31Z Content type: news Language: en Sources: [Cloud Native Computing Foundation]() Topics: [Kubernetes](), [AI Platform](), [AI Inference](), [Training AI Models](), [kueue](), [Fine-tuning](), [Cloud Native Ecosystem](), [datacenter]() Tags: [ai](), [ai-infrastructure](), [ai-training](), [announcements](), [case-study](), [china](), [compute](), [cost](), [fine-tuning](), [inference](), [kubernetes](), [kueue](), [lora]() ### AI overview China Merchants Bank won a CNCF case-study contest for a Kubernetes-based AI platform that shares nearly 10,000 accelerator cards across training, fine-tuning, and online inference. The bank reports increased average accelerator utilization and lower inference costs. ### Source excerpt New cloud native platform lifted average accelerator compute utilization from 35% to more than 60% and cut inference cost per 1 million tokens by more than 60% Key Highlights SHANGHAI, China - KubeCon + CloudNativeCon +... ## Why Gusto hired Evil Martians for Sidekiq infrastructure DevFeed: [Why Gusto hired Evil Martians for Sidekiq infrastructure]() Original publisher: [Read original article]() Author: Irina Nazarova (inazarova@evilmartians.com) Published: 2026-09-02T00:00:00Z Content type: article Language: en Sources: [Evil Martians]() Topics: [Sidekiq](), [Rails](), [Redis](), [Ruby]() Tags: [ai](), [case-study](), [engineering](), [infrastructure](), [rails](), [redis](), [ruby](), [scale](), [sidekiq]() ### AI overview This case study examines why Gusto hired Evil Martians to work on the Sidekiq and Redis infrastructure supporting background jobs in its large Rails monolith. It discusses the operational demands of running payroll-related jobs at scale and the tradeoffs involved when a company uses consultants instead of waiting for a perfect hire. ### Source excerpt Gusto runs payroll for 500,000+ businesses on one of the largest Rails monoliths anywhere. Why a team this strong hired Evil Martians for Sidekiq at scale, what running background jobs at that size actually takes, and when to stop waiting for the perfect hire. ## Type The Repo Name DevFeed: [Type The Repo Name]() Original publisher: [Read original article]() Author: Jon C. Phillips Published: 2026-08-24T01:11:00Z Content type: opinion Language: en Sources: [Jon C. Phillips]() Topics: [GitHub](), [data]() Tags: [articles](), [audience-building](), [case-study](), [code](), [conversion](), [design](), [digital-products](), [github](), [music](), [photography](), [product](), [product-engineering](), [safety](), [side-projects](), [type-the-repo-name](), [web-development]() ### AI overview The article argues that reducing friction is beneficial for routine tasks but harmful when it removes protection, learning, or meaningful engagement. It recommends deliberately adding safeguards for irreversible actions, preserving effort in learning experiences, and making users care about outcomes. ### Source excerpt No click-to-confirm, no checkbox. You type the whole thing out character by character while a red button waits, and it's the most annoying interaction in the entire product. ## Keleusma Research Spike: What It Costs to Compile a Data Structure Whose Shape Is Already Decided DevFeed: [Keleusma Research Spike: What It Costs to Compile a Data Structure Whose Shape Is Already Decided]() Original publisher: [Read original article]() Author: Brendan Sechter Published: 2026-08-09T09:00:00Z Content type: article Language: en Sources: [Brendan A R Sechter's Development Blog]() Topics: [Compiler](), [Back end](), [Data structures]() Tags: [arrays](), [case-study](), [compiler](), [compilers](), [data-structure](), [engineering](), [research](), [verification]() ### AI overview This case study examines the cost of compiling aggregate data types in the Keleusma compiler backend. Measurements of 331 aggregate operations found that most reduce to constant offsets and typed loads, challenging an estimate based on the feature's general name rather than its actual instances. ### Source excerpt The largest remaining item in a compiler backend was estimated at a quarter's work. Measured, it is pointer arithmetic over compile-time constants, and two of the three representation forms it was supposed to need account for two operations in the entire corpus. The item is aggregate data types, meaning structs, tuples, arrays and enumerations. It blocks 34.5 percent of the corpus, more than every other unimplemented feature combined, and it had never been scoped because everyone knew it was large. Everyone was reasoning from the wrong artefact. Aggregates are large in a compiler that must decide their layout. This compiler decided it already, in an earlier pass, and bakes the answer into the instruction stream. What reaches the backend is not a type system. It is a byte offset and a scalar kind. The measurement that establishes this took twenty minutes to write and two and a half seconds to run. It reports that of 331 aggregate operations in the corpus, 300 are a constant offset and a typed load, 2 need anything resembling a value representation, and 0 use the general mechanism the instruction set still carries. This article reports that, and reports why the author's own recommendation to run it deserves more scepticism than the result. What this is a case study of The setting is compiler backend scoping and the project is Keleusma, whose backend is described in the first, second and third articles of this series. No compiler background is required. The general shape is estimating the cost of a feature from its name rather than from its instances. "Aggregate data types" names something with a large literature, a hard general case, and a well-known set of representation decisions. None of that is evidence about the work in front of you, and the gap between the category and the instance is where the estimate went wrong. The transferable question is what remains once a decision has already been made upstream. The answer is often mechanical, and the mechanical residue ## Why Two Similar Compiler Cases Cannot Share One Calling Convention DevFeed: [Why Two Similar Compiler Cases Cannot Share One Calling Convention]() Original publisher: [Read original article]() Author: Brendan Sechter Published: 2026-08-07T09:00:00Z Content type: article Language: en Sources: [Brendan A R Sechter's Development Blog]() Topics: [Compiler](), [interface](), [Back end](), [test](), [Mathematics]() Tags: [article](), [backend](), [case-study](), [class](), [compiler](), [compilers](), [engineering](), [interface](), [mathematics](), [measurement](), [verification]() ### AI overview This compiler backend case study argues that two similar cases cannot be unified when one must report two values through an interface with only one available slot. A counting argument shows that the apparent similarity of nine measured occurrences is irrelevant to the shared interface design. The article also identifies an earlier rule that unnecessarily excluded ten of twenty-four cases. ### Source excerpt A system had grown two ways of doing what looked like one thing. The obvious move was to tidy them into one. The tidying turns out to be impossible, and the reason it is impossible is the reason the two ways exist. The argument that settles it needs no specialist knowledge and fits in a sentence. One of the two cases has two things to report and only one slot to report them in. Whichever thing the slot is given, the other is lost. The other case has only one thing to report, so the single slot is exactly enough. That is a counting argument, it is decided before any code is written, and it is not the argument an engineer reaches for by default. The engineer's instinct is to look at the cases and ask whether they resemble one another. They did. Every one of the nine measured occurrences had exactly the shape that invited the tidy-up, and the measurement encouraged precisely the wrong conclusion. The resemblance was real and it was irrelevant, because the defect was never in the instances. It was in the interface they would have had to share. This article is about that distinction, which is between evidence about members of a class and evidence about the channel the class must pass through. The second dominates the first and is cheaper to check. The article reports the measurement, the way the measurement pointed the wrong direction, and the argument that settled it. It also reports a rule this author shipped one increment earlier which turns out to be stricter than the property it enforces, excluding ten of twenty-four cases for no reason. No test found that. It surfaced while gathering data for this article. How to read this The general argument is in the opening, in the section called The Argument That Settled It, and in Pattern Extraction. Those three need nothing but attention. The sections between them work the argument through a real case with real numbers, and they use the vocabulary of the trade. Every term is glossed at first use, but a reader who wants the r ## Making Navigations Instant in v0 DevFeed: [Making Navigations Instant in v0]() Original publisher: [Read original article]() Author: Jude Gao Published: 2026-08-06T13:00:00Z Content type: article Language: en Sources: [Next.js Blog]() Topics: [Next.js](), [Caching](), [React](), [browser](), [App](), [coding](), [Vercel]() Tags: [agent](), [app](), [browser](), [caching](), [case-study](), [code](), [coding](), [next-js](), [vercel]() ### AI overview This case study explains how v0 achieved instant navigations with Next.js 16.3. The approach combines runtime partial prerendering and browser caching for dynamic, personalized content, while a coding agent uses failing tests to identify slow navigations, apply fixes, and verify routes. ### Source excerpt The case study behind Instant Navigations in Next.js 16.3, and how we made v0's navigations instant using tests and a coding agent. ## Better code, fewer tokens: The benefits of Code Connect in MCP DevFeed: [Better code, fewer tokens: The benefits of Code Connect in MCP]() Original publisher: [Read original article]() Author: Tom Weightman Published: 2026-08-05T18:16:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Figma](), [MCP Server](), [Model Context Protocol](), [AI-assisted coding](), [agentic-coding](), [Front end](), [Code quality]() Tags: [agentic-coding](), [agents](), [case-study](), [code](), [code-quality](), [coding](), [design](), [figma](), [mcp]() ### AI overview Figma's Code Connect gives coding agents production-component context through the Figma MCP server. Evaluations found shorter task durations, higher code quality, and lower token usage when Code Connect templates were available. ### Source excerpt When going from design to code, agents lack the context of your production components. With Code Connect in Figma's MCP, they get that context. We measured its impact on token usage, task duration, and code quality. ## +14% activated users for AppSignal: designing a new homepage in code DevFeed: [+14% activated users for AppSignal: designing a new homepage in code]() Original publisher: [Read original article]() Author: Travis Turner (richardturner@evilmartians.com) Published: 2026-08-04T00:00:00Z Content type: article Language: en Sources: [Evil Martians]() Topics: [Application Performance Management (APM)](), [error tracking](), [Tooling](), [Code](), [Figma](), [Artificial Intelligence]() Tags: [agentic-development](), [ai](), [apm](), [case-study](), [code](), [design](), [design-for-devtools](), [developer-marketing](), [developer-products](), [engineering](), [error-tracking](), [figma](), [product-development](), [tooling]() ### AI overview Evil Martians designed and shipped a new AppSignal homepage primarily in code, combining code, Figma, and AI during exploration and prototyping. The selected direction was validated through an A/B test that showed a 14% increase in activated users over the previous site. ### Source excerpt We designed and shipped a fresh AppSignal homepage in code, then validated it in an A/B test with a 14% lift in activated users. ## How TRM Built a Custom Internal Harness for Product and Engineering Work DevFeed: [How TRM Built a Custom Internal Harness for Product and Engineering Work]() Original publisher: [Read original article]() Author: Luca Rossi Published: 2026-07-29T07:31:16Z Content type: article Language: en Sources: [Refactoring]() Topics: [Development](), [Artificial Intelligence]() Tags: [case-study](), [development](), [engineering](), [harness](), [product-development]() ### AI overview This case study describes how TRM built a custom internal harness for product and engineering work. ### Source excerpt How TRM built a custom internal harness for product and engineering work. ## The PM's Guide to Governance with Eric Ries, author of The Lean Startup DevFeed: [The PM's Guide to Governance with Eric Ries, author of The Lean Startup]() Original publisher: [Read original article]() Author: Aakash Gupta Published: 2026-07-20T22:11:52Z Content type: article Language: en Sources: [Product Growth]() Topics: [structure](), [trust](), [anthropic](), [Claude](), [OpenAI]() Tags: [anthropic](), [case-study](), [claude](), [governance](), [guide](), [openai]() ### AI overview A discussion with Eric Ries examines governance and organizational structures intended to help companies remain resilient and avoid governance failures. The article also covers Ries's use of AI tools, including Claude, in writing Incorruptible. ### Source excerpt The Lean Startup creator on why Anthropic holds and OpenAI cracked, and how to structure yours ## How Discord Fans Out One Message to a Million Users DevFeed: [How Discord Fans Out One Message to a Million Users]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-07-07T13:22:26Z Content type: article Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Discord](), [Publish-subscribe pattern](), [real-time](), [WebSocket](), [Back end](), [Processes](), [Authorization]() Tags: [backend](), [case-study](), [channel](), [discord](), [engineering](), [permission](), [real-time](), [scale](), [server](), [websocket]() ### AI overview This case study explains how Discord fans out a single message to many online users. It describes a real-time backend built around pub/sub, guild routing processes, permission checks, session processes, and WebSocket delivery. ### Source excerpt A case study in fanout, bottlenecks, and the engineering decisions behind Discord's scale. ## How Evaluation-Driven Development (EDD) Works DevFeed: [How Evaluation-Driven Development (EDD) Works]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-06-23T08:57:02Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [Development](), [AI Agent](), [Ground truth / benchmark quality](), [dataset]() Tags: [agent](), [ai](), [ai-evals](), [case-study](), [development](), [evaluation](), [experiment](), [saas](), [test](), [tests]() ### AI overview This case study explains Evaluation-Driven Development (EDD) for AI agents: measure a new feature, compare results before and after changes, and detect regressions before merging. It also discusses generating realistic test data when historical datasets, traces, or ground truth are unavailable. ### Source excerpt Turn every AI agent change into a measured experiment you compare before and after to detect regressions and measure performance. ## Detecting Customer Churn with Structured Metrics and Behavioral Transitions DevFeed: [Detecting Customer Churn with Structured Metrics and Behavioral Transitions]() Original publisher: [Read original article]() Author: Mithil Oswal Published: 2026-06-17T09:05:24Z Content type: article Language: en Sources: [Helpshift]() Topics: [data](), [Framework](), [Support](), [Machine learning](), [Feature Engineering]() Tags: [analytics](), [case-study](), [churn](), [churn-analysis](), [churn-prediction](), [churn-rate](), [customer-churn](), [data](), [engineering](), [framework](), [metric](), [models]() ### AI overview This case study describes a Churn Intelligence Framework based on structured descriptive analytics rather than relying primarily on black-box predictive models. It uses revenue and support-ticket volume dynamics across current and previous periods to classify customers as attrited, declining, growing, new, or stable, with the goal of identifying multi-period decline before full attrition. ### Source excerpt Photo by Deng Xiang on UnsplashOne in a Million Ways to Detect Customer Churn -- Powered by Pure Metric EngineeringA real-world case study on building a Churn Intelligence Framework using revenue dynamics, structured KPI design, and behavioral transitions.😯 Wow, Churn Prediction sounds impressivePhoto by Ksenia Yakovleva on Unsplash Until you realize that most ML models struggle in production. -- Data fluctuates 🔢 -- Features change 💱 -- Stakeholders don't trust black-box outputs ⬛ Teams jump into feature engineering and classification algorithms, chasing accuracy scores -- while the business still lacks a clear behavioral definition of decline. The result? Black-box probabilities that stakeholders don't trust and Customer Success teams don't know how to act on. Predictive models attempt to forecast an outcome. But churn isn't just an outcome. It's a progression.So then what's new here? We use fundamentally structured descriptive analytics, aka real numbers. The objective was not to build another dashboard. It was to introduce intelligence into the existing reporting system. Specifically, the framework was designed to: Understand churn behavior structurally rather than as a single percentage metric or a boolean value. Track support ticket growth and decline across relative time periods, recognizing that volume trends directly influence revenue stability. Enable proactive client retention by identifying multi-period decline before full attrition occurs. Setting the ground❗At Helpshift, we used support ticket volume dynamics as a proxy for revenue. ➡ Definitions:- Start Date (filter) = Report start date that defines the Current Period End Date (filter) = Report end date that defines the Current Period Current Period = Revenue / issue volume for the current period timeframe Previous Period = Revenue / issue volume for the previous period timeframe, where Previous Period has been calculated by pulling back the "Current Period" dates by X days, where X is the difference betwe ## Building Reliable Agentic AI Systems DevFeed: [Building Reliable Agentic AI Systems]() Original publisher: [Read original article]() Author: Martin Fowler (martin@martinfowler.com) Published: 2026-06-16T12:11:00Z Content type: article Language: en Sources: [Martin Fowler]() Topics: [Artificial Intelligence](), [Retrieval Augmented Generation (RAG)](), [text2sql](), [AI search](), [Generative AI](), [AI-generated research reports](), [data](), [Orchestration](), [observability](), [Prompt Engineering]() Tags: [agentic-ai](), [ai](), [building](), [case-study](), [data](), [drug-discovery](), [efficiency](), [engineering](), [generative-ai](), [information-retrieval](), [language-models](), [llms](), [orchestration](), [production](), [rag](), [research](), [retrieval](), [safety](), [sql](), [technical]() ### AI overview This case study describes PRINCE, a cloud-hosted platform developed by Bayer AG with Thoughtworks for pharmaceutical research. It combines Agentic Retrieval-Augmented Generation and Text-to-SQL to help researchers query decades of safety study reports, answer complex questions, and draft regulatory documents. The article focuses on context engineering, orchestration, recovery, observability, transparency, explainability, human oversight, governance, and compliance in production-ready agentic AI systems. ### Source excerpt One of the most interesting projects my colleagues have done with LLMs has been building a system with Bayer to allow pharmaceutical researchers to query decades of information about studies buried in PDF reports. Sarang Sanjay Kulkarni describes its evolution from keyword-based search to an intelligent research assistant capable of answering complex questions and drafting regulatory documents. more... ## 2 Martians, greenfield to MVP in 4 weeks: agentic coding on Rails DevFeed: [2 Martians, greenfield to MVP in 4 weeks: agentic coding on Rails]() Original publisher: [Read original article]() Author: Travis Turner (richardturner@evilmartians.com) Published: 2026-06-09T00:00:00Z Content type: article Language: en Sources: [Evil Martians]() Topics: [agentic-coding](), [Rails](), [coding](), [React](), [Storybook](), [Open Source](), [stripe]() Tags: [agentic](), [agentic-coding](), [ai](), [app](), [authentication](), [building](), [case-study](), [coding](), [designer](), [developer-marketing](), [developer-products](), [google-analytics](), [onboarding](), [open-source](), [payments](), [platform](), [product](), [product-development](), [production](), [prototype](), [rails](), [react](), [skills](), [storybook]() ### AI overview A designer and an engineer describe how they shipped Thicket, a production MVP, in four weeks using agentic coding with Rails, Inertia, React, and Storybook. They discuss their workflow, project-specific skills, user validation, and the resulting open-source practices. ### Source excerpt A designer and an engineer shipped a production MVP in four weeks on Rails + Inertia. In this post, we share our agentic coding stack, the skills we built, and why it clicked. ## The Most Expensive Milliseconds Are Unmeasured DevFeed: [The Most Expensive Milliseconds Are Unmeasured]() Original publisher: [Read original article]() Author: Divya Gupta Arora Published: 2026-06-03T17:11:58Z Content type: article Language: en Sources: [Expedia]() Topics: [Mobile](), [render](), [ui]() Tags: [case-study](), [devops](), [engineering](), [login](), [mobile](), [mobile-apps](), [mobile-performance](), [observability](), [performance](), [release](), [software-engineering](), [technology-investments](), [time-to-interactive](), [ui](), [visibility]() ### AI overview This Expedia Group engineering case study describes extending Native Time to Interactive across mobile login screens. The metric was used to evaluate technology investments, compare platform behavior, and detect performance regressions that existing system-health signals did not reveal. ### Source excerpt Expedia Group Technology -- EngineeringHow a screen-level performance metric reshaped platform decisions, engineering ownership, and release disciplinePhoto by Pietro De Grandi on Unsplash For the last few years, my responsibility has been straightforward to state but hard to execute -- owning the traveler login experience across mobile platforms. Not just whether a feature works, but whether it feels responsive, predictable, and trustworthy in the moments that matter most. During login, those moments are unforgiving: if a login screen hesitates travelers don't interpret it as 'a slow render', they interpret it as risk. And when the majority of travelers interact through our mobile apps, performance stops being a technical concern and becomes a product promise. This post is a case study of how, within Expedia Group™'s login domain, we extended Native Time to Interactive (NTTI) across login screens -- moving performance from a late-stage check to a first-class signal we can use to validate technology investments, compare platform behavior, and prevent silent regressions as we ship. The Problem: Mobile reliability rarely fails loudly Mobile performance rarely breaks with a crash. It usually degrades quietly, a button takes a beat longer to respond a screen looks ready, but taps don't register the UI stutters just enough to feel "off" Those are the expensive milliseconds -- because they erode trust without triggering obvious alarms. In our login flows, we were shipping consistently, evolving a major part of our stack, and supporting increasing product complexity. Yet we didn't have a consistent way to answer the user's real question -- "When can I actually use this screen?" Why our existing signals failed We were not blind. We tracked many useful things, crashes and ANRs backend latency and service SLIs some component-level timing signals limited Time to Interactive tracking on onboarding and initial login But we had a gap -- we had visibility into system health, not screen ## Introducing Trusted Remote Execution: Policy-Enforced Scripts for AI Agents and Humans DevFeed: [Introducing Trusted Remote Execution: Policy-Enforced Scripts for AI Agents and Humans]() Original publisher: [Read original article]() Author: Nick MacDonald Published: 2026-05-04T15:34:36Z Content type: release Language: en Sources: [AWS Open Source Blog]() Topics: [AI Bots](), [SDKs]() Tags: [agent](), [agentic](), [agents](), [ai](), [ai-agents](), [case-study](), [code](), [logs](), [model](), [open](), [open-source](), [operations](), [policy](), [production](), [remote](), [review](), [safety](), [sandboxes](), [scripting](), [software]() ### AI overview Trusted Remote Execution (Rex) is an open-source scripting runtime that authorizes each host operation against a Cedar policy. It is designed to give service owners runtime control over scripts generated by AI agents or used by humans. ### Source excerpt Today, we're announcing Trusted Remote Execution (Rex, for short) -- an open source scripting runtime where every system operation is authorized by policy. Scripts are written in Rhai, a lightweight language with no built-in system access. The only way to reach the host is through operations Rex explicitly provides, which are authorized against a Cedar [...] ## How Sample Vault syncs gigabyte-scale databases from cloud to local with Turso DevFeed: [How Sample Vault syncs gigabyte-scale databases from cloud to local with Turso]() Original publisher: [Read original article]() Author: Jeff Olson Published: 2026-05-04T00:00:00Z Content type: article Language: en Sources: [Turso Blog]() Topics: [Turso](), [Local-First](), [Databases](), [Cloud](), [data](), [App](), [Artificial Intelligence]() Tags: [ai](), [case-study](), [cloud](), [databases](), [embedded-replicas](), [local](), [offline](), [sync](), [turso]() ### AI overview Sample Vault uses Turso to synchronize gigabyte-scale, per-user audio metadata databases bidirectionally between the cloud and local devices. The local replicas support offline queries across libraries containing more than 100,000 audio files, while cloud processing generates spectral, classification, genre, mood, and AI-derived metadata. ### Source excerpt Sample Vault uses Turso for cloud-to-local bidirectional sync with per-user databases, enabling offline-first access to gigabyte-scale audio metadata without custom sync code. ## Weave CLI: A Case Study in Shipping Retrieval-Augmented Generation Systems DevFeed: [Weave CLI: A Case Study in Shipping Retrieval-Augmented Generation Systems]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-04-29T11:04:33Z Content type: article Language: en Sources: [Decoding ML]() Topics: [Retrieval Augmented Generation (RAG)](), [Command-line interface](), [Databases](), [Open Source]() Tags: [case-study](), [cli](), [databases](), [open-source](), [rag](), [vector-database]() ### AI overview An interview with Michael Maximilien examines the practical difficulties of building and evaluating RAG systems, including vector-database selection, embedding models, chunking, ingestion failures, and unreliable comparisons. Maximilien describes Weave CLI, an open-source command-line tool that unifies 11 vector databases into one workflow. ### Source excerpt You iterate. You evaluate. Weave CLI unifies 11 vector databases into one workflow. ## Keeping Meeting Apps Alive in the Background on iOS and watchOS DevFeed: [Keeping Meeting Apps Alive in the Background on iOS and watchOS]() Original publisher: [Read original article]() Author: Roland Published: 2026-03-26T14:55:52Z Content type: tutorial Language: en Sources: [RisingStack]() Topics: [iOS](), [watchOS](), [App](), [Artificial Intelligence]() Tags: [ai](), [apps](), [audio](), [case-study](), [code](), [examples](), [ios](), [meetings](), [pipeline](), [pitfalls](), [production](), [watchos]() ### AI overview This tutorial explains how a real-world meeting app handled reliable background recording and post-recording processing on iOS and watchOS. It covers audio interruptions, resuming recordings, AVAudioSession configuration with mixWithOthers, and background transcription and AI processing, including BGContinuedProcessingTask on iOS 26+. ### Source excerpt Building an app that records meetings, generates transcriptions, and produces AI analysation sounds straightforward - until you try to make it reliable while the user goes in and out of your app, takes calls, plays music, or checks notifications. This post walks through how we handled background recording and post-recording processing on iOS and watchOS [...] The post Keeping Meeting Apps Alive in the Background on iOS and watchOS appeared first on RisingStack Engineering. [Next page]()