# engineering-culture Published articles for engineering-culture. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Becoming an AI Team DevFeed: [Becoming an AI Team]() Original publisher: [Read original article]() Author: Pinterest Engineering Published: 2026-09-01T15:01:06Z Content type: opinion Language: en Sources: [Pinterest Engineering Blog - Medium]() Topics: [AI Strategy](), [AI Development](), [code productivity](), [Refactoring]() Tags: [ai](), [developer-productivity](), [engineering](), [engineering-culture](), [infrastructure](), [pinterest](), [refactoring]() ### AI overview The article argues that becoming an AI team requires changing team ownership, planning, operating models, and roles--not merely adding AI tools. It describes AI-assisted automation, analysis, and prototyping as ways to improve infrastructure reliability, cost efficiency, and developer productivity. ### Source excerpt John Grass | Sr. Manager, Engineering A Fundamental Transformation An AI team is fundamentally more than just a group whose members incorporate AI tools into their existing workflows. The journey to becoming an AI team necessitates a fundamental and comprehensive paradigm shift in how the team defines ownership, engages in strategic planning, and, most critically, executes on its core goals and objectives. This transformation is not merely an addition of new technology; it is a restructuring of the team's operating model, philosophy, and individual roles. Becoming an AI team requires a holistic shift: team members transition from routine, manual execution to empowered, AI-augmented strategists and problem-solvers. In an AI-centric environment, every single member is significantly empowered, not only through access to cutting-edge AI tools and sophisticated models but through an expanded scope of responsibility and influence. These new capabilities allow individuals to automate routine tasks, accelerate data analysis, and rapidly prototype solutions, freeing up cognitive resources for higher-level, more strategic thinking. The expectation shifts from simply completing tasks to orchestrating intelligent systems and focusing on solving problems that were previously intractable. Consequently, the very nature of the work for both individual contributors and managers will be fundamentally different from anything that has come before. At Pinterest, this isn't theoretical. Our infrastructure teams sit at the core of a product that serves billions of Pins, boards, ads, and real-time signals. That reality has forced us to treat "becoming an AI team" as an operational necessity, not a side project as we cannot keep scaling reliability, cost efficiency, and developer productivity using only traditional playbooks. AI Team Capabilities The advent of accessible Artificial Intelligence (AI) tools has fundamentally raised the performance ceiling for what development teams can accomp ## Meta's engineering culture and team-reduction plans amid competition from AI-native startups DevFeed: [Meta's engineering culture and team-reduction plans amid competition from AI-native startups]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-08-27T17:59:17Z Content type: article Language: en Sources: [The Pragmatic Engineer]() Topics: [Meta](), [Artificial Intelligence](), [engineering-culture](), [GitHub]() Tags: [ai](), [culture](), [engineering](), [engineering-culture](), [github](), [meta](), [startups]() ### AI overview The article examines why Meta changed its engineering culture and considered reducing teams amid concerns about AI-native startups doing more with less. It also discusses Ramp's AI infrastructure and increased GitHub load. ### Source excerpt We find out why Meta destroyed its standout engineering culture: it feared AI-native startups doing more with less. Also: thoughts on Ramp's AI infra, GitHub's load doubles in four months, and more ## Why performant code matters (but gets widely ignored), with Casey Muratori DevFeed: [Why performant code matters (but gets widely ignored), with Casey Muratori]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-08-26T15:59:59Z Content type: opinion Language: en Sources: [The Pragmatic Engineer]() Topics: [Code](), [Software](), [engineering-culture]() Tags: [code](), [developer](), [engineering-culture](), [performance](), [software]() ### AI overview This podcast episode features Casey Muratori discussing why software performance matters, why it is often overlooked, and how developers can write faster code. ### Source excerpt Casey Muratori explains why software performance matters, how developers can write faster code, and why he challenges conventional engineering practices. ## Signatures, be true: domain errors and functional handling in Kotlin DevFeed: [Signatures, be true: domain errors and functional handling in Kotlin]() Original publisher: [Read original article]() Author: Alina Dolgikh Published: 2026-08-19T15:52:05Z Content type: article Language: en Sources: [Kotlin : A concise multiplatform language developed by JetBrains | The JetBrains Blog]() Topics: [Kotlin](), [Code](), [Exception](), [API](), [Database](), [engineering-culture]() Tags: [api](), [article](), [backend](), [code](), [database](), [engineering-culture](), [exception](), [how-to](), [kotlin]() ### AI overview An article about making expected domain failures explicit in Kotlin function signatures through sealed types and functional error handling. It argues that business-logic failures should be visible in the function signature, API contract, and client handling code rather than hidden in implementations or exception handlers. ### Source excerpt Here's a function that signs a document: In Kotlin, Unit means the function completes without returning a meaningful value - roughly equivalent to void in Java. Got it? Now, tell me what could go wrong. You can't. Yet, the code might be invalid. The signing window might have closed. The database might be down. The [...] ## Cloud provider postmortems: volume vs depth DevFeed: [Cloud provider postmortems: volume vs depth]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-08-05T00:00:00Z Content type: opinion Language: en Sources: [Sridhar Rajarao]() Topics: [Cloud](), [incident](), [SRE](), [site-reliability-engineering](), [engineering-culture]() Tags: [2017](), [2025](), [2026](), [aws](), [azure](), [cloud](), [dynamodb](), [engineering-culture](), [gcp](), [incident](), [incident-management](), [postmortems](), [s3](), [sre](), [transparency](), [writeup]() ### AI overview The article compares public postmortem practices among Google Cloud, Azure, and AWS. It argues that Google Cloud emphasizes high volume and speed, Azure emphasizes detailed transparency and customer accountability, and AWS publishes fewer writeups with greater depth and industry influence. ### Source excerpt GCP publishes 100+ postmortems a year. AWS publishes almost none. Azure has become the transparency leader. What each posture reveals about engineering culture, and what SREs should steal from all three. ## Frictionless: A recap of trivago Tech Get Together 2026 DevFeed: [Frictionless: A recap of trivago Tech Get Together 2026]() Original publisher: [Read original article]() Author: Ekaterina Falikova Employer Branding manager Published: 2026-06-30T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [Learning](), [Artificial Intelligence](), [Deployment]() Tags: [2026](), [ai](), [artificial-intelligence](), [community](), [deployment](), [ecosystem](), [engineering-culture](), [event](), [net-11](), [recap](), [tech](), [workflows]() ### AI overview A recap of trivago Tech Get Together 2026, a two-day event where more than 200 tech employees shared work, built connections, and explored the theme of frictionless experiences in product development. About half of the talks covered teams applying AI to codebases, infrastructure, and workflows. ### Source excerpt Every year, our tech community gathers for two days to learn, share knowledge, and connect. Over 200 tech talents, all in the same room, presenting their work, celebrating wins, and learning fro... ## Beyond Vibe Coding: A Designer's Case for Directed Generation DevFeed: [Beyond Vibe Coding: A Designer's Case for Directed Generation]() Original publisher: [Read original article]() Author: Jim Gulsen Published: 2026-06-25T03:08:30Z Content type: opinion Language: en Sources: [UX Magazine]() Topics: [Vibe coding](), [Artificial Intelligence](), [User experience (UX)](), [engineering-culture](), [coding]() Tags: [2025](), [ai](), [design](), [engineering-culture](), [generation](), [model](), [prompt](), [ux](), [vibe-coding]() ### AI overview This opinion argues that "vibe coding" describes passive, low-accountability use of generated output, but does not accurately describe intentional AI-assisted design. It proposes directed generation, in which the designer's judgment, authority, and understanding guide the AI-assisted process. ### Source excerpt The name got there first "Vibe coding" is a useful description of a specific, low-accountability behavior. You describe something loosely, accept what the model generates, and don't concern yourself too much with understanding the output. Andrej Karpathy named it accurately in early 2025 -- for the thing he was actually describing. The problem is what The post Beyond Vibe Coding: A Designer's Case for Directed Generation appeared first on UX Magazine. ## AI as the Next Abstraction Layer: How I see engineering evolving at Thumbtack DevFeed: [AI as the Next Abstraction Layer: How I see engineering evolving at Thumbtack]() Original publisher: [Read original article]() Author: Ananda Kanagaraj Sankar Published: 2026-05-22T23:53:03Z Content type: opinion Language: en Sources: [Thumbtack Engineering - Medium]() Topics: [Artificial Intelligence](), [agentic workflows](), [Development](), [Software Engineering](), [engineering-leadership]() Tags: [agentic-workflows](), [ai](), [ai-agent](), [chatgpt](), [copilot](), [development](), [engineering](), [engineering-culture](), [generative-ai-tools](), [software-engineering]() ### AI overview A personal perspective on how AI-assisted development is evolving at Thumbtack, from informal experimentation with ChatGPT and Copilot to end-to-end agentic workflows. The article frames AI as a new abstraction layer and highlights its non-determinism as a major difference from earlier abstractions. ### Source excerpt Over the past year, the way we use AI at Thumbtack has gone through a few phases. Early on it was mostly curiosity, people experimenting with ChatGPT and Copilot on side projects, sharing tips in Slack. Then the models got noticeably better at working inside real, mature codebases (not just greenfield projects) and the conversation shifted. It stopped being about whether we should adopt AI-assisted development and became about how. Lately, it is moving towards the adoption of end to end agentic workflows for development. I've been thinking a lot about what this shift means, not just for our codebase or our velocity, but how it impacts what it actually feels like to be an engineer here. What follows is my personal perspective, shaped by leading engineering on our monetization teams and informed by how Thumbtack's engineering leadership has been approaching this across teams. Another layer of abstraction If you zoom out, software engineering has always been a story of rising abstraction. We went from assembly to C, from C to Java, from hand-rolled servers to cloud-native infrastructure. And each time, the craft shifted rather than shrinking. For example, managing memory manually was replaced by designing distributed systems. I think AI-assisted development is the next version of that pattern.The difference this time isn't that the new layer takes on implementation work. Compilers always did some of that, with their own undefined behavior and implementation-defined choices. The difference is the leap in non-determinism. The earlier abstractions were designed to be mostly deterministic and mostly non-leaky, and AI tools break that pattern. The same prompt produces different code on different days, with different trade-offs and different bugs -- that's a bigger shift than swapping languages. I keep coming back to the same historical pattern: the engineers who thrive are the ones who can operate at the new layer, not the ones who insist on staying anchored to the old one. ## An Engineer's Guide to Better AI Skills: Implementing a Testing Process to Optimize Agent... DevFeed: [An Engineer's Guide to Better AI Skills: Implementing a Testing Process to Optimize Agent...]() Original publisher: [Read original article]() Author: Pinterest Engineering Published: 2026-05-12T16:01:00Z Content type: article Language: en Sources: [Pinterest Engineering Blog - Medium]() Topics: [Agent Skill](), [Artificial Intelligence](), [Claude Code](), [codex](), [Bash](), [iOS](), [JSON]() Tags: [agent](), [agent-skill](), [agentic-coding](), [agents](), [ai](), [ai-agents](), [analysis](), [architecture](), [bash](), [claude-code](), [code](), [codex](), [debug](), [engineering](), [engineering-culture](), [how-to](), [openai](), [pinterest](), [programming](), [skills](), [testing](), [workflows]() ### AI overview An engineer's guide to testing AI skill invocation in repositories and custom skills. It describes a Bash-based harness that runs positive and negative prompts, captures verbose logs, parses JSON-streamed output, and measures invocation reliability across an internal Codex fork and Claude Code. ### Source excerpt An Engineer's Guide to Better AI Skills: Implementing a Testing Process to Optimize Agent Performance in Any Repository or Skill Author: Daniel Reed The tech industry is currently seeing a massive overhaul in the way we work and many are enjoying the benefits of AI agents, particularly when automating engineer workflows and serving domain-specific knowledge. However, relying on agents to consistently invoke a custom skill can be surprisingly unreliable at times. When adopting a new skill intended to help agents write code for Pinterest's iOS architecture (I'll call it rx-mvvm) we discovered that sometimes our knowledge skill wasn't being loaded into our agents. To address this, we conducted a series of tests on Pin-agent (an internal fork of OpenAI's Codex) and Claude Code to quantify the reliability of skill invocation and identify some best practices to maximize performance. This was a direct result of observing agents struggling to meet the skills bar during architectural reviews. We found that by applying different techniques we could track and drastically improve skill invocation rates on both tested agents. How to Build A Skill Test Harness Building a reliable test harness for agent skill invocation requires three key components working in concert. The Core Tool is a Bash script that orchestrates automated testing by piping prompts to your agent and capturing verbose output logs. The core execution is simple: if echo "$prompt" | claude --print --verbose --output-format stream-json > "$log_file" 2>&1; then command_success=true fi The script runs all test cases in sequence, collecting logs for later analysis. We ran the entire suite multiple times to account for the nondeterministic nature of agents. Prompts were categorized into two categories defined as arrays: Positive Cases -- 15 prompts covering the full spectrum of skill domains: CORE_PROMPTS=( "load the rx-mvvm-architecture skill" "check if this follows rx-mvvm patterns" # ... 13 more cases ) Negative Case ## How we standardized MariaDB in our Integration Server DevFeed: [How we standardized MariaDB in our Integration Server]() Original publisher: [Read original article]() Author: Eloy Gonzalez Published: 2026-03-26T18:59:43Z Content type: article Language: en Sources: [Wealthfront]() Topics: [MariaDB](), [Testing](), [migration](), [Databases](), [Jenkins](), [CI/CD](), [Back end]() Tags: [architecture](), [backend](), [careers](), [ci-cd](), [data](), [engineering-culture](), [hibernate](), [infrastructure](), [integrity](), [jenkins](), [locking](), [migration](), [modularization](), [production](), [sql](), [testing](), [tests](), [wealthfront-engineering]() ### AI overview Wealthfront describes migrating its Integration Server from HSQLDB to MariaDB to improve parity with production. The article covers the migration strategy, architectural hurdles, and benefits for data integrity, concurrency, locking, and production-like testing. ### Source excerpt Engineering at Wealthfront is centered on the idea that code should be written to facilitate testing, not the other way around. Without a staging environment to fall back on, we maximize confidence through a sophisticated, multi-layered testing strategy. While unit tests provide our most rigorous line of defense, our Integration Server is the workhorse that... Read more ## Why Agentic Coding Tools Require Software Engineers to Focus on Building Products DevFeed: [Why Agentic Coding Tools Require Software Engineers to Focus on Building Products]() Original publisher: [Read original article]() Author: Michael Werle Published: 2026-03-06T15:07:10Z Content type: opinion Language: en Sources: [Indeed]() Topics: [ai-coding](), [AI Development](), [Software Engineering](), [Model Context Protocol](), [ide]() Tags: [agentic-coding](), [ai](), [ai-coding](), [context-engineering](), [engineering](), [engineering-culture](), [how-to](), [mcp](), [sandboxing](), [software-engineering](), [unsorted]() ### AI overview This opinion article argues that software engineers should adapt to agentic coding tools by focusing less on manual coding and more on building products that solve customer problems. It describes the author's changing views on AI-assisted programming and highlights context engineering, MCP, skills, sandboxing, subagents, and related tools, while acknowledging significant security and automation risks. ### Source excerpt Why agentic coding tools demand a new identity for software engineers After more than two decades of professional software engineering, I have arrived at a set of conclusions that I find very uncomfortable. The era of mostly manual coding has ended. IDEs, in their current form, are no longer necessary. Traditional software development languages are [...] ## Kogan.com's Engineering Culture Emphasizes Ownership, Collaboration, and Continuous Delivery DevFeed: [Kogan.com's Engineering Culture Emphasizes Ownership, Collaboration, and Continuous Delivery]() Original publisher: [Read original article]() Author: Kogan Dev Blog Published: 2026-02-22T03:33:30Z Content type: article Language: en Sources: [Kogan.com]() Topics: [engineering-culture](), [Development](), [Testing](), [CI/CD](), [Deployment](), [observability]() Tags: [ci-cd](), [code](), [culture](), [deployment](), [development](), [engineering](), [engineering-culture](), [learning-growth](), [observability](), [testing]() ### AI overview This article describes Kogan.com's engineering culture, emphasizing ownership, pragmatic decision-making, collaboration across teams, and continuous improvement. It also outlines a delivery process involving clear requirements, incremental work, peer review, automated testing, CI checks, CI/CD deployment, and production monitoring. ### Source excerpt At Kogan.com, engineering is about building software that is useful and seeing them used at scale. We work in a fast moving e-commerce environment, so the problems are real and often complex. Performance, reliability, scale, legacy constraints, new features, tight feedback loops. We ship frequently, deploy daily, and continuously improve what is already live. You can see the impact of your work quickly, and so can our customers. Yes, there are hackathons, plenty of snacks, meetup pizzas and team events. But what defines the culture is ownership. Engineers are trusted to make decisions, go deep into systems, challenge assumptions, and drive outcomes. That might mean building a new platform, untangling and modernising legacy code, improving observability, or removing bottlenecks that affect millions of users. Teams are pragmatic and hands on. We care about clean code and good architecture, but we also care about delivering value. There is a strong bias toward action and continuous improvement over perfection. Collaboration is key. Engineers work closely with product, design, data, and commercial teams. Context is shared openly, trade offs are discussed honestly, and ideas are judged on merit. To bring our culture to life, we spoke to three of our engineers about the engineering culture here: Shams SaatichoHow do ideas go from concept to production here? Ideas come from stakeholders, marketing, product, UX or engineering. The first step is clarity. What problem are we solving? What measurable outcomes define success? What constraints or trade offs exist? Engineers and stakeholders align on requirements and break larger initiatives into small, testable increments with clear acceptance criteria. Once scoped, work is prioritised in the backlog. During development, changes go through peer review, automated testing and CI checks, plus user acceptance testing where needed. Deployment happens through CI/CD. Shipping is not the finish line. We monitor production metrics and be ## New Leaders, New Perspectives: Why We Joined Wealthfront Engineering DevFeed: [New Leaders, New Perspectives: Why We Joined Wealthfront Engineering]() Original publisher: [Read original article]() Author: Sanjana Kaundinya Published: 2026-02-12T23:10:24Z Content type: article Language: en Sources: [Wealthfront]() Topics: [engineering-culture](), [DevOps](), [Back end](), [Kafka]() Tags: [backend](), [devops](), [engineering](), [engineering-culture](), [kafka](), [perspectives](), [wealthfront-engineering]() ### AI overview Wealthfront interviews two new engineering managers about joining the company, its engineering culture, and how individual ownership shapes software design and project direction. ### Source excerpt At Wealthfront, our engineering culture is something you experience as soon as you join the organization, and new hires are regularly surprised by how quickly and fully they begin to contribute and drive impact. We sat down with two of our new engineering managers, Melissa and Sanjana, to hear how quickly they were able to... Read more ## How Service Level Objectives Align Developers and Product Managers DevFeed: [How Service Level Objectives Align Developers and Product Managers]() Original publisher: [Read original article]() Author: Anis Khan Site Reliability Engineering is my role Cost optimization is my goal GitHub profile Linkedin profile Published: 2026-02-02T00:00:00Z Content type: opinion Language: en Sources: [Trivago]() Topics: [Availability](), [User Experience](), [data]() Tags: [availability](), [developer](), [engineering-culture](), [maintainability](), [opinions](), [outages](), [performance](), [site-reliability-engineering](), [technical](), [uptime]() ### AI overview This article argues that Service Level Objectives (SLOs) can reduce conflict between developers and product managers by creating a shared, data-driven agreement about reliability and user experience. It explains how a 99.9% availability target creates a 43.2-minute monthly downtime budget and shows how that budget can guide decisions about releasing features versus restoring stability. ### Source excerpt It's a scenario developers relate to a little too well. The product manager always comes with more and more feature requests. They also want to release fast by giving a tight deadline. While you... ## The 5 Worst Tech Trends of 2025 DevFeed: [The 5 Worst Tech Trends of 2025]() Original publisher: [Read original article]() Author: Brian Jenney Published: 2025-12-29T20:01:09Z Content type: opinion Language: en Sources: [Brian Jenney]() Topics: [Artificial Intelligence](), [coding](), [engineering-culture](), [Job](), [Bootcamp]() Tags: [2025](), [ai](), [coding](), [developers](), [engineering-culture](), [job](), [junior-developer]() ### AI overview An opinionated review of negative technology-industry trends observed in 2025, focusing on claims about AI productivity, a harder junior developer job market, and coding bootcamp closures. The supplied text is incomplete and ends during the bootcamp section. ### Source excerpt (And How to Win in 2026) ## Making time: Space to think, build, and create (or, This Shit is Fun!) DevFeed: [Making time: Space to think, build, and create (or, This Shit is Fun!)]() Original publisher: [Read original article]() Published: 2025-12-08T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [chainguard](), [Google](), [ibm](), [Open Source](), [GitHub]() Tags: [chainguard](), [chainguard-culture](), [chainguard-engineering](), [chainguard-engineering-culture](), [engineering](), [engineering-culture](), [github](), [innovation](), [making-time](), [mentoring](), [projects](), [prototypes](), [work]() ### AI overview The article describes Chainguard's "Making Time," a lightly structured program that gives engineers protected time for self-directed, strategic work outside the immediate product roadmap. It explains how similar programs at IBM and Google supported innovation and presents Chainguard's approach as a way to encourage prototyping, technical improvements, documentation review, mentoring, and long-term engineering impact. ### Source excerpt See how we give engineers protected space for self-directed, high-impact work; boosting innovation and improving engineering morale. ## Mob Programming: Smells Like Team Spirit DevFeed: [Mob Programming: Smells Like Team Spirit]() Original publisher: [Read original article]() Author: Dmytro Kurets Published: 2025-12-04T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [Programming](), [Code](), [pull-requests](), [Back end](), [Front end](), [Data analysis](), [Refactoring]() Tags: [back-end](), [code](), [collaboration](), [data-analysis](), [engineering](), [engineering-culture](), [front-end](), [keyboard](), [pairing](), [programming](), [review](), [team]() ### AI overview The article explains mob programming as structured, shared-focus collaboration with one driver, rotating typing roles, and navigators guiding the work. It describes a trivago team experimenting with collaborative review and coding changes to address pull requests piling up and slowing delivery. ### Source excerpt Mob programming often sparks hot takes. "It's counter-productive." "One person could do it faster." "Why waste five people on one keyboard?" If that's what it looks like in your head--one senior ... ## Turso crosses the mark of 128 contributors DevFeed: [Turso crosses the mark of 128 contributors]() Original publisher: [Read original article]() Author: Glauber Costa Published: 2025-07-24T00:00:00Z Content type: article Language: en Sources: [Turso Blog]() Topics: [Turso](), [Rust](), [SQLite](), [Open Source](), [Database](), [systems]() Tags: [code](), [database](), [engineering-culture](), [open-source](), [p2p](), [rust](), [software](), [sqlite](), [turso]() ### AI overview Turso has reached 128 contributors to its open-source, fully compatible SQLite rewrite in Rust. The article introduces bit-aloo, the 128th contributor, and describes their background, interest in SQLite internals, and positive experience with Turso's engineering culture and contribution process. ### Source excerpt Turso just crossed 128 contributors on its open SQLite rewrite in Rust. Meet bit-aloo, our 128th contributor, on contributing to database internals. ## ☀ While I'm out: A 6-part deep dive on TDD, engineering culture and leadership DevFeed: [☀ While I'm out: A 6-part deep dive on TDD, engineering culture and leadership]() Original publisher: [Read original article]() Author: Denis Čahuk Published: 2025-07-17T14:03:59Z Content type: article Language: en Sources: [Crafting Tech Teams]() Topics: [Test-driven development](), [Testing](), [engineering-culture]() Tags: [engineering-culture](), [leadership](), [mocking](), [tdd](), [testing](), [unit-testing]() ### AI overview The author announces a six-part Substack series based on earlier coaching streams and webinars. The series covers testing, mocking, TDD, engineering culture, team dynamics, and leadership, with posts scheduled from July 19 through July 26. ### Source excerpt A Summer Deep-Dive on Testing, Design, and Teams from our previous webinars and coaching streams. ## How Dust uses Temporal to run real-time AI agents across workplace tools DevFeed: [How Dust uses Temporal to run real-time AI agents across workplace tools]() Original publisher: [Read original article]() Author: Clair Byrd Published: 2025-05-29T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Artificial Intelligence](), [real-time](), [GitHub](), [Slack](), [engineering-culture]() Tags: [agentic-ai](), [ai](), [community](), [engineering-culture](), [github](), [notion](), [real-time](), [slack]() ### AI overview The article describes how Dust embeds AI agents in tools such as Slack, GitHub, Notion, Confluence, and Salesforce. It explains that Temporal provides the orchestration backbone for running these event-triggered workflows at scale and with reliability. ### Source excerpt Dust uses Temporal to power real-time AI agents that work inside Slack, GitHub, Notion, and more; scaling to 10M+ Activities/day with ease. ## Odysseus to AI: Matt Greenwood on the Dev Interrupted Podcast DevFeed: [Odysseus to AI: Matt Greenwood on the Dev Interrupted Podcast]() Original publisher: [Read original article]() Author: Emily Majewski Published: 2025-05-27T16:55:02Z Content type: opinion Language: en Sources: [Two Sigma Engineering]() Topics: [Artificial Intelligence](), [engineering-culture](), [AI Inference](), [Development](), [Large Language Model](), [Machine learning]() Tags: [agentic](), [ai](), [culture](), [dev](), [engineering-culture](), [llms](), [machine-learning](), [podcast]() ### AI overview A Dev Interrupted podcast discussion with Two Sigma Chief Innovation Officer Matt Greenwood explores AI advisory agents, intentional innovation, engineering culture, and the use of LLMs and other AI approaches in systematic investment processes. Greenwood describes an "epsilon and omega" approach of iterative steps toward long-term goals and an advisory, oracle, and operational framework for AI applications. ### Source excerpt The post Odysseus to AI: Matt Greenwood on the Dev Interrupted Podcast appeared first on Two Sigma. ## How Dropbox leverages testing to maintain high level of trust at scale DevFeed: [How Dropbox leverages testing to maintain high level of trust at scale]() Original publisher: [Read original article]() Author: Jose Alcérreca Published: 2025-04-23T17:26:09Z Content type: article Language: en Sources: [Android Developers - Medium]() Topics: [dropbox](), [Testing](), [Android](), [test](), [engineering-culture]() Tags: [android](), [automated](), [case-study](), [dropbox](), [e2e-testing](), [engineering](), [engineering-culture](), [featured](), [quality](), [testing]() ### AI overview This article describes how Dropbox evolved its Android testing strategies to maintain user trust and timely feedback at scale. It discusses the testing pyramid, automated and end-to-end testing, Android integration, and the costs of an early monolithic app structure. ### Source excerpt This is part 2 of the Testing at scale series of articles where we asked industry experts to share their testing strategies. In this article, Ryan Harter, Staff Engineer at Dropbox, shares how the shape of Dropbox's testing pyramid changed over time, and what tools they use to get timely feedback. With more than one billion downloads, the Dropbox app for Android has to maintain a high quality bar for a diverse set of use cases and users. With less than 30 Android engineers, manual testing and #yolo isn't enough to maintain confidence in our codebase, so we employ a variety of different testing strategies to ensure we can continually serve our users needs. Since Dropbox makes it easy to access your files across all of your devices, the Android app has to support viewing as many of those files as possible, including media files, documents, photos, and all of the variations within these categories. Additionally, features like Camera Uploads, which automatically backs up all of your most important photos, require deep integration with the Android OS in ways that have changed significantly over the years and across Android versions. All of this needs to continually work for our users, without them having to worry about the complexity, because the last thing anyone wants is to worry that they might lose their data. While the size and distribution of the Android team at Dropbox has changed throughout the years, it's imperative that we're able to consistently build and refine features within the app while maintaining the level of trust from our users that we've become known for. To help underscore how Dropbox has been able to foster that trust, I'd like to share some ways that our testing strategies have changed over the years. How it started While automated testing has always been an important part of engineering culture at Dropbox, it hasn't always been easy on Android. Years ago Dropbox invested in testing infrastructure that leaned heavily on End-to-End (E2E) testing. B ## Come build the future of Incident Response DevFeed: [Come build the future of Incident Response]() Original publisher: [Read original article]() Author: Norberto Lopes Published: 2025-03-20T08:30:00Z Content type: opinion Language: en Sources: [The incident.io Blog]() Topics: [Incident response](), [incident management](), [Artificial Intelligence](), [engineering-culture]() Tags: [2025](), [ai](), [career](), [changelog](), [culture](), [engineering](), [engineering-culture](), [incident](), [incident-channel](), [incident-management](), [incident-response](), [outage](), [post-mortem](), [slack-incident]() ### AI overview incident.io presents its product vision, engineering culture, and hiring appeal for 2025. The article emphasizes investment in AI to make incidents easier to handle, alongside continued development of on-call and incident-response capabilities. ### Source excerpt Ever curious about why incident.io is the most exciting place to build your career in 2025? Let us share our incredible product momentum, ambitious roadmap, and unique engineering culture that's attracting the talent eager to redefine incident response. ## Life of SRE as a Salesperson DevFeed: [Life of SRE as a Salesperson]() Original publisher: [Read original article]() Author: Anis Khan Site Reliability Engineering is my role Cost optimization is my goal GitHub profile Linkedin profile Published: 2025-03-17T00:00:00Z Content type: opinion Language: en Sources: [Trivago]() Topics: [site-reliability-engineering](), [site-reliability-engineer](), [Platform Engineering](), [Developer experience](), [observability](), [cloud-infrastructure]() Tags: [developer](), [developer-experience](), [development](), [engineering-culture](), [infrastructure](), [observability](), [platform-engineering](), [site-reliability-engineering](), [sre]() ### AI overview A Site Reliability Engineer at trivago describes acting as a salesperson or influencer for technology teams by helping product teams adopt practices, tools, and solutions developed with Platform Engineering, Developer Experience, and Observability teams. The article also outlines trivago's SRE mission and squad structure. ### Source excerpt If you are a Developer or a Product person, you might have this feeling of achievement when you work on a specific product. When it's launched successfully in the market, you s... [Next page]()