# Productivity Published articles for Productivity. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Improving Test Coverage in the Interview Coach Application with GitHub Copilot DevFeed: [Improving Test Coverage in the Interview Coach Application with GitHub Copilot]() Original publisher: [Read original article]() Author: Aaron Powell Published: 2026-09-15T16:00:21Z Content type: tutorial Language: en Sources: [Visual Studio Blog]() Topics: [test-coverage](), [Test coverage](), [GitHub Copilot](), [App](), [Visual Studio](), [Code]() Tags: [agent](), [analysis](), [code-coverage](), [copilot](), [github-copilot](), [productivity](), [projects](), [run](), [test](), [test-coverage](), [testing](), [tests](), [unit-tests](), [visual-studio]() ### AI overview This tutorial explains how to improve test coverage in the Interview Coach application by establishing a baseline, using GitHub Copilot's Test Agent to generate unit tests, and analyzing coverage across the solution. The article reports coverage increasing from 37% to 81%. ### Source excerpt Today I want to look at how we can improve on the test coverage that we have in the Interview Coach application. This application has some unit tests in it already, but I'm sure there is room for improvement. Defining our baseline Before we start writing tests, it's a good idea to have a baseline [...] The post Today I will... improve test coverage appeared first on Visual Studio Blog. ## Small Teams, Ambitious Projects DevFeed: [Small Teams, Ambitious Projects]() Original publisher: [Read original article]() Author: Dalai Felinto Published: 2026-09-10T15:56:53Z Content type: opinion Language: en Sources: [Blender]() Topics: [Development](), [Open Source](), [Software]() Tags: [animation](), [collaboration](), [development](), [general-development](), [open-source](), [productivity](), [testing](), [workflow]() ### AI overview Blender outlines strategic development targets for the next two years, focused on improving productivity and collaboration for individuals and small teams. Priorities include dynamic overrides, animation workflow improvements, scalable rigging, and smoother transitions from storyboards to previs. ### Source excerpt A first-look overview of Blender development's strategic targets for the next two years. ## Article: When Spec-Driven Development Pays Off DevFeed: [Article: When Spec-Driven Development Pays Off]() Original publisher: [Read original article]() Author: Nitin Garg Published: 2026-09-10T09:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [AI-assisted coding](), [AI Development](), [Software Engineering](), [code productivity]() Tags: [ai](), [ai-assisted-coding](), [ai-coding](), [ai-development](), [ai-ml-data-engineering](), [architecture](), [article](), [artificial-intelligence](), [code-generation](), [generative-ai](), [ml-data-engineering](), [productivity](), [reasoning](), [review](), [security](), [software-development](), [spec-driven-development](), [when-spec-driven-development-pays-off]() ### AI overview The article argues that AI-assisted coding shifts the main constraint from writing code to verifying it. It presents specification-first development as a governance approach for hard, multi-constraint work, while noting its time and cost and warning that apparent gains may instead come from reasoning. ### Source excerpt AI coding assistants have become a core part of software development. AI-generated code has shown productivity gains, but it's also contributing to security weaknesses and familiar bug patterns. In this article, author Nitin Garg highlights the bottleneck has moved from code generation to code verification, and how to detect & mitigate it when the AI-generated behavior diverges from the intent. By Nitin Garg ## Today I will... find hidden latency across a distributed .NET application DevFeed: [Today I will... find hidden latency across a distributed .NET application]() Original publisher: [Read original article]() Author: Justin Yoo Published: 2026-09-08T16:00:22Z Content type: tutorial Language: en Sources: [Visual Studio Blog]() Topics: [.NET](), [Processes](), [GitHub Copilot]() Tags: [copilot](), [github-copilot](), [github-copilot-profiler-agent](), [latency](), [net](), [performance](), [processes](), [productivity](), [profile](), [visual-studio](), [visual-studio-2026](), [visual-studio-profiler]() ### AI overview A Visual Studio profiling walkthrough for finding hidden latency in a distributed .NET application by isolating the process responsible for a slow interaction and determining whether it is computing or waiting. ### Source excerpt When a distributed application feels slow, the user sees one delay. The code behind that delay may run across a web frontend, backend services, databases, and external APIs. If you profile only the frontend while the backend is slow, the profiler can show that the frontend is healthy without revealing the actual bottleneck. This article [...] The post Today I will... find hidden latency across a distributed .NET application appeared first on Visual Studio Blog. ## How to Grow Past Senior Engineer DevFeed: [How to Grow Past Senior Engineer]() Original publisher: [Read original article]() Author: Gregor Ojstersek Published: 2026-09-08T15:30:36Z Content type: opinion Language: en Sources: [Engineering Leadership]() Topics: [coding](), [meetings](), [implementation]() Tags: [career](), [productivity](), [software-engineer](), [team-lead]() ### AI overview An experienced software engineer shares four practical ways to move beyond the senior engineer level: discuss career goals with a manager, track accomplishments, become a go-to person in a project, technology, or domain, and propose and own an impactful codebase improvement. ### Source excerpt My 4 recommended starting moves that'll help you gain trust and credibility to grow to the next step! ## Meta Virtual Display Adds Input Forwarding and Windows-Like Features to macOS DevFeed: [Meta Virtual Display Adds Input Forwarding and Windows-Like Features to macOS]() Original publisher: [Read original article]() Author: Craig Storm Published: 2026-09-08T14:50:34Z Content type: news Language: en Sources: [UploadVR]() Topics: [Meta](), [macOS](), [Windows](), [App]() Tags: [features](), [headsets-tech](), [improvements](), [mac](), [macos](), [productivity](), [remote](), [windows]() ### AI overview Meta Virtual Display has gained input forwarding on Windows and macOS, while the macOS version now supports a quick-connect prompt and display formats previously available on Windows. Testing found that support for older Intel Macs varies by model. ### Source excerpt The Meta Virtual Display feature of Quest headsets has continued to receive improvements, including input forwarding, and the Mac version now offers functionality previously exclusive to Windows. ## 1Password increases engineering productivity 21% with Codex DevFeed: [1Password increases engineering productivity 21% with Codex]() Original publisher: [Read original article]() Published: 2026-09-08T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI-assisted coding]() Tags: [codex](), [engineering](), [features](), [internal-tools](), [productivity](), [pull-request](), [security](), [site-reliability-engineering](), [software-delivery](), [user-stories](), [writing-code]() ### AI overview 1Password reports using Codex across its software delivery lifecycle to turn user stories into near-final prototypes and features more quickly. It cites improved engineering productivity and shorter pull request cycle times while maintaining security standards. ### Source excerpt Engineers at 1Password use Codex to rapidly build new features and internal tools, reaching production-readiness while maintaining rigorous security policies. ## How AI Coding Fits a Scarcity Loop of Opportunity, Unpredictable Rewards, and Quick Repetition DevFeed: [How AI Coding Fits a Scarcity Loop of Opportunity, Unpredictable Rewards, and Quick Repetition]() Original publisher: [Read original article]() Author: Fran Soto Published: 2026-09-06T04:00:42Z Content type: opinion Language: en Sources: [Strategize Your Career]() Topics: [coding](), [Artificial Intelligence](), [ai-coding]() Tags: [ai](), [ai-coding](), [code](), [coding](), [productivity]() ### AI overview This commentary applies the three-part scarcity loop from Scarcity Brain--opportunity, unpredictable reward, and quick repetition--to coding. It argues that AI lowers the friction of writing code, makes prompt outcomes uncertain, and enables faster or parallel feedback loops, which can encourage repeated prompting without necessarily representing progress. ### Source excerpt The 3 rules from Scarcity Brain that turn AI coding into a loop you want to repeat, without confusing more output with progress. ## Why Data Teams Should Focus on Business Value, Not Just Technical Decisions DevFeed: [Why Data Teams Should Focus on Business Value, Not Just Technical Decisions]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-09-05T16:08:27Z Content type: opinion Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [data](), [coding](), [Microservice](), [Cloud]() Tags: [architecture](), [business-value](), [data-platform](), [productivity](), [technical]() ### AI overview This opinion article argues that engineers and data teams often focus heavily on technical choices--such as architecture, platforms, and infrastructure--while neglecting the business reasons for building an application or feature. It encourages teams to prioritize meaningful outcomes and business value. ### Source excerpt Why data teams need to spend less time debating the stack and more time questioning the work ## Today I will... review GitHub PRs DevFeed: [Today I will... review GitHub PRs]() Original publisher: [Read original article]() Author: Aaron Powell Published: 2026-09-01T16:00:27Z Content type: tutorial Language: en Sources: [Visual Studio Blog]() Topics: [Pull Request](), [GitHub](), [GitHub Copilot](), [Syntax Highlighting]() Tags: [copilot](), [git](), [github](), [productivity](), [pull-requests](), [review](), [tooling](), [visual-studio](), [visual-studio-2026]() ### AI overview A tutorial on reviewing GitHub pull requests from Visual Studio. It covers opening PRs, examining diffs, adding buffered inline comments, approving or merging, and using Copilot during review. ### Source excerpt Today I will show you how we can tackle one of the most common tasks when working in a development team or managing an open-source project, and that is working with Pull Requests. While we're going to be using the GitHub tooling within Visual Studio for this, the approach would be very similar with Azure [...] The post Today I will... review GitHub PRs appeared first on Visual Studio Blog. ## Enterprise AI Agents: Common Roadblocks and How to Overcome Them DevFeed: [Enterprise AI Agents: Common Roadblocks and How to Overcome Them]() Original publisher: [Read original article]() Author: JEFF MILLS, CHIEF CUSTOMER OFFICER, AI SERVICES @ TOPTAL Published: 2026-09-01T07:00:00Z Content type: article Language: en Sources: [Toptal Blog]() Topics: [AI Agent](), [Artificial Intelligence](), [AI Platforms/Deployment]() Tags: [agentic](), [agents](), [ai](), [ai-agent](), [ai-agents](), [enterprise](), [operations](), [productivity](), [workflows]() ### AI overview This article examines why enterprises struggle to scale AI agents into daily operations and outlines the organizational readiness and workflow integration challenges behind that gap. ### Source excerpt AI agents promise autonomy at scale, but many organizations fail to deploy them in day-to-day operations. Drawing on decades of experience in business strategy and technology, two Toptal leaders explore what it takes to bring AI agents into real workflows. ## Polimill builds Japan's next-generation public AI infrastructure DevFeed: [Polimill builds Japan's next-generation public AI infrastructure]() Original publisher: [Read original article]() Published: 2026-08-31T07:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI Chat](), [AI search]() Tags: [ai-infrastructure](), [ai-platform](), [codex](), [data](), [generative-ai](), [government](), [gpt](), [japan](), [openai](), [platform](), [productivity](), [public-ai](), [public-sector](), [search](), [workflows]() ### AI overview Polimill's QommonsAI uses OpenAI GPT models to organize and search municipal administrative knowledge across Japan, supporting public-sector workflows and development productivity. ### Source excerpt Polimill uses OpenAI GPT models and Codex to help municipalities search and use administrative knowledge while accelerating development. ## How do you actually measure the impact of AI on design? DevFeed: [How do you actually measure the impact of AI on design?]() Original publisher: [Read original article]() Author: Shane Johnston Published: 2026-08-28T19:30:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Artificial Intelligence](), [Figma]() Tags: [ai](), [ai-impact](), [ai-tools](), [collaboration](), [design](), [developers](), [product-development](), [productivity](), [research](), [workflows]() ### AI overview Figma describes a multi-year study measuring how AI affects design and product development. Its AI impact index compares expectations with reported experience across collaboration, personal productivity, projects, and products, using survey responses from designers, developers, and product managers. ### Source excerpt In 2024, we kicked off a study to understand how AI would reshape design and product development. Now, AI is helping us run it. ## Six Heroku CLI Commands for Improving Terminal Workflows DevFeed: [Six Heroku CLI Commands for Improving Terminal Workflows]() Original publisher: [Read original article]() Author: Andy Smith Published: 2026-08-25T16:28:53Z Content type: tutorial Language: en Sources: [Heroku]() Topics: [Command-line interface](), [Heroku](), [Developer experience](), [code productivity]() Tags: [cli](), [command-line](), [commands](), [config](), [developer-experience](), [developer-tools](), [ecosystem](), [heroku](), [heroku-postgres](), [productivity](), [terminal](), [tools](), [variables]() ### AI overview This tutorial presents six Heroku CLI commands intended to reduce context switching and improve terminal-based workflows. The supplied content discusses exporting app configuration for local environments and checking active Heroku Postgres database queries, while warning about production configuration handling and credential leaks. ### Source excerpt Improving developer experience in the command line relies on minimizing context switches. While most engineers use the Heroku CLI strictly for basic tasks like pushing code, it contains a powerful suite of command line tools built to optimize your workflow. If you want to keep your focus locked on writing code, these six Heroku CLI commands will instantly upgrade your daily productivity. The post 6 Lesser-Known Heroku CLI Commands You Probably Aren't Using appeared first on Heroku. ## Proficient Android AI training program addresses AI-agent workflows for Android teams DevFeed: [Proficient Android AI training program addresses AI-agent workflows for Android teams]() Original publisher: [Read original article]() Author: Jorge Castillo Published: 2026-08-25T07:21:15Z Content type: opinion Language: en Sources: [Effective Android]() Topics: [Android](), [android-development](), [AI-assisted coding](), [AI Agent](), [Development](), [Mobile]() Tags: [ai-agents](), [ai-coding-agents](), [android](), [android-development](), [productivity](), [workflows]() ### AI overview The author describes common problems Android teams encounter when using AI coding agents, including repeated context, nondeterministic output, poorly reviewed code, generic prompts, fragile tests, and oversized pull requests. The article announces Proficient Android AI, a practical training program focused on improving AI-agent workflows for Android engineers and teams. ### Source excerpt Proficient Android AI teaches Android engineers how to get ahead with AI agents now: better output, fewer wasted tokens, more deterministic results, and workflows built for real Android teams. ## Internal Developer Platform Golden Paths Guide DevFeed: [Internal Developer Platform Golden Paths Guide]() Original publisher: [Read original article]() Author: Rashmi Hegde Published: 2026-08-25T00:00:00Z Content type: tutorial Language: en Sources: [Harness Blog]() Topics: [internal developer platform](), [Platform Engineering](), [Developer Platform](), [Template]() Tags: [catalog](), [guide](), [internal-developer-platform](), [platform-engineering](), [productivity](), [self-service](), [visibility](), [workflows]() ### AI overview This guide explains how platform engineers can evolve an internal developer platform from a basic service catalog into maintained golden paths. It describes opinionated workflows, self-service capabilities, and embedded operational practices intended to improve developer adoption, speed, and reliability. ### Source excerpt Transform your service catalog into golden paths that developers love. Build an IDP that boosts productivity. Learn more. | Blog ## Why Engineering Culture Matters for AI-Enabled Productivity DevFeed: [Why Engineering Culture Matters for AI-Enabled Productivity]() Original publisher: [Read original article]() Author: Gregor Ojstersek Published: 2026-08-24T14:08:28Z Content type: opinion Language: en Sources: [Engineering Leadership]() Topics: [Artificial Intelligence](), [Software]() Tags: [ai](), [culture](), [engineering](), [productivity]() ### AI overview The article argues that AI tools can improve engineering productivity only when organizations provide a healthy culture and environment. It emphasizes that poor culture reduces psychological safety, while strong culture supports effective use of AI. ### Source excerpt AI definitely helps with productivity, but only when you have the right culture in place first! ## Hedge 316: AI Governance DevFeed: [Hedge 316: AI Governance]() Original publisher: [Read original article]() Author: Russ Published: 2026-08-20T15:38:28Z Content type: article Language: en Sources: [rule 11 reader]() Topics: [ai-governance](), [Artificial Intelligence](), [Network]() Tags: [ai](), [ai-governance](), [audio](), [cost](), [governance](), [hedge](), [privacy](), [productivity]() ### AI overview This podcast episode examines AI governance for AI operations and general network use, focusing on privacy, cost control, accuracy, productivity measurement, and effective deployment. ### Source excerpt Deploying AI for AI Ops, or even just for general use in your network, is very simple--but we often forget that these kinds of new technologies need to be governed. From privacy through cost, operators need to decide how to govern their AI deployments to control costs, ensure accuracy, measure productivity, and make certain these systems are being used effectively. Colin Cosgrove joins Russ and Tom to look at AI governance. ## Conceptual integrity and counting lines of code DevFeed: [Conceptual integrity and counting lines of code]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-08-19T22:46:07Z Content type: opinion Language: en Sources: [Simon Willison]() Topics: [AI Development](), [AI-assisted coding](), [code productivity](), [Software Engineering](), [Programming]() Tags: [agents](), [ai](), [ai-2-235](), [code](), [coding-agents](), [coding-agents-248](), [generative-ai](), [generative-ai-1-981](), [llms](), [llms-1-947](), [podcast-appearances](), [podcast-appearances-41](), [productivity](), [software-development](), [software-engineer]() ### AI overview This article presents transcript highlights from a Talking Postgres discussion about AI-assisted software development. It argues that lines of code can sometimes indicate productivity when coding agents produce maintainable, tested, production-quality code, while emphasizing that skill and experience remain necessary. It also argues that cognitive capacity and conceptual integrity limit how much agent-generated code engineers can effectively manage. ### Source excerpt Last week I recorded an episode of the Talking Postgres podcast with Claire Giordano on the subject of "How AI is changing software development". We had a really great conversation. Here are a couple of my highlights from a lightly edited transcript (prompt to Claude: "very minor edits to remove disfluencies"). This is the latest version of an argument I've been trying to build about why sometimes it does make sense to talk about lines of code as an indicator of productivity with coding agents, at 35:01: A lot of people will tell you it makes no sense to measure productivity in lines of code. I'd actually disagree, because there's a hard limit. In the before-times, a software engineer could produce a few hundred lines of production-ready code per day -- and 200 lines of working, debugged, production-level code is an incredibly good day. Most days you'd produce 50 or 60. If agents let you produce a thousand lines of debugged code, that really is a very meaningful improvement -- as long as the code is the same quality: maintainable, tested, all of that. You can get to that point with agents, but it takes a huge amount of skill and knowledge and experience. That's what senior engineers are made of. I can do way more work as a single engineer than I could without agents. So you could argue, why should a company have more than one engineer? Beyond the obvious bus factor thing -- a team of one is a very badly designed team -- the answer is that the new limiting factor is cognitive capacity. I can churn out code a hundred times faster. I don't have the cognitive capacity to stay on top of 100 times the amount of code. So you still need a team of engineers, so you can load balance that cognitive capacity across the team. And this section on conceptual integrity at 46:03, which Claire equated to the Winchester Mystery House! Simon: There's a concept in The Mythical Man-Month -- conceptual integrity -- where well-designed software has an integrity to it: there are no surprises in i ## Measuring time savings from Figma Make DevFeed: [Measuring time savings from Figma Make]() Original publisher: [Read original article]() Author: Remy Stewart Published: 2026-08-11T19:59:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Figma](), [Data Science](), [Artificial Intelligence](), [A/B Testing]() Tags: [a-b-testing](), [ai](), [ai-tools](), [data-science](), [figma](), [productivity](), [research](), [testing]() ### AI overview Figma's Data Science team describes a randomized controlled trial measuring how Figma Make affects users' design work. Across 100 participants, design work was 20% faster and 16% easier overall, while PMs saw tasks become 23% faster and 37% easier. The article explains why confounders make AI time savings difficult to measure and discusses the limitations of online A/B testing and causal inference using log data. ### Source excerpt The Figma Data Science team assumed that AI saves users time--but quantifying it required a new approach to research design. ## AI Adoption Metrics: Connect Usage to Real Engineering Impact DevFeed: [AI Adoption Metrics: Connect Usage to Real Engineering Impact]() Original publisher: [Read original article]() Author: Tomasz Skora Published: 2026-08-10T11:32:12Z Content type: opinion Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [AI Strategy](), [code productivity](), [AI Development](), [data](), [Claude](), [cursor](), [GitHub Copilot]() Tags: [2025](), [ai](), [ai-adoption](), [ai-tools](), [claude](), [copilot](), [cursor](), [development](), [metrics](), [productivity](), [token](), [tokens]() ### AI overview The article argues that AI adoption metrics should connect tool usage with engineering context and outcomes. License activity, user engagement, token consumption, and prompt volume do not by themselves show whether teams are working more effectively. Meaningful evaluation should consider teams, services, processes, ownership, workflows, delivery stability, test coverage, incidents, and concrete results such as reduced latency. ### Source excerpt See why AI adoption metrics need engineering context: how connecting usage to teams, services, and outcomes reveals real impact. ## Premium seats are coming to ChatGPT Business DevFeed: [Premium seats are coming to ChatGPT Business]() Original publisher: [Read original article]() Published: 2026-08-10T00:00:00Z Content type: release Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Artificial Intelligence](), [codex]() Tags: [ai](), [chatgpt](), [codex](), [cost](), [developers](), [product](), [productivity](), [products](), [update]() ### AI overview ChatGPT Business introduces Premium seats with 5x more usage than Standard seats, no five-hour usage limit, weekly usage resets, and optional shared workspace credits. Premium seats cost $125 per user per month, or $100 per month when billed annually, while Standard seats remain available at lower prices. ### Source excerpt Premium seats are now available on ChatGPT Business, with 5x more usage, no five-hour usage limit, and flexible seat options for every teammate. ## How HSP GRUPPE builds AI capabilities for tax advisory DevFeed: [How HSP GRUPPE builds AI capabilities for tax advisory]() Original publisher: [Read original article]() Published: 2026-08-07T09:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI Chat]() Tags: [ai](), [chatgpt](), [enterprise](), [knowledge-sharing](), [learning](), [productivity](), [security](), [tax]() ### AI overview HSP GRUPPE describes embedding ChatGPT Enterprise across tax advisory and related professional work, supported by adoption programs, governance, and professional review. ### Source excerpt Discover how HSP GRUPPE uses ChatGPT Enterprise to boost productivity, improve work quality, and create more capacity for tax advisory and client service. ## How to measure ROI for an enterprise CMS migration DevFeed: [How to measure ROI for an enterprise CMS migration]() Original publisher: [Read original article]() Author: Webflow Team Published: 2026-08-07T00:00:00Z Content type: article Language: en Sources: [Webflow Blog]() Topics: [Content Management System](), [migration](), [workflow automation](), [Web](), [code productivity]() Tags: [analysis](), [article](), [automation](), [business](), [content](), [core-web-vitals](), [developer](), [development](), [efficiency](), [enterprise](), [how-to](), [metrics](), [migration](), [platform](), [productivity](), [strategy](), [tools](), [web](), [workflow](), [workflow-automation]() ### AI overview This article explains how enterprises can measure the return on investment of migrating to a modern content management system. It covers migration costs, expected gains, baseline metrics, operational efficiency, development costs, website performance, and developer productivity. ### Source excerpt Find out how to measure the ROI for your enterprise's CMS migration. Learn about the timeline you can expect and the improvements your team might see. [Next page]()