Beyond the Desktop: The Future of Development with Microsoft Dev Box and GitHub Codespaces
The modern developer platform has already moved past the desktop. We’re no longer defined by what’s installed on our laptops, instead we look at what tooling we can use to move from idea to production. An organisations developer platform strategy is no longer a nice to have, it sets the ceiling for what’s possible, an organisation can’t iterate it's way to developer nirvana if the foundation itself is brittle. A great developer platform shrinks TTFC (time to first commit), accelerates release velocity, and maybe most importantly, helps alleviate everyday frictions that lead to developer burnout. Very few platforms deliver everything an organization needs from a developer platform in one product. Modern development spans multiple dimensions, local tooling, cloud infrastructure, compliance, security, cross-platform builds, collaboration, and rapid onboarding. The options organizations face are then to either compromise on one or more of these areas or force developers into rigid environments that slow productivity and innovation. This is where Microsoft Dev Box and GitHub Codespaces come into play. On their own, each addresses critical parts of the modern developer platform: Microsoft Dev Box provides a full, managed cloud workstation. DevBox gives developers a consistent, high-performance environment while letting central IT apply strict governance and control. Internally at Microsoft, we estimate that usage of DevBox by our development teams delivers savings of 156 hours per year per developer purely on local environment setup and upkeep. We have also seen significant gains in other key SPACE metrics reducing context-switching friction and improving build/test cycles. Although the benefits of DevBox are clear in the results demonstrated by our customers it is not without its challenges. The biggest challenge often faced by DevBox customers is its lack of native Linux support. At the time of writing and for the foreseeable future DevBox does not support native Linux developer workstations. While WSL2 provides partial parity, I know from my own engineering projects it still does not deliver the full experience. This is where GitHub Codespaces comes into this story. GitHub Codespaces delivers instant, Linux-native environments spun up directly from your repository. It’s lightweight, reproducible, and ephemeral ideal for rapid iteration, PR testing, and cross-platform development where you need Linux parity or containerized workflows. Unlike Dev Box, Codespaces can run fully in Linux, giving developers access to native tools, scripts, and runtimes without workarounds. It also removes much of the friction around onboarding: a new developer can open a repository and be coding in minutes, with the exact environment defined by the project’s devcontainer.json. That said, Codespaces isn’t a complete replacement for a full workstation. While it’s perfect for isolated project work or ephemeral testing, it doesn’t provide the persistent, policy-controlled environment that enterprise teams often require for heavier workloads or complex toolchains. Used together, they fill the gaps that neither can cover alone: Dev Box gives the enterprise-grade foundation, while Codespaces provides the agile, cross-platform sandbox. For organizations, this pairing sets a higher ceiling for developer productivity, delivering a truly hybrid, agile and well governed developer platform. Better Together: DevBox and GitHub Codespaces in action Together, Microsoft Dev Box and GitHub Codespaces deliver a hybrid developer platform that combines consistency, speed, and flexibility. Teams can spin up full, policy-compliant Dev Box workstations preloaded with enterprise tooling, IDEs, and local testing infrastructure, while Codespaces provides ephemeral, Linux-native environments tailored to each project. One of my favourite use cases is having local testing setups like a Docker Swarm cluster, ready to go in either Dev Box or Codespaces. New developers can jump in and start running services or testing microservices immediately, without spending hours on environment setup. Anecdotally, my time to first commit and time to delivering “impact” has been significantly faster on projects where one or both technologies provide local development services out of the box. Switching between Dev Boxes and Codespaces is seamless every environment keeps its own libraries, extensions, and settings intact, so developers can jump between projects without reconfiguring or breaking dependencies. The result is a turnkey, ready-to-code experience that maximizes productivity, reduces friction, and lets teams focus entirely on building, testing, and shipping software. To showcase this value, I thought I would walk through an example scenario. In this scenario I want to simulate a typical modern developer workflow. Let's look at a day in the life of a developer on this hybrid platform building an IOT project using Python and React. Spin up a ready-to-go workstation (Dev Box) for Windows development and heavy builds. Launch a Linux-native Codespace for cross-platform services, ephemeral testing, and PR work. Run "local" testing like a Docker Swarm cluster, database, and message queue ready to go out-of-the-box. Switch seamlessly between environments without losing project-specific configurations, libraries, or extensions. 9:00 AM – Morning Kickoff on DevBox I start my day on my Microsoft DevBox, which gives me a fully-configured Windows environment with VS Code, design tools, and Azure integrations. I select my teams project, and the environment is pre-configured for me through the DevBox catalogue. Fortunately for me, its already provisioned. I could always self service another one using the "New DevBox" button if I wanted too. I'll connect through the browser but I could use the desktop app too if I wanted to. My Tasks are: Prototype a new dashboard widget for monitoring IoT device temperature. Use GUI-based tools to tweak the UI and preview changes live. Review my Visio Architecture. Join my morning stand up. Write documentation notes and plan API interactions for the backend. In a flash, I have access to my modern work tooling like Teams, I have this projects files already preloaded and all my peripherals are working without additional setup. Only down side was that I did seem to be the only person on my stand up this morning? Why DevBox first: GUI-heavy tasks are fast and responsive. DevBox’s environment allows me to use a full desktop. Great for early-stage design, planning, and visual work. Enterprise Apps are ready for me to use out of the box (P.S. It also supports my multi-monitor setup). I use my DevBox to make a very complicated change to my IoT dashboard. Changing the title from "IoT Dashboard" to "Owain's IoT Dashboard". I preview this change in a browser live. (Time for a coffee after this hardwork). The rest of the dashboard isnt loading as my backend isnt running... yet. 10:30 AM – Switching to Linux Codespaces Once the UI is ready, I push the code to GitHub and spin up a Linux-native GitHub Codespace for backend development. Tasks: Implement FastAPI endpoints to support the new IoT feature. Run the service on my Codespace and debug any errors. Why Codespaces now: Linux-native tools ensure compatibility with the production server. Docker and containerized testing run natively, avoiding WSL translation overhead. The environment is fully reproducible across any device I log in from. 12:30 PM – Midday Testing & Sync I toggle between DevBox and Codespaces to test and validate the integration. I do this in my DevBox Edge browser viewing my codespace (This could be a second VS Code Desktop) and I use the same browser to view my frontend preview. I update the environment variable for my frontend that is running locally in my DevBox and point it at the port running my API locally on my Codespace. In this case it was a web socket connection and HTTPS calls to port 8000. I can make this public by changing the port visibility in my codespace. https://fluffy-invention-5x5wp656g4xcp6x9-8000.app.github.dev/api/devices wss://fluffy-invention-5x5wp656g4xcp6x9-8000.app.github.dev/ws This allows me to: Preview the frontend widget on DevBox, connecting to the backend running in Codespaces. Make small frontend adjustments in DevBox while monitoring backend logs in Codespaces. Commit changes to GitHub, keeping both environments in sync and leveraging my CI/CD for deployment to the next environment. We can see the DevBox running local frontend and the Codespace running the API connected to each other, making requests and displaying the data in the frontend! Hybrid advantage: DevBox handles GUI previews comfortably and allows me to live test frontend changes. Codespaces handles production-aligned backend testing and Linux-native tools. DevBox allows me to view all of my files in one screen with potentially multiple Codespaces running in browser of VS Code Desktop. Due to all of those platform efficiencies I have completed my days goals within an hour or two and now I can spend the rest of my day learning about how to enable my developers to inner source using GitHub CoPilot and MCP (Shameless plug). The bottom line There are some additional considerations when architecting a developer platform for an enterprise such as private networking and security not covered in this post but these are implementation details to deliver the described developer experience. Architecting such a platform is a valuable investment to deliver the developer platform foundations we discussed at the top of the article. While in this demo I have quickly built I was working in a mono repository in real engineering teams it is likely (I hope) that an application is built of many different repositories. The great thing about DevBox and Codespaces is that this wouldn’t slow down the rapid development I can achieve when using both. My DevBox would be specific for the project or development team, pre loaded with all the tools I need and potentially some repos too! When I need too I can quickly switch over to Codespaces and work in a clean isolated environment and push my changes. In both cases any changes I want to deliver locally are pushed into GitHub (Or ADO), merged and my CI/CD ensures that my next step, potentially a staging environment or who knows perhaps *Whispering* straight into production is taken care of. Once I’m finished I delete my Codespace and potentially my DevBox if I am done with the project, knowing I can self service either one of these anytime and be up and running again! Now is there overlap in terms of what can be developed in a CodeSpace vs what can be developed in Azure DevBox? Of course, but as organisations prioritise developer experience to ensure release velocity while maintaining organisational standards and governance then providing developers a windows native and Linux native service both of which are primarily charged on the consumption of the compute* is a no brainer. There are also gaps that neither fill at the moment for example Microsoft DevBox only provides windows compute while GitHub Codespaces only supports VS Code as your chosen IDE. It's not a question of which service do I choose for my developers, these two services are better together! * Changes have been announced to DevBox pricing. A W365 license is already required today and devboxes will continue to be managed through Azure. For more information please see: Microsoft Dev Box capabilities are coming to Windows 365 - Microsoft Dev Box | Microsoft LearnLangChain v1 is now generally available!
Today LangChain v1 officially launches and marks a new era for the popular AI agent library. The new version ushers in a more opinionated, streamlined, and extensible foundation for building agentic LLM applications. In this post we'll breakdown of what’s new, what changed, and what “general availability” means in practice. Join Microsoft Developer Advocates, Marlene Mhangami and Yohan Lasorsa, to see live demos of the new API and find out more about what JavaScript and Python developers need to know about v1. Register for this event here. Why v1? The Motivation Behind the Redesign The number of abstractions in LangChain had grown over the years to include chains, agents, tools, wrappers, prompt helpers and more, which, while powerful, introduced complexity and fragmentation. As model APIs evolve (multimodal inputs, richer structured output, tool-calling semantics), LangChain needed a cleaner, more consistent core to ensure production ready stability. In v1: All existing chains and agent abstractions in the old LangChain are deprecated; they are replaced by a single high-level agent abstraction built on LangGraph internals. LangGraph becomes the foundational runtime for durable, stateful, orchestrated execution. LangChain now emphasizes being the “fast path to agents” that doesn’t hide but builds upon LangGraph. The internal message format has been upgraded to support standard content blocks (e.g. text, reasoning, citations, tool calls) across model providers, decoupling “content” from raw strings. Namespace cleanup: the langchain package now focuses tightly on core abstractions (agents, models, messages, tools), while legacy patterns are moved into langchain-classic (or equivalents). What’s New & Noteworthy for Developers Here are key changes developers should pay attention to: 1. create_agent becomes the default API The create_agent function is now the idiomatic way to spin up agents in v1. It replaces older constructs (e.g. create_react_agent) with a clearer, more modular API that is middleware-centric. You can now compose middleware around model calls, tool calls, before/after hooks, error handling, etc. 2. Standard content blocks & normalized message model Responses from models are no longer opaque strings. Instead, they carry structured content_blocks which classify parts of the output (e.g. “text”, “reasoning”, “citation”, “tool_call”). If needed for backward compatibility or client serialization, you can opt in to serializing those blocks back into the .content field by setting output_version="v1". 3. Multimodal and richer model inputs / outputs LangChain now supports more than just text-based interactions. Models can accept and return files, images, video, etc., and the message format reflects this flexibility. This upgrade prepares us well for the next generation of models with mixed modalities (vision, audio, etc.). 4. Middleware hooks, runtime context, and finer control Because create_agent is designed as a pluggable pipeline, developers can now inject logic before/after model calls, tool calls, error recoveries, fallback strategies, request transformations, and more. New middleware such as retry, fallback, call limits, and context editing have been added. The notion of a runtime and context object accompanies each agent execution, making it easier to carry state or metadata through the pipeline. 5. Simplified, leaner namespace & migration path Many formerly top-level modules or helper classes have been removed or relocated to langchain-classic (or similarly stamped “legacy”) to declutter the main API surface. A migration guide is available to help projects transition from v0 to v1. While v1 is now the main line, older v0 is still documented and maintained for compatibility. What “General Availability” Means (and Doesn’t) v1 is production-ready, after months of testing the alpha version The stable v0 release line remains supported for those unwilling or unable to migrate immediately. Breaking changes in public APIs will be accompanied by version bumps (i.e. minor version increments) and deprecation notices. The roadmap anticipates minor versions every 2–3 months (with patch releases more frequently). Because the field of LLM applications is evolving rapidly, the team expects continued iterations in v1—even in GA mode—with users encouraged to surface feedback, file issues, and adopt the migration path. (This is in line with the philosophy stated in docs.) Developer Callouts & Suggested Steps Here are practical tips to get developers onboard: Try the new API Now! LangChain Azure AI and Azure OpenAI have migrated to LangChain v1 and are ready to test! Try out our getting started sample here. Learn more about using LangChain and Azure AI: Python: https://docs.langchain.com/oss/python/integrations/providers/azure_ai JavaScript: https://docs.langchain.com/oss/javascript/integrations/providers/microsoft Join us for a Live Stream on Wednesday 22 October 2025 Join Microsoft Developer Advocates Marlene Mhangami and Yohan Lasorsa for a livestream this Wednesday to see live demos and find out more about what JavaScript and Python developers need to know about v1. Register for this event here.Deployment and Build from Azure Linux based Web App
TOC Introduction Deployment Sources From Laptop From CI/CD tools Build Source From Oryx Build From Runtime From Deployment Sources Walkthrough Laptop + Oryx Laptop + Runtime Laptop CI/CD concept Conclusion 1. Introduction Deployment on Azure Linux Web Apps can be done through several different methods. When a deployment issue occurs, the first step is usually to identify which method was used. The core of these methods revolves around the concept of Build, the process of preparing and loading the third-party dependencies required to run an application. For example, a Python app defines its build process as pip install packages, a Node.js app uses npm install modules, and PHP or Java apps rely on libraries. In this tutorial, I’ll use a simple Python app to demonstrate four different Deployment/Build approaches. Each method has its own use cases and limitations. You can even combine them, for example, using your laptop as the deployment tool while still using Oryx as the build engine. The same concepts apply to other runtimes such as Node.js, PHP, and beyond. 2. Deployment Sources From Laptop Scenarios: Setting up a proof of concept Developing in a local environment Advantages: Fast development cycle Minimal configuration required Limitations: Difficult for the local test environment to interact with cloud resources OS differences between local and cloud environments may cause integration issues From CI/CD tools Scenarios: Projects with established development and deployment workflows Codebases requiring version control and automation Advantages: Developers can focus purely on coding Automatic deployment upon branch commits Limitations: Build and runtime environments may still differ slightly at the OS level 3. Build Source From Oryx Build Scenarios: Offloading resource-intensive build tasks from your local or CI/CD environment directly to the Azure Web App platform, reducing local computing overhead. Advantages: Minimal extra configuration Multi-language support Limitations: Build performance is limited by the App Service SKU and may face performance bottlenecks The build environment may differ from the runtime environment, so apps sensitive to minor package versions should take caution From Runtime Scenarios: When you want the benefits and pricing of a PaaS solution but need control similar to an IaaS setup Advantages: Build occurs in the runtime environment itself Allows greater flexibility for low-level system operations Limitations: Certain system-level settings (e.g., NTP time sync) remain inaccessible From Deployment Sources Scenarios: Pre-package all dependencies and deploy them together, eliminating the need for a separate build step. Advantages: Supports proprietary or closed-source company packages Limitations: Incompatibility may arise if the development and runtime environments differ significantly in OS or package support Type Method Scenario Advantage Limitation Deployment From Laptop POC / Dev Fast setup Poor cloud link Deployment From CI/CD Auto pipeline Focus on code OS mismatch Build From Oryx Platform build Simple, multi-lang Performance cap Build From Runtime High control Flexible ops Limited access Build From Deployment Pre-built deploy Use private pkg Env mismatch 4. Walkthrough Laptop + Oryx Add Environment Variables SCM_DO_BUILD_DURING_DEPLOYMENT=false (Purpose: prevents the deployment environment from packaging during publish; this must also be set in the deployment environment itself.) WEBSITE_RUN_FROM_PACKAGE=false (Purpose: tells Azure Web App not to run the app from a prepackaged file.) ENABLE_ORYX_BUILD=true (Purpose: allows the Azure Web App platform to handle the build process automatically after a deployment event.) Add startup command bash /home/site/wwwroot/run.sh (The run.sh file corresponds to the script in your project code.) Check sample code requirements.txt — defines Python packages (similar to package.json in Node.js). Flask==3.0.3 gunicorn==23.0.0 app.py — main Python application code. from flask import Flask app = Flask(__name__) @app.route("/") def home(): return "Deploy from Laptop + Oryx" if __name__ == "__main__": import os app.run(host="0.0.0.0", port=8000) run.sh — script used to start the application. #!/bin/bash gunicorn --bind=0.0.0.0:8000 app:app .deployment — VS Code deployment configuration file. [config] SCM_DO_BUILD_DURING_DEPLOYMENT=false Deployment Once both the deployment and build processes complete successfully, you should see the expected result. Laptop + Runtime Add Environment Variables (Screenshots omitted since the process is similar to previous steps) SCM_DO_BUILD_DURING_DEPLOYMENT=false Purpose: Prevents the deployment environment from packaging during the publishing process. This setting must also be added in the deployment environment itself. WEBSITE_RUN_FROM_PACKAGE=false Purpose: Instructs Azure Web App not to run the application from a prepackaged file. ENABLE_ORYX_BUILD=false Purpose: Ensures that Azure Web App does not perform any build after deployment; all build tasks will instead be handled during the startup script execution. Add Startup Command (Screenshots omitted since the process is similar to previous steps) bash /home/site/wwwroot/run.sh (The run.sh file corresponds to the script of the same name in your project code.) Check Sample Code (Screenshots omitted since the process is similar to previous steps) requirements.txt: Defines Python packages (similar to package.json in Node.js). Flask==3.0.3 gunicorn==23.0.0 app.py: The main Python application code. from flask import Flask app = Flask(__name__) @app.route("/") def home(): return "Deploy from Laptop + Runtime" if __name__ == "__main__": import os app.run(host="0.0.0.0", port=8000) run.sh: Startup script. In addition to launching the app, it also creates a virtual environment and installs dependencies, all build-related tasks happen here. #!/bin/bash python -m venv venv source venv/bin/activate pip install -r requirements.txt gunicorn --bind=0.0.0.0:8000 app:app .deployment: VS Code deployment configuration file. [config] SCM_DO_BUILD_DURING_DEPLOYMENT=false Deployment (Screenshots omitted since the process is similar to previous steps) Once both deployment and build are completed, you should see the expected output. Laptop Add Environment Variables (Screenshots omitted as the process is similar to previous steps) SCM_DO_BUILD_DURING_DEPLOYMENT=false Purpose: Prevents the deployment environment from packaging during publish. This must also be set in the deployment environment itself. WEBSITE_RUN_FROM_PACKAGE=false Purpose: Instructs Azure Web App not to run the app from a prepackaged file. ENABLE_ORYX_BUILD=false Purpose: Prevents Azure Web App from building after deployment. All build tasks will instead execute during the startup script. Add Startup Command (Screenshots omitted as the process is similar to previous steps) bash /home/site/wwwroot/run.sh (The run.sh corresponds to the same-named file in your project code.) Check Sample Code (Screenshots omitted as the process is similar to previous steps) requirements.txt: Defines Python packages (like package.json in Node.js). Flask==3.0.3 gunicorn==23.0.0 app.py: The main Python application. from flask import Flask app = Flask(__name__) @app.route("/") def home(): return "Deploy from Laptop" if __name__ == "__main__": import os app.run(host="0.0.0.0", port=8000) run.sh: The startup script. In addition to launching the app, it activates an existing virtual environment. The creation of that environment and installation of dependencies will occur in the next section. #!/bin/bash source venv/bin/activate gunicorn --bind=0.0.0.0:8000 app:app .deployment: VS Code deployment configuration file. [config] SCM_DO_BUILD_DURING_DEPLOYMENT=false Deployment Before deployment, you must perform a local build process. Run commands locally (depending on the language, usually for installing dependencies). python -m venv venv source venv/bin/activate pip install -r requirements.txt After completing the local build, deploy your app. Once deployment finishes, you should see the expected result. CI/CD concept For example, when using Azure DevOps (ADO) as your CI/CD tool, its behavior conceptually mirrors deploying directly from a laptop, but with enhanced automation, governance, and reproducibility. Essentially, ADO pipelines translate your manual local deployment steps into codified, repeatable workflows defined in a YAML pipeline file, executed by Microsoft-hosted or self-hosted agents. A typical azure-pipelines.yml defines the stages (e.g., build, deploy) and their corresponding jobs and steps. Each stage runs on a specified VM image (e.g., ubuntu-latest) and executes commands, the same npm install, pip install which you would normally run on your laptop. The ADO pipeline acts as your automated laptop, every build command, environment variable, and deployment step you’d normally execute locally is just formalized in YAML. Whether you build inline, use Oryx, or deploy pre-built artifacts, the underlying concept remains identical: compile, package, and deliver code to Azure. The distinction lies in who performs it. 5. Conclusion Different deployment and build methods lead to different debugging and troubleshooting approaches. Therefore, understanding the selected deployment method and its corresponding troubleshooting process is an essential skill for every developer and DevOps engineer.Microsoft Teams Workflows v2: New Features, Faster Automation Explained
🚀 Big Update for Microsoft Teams Users: Workflows v2 is Here! Microsoft is rolling out a completely redesigned Workflows experience in Teams, built from the ground up to make automation simpler, faster, and more intuitive. Powered by Power Automate, this update helps you automate repetitive tasks and streamline processes in just a few clicks. ✅ What’s New? A streamlined UI across Teams chats, channels, and SharePoint Redesigned template catalog for quick workflow discovery Natural language-to-flow capabilities 📅 Rollout Timeline Public Preview: September 2025 General Availability: October 2025 No admin action required—existing workflows remain unaffected. This is part of Microsoft’s broader effort to make automation accessible for everyone. 🔗 Learn more: https://www.microsoft.com/en-us/microsoft-365/roadmap?filters=&searchterms=491632 👉 How will you use Workflows v2 to boost productivity in Teams? Share your thoughts below! https://youtu.be/teIRFc4eYoA #MicrosoftTeams #PowerAutomate #Workflows #Productivity #Automation #Microsoft365
Please tell me how to disable the Pin Copilot message
Morning! I wrote a message yesterday but nobody replied, so here's another one so it doesn't get lost Can somebody tell me how to disable the annoying "Pin Copilot Chat" popup? every morning I have to say "Maybe Later" when I really mean to say NEVER IN A THOUSAND YEARS
Shopify Campaign Buy X Get Y fullfillment.
Hi, I understand from an article that campaign articles Buy X Get Y (campaign items) in Shopify must currently be fulfilled manually. But I’m wondering if anyone has found a workaround for this limitation. Specifically: - Is it possible to adjust the Shopify Connector so that the fulfillment process can be automated? - Alternatively, can we fulfill the order in Business Central and then have that fulfillment successfully sync back to Shopify? Here’s what I’ve found so far: - There is a synchronization function in Business Central: Sync Shipments to Shopify. - The Sales Shipment Line (Table 111) in BC has a row number that matches with the Shopify Order Line (Table 30119). - However, the Shopify Fulfillment Order Line (Table 30144) does not have a row number that links back to the Shopify Order Line (30119). This creates a problem: If the order contains both “normal” items and campaign items, then the normal items are fulfilled as expected. But the campaign items are ignored during the fulfillment process, because of the Shopify Product IDs and Variant IDs are identical. Has anyone found a way to handle this? Maybe through a modification of the connector? Or another method to make sure campaign items also get fulfilled automatically when syncing shipments from BC to Shopify? Any tips, experiences, or suggestions would be greatly appreciated!
