# Conda Published articles for Conda. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Python Environments Extension for VS Code- April Update DevFeed: [Python Environments Extension for VS Code- April Update]() Original publisher: [Read original article]() Author: Vedha Ranganathan Published: 2026-04-27T20:07:30Z Content type: release Language: en Sources: [Microsoft Python Engineering]() Topics: [Python](), [Extension](), [Visual Studio Code](), [Package Management](), [Conda](), [Poetry](), [ssh]() Tags: [2026](), [conda](), [crash](), [extension](), [microsoft](), [poetry](), [python](), [recovery](), [release](), [remote](), [ssh](), [terminal](), [update](), [vs-code]() ### AI overview Microsoft's April 2026 release update for the Python Environments extension in Visual Studio Code describes faster startup and environment resolution, narrower workspace scanning, improved crash recovery, a Conda environment fix, automatic package-list refreshes, and multi-project terminal support. ### Source excerpt The April 2026 release update includes the Python Environments extension... Keep on reading to learn more! The post Python Environments Extension for VS Code- April Update appeared first on Microsoft for Python Developers Blog. ## Why AI developers are grumpy about containers DevFeed: [Why AI developers are grumpy about containers]() Original publisher: [Read original article]() Published: 2024-10-31T00:00:00Z Content type: opinion Language: en Sources: [Chainguard: Unchained]() Topics: [AI Development](), [Containers](), [Security](), [Vulnerabilities](), [Cloud Native Ecosystem]() Tags: [ai](), [ai-containers](), [artificial-intelligene](), [cloud-native](), [conda](), [container-images](), [containers](), [data-science](), [developers](), [kafka](), [machine-learning](), [pytorch](), [security](), [vulnerabilities]() ### AI overview Chainguard summarizes interviews with machine learning specialists, AI developers, and data scientists about using containers in AI applications. The reported concerns include bloated images, security priorities beyond container CVEs, difficult dependencies, limited container expertise among some machine learning specialists, and reliance on inherited Dockerfile defaults and templates. ### Source excerpt Chainguard interviewed AI developers about containers and how they are used in the creation of AI applications. See the results. ## Chainguard Releases 58 New Container Images in March 2024 DevFeed: [Chainguard Releases 58 New Container Images in March 2024]() Original publisher: [Read original article]() Published: 2024-04-04T00:00:00Z Content type: release Language: en Sources: [Chainguard: Unchained]() Topics: [chainguard images](), [container images](), [supply-chain-security](), [Security](), [Kubernetes](), [.NET](), [VLC]() Tags: [chainguard](), [chainguard-images](), [conda](), [container-images](), [fips-compliance](), [helm](), [kubernetes](), [kyverno](), [net](), [security](), [software-supply-chain](), [velero]() ### AI overview Chainguard released 58 new container images in March 2024, including publicly available images for Velero and .NET. The article highlights FIPS compliance, smaller image size for Velero, daily builds from source, and performance optimizations for .NET images. ### Source excerpt Discover the latest Chainguard Images in March 2024, offering unparalleled security with low-to-zero CVEs for fortified software supply chain. ## Building reproducible Python environments with XARs DevFeed: [Building reproducible Python environments with XARs]() Original publisher: [Read original article]() Author: Pavel Senchanka Published: 2023-04-14T00:00:00Z Content type: article Language: en Sources: [Jane Street]() Topics: [Python](), [Conda](), [Deployment](), [Docker](), [OCaml](), [Open Source]() Tags: [build-system](), [conda](), [declarative](), [deployment](), [docker](), [open-source](), [python](), [reproducibility]() ### AI overview Jane Street describes how it built js-python, a system for creating and deploying centrally built, reproducible Python environments. The system integrates Python environments with OCaml code and uses XAR for deployment after limitations with Conda and other tools became apparent. ### Source excerpt Our traders and researchers love Python for its agility and for its huge open-source ecosystem, especially when it comes to machine learning. But the heavy use of notebooks can make it difficult to support. Notebooks have a very different lifecycle than regular code, and aren't always rigorously version controlled. And while most of our code (much of it written in OCaml) lives in a monorepo, putting all notebooks there is difficult; many notebooks end up being stored all over the place. ## From YAGNI to YDNIY DevFeed: [From YAGNI to YDNIY]() Original publisher: [Read original article]() Author: Itamar Turner-Trauring Published: 2020-09-18T00:00:00Z Content type: tutorial Language: en Sources: [Itamar Turner-Trauring]() Topics: [Python](), [Conda](), [jupyter](), [Linux](), [macOS](), [pip](), [data](), [C++]() Tags: [c-plus-plus](), [conda](), [jupyter](), [jupyter-notebooks](), [linux](), [macos](), [python](), [releases]() ### AI overview The article introduces YDNIY, or "You Don't Need It Yet," as a complement to YAGNI. It argues that features that are necessary but not release blockers can be delayed when a product remains useful without them. The author illustrates this with the staged development of the Python memory profiler Fil across Linux, macOS, Jupyter, Conda, and multiple memory-allocation APIs. ### Source excerpt How do you ship a product on schedule? One useful approach is applying the You Ain't Gonna Need It principle, or YAGNI for short: leave out all the things that seem nice-to-have, but you have no proof you actually need. But beyond the things you don't need, there are still plenty of features you pretty clearly do need... but are not blockers on releasing your product. So beyond YAGNI, there's also YDNIY: You Don't Need It Yet. Let's see an example of this principle in practice, visualize the principle as a flowchart, and then compare it to another popular acronymed concept, the Minimum Viable Product. A real world example: shipping a new memory profiler In March 2020 I shipped the initial release of a new memory profiler for Python, Fil. Here's how it changed over time in terms of features, from May to August 2020: 0.3.0, initial release: Installable via pip packaging tool, runs only on Linux, only profiles complete program runs. 0.3.3: Support for an additional memory allocation API. 0.4.0: Support for out-of-memory situations. 0.5.0: macOS support. 0.6.0: Support for mmap() allocation API. 0.7.0: Support for C++ memory allocation API. 0.9.0: Much faster and lower overhead in some use cases, added support for yet another memory allocation API. 0.10.0: Support for running inside Jupyter notebooks, and native support for installing via the Conda packaging tool. All of the features I added in later releases were clearly necessary from the start; YAGNI did not apply. Lots of people use macOS, the target audience of data scientists and scientists often use Conda and Jupyter, all those memory allocation APIs are used in the real world, and so on. But even a tool that only runs complete programs on Linux, and only tracks the most popular memory allocation APIs, is still useful to some people. If I had waited until all those features were implemented to ship an initial release, all the people who used the profiler during the first four months of its existence would have had