# Learning Published articles for Learning. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## AI Is Changing How We Code. It's Also Changing How We Learn. DevFeed: [AI Is Changing How We Code. It's Also Changing How We Learn.]() Original publisher: [Read original article]() Author: James Rempt Published: 2026-09-16T14:00:09Z Content type: opinion Language: en Sources: [Visual Studio Blog]() Topics: [Artificial Intelligence](), [Development](), [AI Development](), [Software]() Tags: [ai](), [code](), [developers](), [development](), [learning](), [software](), [software-development](), [visual-studio]() ### AI overview The article argues that AI tools are changing software development by helping developers write and understand code, troubleshoot problems, learn frameworks, and get answers quickly. It also emphasizes the continuing value of learning directly from other developers through practical education, hands-on discussion, and professional networking at events such as VSLive!. ### Source excerpt AI is changing software development at a pace no one can ignore. Developers now have powerful AI tools that can help us write code, understand unfamiliar codebases, troubleshoot problems, learn new frameworks, and get answers in seconds. That is an incredible opportunity. But as these tools become more capable, I believe something else is becoming more valuable [...] The post AI Is Changing How We Code. It's Also Changing How We Learn. appeared first on Visual Studio Blog. ## Hackathons Can Be Healthy DevFeed: [Hackathons Can Be Healthy]() Original publisher: [Read original article]() Author: Khan Academy Published: 2016-07-11T22:00:00Z Content type: opinion Language: en Sources: [Khan Academy]() Topics: [Hackathon](), [Learning](), [Open Source]() Tags: [eng-leads](), [engineering](), [hackathon](), [hackathons](), [inclusive](), [learning](), [news](), [open-source]() ### AI overview Khan Academy describes its twice-yearly internal hackathons and the benefits of collaboration, creativity, team bonding, morale, and learning. The article also explains how the company adapted its hackathon format to be more inclusive, sustainable, and healthy. ### Source excerpt By Tom Yedwab As an organization whose raison d'être is learning - we in fact have a campaign ... Read more ## Kotlin for Python developers DevFeed: [Kotlin for Python developers]() Original publisher: [Read original article]() Author: Khan Academy Published: 2018-11-29T23:00:00Z Content type: tutorial Language: en Sources: [Khan Academy]() Topics: [Kotlin](), [Python](), [Learning](), [Programming]() Tags: [content-platform](), [developers](), [engineering](), [guide](), [kotlin](), [learning](), [news](), [programming](), [python]() ### AI overview Khan Academy Engineering publicly released a Kotlin for Python developers guide after adopting Kotlin as an alternative backend language alongside Python. The textbook-style resource explains Kotlin in terms familiar to Python developers, while noting that it is not comprehensive and is provided as-is. ### Source excerpt By Aasmund Eldhuset Earlier this year, the Khan Academy Engineering organization decided to introduce Kotlin as an alternative ... Read more ## Agentic Engineering in Python: From Vibes to Evidence DevFeed: [Agentic Engineering in Python: From Vibes to Evidence]() Original publisher: [Read original article]() Author: Ben Batman Published: 2026-09-14T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Loop Engineering](), [AI Agent](), [Python](), [Transformers](), [Code review](), [Test-driven development]() Tags: [agentic](), [agentic-engineering](), [ai-coding-agents](), [cheat-sheet](), [code-review](), [learning](), [python](), [tests]() ### AI overview This tutorial presents agentic engineering in Python as a disciplined workflow for delegating bounded tasks to AI coding agents while retaining human control over intent, constraints, review, and release decisions. It explains execution and acceptance loops, the Engineering Evidence Ladder, and the RECAP review method for evaluating agent-generated changes with tests, type hints, refactoring, and code review. ### Source excerpt Move from vibe coding to agentic engineering in Python, using tests, types, and code review to prove an AI agent's changes are safe to keep. ## Primer on Jinja Templating DevFeed: [Primer on Jinja Templating]() Original publisher: [Read original article]() Author: Philipp Acsany Published: 2026-09-13T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Template](), [Python](), [web applications](), [Front end](), [Flask](), [modern web development](), [HTML](), [Django]() Tags: [building](), [code](), [content](), [django](), [features](), [framework](), [how-to](), [html](), [learn](), [learning](), [python](), [tutorial](), [web-applications]() ### AI overview A practical introduction to Jinja templating in Python. The tutorial covers installation, template creation and rendering, dynamic content, conditionals, loops, filters, macros, nesting, standalone text generation, and integration with Flask. ### Source excerpt With Jinja, you can build rich templates that power the front end of your web applications. But you can use Jinja without a web framework running in the background. Anytime you want to create text files with programmatic content, Jinja can help you out. ## Deciding how to progress your career and find work you find interesting/challenging. DevFeed: [Deciding how to progress your career and find work you find interesting/challenging.]() Original publisher: [Read original article]() Author: Peter Lawrey (noreply@blogger.com) Published: 2026-09-12T07:32:17Z Content type: opinion Language: en Sources: [Vanilla Java]() Topics: [Job](), [Learning](), [Artificial Intelligence](), [Code]() Tags: [ai](), [career](), [code](), [job](), [learning](), [opinion](), [work]() ### AI overview An opinion article about progressing in a career by creating growth opportunities within a current role. It recommends teaching others, understanding users and systems, improving quality and efficiency, taking responsibility for outcomes, and focusing on work that AI cannot easily automate. ### Source excerpt When you start a new role, many challenges are placed on you. Once you have been in a role for a while, you may feel like you aren't learning as much, your career isn't progressing, and you wonder what job you might move to next. Having been in that situation many times, my suggestion is that rather than waiting to be given challenges or the next role, you take the opportunity to challenge yourself to grow in your current role. This doesn't mean accepting a role with too little growth, but rather see if there are opportunities to grow while you have some capacity, if you drive them. For example, imagine the roles you would wish to have, how much of that could you be doing now without being asked, possibly in your own time? I am not suggesting doing more unpaid work, rather extending the work you have to benefit your career. Ways to extend your current role to help you grow Teach others. Often, you don't really understand something until you have had to explain it to someone else. Understand the people using your work. Broaden your understanding of the people or systems using what you are producing. How can what you're doing be more efficient, high-quality, more valuable to the end users, and easier to maintain? Take responsibility for a larger part of an outcome. Instead of only producing a piece of work, help define what success means, consider alternatives, check whether it works in practice and learn from what happens afterwards. Say you are working heavily with AI. Explore all the ways it currently fails, rather than sticking to what it does safely. Anything AI does easily now will be increasingly automated, and there probably isn't a future in that. Instead, focus on what AI can't do and where you add value. That is likely to be in demand. Consider two approaches to using AI to write for illustration: 2,000 words of content on a subject (or 200 lines of code @ ~10 words per line). You could prompt it for an answer of about 2,500 words and then edit out anything ## Worth Reading 091126 DevFeed: [Worth Reading 091126]() Original publisher: [Read original article]() Author: Russ Published: 2026-09-11T15:39:48Z Content type: article Language: en Sources: [rule 11 reader]() Topics: [Artificial Intelligence](), [data](), [Internet](), [Programming](), [Software](), [systems]() Tags: [ai](), [articles](), [data](), [errors](), [internet](), [junior](), [learning](), [programming](), [rpki](), [software](), [systems](), [worth-reading]() ### AI overview A reading roundup connecting the AI bubble, Internet Route Registry and RPKI safety, silent data corruptions, and the effects of AI-assisted programming on junior developers and software engineering. ### Source excerpt Recognizing that AI is a bubble requires one to apply the right test to the right companies and that requires one to understand the fundamental nature of the bubble in question. Based on the insights from those articles, we now shift attention to which parts of the Internet Route Registry (IRR) landscape can - or cannot - be replaced safely with information from the Resource Public Key Infrastructure (RPKI) system. While many faults manifest as crashes or exceptions, others are far more subtle to identify. Silent data corruptions (SDCs), also known as silent data errors or silent errors, are violations of data integrity that occur without immediate, observable, and explicit indications. Aligning intended learning outcomes, assessments, and teaching practices with the realities of AI-assisted programming. AI is transforming the role of the junior developer. But as routine tasks become increasingly automated, how will the next generation of software engineers master the craft of maintaining complex systems? ## Cypress Ambassador Spotlight: Maksym Donets DevFeed: [Cypress Ambassador Spotlight: Maksym Donets]() Original publisher: [Read original article]() Published: 2026-09-10T16:12:22Z Content type: article Language: en Sources: [Cypress Blog]() Topics: [Cypress](), [Testing](), [Cloud](), [Automation]() Tags: [ambassador](), [automation](), [bugs](), [cloud](), [community](), [cypress](), [debugging](), [learning](), [qa](), [testing]() ### AI overview An ambassador spotlight featuring Maksym Donets, an Automation QA Engineer who discusses learning, sharing, helping developers debug failed pipelines with Cypress Cloud, and using cy.session() with cy.request() to simplify session handling and speed up tests. He also shares advice for getting started with Cypress and mentions catching bugs with end-to-end testing. ### Source excerpt Welcome back to another edition of our Ambassador Spotlight series, where we shine a light on the Cypress community members who make testing better for everyone through the blogs they write, the talks they give, and the day-to-day help they offer to fellow developers. Curious about the program? You can learn more about it here. This time, we're catching up with Maksym Donets, an Automation QA Engineer with a straightforward philosophy on testing and an even more straightforward philosophy on li ## The 13 Best Video Training Platforms for Corporate L&D in 2026 DevFeed: [The 13 Best Video Training Platforms for Corporate L&D in 2026]() Original publisher: [Read original article]() Author: Jon Whitehead Published: 2026-09-10T00:01:56Z Content type: comparison Language: en Sources: [DaCast]() Topics: [hosting](), [Learning](), [Streaming](), [Monitoring]() Tags: [2026](), [article](), [comparison](), [corporate](), [hosting](), [learning](), [platforms](), [streaming](), [the-video-experts-blog](), [training]() ### AI overview A comparison of 13 video training platforms for corporate learning and development teams in 2026. It evaluates capabilities such as LMS integration, completion tracking, compliance recording, reporting, and pricing, identifying Dacast, Kaltura, and Panopto as having the clearest corporate training fit among the listed platforms. ### Source excerpt By Dacast Editorial Team | Reviewed by Jon Whitehead, COO at Dacast | Updated September 2026 For corporate L&D teams, the best video training platforms have become core infrastructure rather than a nice-to-have. They provide a reliable and interactive learning experience for onboarding, compliance, and enablement content. Leveraging educational video hosting and video-on-demand (VOD) capabilities, [...] The post The 13 Best Video Training Platforms for Corporate L&D in 2026 appeared first on Dacast. ## Museas & Astronoma On Apple Vision Pro Support Curiosity & Discovery DevFeed: [Museas & Astronoma On Apple Vision Pro Support Curiosity & Discovery]() Original publisher: [Read original article]() Author: Laura Mingail Published: 2026-09-07T23:28:57Z Content type: article Language: en Sources: [UploadVR]() Topics: [3D](), [App]() Tags: [3d](), [apple](), [apps](), [art](), [design](), [discovery](), [education](), [exploration](), [interview](), [learning](), [science](), [technologies](), [vision]() ### AI overview An interview with Miguel Garcia Gonzalez examines how the Apple Vision Pro apps Museas and Astronoma use immersive design, real-world source material, and selective 3D presentation to encourage curiosity and exploration of art and science. ### Source excerpt What makes exploring art and science in spatial apps so compelling? We look at Apple Vision Pro apps Museas and Astronoma to learn how to design for curiosity and discovery. ## How to build a secure-by-default AI coding agent DevFeed: [How to build a secure-by-default AI coding agent]() Original publisher: [Read original article]() Author: Phoebe Sajor Published: 2026-09-04T07:40:00Z Content type: tutorial Language: en Sources: [Stack Overflow Blog]() Topics: [AI Bots]() Tags: [acquisitions](), [agent](), [ai](), [ai-coding](), [ai-infrastructure](), [coding](), [data](), [data-science](), [engineering](), [enterprise](), [how-to](), [infrastructure](), [learning](), [machine-learning](), [platform](), [podcast](), [products](), [python](), [se-stackoverflow](), [se-tech](), [security](), [software](), [supply-chain]() ### AI overview An interview about building a secure-by-default AI coding agent, the limits of prompts as security guardrails, and securing the AI software supply chain. ### Source excerpt Ryan chats with Greg Jennings, VP of Engineering for AI Products at Anaconda, about what it takes to build a secure-by-default AI coding agent, why prompts shouldn't be treated as strict security guardrails, and how Anaconda is using strategic acquisitions to secure the AI software supply chain. ## Transfer learning for genomic prediction in underrepresented populations DevFeed: [Transfer learning for genomic prediction in underrepresented populations]() Original publisher: [Read original article]() Published: 2026-09-03T18:20:31Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Fine-tuning](), [Model Development](), [Google](), [datasets]() Tags: [datasets](), [evaluation](), [general-science](), [google](), [healthcare](), [learning](), [machine-intelligence](), [research](), [training]() ### AI overview Google Research evaluates transfer learning for polygenic risk-score prediction across populations. European-cohort transfer learning improves prediction for small underrepresented target cohorts but can reduce accuracy as target cohorts grow, particularly for population-specific traits. ### Source excerpt General Science ## From MIT to IBM, expediting AI and quantum deployment DevFeed: [From MIT to IBM, expediting AI and quantum deployment]() Original publisher: [Read original article]() Author: Lauren Hinkel | MIT-IBM Computing Research Lab Published: 2026-09-02T20:25:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [ibm](), [Reinforcement learning](), [Computing](), [LLMs]() Tags: [academic](), [ai](), [ai-agents](), [algorithms](), [alumni-ae](), [aram-harrow](), [artificial-intelligence](), [atari-games](), [careers](), [collaboration](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [computing](), [data](), [electrical-engineering-and-computer-science-eecs](), [fair-ai](), [graduate-postdoctoral](), [graduate-students](), [ibm](), [industry](), [irene-ko](), [isaac-chuang](), [language-models](), [learning](), [luca-daniel](), [machine-learning](), [mit-ibm-computing-research-lab](), [mit-ibm-watson-ai-lab](), [mit-schwarzman-college-of-computing](), [pulkit-agrawal](), [quantum-computing](), [quantum-machine-learning](), [research](), [school-of-engineering](), [srinivasan-arunachalam](), [trustworthy-ai](), [vllm-hook](), [zhang-wei-hong]() ### AI overview MIT graduate students and a former postdoc who moved to IBM describe how work with the MIT-IBM Computing Research Lab helped translate rigorous research into industry applications. Their areas include quantum machine learning, reinforcement learning, AI agents, and trustworthy and fair AI. ### Source excerpt MIT affiliates engage with the MIT-IBM Computing Research Lab to bring rigorous theory to production systems. ## Djangonaut Space - Session 7 Accepting Applications DevFeed: [Djangonaut Space - Session 7 Accepting Applications]() Original publisher: [Read original article]() Author: Djangonaut Space Mission Control Published: 2026-09-01T17:13:15Z Content type: news Language: en Sources: [The Django weblog]() Topics: [Maintainers](), [Code](), [Pull Request]() Tags: [applications](), [community](), [discord](), [django](), [framework](), [leadership](), [learning](), [maintainers](), [open-source](), [pull-request](), [python]() ### AI overview Djangonaut Space is accepting applications for its seventh cohort, a free eight-week group mentoring program that helps people contribute to Django and potentially develop future leadership roles in its community. ### Source excerpt We are thrilled to announce that Djangonaut Space, a mentorship program for contributing to Django, is open for applicants for our next cohort! 🚀 Session 7 launches October 12th, 2026. We are currently accepting applications until September 6th, 2026 Anywhere on Earth. More details can be found in the website. Djangonaut Space is a free, 8-week group mentoring program where individuals will work self-paced in a semi-structured learning environment. It seeks to help members of the community who wish to level up their current Django code contributions and potentially take on leadership roles in Django in the future. Rodrigo Vieira, Djangonaut Session 6 "Participating as a mentee in the Djangonaut Space was a highly valuable experience. The program provided clear, step-by-step guidance on how to navigate the core framework, find the right issues to tackle, and confidently send my PRs. Beyond the code, the community on Discord was very active, sharing experiences, wins and support. We had access to regular talks, Django Fellows and long-time maintainers who generously shared their time and expertise. It was a welcoming environment to connect with other newcomers from all over the world, allowing us to share our journeys and learn together. The highlight of the program for me was seeing my first pull request finally merged and mentioned in Django News. I'm looking forward to keep involved as an alumni and help others now." Eliana, Djangonaut Session 1 "I'm so grateful to have been a part of the Djangonaut Space program. It's a wonderfully warm, diverse, and welcoming space, and the perfect place to get started with Django contributions. The community is full of bright, talented individuals who are making time to help and guide others, which is truly a joy to experience. Before Djangonaut Space, I felt as though I wasn't the kind of person who could become a Django contributor; now I feel like I found a place where I belong." Enthusiastic about contributing to Django but ## Agentic Skill Decay DevFeed: [Agentic Skill Decay]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-08-31T14:30:37Z Content type: opinion Language: en Sources: [Elevate]() Topics: [Learning](), [Software Engineering](), [Development]() Tags: [agentic](), [agents](), [engineering](), [learning](), [software-engineering]() ### AI overview The article argues that coding agents can complete tasks while bypassing the deliberate practice that builds expertise and judgment. It recommends forming hypotheses, understanding domains, reading diffs, debugging, reviewing code, and deliberately practicing decision-making, specification, steering, and verification. ### Source excerpt Agents can finish the task without teaching you anything. Building expertise now has to be deliberate. ## Async Programming in Python: From Generators to asyncio DevFeed: [Async Programming in Python: From Generators to asyncio]() Original publisher: [Read original article]() Author: Doug Farrell Published: 2026-08-31T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Python](), [Programming](), [Concurrent Programming](), [IO](), [Code](), [Python 3.14]() Tags: [automation](), [batch](), [code](), [learning](), [programming](), [python](), [tutorial]() ### AI overview A tutorial on Python asynchronous programming, explaining how event loops, coroutines, async and await enable a single-threaded program to handle slow I/O without blocking. It progresses from synchronous code and generators to concurrent task execution with asyncio, using examples compatible with Python 3.11 and later. ### Source excerpt Learn how Python async programming works. Write async functions with async and await, and run slow I/O operations concurrently with asyncio. ## How to use Gemini Notebook to learn faster DevFeed: [How to use Gemini Notebook to learn faster]() Original publisher: [Read original article]() Author: Jeff Morhous Published: 2026-08-26T10:06:43Z Content type: tutorial Language: en Sources: [The AI-Augmented Engineer]() Topics: [Learning](), [Artificial Intelligence](), [App](), [Google](), [Obsidian](), [Markdown]() Tags: [ai](), [app](), [drive](), [gemini](), [how-to](), [learn](), [learning](), [markdown](), [notes]() ### AI overview This tutorial explains how to use Gemini Notebook as a source-grounded learning tool. It covers adding sources such as pasted text, uploaded files, links, and Google Drive content, then generating study materials including briefing documents, quizzes, flashcards, and podcasts. ### Source excerpt Gemini Notebook can help you learn anything twice as fast. I'll show you how. ## Learning never stops: How AI makes learning continuous DevFeed: [Learning never stops: How AI makes learning continuous]() Original publisher: [Read original article]() Published: 2026-08-26T10:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [ChatGPT](), [OpenAI]() Tags: [ai](), [chatgpt](), [learning](), [openai](), [product]() ### AI overview OpenAI's report describes how students and educators use ChatGPT to extend learning beyond the classroom through on-demand guidance, feedback, practice, and individualized support. ### Source excerpt OpenAI's new report explores how students and educators use ChatGPT to make learning more continuous, with support that extends beyond the classroom. ## Forever a Student DevFeed: [Forever a Student]() Original publisher: [Read original article]() Published: 2026-08-26T00:00:00Z Content type: opinion Language: en Sources: [Jonathan Snook]() Topics: [Web Development](), [Learning](), [CSS](), [browsers](), [Testing](), [Database]() Tags: [blog](), [browsers](), [css](), [development](), [learning](), [testing](), [web-development]() ### AI overview The author reflects on learning web development, sharing work in progress, and accepting that mistakes are part of learning. They describe how writing, prototyping, browser testing, and research help deepen understanding, and argue that people should not be discouraged from sharing what they are learning. ### Source excerpt When I was learning CSS's grid feature (oh gosh, nearly ten years ago), I posted a few short videos talking about my understanding of the feature and how it worked. I was surprised to get scolded for sharing inaccurate information, "especially as a teacher". Wait, what?! Who, me?! I get things wrong all the time. That's part of learning. But it was the teacher remark that threw me because I've never considered myself a teacher. Yes, I've spoken at conferences and put on workshops but the label of teacher isn't one that I feel comfortable with. Perhaps I associate the word with all-knowing authority figures standing in front of a class and I'm far from all-knowing, or an authority, or have much of a figure. I have stood in front of classes, though. I imagine myself more like Stuart from the Mad TV sketch saying "look what I can do!" When I first got into web development, I built a web page that displayed a table pulled from a database. When I showed my friend, he wasn't all that excited. "Is that it?" It was, after all, just a table on a page. (Which, hey, it was 1996. Everything was just a table on a page.) This blog has been a 25+ year journey of me saying "look what I can do" over and over again. I enjoy the process of learning new things and sharing what I've learned. Writing about what I've learned helps me learn even more because I build prototypes and test in different browsers as I try to understand all the nuances of something in my effort to write about a topic. Even with the research and prototypes and testing and writing, I'll get things wrong but one of the things I have learned is not to admonish people sharing what they're learning, even if they get it wrong. We should all be forever a student. Have something to say? Tell me about it. ## The Python print() Function: Go Beyond the Basics DevFeed: [The Python print() Function: Go Beyond the Basics]() Original publisher: [Read original article]() Author: Real Python Published: 2026-08-25T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Python](), [coding](), [Terminal](), [User Interfaces]() Tags: [coding](), [course](), [deep-dive](), [learning](), [python](), [q-a](), [resources](), [tutorial](), [video]() ### AI overview A course on Python's print() function that covers lesser-known features, common mistakes, newlines, character encodings, buffering, file output, unit-test mocking, and terminal user interfaces. ### Source excerpt Learn about Python's print() function, discover its lesser-known features, avoid common mistakes, and know when to use a better alternative. ## Senior Engineer ≠ Years of Experience DevFeed: [Senior Engineer ≠ Years of Experience]() Original publisher: [Read original article]() Author: Valentina Jemuović Published: 2026-08-24T06:01:00Z Content type: opinion Language: en Sources: [Optivem Journal]() Topics: [Software Engineering](), [Development](), [engineering-culture]() Tags: [effective](), [engineering](), [experience](), [learning](), [mistakes](), [software]() ### AI overview This opinion argues that years of software engineering experience do not reliably measure engineering ability. Repeated habits, including poor design, avoiding tests, and overengineering, can become more entrenched over time, while continuous learning, experimentation, feedback, and willingness to change are presented as stronger indicators of effectiveness. ### Source excerpt "I've been doing this for 15 years, so I already know." ## Introducing ChatGPT for Teens: Built for learning, backed by protections DevFeed: [Introducing ChatGPT for Teens: Built for learning, backed by protections]() Original publisher: [Read original article]() Published: 2026-08-18T11:00:00Z Content type: release Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Artificial Intelligence]() Tags: [ai](), [chatgpt](), [features](), [learning](), [product](), [safety]() ### AI overview OpenAI introduces ChatGPT for Teens, an experience designed to help teenagers learn, think critically, and use AI confidently. It includes Study Mode, guided homework support, quizzes, learning visualizations, Study Hours, stronger safety protections, and parental controls. ### Source excerpt ChatGPT for Teens helps teens learn, think critically, and use AI with confidence, with stronger built-in protections, healthy-use features, and additional controls for parents. ## WordPress.com Education launches free classroom plans for teachers and students DevFeed: [WordPress.com Education launches free classroom plans for teachers and students]() Original publisher: [Read original article]() Author: L. Jeffrey Zeldman Published: 2026-08-17T15:12:09Z Content type: release Language: en Sources: [Jeffrey Zeldman]() Topics: [WordPress](), [Website]() Tags: [automattic](), [blogs-and-blogging](), [career](), [community](), [courseware](), [creativity](), [education](), [free](), [free-software](), [launches](), [learning](), [open-source](), [platforms](), [plugins](), [publishing](), [sftp](), [software](), [ssh](), [students](), [teaching](), [websites](), [wordpress]() ### AI overview Automattic has launched WordPress.com Education, a free classroom product that gives students WordPress.com domains, optional .blog or .art domain names, and plugin support. Teachers can provide access free for the first year without entering payment details. ### Source excerpt Meet WordPress.com Education, a new, free product for teachers and students. The post For students who make websites appeared first on Jeffrey Zeldman Presents. ## Lessons Learned from Fixing Flaky Tests with Claude DevFeed: [Lessons Learned from Fixing Flaky Tests with Claude]() Original publisher: [Read original article]() Author: Henrik Warne Published: 2026-08-15T15:50:33Z Content type: article Language: en Sources: [Henrik Warne]() Topics: [Claude](), [Testing]() Tags: [claude](), [flaky-tests](), [integration](), [learning](), [llm](), [net-9](), [parallel](), [programming](), [running](), [testing](), [tests]() ### AI overview The article describes lessons learned from using Claude to diagnose and fix flaky integration tests after parallelizing a test suite. It explains that blindly accepting plausible fixes was ineffective, while understanding and reviewing each change was important. ### Source excerpt We recently sped up our integration tests by a factor of ten by running many of them in parallel. There were several flaky tests in the new test suite. Claude was really useful in getting rid of almost all flakyness, ... Continue reading -> [Next page]()