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Friday, May 15, 2026

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Thursday, May 14, 2026

Wednesday, May 13, 2026

Tuesday, May 12, 2026

From Classroom to Code II: Innovative Qt Apps by Future DevelopersLast year , we shared the story of a new collaboration with the Cologne University of Applied Sciences ( German: TH Köln) for a new course titled Engineering Desktop Applications with C++ and Qt (EDA) . The first edition gave students the chance to explore modern C++ and Qt development in a hands-on setting, with teams designing and building their own music player application. Now, the collaboration has successfully entered its second round.📝Qt Blog
Code Review Responses: Add Context When It Counts@media only screen and (max-width: 600px) { .body { overflow-x: auto; } .post-content table, .post-content td { width: auto !important; white-space: nowrap; } } This article was adapted from a Google Tech on the Toilet (TotT) episode. You can download a printer-friendly version of this TotT episode and post it in your office. By Saicharan Nimmala When responding to code review comments, responses like “Done,” “Updated,” or “Fixed” are commonly used to indicate addressing a suggestion. However, sometimes, a little extra context adds a lot of clarity. Next time you resolve a code review comment, ask yourself: "Is how I addressed the comment completely obvious from the code change and comment thread?" If not, supplement your response with a brief note to clarify the “why” or “how.” Your reviewers will thank you. When is it helpful to add context to a code review comment response? Here are a few examples: Your code change doesn't fully explain how you addressed the comment . Providing a brief summary helps the reviewer verify the changes without re-examining every line of the delta, and creates a clearer historical record. Reviewer: This approach seems risky. It might not handle all the edge cases properly. Less helpful response: More helpful response: Author: Updated. Good catch. I've added checks for null, empty, and negative inputs, each with a new test case. Thanks! You made a design choice or trade-off that isn't self-evident. Capturing the reasoning behind a choice provides valuable context. Note that non-obvious design choices within the code should ideally be explained in code comments or the commit description as well. Reviewer: Consider using a more performant library for this data transformation. Less helpful response: More helpful response: Author: I’ll go with Y. Done. I considered Library X, but stuck with Library Y because our datasets here are typically small, so the performance difference is negligible, and Library Y has a much simpler API. An offline discussion influenced the solution. Briefly summarizing the outcome or key reasoning from an offline sync ensures that other reviewers, who only see the final code change, can grasp the “why”. Reviewer: This logic seems a bit complex. Consider a simpler way to handle these. Less helpful response: More helpful response: Author: Fixed. As we discussed offline, this complexity is required to maintain backward compatibility with legacy data formats. I’ve added a comment in the code to clarify this. Thank s! There are multiple ways to address the comment. Clearly stating which option you selected and the reasoning behind that choice over other alternatives helps reviewers. Learn more code review practices in Google’s code review guide: google.github.io/eng-practices/review .📝Google Testing Blog
Introducing the Documentation MCP Tool for QtHow a Documentation MCP Tool Saves LLM Token Usage Every time an AI agent searches the web for Qt documentation today, it receives full HTML pages loaded with navigation chrome, cookie banners, related-article sidebars, and search-engine snippets that have nothing to do with the answer - burning thousands of LLM tokens before a single line of useful content appears. Qt's new official Model Context Protocol (MCP) tool for Qt documentation solves this directly.📝Qt Blog

Monday, May 11, 2026

Introducing the QML Coding Skill for Agentic WorkflowsThe Challenge: Elevating AI-Generated QML to Best-Practise Quality Frontier Large Language Models have become genuinely capable QML authors. Benchmarks show models like Claude, GPT, and Gemini achieving between 75% and 86% accuracy on the QML100 benchmark for single-turn coding tasks - a result that reflects the depth of Qt’s open-source ecosystem and the decades of publicly available QML code that has served as training material. For everyday UI components, a well-prompted AI agent can produce working, readable QML on the first attempt.📝Qt Blog

Sunday, May 10, 2026

Saturday, May 9, 2026