.NET Tips and Tricks

Blog archive

Wrapping Lines in Visual Studio

I was teaching Learning Tree's ASP.NET MVC course a few weeks back. The author of that course decided that having code lines extend past the right-hand edge of the code window wasn't a good idea if you're an instructor demoing some code. To eliminate those disappearing lines on the demo computer we use in the course, he turned on word-wrap for Visual Studio. This choice keeps all of the code on the screen by wrapping long lines of code back to the left hand margin.

If you like that idea, it's easy to turn on that option. Go to Tools | Options | Text Editor | All Languages and select the "Word wrap" choice on the right. That's all you need to do but, if you want, you can also have Visual Studio put a U-turn arrow at the end of each line that's too long to fit in the window -- just click the "Show Visual Glyphs for word wrap" option under the Word wrap choice.

Posted by Peter Vogel on 12/15/2015


comments powered by Disqus

Featured

  • Hands On with GitHub Copilot App Technical Preview: Turning a Blazor Issue into a PR

    GitHub's brand-new Copilot desktop app, in technical preview, handled a small Blazor issue from planning through pull request creation, but the hands-on test also showed why developers still need to verify agent work in the running app before merging.

  • At Build 2026, Microsoft Sets Up Windows as an OS for AI Agents

    Microsoft's Build 2026 Windows developer announcements point to a broader platform strategy for agentic AI, spanning terminal workflows, local models, app-building skills, Cloud PCs and operating system-level containment.

  • Slammed by Copilot Usage-Based Billing on Day 1, Facing $180 Bill for June

    A journalist using GitHub Copilot Pro details how a broken editorial workflow on day one of usage-based billing led to runaway token consumption, a projected $180 monthly bill, and practical tactics for cutting AI credit burn.

  • AdaBoost.R2 Regression Using C#

    AdaBoost.R2 regression works by building an ensemble of decision trees, training them on reweighted data, and combining their predictions with a weighted median, while also showing how parameter choices affect accuracy and overfitting.

Subscribe on YouTube