Resource Center

Cursor has multiple usage modes — from the VS Code fork to the agents window, the command line interface, the iOS app, and the website in a browser. Cursor also has fully functional cloud environments on demand that can be run programmatically as part of any application using the SDK. When practicing AI-driven development, merely using Cursor is not automatically going to increase your quality, your software stability, or your speed of software delivery. In this training, Jeffrey Palermo, Chief Technology Officer of Clear Measure, an AI-driven software architecture company, gives you the right architectural thinking about how Cursor fits into your existing development environment, and what architectural elements you need in order to up-level your existing DevOps environment to a fully featured AI DevOps environment. You will see ways to completely delegate some features to Cursor while choosing to develop more complex features interactively, using the different Cursor usage modes. You will learn how to measure your Cursor implementation to know if the quality of your software is increasing or decreasing, if the stability of your system in production is getting better or worse, and if your team is actually speeding up and delivering more — or silently slowing down.
And you will walk away knowing how to deliver a report that makes sense to management so that they can see the return on investment of your Cursor investment. Webinar transcript: https://clearmeasure.com/cursor-for-ai-driven-development-transcript/

In this episode, host Jonathan “J.” Tower sat down with Jeffrey Palermo, CTO and Chairman at Clear Measure, to talk about what changes in software development once AI writes much of the code, and what doesn’t. Jeffrey argues this shift is different in kind from the web, agile, mobile, and cloud transitions that came before it, because it changes how we interact with a computer rather than just how we write code. He’s equally direct about the limits. A language model is very good at answering questions and has no way to come up with one worth asking, which is the part that still has to come from us. From there, J. and Jeffrey get into why the definition of “good” has to come from outside the model. In practice, that means automated tests as the external standard and builds fast enough to keep pace with how quickly AI now changes code. Jeffrey makes the case that the ten-minute build that felt fast in the DevOps era is too slow now, and that getting down to two minutes means running every stage in parallel instead of in sequence. They also dig into which code is structural and which is decoration (his skyscraper analogy is worth the listen), why Fred Brooks’s surgical team is relevant again, and his prediction that the separate IT department is on its way out. If you’re trying to work out what software engineering discipline looks like on the other side of AI, this episode is for you.

Jeffrey Palermo, CTO & Chairman of Clear Measure, walks through the AI Software Factory pattern in this Austin .NET User Group presentation, an executive-level framework for orchestrating how software moves through an organization to increase throughput while keeping quality high.

The session makes the case that AI builds on top of good software delivery fundamentals, not around them. Teams still struggling with escaped defects, production instability, or an incomplete DevOps environment will not benefit from AI until those are solved.

Live demos cover automated production incident resolution, a software delivery scorecard with forecasting, and auto-generated architecture documentation pulled from source code. The core takeaway is that AI works best on easy, well-defined work. Start there, measure the impact, and let human judgment handle the rest.

This builds on our AI Software Factory demonstrations that we have already done. For implementing the AI Software Factory pattern in your organization, the first objective is Clarity. That starts with measurement. If we believe that AI automation will help software delivery, we need measurements in place to give us visibility into the success of the AI implementation. Join us to learn how to start measuring and reporting on software delivery throughput, as well as DORA metrics and others.