Juan C.Cilleruelo

The AI that is giving me the most satisfying responses is, without a doubt, Grok. GPT is excellent, too. I use it through the REST service. It is the most simple to implement. Of course, what I expressed in the post is my personal experience filtered through my capacity to prompt the AI. That is an essential part of the method. If you ask your AI like you ask Google, you are definitively losing your time. I read some books and other documents about AI prompting. If you are looking for a magic collaboration with AI, you only need to wait one year or maybe two. But then you will be out of date. The time to integrate AI within your toolbox is now. Really yesterday, but you can run. Everyone has excuses. But what you need at this moment are reasons. Don't waste your time explaining why not. Do it!

Programming with AI Assistance Introduction I’ll take a few minutes to explore the current relationship between AI and programming, as of March 4, 2025. AI evolves so rapidly that claims need constant reassessment. A year ago, I argued AIs relied solely on their knowledge base, not internet searches—a point now outdated, as they do both. So, let’s dive into the key question: Can AI fully replace a programmer today or soon? Can AI Replace Programmers? The short answer is no, and here’s why. Claiming AI can replace a programmer assumes it can flawlessly interpret a designer’s or user’s instructions without ambiguity, generate error-free code, and fix mistakes after the fact. It also implies the AI can review and adapt existing code to meet new or corrected requirements as an application evolves. Picture a dialogue with an AI to build a program. It could stretch over days or weeks, requiring constant backtracking to resolve misunderstandings. Each revision would alter the program, spawning fresh errors—something programmers know all too well. Iterations might edge us closer to the goal, but sifting through endless chat logs to spot where communication faltered would be exhausting. Now, suppose we had a tool tailored for this AI interaction, resembling an IDE (Integrated Development Environment). It could let us search and document requirements, track how new ones affect old ones, and perhaps include a UML generator. Sounds helpful, right? Maybe not—it’d likely just pile another layer of complexity onto development, one still reliant on skilled programmers or analysts to feed it. Even if we fed this knowledge into an AI, it’d need deep familiarity with IDEs or command-line tools to produce the final program. More critically, someone must verify the output meets requirements and works—not just compiles cleanly. Maintenance adds further hurdles: when users report issues in production, do we tweak the original requirements and regenerate the code, or prompt the AI to patch its own prior work? It’s a tangled mess, don’t you agree? Those videos touting “code an app with AI, no skills needed” are like ads promising “speak English like a native.” It’s a hollow pitch—you won’t master it without the foundation, though exposure might sharpen your skills. AI as a Programmer’s Ally So, are those videos about coding with AI useful? No. Their makers aim to entertain you (and rake in ad revenue) while flexing their cleverness—not to teach you AI mastery. Their business would dry up if they did. But here’s a better question: Can AI boost a programmer’s performance? Absolutely, without a doubt. Practical AI Techniques AI won’t replace us—it empowers us. Here’s how I use it daily: Setup: I keep profiles on key AIs—Grok, GPT, DeepSeek, Mistral—ready in browser tabs that auto-open. Even if I rarely touch the last two, they’re there when needed. Function Generation: For standalone functions with clear inputs and outputs, I ask the AI to draft them. Early results may not compile, but they give me a head start. With practice, I’ve honed prompts to get working, compilable code on the first go. Bug Hunting: When my code has a sneaky bug, I pass it to the AI with a description of the unwanted behavior. It often pinpoints the fix. HTML Cleanup: Hand-edited HTML can turn into a cryptic mess. When it’s unreadable, I hand it to the AI to refine and flag errors—a real time-saver. Instant Help: The F1 key once gave contextual IDE help; now I ask the AI for explanations on terms, classes, or functions. It delivers detailed answers and examples, often tailored to my project if we’ve been chatting. Documentation: Most coders dread documenting modules, yet it’s vital for maintenance—the costliest phase of software life. I task the AI with it, specifying depth and skipping obvious lines or pseudocode comments. Performance Tweaks: Facing a bottleneck? The AI can estimate runtimes from source code alone and suggest optimizations—no execution needed. Unit Tests: Tedious, repetitive unit tests are perfect for AI. Give it a controller interface, and it churns out tests fast, ensuring reliability even after changes or integrations. REST Integration: Beyond chat, I’ve built REST interfaces in my programs to query the AI directly with precise prompts, embedding its responses into the app. For example, I use a Stub program to generate varied test data (e.g., JSON arrays of names, split by nationality or location) instead of relying on monotonous random lists. It’s efficient and spares me manual coding. Mastering these techniques—especially REST-driven data generation—lets you apply AI creatively in client projects. The possibilities far exceed this article’s scope, but paired with the next approach, they’ll transform you into a sharper developer. Beyond the Technical: Prospective Thinking AI shines beyond pure coding tasks in what I call "prospective interactions." Before starting a project, I weigh my options—techniques, code structure—and consult the AI. I list my alternatives, and it reasons through the best path, explaining why. I don’t always follow it, but it clarifies my choices. Better yet, I’ll ask it for fresh angles I hadn’t considered. That’s when coding becomes exhilarating—you shift from a technical grinder to a creative problem-solver. That’s the real power of AI as a programming partner.

No. Really no!

Tired of creating the descendants of TActiveRecord by hand? GitHub MVC_AR_EntityGenerator is an OpenSource project developed in Delphi XE 12 that takes the metadata of your Database and creates one class ActiveRecord of each Table in the Database. You can generate the AR of one table or one AR class for each table in the Database. You can indicate in which folder the AR file should be deployed, and the program will remember it. Each AR is generated in an independent .pas file. And this is only the Beta version. You save all the information used to generate the AR; when you need to develop it again, you only need to use it. In the next version, we plan to implement more types of AR, like, for example, AR with a Master-Detail Relationship. In the current version, you can change the name of the Class generated. This is useful when you want, for example, the AR class representing one member of the entity Customers, instead of being named TCustomers, to be named TCustomer; that is more accurate. I want to implement, too, a connection with the AI (probably Chat-GPT) to ask directly for the translation of the plural table names to singular class names. In this target, I want the AI to modify the table name to respect the Camel Style of Pascal Class names. For example, a table named Items_Owned_by_user is currently created as TItemsownedbyuser. The AI can easily convert this to TItemsOwnedByUser, which is more accurate to the style of Pascal code. And many more things can be made with an open-source project. Do you don't think it!! Try it, and let us know what you think! Thanks PD: The project is also an excellent example of using SQLite as a memory store for the data and treating this data as a standalone project, in contrast to the concept of a Database on Disk, which is more current in Delphi.

Thanks!

"640 Kb of RAM is sufficient for many years of PC evolution. Don't worry". I think that in the last quarter of 2024, there will be a lot of PCs with ARM. Probably running poorly programs compiled natively for the Win64 platform. I think that the concern of Embarcader must be to develop a WinArm compiler for Windows as soon as possible.

The problem, at least for me, is not about running Delphi on Windows ARM. Instead, is about when is Embarcadero going to include Windows for Arm native compilation for Windows.

Microsoft is an enterprise, not an NGO. Because of this, you cant think about this: Why Windows for Arm is not still a commercial product? 99.9%??? jajaja. Try to do something serious with it and you are going to see the problems.

Sure? Intel apps for Mac OS work well in Arm Macs, thanks to Rosetta 2. Probably, the problem is that Microsoft does not have this technology. Arm technology allows, with the same batteries, more than 20 hours between charges, for example. Arm technology allows a much more power computation, without consuming more energy. Really do you think that the future is Intel architecture? Or do you think that Intel is ready to create processors without these limitations?

I think it's speaking about the gap that is currently becoming between Apple and Windows technologies. Since Apple is embracing ARM architecture, Microsoft is still waiting to no one know what, while in the field of hardware, the technology was ready a time ago. Even Chrome OS was adopted ARM quickly. It was very easy in this case, of course.

It was because of copy&paste in these forums. It's not intentional.

Ctrl+Alt+Del: Snapdragon X Elite has one fatal flaw – Windows on Arm

https://www.neowin.net/news/microsoft-launches-arm-advisory-service-to-help-developers-make-windows-arm-based-apps/ Microsoft has just launched a new and free program for Windows developers who need assistance in adapting their Windows apps to run on Arm processor-based PCs. The program is called the Arm Advisory Service.

I think you need to work with Mac for a year before making opinions of it.

Try this: Matlab.