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I use the keyboard for code and the mouse for UI design - like they were meant to 🙂 Why would you use the mouse for code?

TOSVersion.Architecture gives you the architecture of the processor that the app is executing on. Please ignore the fact that the documentation is outdated. Possible values are: arIntelX86, arIntelX64, arARM32, or arARM64.

Hi, i just made a new performance profiler for delphi 12 that have the advantage to work on iOS and Android too! it's about instrumenting the source code, it's work pretty well. you can found it here : https://github.com/MagicFoundation/Alcinoe?tab=readme-ov-file#alcinoe-code-profiler

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 A year ago, I argued AIs relied solely on their knowledge base, not internet searches—a point now outdated, as they do both.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.

I personally think it's strange to introduce major features in minor releases and fix minor bugs in major releases. I think major releases (11, 12, etc) should get new features, minor releases (11.1, 11.2, 12.1, 12.2, etc) should be mostly focused on fixing minor problems and bugs. I guess people don't want to wait for new features but then what's the point of a major versus minor release?

Golden socialism. It was almost stable there Irony of course.

P.S. Good luck with the writing