u/Few_Boss_9507

Primul gand a fost sa mi creez singur o aplicatie care să mă ajute să-mi monitorizez mai ușor progresul în sală un fel de carnețel pentru sală și care să mă ajute totodată la calcularea caloriilor și proteinelor adică un fel de calculator caloric. Fiind un programator în industria aceasta de Softwere Engineer, m-am pus pe treabă. Am ajuns la o versiune finală și am zis de ce nu poate să ajute și alți oameni.
Știind de la început și văzând piața aplicațiilor care vând un abonament lunar destul de scump pentru ceea ce oferă, am zis ca eu sa ofer o plata unica pe viata, doar 50 de lei, in cazul in care alte aplicatii ofere 80-100 de lei pe luna.
Aplicatia ofera functii avansate create impreuna cu AI pentru a ti arata cum sa progresezi in programul tau la sala si ofera si functii pentru nutritie.
Ce ziceti, voi atrage public sau nu? De unde pot sa fac rost de public? La capitolul asta ma descurc mai greu. Any help please? Multumesc!❤️

I'm a 16 year old developer with a solid background in C++ (STL, templates and generic programming, deep memory management, OOP patterns like Pimpl and Observer). I'm asking this question because I just finished an update to a project and I was wondering where I see myself in a few years. I came here to ask for some advice. Given the current and future state of artificial intelligence, where do you think I should focus at my current stage and in a few years? In what IT field? Can you give me some advice? I recently asked chatgpt and he told me the following: "Here are the major technical directions where AI logic hits a wall, perfect for a developer focused on architecture and C++:

1. Parsers, Compilers, and Lexical Analysis

AI generates text, but the systems that transform text into precise and reliable instructions remain a purely engineering field.

Focus: Developing Domain Specific Languages ​​(DSLs), tokenization, and constructing Abstract Syntax Trees (ASTs) for interpreting commands.

Why it's AI-proof: Optimizing complex grammars and syntax trees requires a mathematical understanding of states and formal logic that LLMs only approximate. It's of ultimate utility when building advanced search bars with dynamic filters and tags.

2. High-Performance Computing (HPC) & Concurrency

Generating a functional sequential algorithm is easy. Rewriting that algorithm to run simultaneously on 32 cores without locking memory is a whole other league.

Focus: Advanced multithreading, synchronization (std::mutex, std::atomic in C++), memory models, and GPGPU programming (CUDA for processing on the video card).

Why it's AI-proof: Optimizing cache memory and avoiding race conditions at the microsecond level requires architectural intuition. AI itself needs this hyper-optimized infrastructure to run.

3. Low-Level and Embedded Systems

Write code that interacts directly with the hardware, manually allocating each byte and controlling execution times in real time (RTOS).

Focus: Microprocessor architecture, drivers, hardware interfaces, communication protocols (SPI, I2C).

Why it's AI-proof: AI can't debug an oscilloscope and doesn't understand the physical constraints (current consumption, interference) of a custom development board.

4. Graphics Engines (Rendering Engines)

Anyone can use Unity or Unreal Engine. Very few know the math and architecture behind how pixels get to the screen.

Focus: Graphics APIs (Vulkan, DirectX 12), linear algebra, shader development, and spatial geometry management systems.

Why it's AI-proof: Rendering pipelines are among the most complex software architectures. Augmented reality (AR), industrial simulations, and giant data visualizations rely on C++ code that is absolutely efficient."