لا تتعلم برمجة في 2026 قبل ما تشوف الفيديو ده

Summary — key tech concepts, roadmap, and practical guidance

Learning programming in 2026 is still worthwhile and important — even more so because AI raises the bar for fundamentals. AI is a powerful productivity tool but cannot replace a developer who lacks core knowledge and judgment.

6-stage roadmap (what to learn and why)

1. Fundamentals

   • Start with any language; recommended: C++, C#, Java (structured, OOP, good for learning problem‑solving patterns).
   • Core topics: variables, functions, arrays, loops, strings, etc.
   • Practice problem solving on sites such as LeetCode, Codeforces, Codewars. Do many simple problems (e.g., 50–100) to build the skill of breaking problems down.
   • Learn Object‑Oriented Programming and build a small OOP project.
   • Essential skills: debugging, refactoring, Object‑Oriented Design (SOLID, design patterns).

2. Core computer‑science concepts (concept‑building)

   • Systems analysis (requirements & planning).
   • Databases (query performance, portability).
   • Networking basics (important for web/mobile).
   • Operating Systems (conceptual/system thinking).
   • System Design (architectural thinking).
   • Get introductions to specializations: cloud computing, cybersecurity, AI.
   • These concepts let you evaluate tech choices and use AI tools effectively.

3. Choose a specialization & framework (but keep fundamentals first)

   • Learn a practical framework for your target area and build projects by hand:
     • Web front‑end: HTML/CSS/JS + React or Angular.
     • Mobile: Flutter.
     • Back‑end: .NET, Laravel, Node.js (examples).
   • Learn version control (Git/GitHub) to store and share code.
   • With a strong foundation, switching frameworks is easier — AI can speed adaptation but can’t replace deep understanding.

4. Learn AI tools

   • Explore a variety of AI tools (chat models like ChatGPT, Gemini, Claude; local agents; testing/security/UX generators).
   • Use them to generate code, suggest edge cases, create tests, find vulnerabilities, design UI/UX, and speed up tasks.
   • Learn to evaluate AI outputs critically — foundation knowledge is necessary to judge correctness.

5. Build projects using AI tools

   • Build 2–3 real projects that integrate AI tools so you can apply them in specific use cases.
   • Use these projects to learn best workflows, determine which tools help vs. harm productivity, and create portfolio pieces.

6. Job preparation

   • Prepare CV/resume, LinkedIn profile, and company/job‑board profiles (e.g., national/regional IT agency lists).
   • Create content and post on LinkedIn so recruiters can find you.
   • The first job search is the hardest — once hired, mobility is easier because demand for programmers remains high.

Practical recommendations & warnings

• Do not skip fundamentals or rely on AI as the primary teacher. AI should assist, not replace learning core concepts.
• Use small projects to validate learning and enable meaningful interactions with AI (code review, translation).
• Solve algorithmic problems iteratively from easy to harder to train problem‑solving thinking.
• After hands‑on project experience, filter AI tools by practical usefulness.

Resources / guides mentioned

• Web diploma (15‑course program) teaching programming from scratch + using AI tools (offered by IT Legend; link in the video description).
• Course on how to land your first job.
• Video (and description) with project ideas and suggested practice projects.
• Problem‑solving websites: LeetCode, Codeforces, Codewars.

Main speaker / sources

• Ali Shaheen — CEO of IT Legend (primary speaker).
• References to problem sites (LeetCode, Codeforces, Codewars) and AI tools (ChatGPT, Gemini, Claude/local agents).