Understanding Clean Code: Emergence ⚡️

Chapter 12 of Clean Code, titled "Emergence," explores how clean, well-structured software emerges from adherence to fundamental design principles.

https://dev.to/alisamir/understanding-clean-code-emergence-19bm

Top 4 Forms of Authentication Mechanisms 🔻

SSH Keys

Cryptographic keys are used to access remote systems and servers securely

OAuth Tokens

Tokens that provide limited access to user data on third-party applications

SSL Certificates

Digital certificates ensure secure and encrypted communication between servers and clients

Credentials

User authentication information is used to verify and grant access to various systems and services

في عالم السوفتوير هتقابل أنماط تصميم زي MVC و MVP و MVVM و MVI.

خلينا ندردش شوية عن كل واحد فيهم ونشوف إيه الفرق بينهم وإزاي كل واحد بيأثر على طريقة كتابة الكود وتنظيمه.

https://www.linkedin.com/posts/dev-alisamir_softwaredeveloper-softwaredevelopment-programming-activity-7248978308028125184-z3_e

من طرق تحسين إمكانية الوصول للمستخدم...(Accessibility)
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تباين الألوان في الموقع أو التصميم بيلعب دور كبير في تحسين الـ Accessibility للمستخدمين، علشان كده لازم تتأكد إنه مناسب لكل الناس ومتوافق مع معايير الـ Accessibility. 💯

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https://www.linkedin.com/posts/dev-alisamir_webdeveloper-webdevelopment-accessibility-activity-7249381038563508226-x_MY

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Big O Notation: The Secret to Writing Efficient Algorithms 🔻

From simple array operations to complex sorting algorithms, understanding the Big O Notation is critical for building high-performance software solutions.

O(1)

This is the constant time notation. The runtime remains steady regardless of input size. For example, accessing an element in an array by index and inserting/deleting an element in a hash table.

O(n)

Linear time notation. The runtime grows in direct proportion to the input size. For example, finding the max or min element in an unsorted array.

O(log n)

Logarithmic time notation. The runtime increases slowly as the input grows. For example, a binary search on a sorted array and operations on balanced binary search trees.

O(n^2)

Quadratic time notation. The runtime grows exponentially with input size. For example, simple sorting algorithms like bubble sort, insertion sort, and selection sort.

O(n^3)

Cubic time notation. The runtime escalates rapidly as the input size increases. For example, multiplying two dense matrices using the naive algorithm.

O(n logn)

Linearithmic time notation. This is a blend of linear and logarithmic growth. For example, efficient sorting algorithms like merge sort, quick sort, and heap sort

O(2^n)

Exponential time notation. The runtime doubles with each new input element. For example, recursive algorithms solve problems by dividing them into multiple subproblems.

O(n!)

Factorial time notation. Runtime skyrockets with input size. For example, permutation-generation problems.

O(sqrt(n))

Square root time notation. Runtime increases relative to the input’s square root. For example, searching within a range such as the Sieve of Eratosthenes for finding all primes up to n.

مسارات تعلم شاملة في مجالات البرمجة 🔻

المسارات دي هتساعدك تخطط رحلة التعلم خلال الـ 12 شهر الجايين (أو أكتر في مجال الويب)، وده من غير ما تضيع وقتك في البحث عن إيه اللي المفروض تتعلمه في كل مجال. ✅

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https://www.linkedin.com/posts/dev-alisamir_softwaredeveloper-softwaredevelopment-roadmap-activity-7250153662952456192-IM-C