๐ป ๐จ๐ป๐ฑ๐ฒ๐ฟ๐๐๐ฎ๐ป๐ฑ ๐๐ถ๐ด ๐ข ๐ป๐ผ๐๐ฎ๐๐ถ๐ผ๐ป!
O(1) - Constant Time: Simple tasks that take the same amount of time no matter how much data you have, like finding an item in a list by its position.
O(log n) - Logarithmic Time: Tasks that take less time as the data grows, like finding an item in a sorted list by repeatedly dividing it in half.
O(n) - Linear Time: Tasks that take more time as the data grows, like counting all items in a list by checking each one.
O(n log n) - Linearithmic Time: Tasks that get a bit slower as the data grows, like sorting a list using efficient methods such as merge sort or quick sort.
O(nยฒ) - Quadratic Time: Tasks that get noticeably slower as the data grows, like sorting a list using simpler methods like bubble sort or finding all pairs in a list.
O(2^n) - Exponential Time: Tasks that get much slower as the data grows, like finding all subsets of a set or solving complex problems like the traveling salesman using a basic approach.
O(n!) - Factorial Time: Tasks that get extremely slow as the data grows, like solving problems that involve checking every possible arrangement of items.
O(1) - Constant Time: Simple tasks that take the same amount of time no matter how much data you have, like finding an item in a list by its position.
O(log n) - Logarithmic Time: Tasks that take less time as the data grows, like finding an item in a sorted list by repeatedly dividing it in half.
O(n) - Linear Time: Tasks that take more time as the data grows, like counting all items in a list by checking each one.
O(n log n) - Linearithmic Time: Tasks that get a bit slower as the data grows, like sorting a list using efficient methods such as merge sort or quick sort.
O(nยฒ) - Quadratic Time: Tasks that get noticeably slower as the data grows, like sorting a list using simpler methods like bubble sort or finding all pairs in a list.
O(2^n) - Exponential Time: Tasks that get much slower as the data grows, like finding all subsets of a set or solving complex problems like the traveling salesman using a basic approach.
O(n!) - Factorial Time: Tasks that get extremely slow as the data grows, like solving problems that involve checking every possible arrangement of items.
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๐จ Attention: A Critical Next.js vulnerability
What Happened?
A massive vulnerability (CVE-2025-29927) was just found in Next.js, a popular framework for building web apps with React. This flaw, rated 9.1/10 for severity, lets attackers slip past security checks using a simple trickโa special header. It affects self-hosted Next.js apps (versions 11.1.4 to 15.2.2) that use middleware to protect pages, like admin dashboards. If unpatched, hackers could access restricted areasโyikes!
How Big Is It?
Pretty darn big! Millions of developers use Next.js, and this bug leaves tons of apps exposed. The good news? Itโs fixed in version 15.2.3 and up. Please upgrade to newest version to stay safe!
https://thehackernews.com/2025/03/critical-nextjs-vulnerability-allows.html
What Happened?
A massive vulnerability (CVE-2025-29927) was just found in Next.js, a popular framework for building web apps with React. This flaw, rated 9.1/10 for severity, lets attackers slip past security checks using a simple trickโa special header. It affects self-hosted Next.js apps (versions 11.1.4 to 15.2.2) that use middleware to protect pages, like admin dashboards. If unpatched, hackers could access restricted areasโyikes!
How Big Is It?
Pretty darn big! Millions of developers use Next.js, and this bug leaves tons of apps exposed. The good news? Itโs fixed in version 15.2.3 and up. Please upgrade to newest version to stay safe!
https://thehackernews.com/2025/03/critical-nextjs-vulnerability-allows.html
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