docs: English JsonRpcBundle docs, REST and MCP Bundle sections, package cards
en/articles/from-tg/series/algorithm-complexity/README.md
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# How efficient is my code?
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**Algorithmic complexity** is one of the fundamental measures of efficiency.
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This series of short articles walks through algorithmic complexity from the simplest to the most complex: for each one — what it means and a code example, so you can understand it in practice.
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Let's start with the simplest, yet very important one — O(1), or constant complexity.
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## Parts of the series
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1. [O(1) — constant complexity](/articles/from-tg/series/algorithm-complexity/o-1)
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2. [O(n) — linear complexity](/articles/from-tg/series/algorithm-complexity/o-n)
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3. [O(log n) — logarithmic complexity](/articles/from-tg/series/algorithm-complexity/o-log-n)
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4. [O(n log n) — linearithmic complexity](/articles/from-tg/series/algorithm-complexity/o-n-log-n)
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5. [O(n²) — quadratic complexity](/articles/from-tg/series/algorithm-complexity/o-n2)
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6. [O(n³) — cubic complexity](/articles/from-tg/series/algorithm-complexity/o-n3)
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7. [O(2ⁿ) — exponential complexity](/articles/from-tg/series/algorithm-complexity/o-2n)
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8. [O(n!) — factorial complexity](/articles/from-tg/series/algorithm-complexity/o-n-factorial)
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---
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*First published in Ukrainian in the Telegram channel [“Created to die”](https://t.me/php_while_true/18) on June 20, 2025.*