ordo-one

ordo-one / FuzzyMatch

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Fuzzy string matches at full speed

claude-code fuzzy-matching fuzzy-search ordo-core-package swift
59
3
100% credibility
Found Feb 13, 2026 at 20 stars 3x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Swift
AI Summary

FuzzyMatch is a Swift library providing high-performance fuzzy string matching for applications like code search, file finders, and financial lookups with support for typos and fast ranking.

How It Works

1
💡 Discover FuzzyMatch

You hear about a smart tool that finds matches even when you misspell names or file titles, making searches lightning fast.

2
📱 Add to your app

You easily include it in your project with a simple copy-paste step, no hassle.

3
🔍 Set up your search

You prepare what you're looking for once, so it's ready to use many times.

4
⚡ Search anything instantly

You type a search and it checks tons of options super quickly, ranking the best fuzzy matches first.

5
⚙️ Tweak for perfection

You adjust settings like how picky it is about typos to fit exactly what you need.

✅ Find everything effortlessly

Your searches now spot perfect or close-enough matches in a flash, saving you time every day.

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Star Growth

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AI-Generated Review

What is FuzzyMatch?

FuzzyMatch is a pure Swift library for high-performance fuzzy string matching, delivering sub-millisecond latency on large datasets like stock tickers or code identifiers. It scores candidates against imprecise queries with typo tolerance via Damerau-Levenshtein distance or faster Smith-Waterman alignment, returning top-N results sorted by relevance. Developers get a simple API for one-shot scoring, zero-allocation batch processing, and a ready-to-run fuzzygrep CLI example.

Why is it gaining traction?

It crushes fuzzy match python github libs and fuzzy string matching javascript alternatives in benchmarks—18M candidates/sec for edit distance, 31M/sec for Smith-Waterman—while handling transpositions that stump fzf or nucleo. Thread-safe with reusable buffers, word-boundary bonuses for camelCase/snake_case, and configurable penalties make rankings feel smart without tuning. No deps, full fuzz testing, and drop-in nucleo compatibility hook Swift users tired of FFI overhead.

Who should use this?

Swift iOS/macOS devs building autocomplete dropdowns, file search UIs, or product catalogs where users mistype. CLI authors replacing fuzzy string search python scripts with native speed. Finance/product teams matching fuzzy string comparison on symbols/ISINs without fuzzy match excel workarounds.

Verdict

Grab it for Swift fuzzy needs—benchmarks and tests are production-grade despite 15 stars and 1.0% credibility score. Early maturity means watch for edge cases, but zero-alloc paths and examples make prototyping instant.

(198 words)

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