Crowdin, a localization management software powered by AI, facilitates the localization of diverse content such as websites, mobile apps, games, desktop and web applications, help centers, blogs, and email campaigns. With a repertoire of over 700 add-ons and integrations, the platform streamlines the localization process and supports over 100 file formats. Crowdin uses cutting-edge technology to simplify translation and localization tasks, providing easy-to-use solutions for seamless implementation.
Crowdin supports more than 100 file formats, including but not limited to files for mobile, software, documents, subtitles, and graphic assets:
.xml, .strings, .json, .html, .xliff, .csv, .php, .resx, .yaml, .xml, .strings and on.
Continuous localization for all your content:
✓ Software
✓ Mobile Apps
✓ Websites
✓ Marketing content
✓ Help center
✓ Games
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Okyline is an Executable Data Design (EDD) platform for declarative data validation contracts and measurable operational data quality.
Instead of maintaining disconnected specifications, validators, tests, and quality dashboards, Okyline uses a single executable contract as the operational source of truth for validation and flow quality monitoring.
The same readable contract drives multi-format validation, deterministic execution, quality measurement, data quality gate, and historical quality analytics across APIs, events, files, LLM structured outputs, and enterprise data flows.
Community Edition provides the open specification, a free Java validation runtime, a public Claude AI assistant for contract generation, and a free online studio for executable JSON validation contracts and JSON Schema transpilation.
Enterprise Edition supports direct validation of JSONL, XML, CSV, FIXED, and EDI flows, data quality gate, and operational quality dashboards, all without databases
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LibFuzzer
LibFuzzer is an in-process, coverage-guided, evolutionary fuzzing engine. LibFuzzer is linked with the library under test, and feeds fuzzed inputs to the library via a specific fuzzing entry point (or target function); the fuzzer then tracks which areas of the code are reached, and generates mutations on the corpus of input data in order to maximize the code coverage. The code coverage information for libFuzzer is provided by LLVM’s SanitizerCoverage instrumentation. LibFuzzer is still fully supported in that important bugs will get fixed. The first step in using libFuzzer on a library is to implement a fuzz target, a function that accepts an array of bytes and does something interesting with these bytes using the API under test. Note that this fuzz target does not depend on libFuzzer in any way so it is possible and even desirable to use it with other fuzzing engines like AFL and/or Radamsa.
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