Search Results for "dsl speed test"
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9 projects for "dsl speed test" with 2 filters applied:
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Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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99.99% Uptime for MySQL and PostgreSQL DatabasesCloud SQL Enterprise Plus delivers near-zero downtime with 35 days of point-in-time recovery. Supports MySQL, PostgreSQL, and SQL Server.
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MockK
Mocking library for Kotlin
All you need to get started is just to add a dependency to MockK library. Simplest example. By default mocks are strict, so you need to provide some behavior. -
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WebP Codec
Library to encode and decode images in WebP format
...As a widely deployed codec, libwebp balances compression efficiency, speed, and integration simplicity across platforms. -
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GoNB
GoNB, a Go Notebook Kernel for Jupyter
Go is a compiled language, but with very fast compilation, that allows one to use it in a REPL (Read-Eval-Print-Loop) fashion, by inserting a "Compile" step in the middle of the loop -- so it's a Read-Compile-Run-Print-Loop — while still feeling very interactive. GoNB leverages that compilation speed to implement a full-featured (at least it's getting there) Jupyter notebook kernel. As a side benefit it works with packages that use CGO — although it won't parse C code in the cells, so it... -
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php-file-iterator
FilterIterator implementation that filters files
...It also plays nicely with glob patterns and SPL iterators, so you can combine it with other filesystem utilities. As part of the PHPUnit tooling suite, it’s optimized for speed and predictable behavior across platforms. -
Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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Union Pandera
Light-weight, flexible, expressive statistical data testing library
The open-source framework for precision data testing for data scientists and ML engineers. Pandera provides a simple, flexible, and extensible data-testing framework for validating not only your data but also the functions that produce them. A simple, zero-configuration data testing framework for data scientists and ML engineers seeking correctness. Access a comprehensive suite of built-in tests, or easily create your own validation rules for your specific use cases. Validate the functions... -
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rechne.exe
Command-line calculator and libray.
Easy-to-use command-line calculator for basic and scientific calculations. -
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JavaScript & Node.js Testing
Comprehensive and exhaustive JavaScript & Node.js testing
This project compiles practical advice for testing JavaScript applications across the stack, emphasizing clarity, speed, and reliability over dogma. It distinguishes between different kinds of tests—unit, integration, end-to-end—and shows when each is appropriate given time and risk constraints. The guide focuses on removing flakiness by isolating side effects, controlling randomness, and stabilizing async code paths. It also demonstrates test design techniques like arranging test data, using expressive assertions, and structuring test suites for readability and discoverability. ... -
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OkReplay
Record and replay OkHttp network interaction in your tests
OkReplay is a tool for recording and replaying HTTP interactions (specifically via OkHttp) in Android tests to improve determinism, test speed, and reliability. During the first run of a test annotated with @OkReplay, the library records outgoing HTTP requests and their responses into tape files. On subsequent runs, it intercepts those requests and serves the recorded responses instead of making real network calls. This allows tests to be executed offline, reduces flakiness due to network variability, and isolates the test environment from external dependencies. ... -
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benchm-ml
A benchmark of commonly used open source implementations
This repository is designed to provide a minimal benchmark framework comparing commonly used machine learning libraries in terms of scalability, speed, and classification accuracy. The focus is on binary classification tasks without missing data, where inputs can be numeric or categorical (after one-hot encoding). It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different implementations. ... -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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