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Showing 2517 open source projects for "so"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Visual Blocks
Visual Blocks for ML is a Google visual programming framework
Visual Blocks is a node-based, in-browser environment for building AI and data-processing workflows with drag-and-drop components. It lets you connect sources, transforms, models, and visualizers into a live graph, so changes propagate instantly and results are observable without writing glue code. Under the hood it leans on web-friendly runtimes (e.g., WebGPU/WebGL/WebNN or TensorFlow.js backends) to execute pipelines locally, which is great for demos, teaching, and privacy-sensitive prototypes. The block abstraction encourages modularity: you can package a preprocessor, a model, and a postprocessor as a reusable composite for others to slot into their graphs. ... -
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TensorStore
Library for reading and writing large multi-dimensional arrays
TensorStore is a high-performance library for reading and writing N-dimensional arrays that live in many different storage systems, from local files to cloud object stores. It separates the logical view (shape, dtype, chunking) from the physical layout so the same code can target Zarr, N5, TIFF pyramids, or custom backends. Rich indexing, slicing, and broadcasting operations make it feel like a familiar array API, while asynchronous I/O pipelines stream chunks efficiently in parallel. Transactional semantics allow atomic updates and consistent snapshots, which is essential for large, shared datasets used by ML and scientific workflows. ... -
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Atheris
A Coverage-Guided, Native Python Fuzzer
...Because many Python libraries are thin wrappers over C/C++ code, Atheris is equally adept at surfacing memory safety issues in extension modules compiled with sanitizers. The tool integrates smoothly with Python’s packaging and unit-test ecosystems, so you can wrap existing tests as fuzz targets and keep results understandable. It supports structured input strategies and custom mutators, which is especially helpful for text and data formats common in Python workloads. In practice, Atheris compresses weeks of edge-case brainstorming into hours of automated exploration with actionable, minimized reproductions. -
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Tunix
A JAX-native LLM Post-Training Library
Tunix is a JAX-native library for post-training large language models, bringing supervised fine-tuning, reinforcement learning–based alignment, and knowledge distillation into one coherent toolkit. It embraces JAX’s strengths—functional programming, jit compilation, and effortless multi-device execution—so experiments scale from a single GPU to pods of TPUs with minimal code changes. The library is organized around modular pipelines for data loading, rollout, optimization, and evaluation, letting practitioners swap components without rewriting the whole stack. Examples and reference configs demonstrate end-to-end runs for common model families, helping teams reproduce baselines before customizing. ... -
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Honggfuzz
Security oriented software fuzzer
honggfuzz is a general-purpose, high-performance fuzzer that mixes coverage feedback with practical crash triage to uncover memory-safety and logic bugs. It supports multiple fuzzing modes—stdin, file, and networking—so targets can be exercised the same way they run in production. Instrumentation via compiler hooks or hardware/perf counters guides mutations toward previously unseen edges, while persistent mode keeps the target process alive to amortize startup costs. The tool integrates tightly with sanitizers and can attach to already running processes, making it convenient for both white-box and black-box fuzzing. ... -
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Synthetic Data Kit
Tool for generating high quality Synthetic datasets
...The kit’s design goal is to shorten the “data prep” bottleneck by turning dataset creation into a repeatable pipeline rather than ad-hoc notebooks. It supports generation of rationales/chain-of-thought variants, configurable sampling, and guardrails so outputs meet format constraints and quality checks. Examples and guides show how to target task-specific behaviors like tool use or step-by-step reasoning, then save directly into training-ready files. -
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Multimodal
TorchMultimodal is a PyTorch library
...The design emphasizes composability: you can mix and match encoder, fusion, and decoder components rather than starting from monolithic models. The repository also includes example scripts and datasets for common multimodal tasks (e.g. retrieval, visual question answering, grounding) so you can test and compare models end to end. Installation supports both CPU and CUDA, and the codebase is versioned, tested, and maintained. -
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GenAI Processors
GenAI Processors is a lightweight Python library
...Processors can be composed sequentially (to build multi-step flows) or in parallel (to fan-out work and merge results), which makes sophisticated agent behaviors easy to express with simple operators. The library offers built-in processors for classic turn-based Gemini calls as well as Live API streaming, so you can mix “batch” and real-time interactions in the same graph. It leans on Python’s asyncio to coordinate concurrency, handle network I/O, and juggle background compute threads without blocking. -
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Image Harmonization Dataset iHarmony4
The first large-scale public benchmark dataset for image harmonization
This repository provides the iHarmony4 dataset, which is a large-scale dataset designed for image harmonization tasks. Image harmonization involves adjusting the appearance of a foreground in a composite image so that it is consistent with the background (in color, tone, illumination, etc.). The iHarmony4 dataset comprises four sub-datasets (HCOCO, HAdobe5k, HFlickr, Hday2night), each making composite images by combining a foreground from one image with a background from another, along with associated ground truth harmonized images and foreground masks. ... -
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PLT (Programming Languages Theory)
Programming Language Theory
...It spans foundational topics like lambda calculus, type systems, interpreters, compilers, and formal semantics, while also pointing to contemporary areas such as effect systems, dependent types, and verification. Each section clusters materials by theme so learners can build understanding step by step instead of grazing at random. The list emphasizes primary sources and high-quality lectures, encouraging readers to engage with rigorous treatments rather than superficial summaries. It’s designed to serve both as a starting guide for newcomers and as a reference index for experienced practitioners, filling in gaps. ... -
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LKMPG
The Linux Kernel Module Programming Guide
...Readers learn about concurrency concerns (spinlocks, mutexes), memory allocation in kernel space, and safe patterns for error handling and cleanup. The guide pairs narrative chapters with compilable code, so you can move from theory to practice and observe behavior directly with dmesg and friends. It also calls out pitfalls—like blocking in the wrong context or leaking references—that matter for stable, production-quality modules. As a teaching resource and a quick reference, lkmpg lowers the barrier to kernel development by showing how to extend Linux safely and idiomatically. -
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Homebrew Core
Default formulae for the missing package manager for macOS (or Linux)
Homebrew-core is the canonical formula repository for Homebrew, the macOS (and Linux) package manager, containing the Ruby “formulae” that describe how to fetch, build, and install thousands of open-source packages. Each formula encodes metadata, dependencies, build instructions, and tests so Homebrew can produce reproducible bottles (prebuilt binaries) or source builds across supported macOS and Linux environments. The repo is the operational center for package maintenance: maintainers review updates, address compatibility patches, and manage version migrations and deprecations to keep the ecosystem healthy. When users install or upgrade packages, Homebrew leverages the core formulae to resolve dependencies, perform sandboxed builds, and run post-install hooks, making development toolchains and end-user apps easy to provision. -
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Fastify
Fast and low overhead web framework, for Node.js
Fastify is a high-performance web framework for Node.js that focuses on minimal overhead and predictable extensibility. It pairs a declarative routing API with JSON Schema–driven validation and serialization, so requests are checked at the edge and responses are produced quickly. The framework’s plugin system uses encapsulation to isolate routes, decorators, and hooks, which keeps large codebases modular and avoids accidental cross-pollution. Built-in hooks (such as onRequest, preHandler, and onSend) and lightweight decorators make it easy to insert cross-cutting concerns like auth, caching, and metrics. ... -
14
Gitit
A wiki using HAppS, pandoc, and git
...(Or darcs or hg, if you're using darcs or mercurial to store the wiki data.) Gitit assumes that the page files (stored in the git repository) are encoded as UTF-8. Even page names may be UTF-8 if the file system supports this. So you should make sure that you are using a UTF-8 locale when running gitit. (To check this, type locale.) The metadata block consists of a list of key-value pairs, each on a separate line. If needed, the value can be continued on one or more additional line, which must begin with a space. -
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Clouddriver
Read and write operations across cloud providers
Clouddriver is the cloud-provider integration layer of Spinnaker, exposing a uniform API and cache for infrastructure resources across multiple clouds. It discovers and tracks entities such as server groups, load balancers, security groups, images, and instances, normalizing them so higher-level services can reason consistently. The service also brokers mutating operations—like creating server groups or resizing clusters—by translating Spinnaker requests into provider-specific calls. A scalable caching subsystem maintains near-real-time views of accounts and regions to keep UI and pipelines responsive. Credentials, permissions, and account configuration are managed centrally, enabling multi-account, multi-region deployments at scale. ... -
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Horde
Horde is a distributed Supervisor and Registry
Horde provides a distributed, fault-tolerant Registry and DynamicSupervisor for Elixir applications, letting you run and manage processes across clustered nodes as if they lived on a single machine. It relies on conflict-free replicated data types (CRDTs) to converge membership and process ownership without a central leader, so cluster topology can change freely as nodes join or leave. With Horde.Registry you register processes globally and look them up anywhere, while Horde.DynamicSupervisor starts and migrates children across nodes, rebalancing as capacity or health changes. Because everything runs under OTP supervision, failures are isolated and recoveries are automatic, even during network partitions or rolling deploys. ... -
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GSY Flutter Demo
Collection of independent Flutter example components
...The project is often used as a learning playground for developers to see practical implementations of both simple widgets and complex app structures. It demonstrates integrations with APIs, database persistence, and plugin usage, making it a real-world reference app. The codebase is modular, so learners can explore sections independently without needing to understand the entire app at once. Overall, it is a well-regarded resource for both beginners and experienced Flutter developers seeking examples of scalable app structures. -
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Cheshire
Clojure JSON and JSON SMILE (binary json format) encoding/decoding
...From 5.0.0 onwards, custom encoding has been moved to be part of the core namespace (not requiring a namespace change). Custom and Core encoding have been combined in Cheshire 5.0.0, so there is no longer any need to require a different namespace depending on what you would like to use. Cheshire will automatically use a BigInteger if needed for non-floating-point numbers. -
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PydanticAI
Agent Framework / shim to use Pydantic with LLMs
When I first found FastAPI, I got it immediately. I was excited to find something so innovative and ergonomic built on Pydantic. Virtually every Agent Framework and LLM library in Python uses Pydantic, but when we began to use LLMs in Pydantic Logfire, I couldn't find anything that gave me the same feeling. PydanticAI is a Python Agent Framework designed to make it less painful to build production-grade applications with Generative AI. -
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Runeset
Fast UTF-8 codepoint sets for Zig
...This design is original, in the sense that I invented it. There may be prior art, it's remarkably difficult to search for "UTF-8 character sets" and find papers on set data structures, so I can't say with high confidence that it's truly novel; in a sense, it's an obvious extension of the widespread practice of using a pair of u64 bitmasks to detect a set of ASCII values. What I can say is: it's effective. The RuneSet struct is just a slice of u64, offering a few variations on set membership tests, depending on how confident you are that the string it's testing is valid UTF-8, and what you would like the test to do in the event that it isn't. -
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Ruma
A set of Rust crates for interacting with the Matrix chat network
...It includes all the features you'd expect from a modern chat platform including instant messaging, group chats, audio and video calls, searchable message history, synchronization across all your devices, and end-to-end encryption. Matrix is federated, so no single company controls the system or your data. You can use an existing server you trust or run your own, and the servers synchronize messages seamlessly. Learn more in the Introduction to Matrix. Rust is a systems programming language from Mozilla built with safety, concurrency, and performance in mind. Its novel approach to memory safety and its rich type system make it an excellent choice for writing fast, secure, and reliable programs. -
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Measurements.jl
Error propagation calculator and library for physical measurements
...Physical measures are typically reported with an error, a quantification of the uncertainty of the accuracy of the measurement. Whenever you perform mathematical operations involving these quantities you have also to propagate the uncertainty, so that the resulting number will also have an attached error to quantify the confidence about its accuracy. Measurements.jl relieves you from the hassle of propagating uncertainties coming from physical measurements, when performing mathematical operations involving them. The linear error propagation theory is employed to propagate the errors. -
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Html Menu Generator
Html menu generator
...Additionally, you can opt for a more verbose and flexible syntax, or for convenience methods that cover most use cases. You can programmatically add html classes and attributes to any item in the menu, or to the menu itself. You can add id, so you can easily target some of these elements with CSS or JS. The Laravel version of the menu package adds some extras like convenience methods for generating URLs and macros. -
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dhall-haskell
Maintainable configuration files
...You will probably want to use the shared caches hosted at cache.dhall-lang.org and dhall.cachix.org when doing Nix development. This is not required, but this will save you a lot of time so that you don't have to build as many dependencies from scratch the first time. If you prefer installing the binaries locally in a nix shell environment instead, just run nix-shell in the top-level directory. This option provides additional flexibility with respect to overriding some of the default parameters (e.g. the compiler version), which makes it particularly useful for developers. -
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json-graphql-server
Get a full fake GraphQL API with zero coding in less than 30 seconds
...Under the hood, it uses the express-graphql module. Please refer to their documentations for details about passing variables, etc. Note that the server is GraphiQL enabled, so you can query your server using a full-featured graphical user interface, providing autosuggest, history, etc. Based on your data, json-graphql-server will generate a schema with one type per entity, as well as 3 query types and 3 mutation types.