6400 projects for "code" with 1 filter applied:

  • Ship Agents Faster Icon
    Ship Agents Faster

    Transform your applications and workflows into powerful agentic systems at global scale.

    Gemini 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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    Train ML Models With SQL You Already Know

    BigQuery automates data prep, analysis, and predictions with built-in AI assistance.

    Build 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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  • 1
    Unla

    Unla

    Gateway service that instantly transforms existing MCP Servers

    Unla is a lightweight, highly available MCP gateway written in Go that turns existing MCP servers or ordinary HTTP APIs into MCP-compliant services through configuration, not code changes. Its goal is to let teams “wire up” tools they already run—internal REST endpoints, third-party APIs, or local MCP servers—and present a single, reliable MCP interface to clients like Claude Desktop, Cursor, and IDEs. The gateway focuses on operational concerns you’d expect in production: multi-instance availability, health checking, and declarative routing that maps upstreams to MCP tools and resources. ...
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  • 2
    vJEPA-2

    vJEPA-2

    PyTorch code and models for VJEPA2 self-supervised learning from video

    VJEPA2 is a next-generation self-supervised learning framework for video that extends the “predict in representation space” idea from i-JEPA to the temporal domain. Instead of reconstructing pixels, it predicts the missing high-level embeddings of masked space-time regions using a context encoder and a slowly updated target encoder. This objective encourages the model to learn semantics, motion, and long-range structure without the shortcuts that pixel-level losses can invite. The...
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  • 3
    OpenAI Realtime Agents

    OpenAI Realtime Agents

    This is a simple demonstration of more advanced, agentic patterns

    ...It also supports a “Chat-Supervisor” pattern where a lightweight realtime chat agent handles user interactions and delegates more complex reasoning or tool usage to a stronger textual model (e.g. GPT-4). Because realtime agents are still a beta feature, the code and API surface are subject to changes and may evolve.
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  • 4
    hithesis

    hithesis

    LaTeX thesis template package for Harbin Institute of Technology

    ...It provides class files and examples that handle title pages, abstracts in multiple languages, acknowledgments, lists of figures/tables, symbols, and properly styled chaptering. The template includes bibliography settings and citation styles that conform to school rules, along with ready-made environments for formulas, algorithms, and code. It supports Chinese and English typesetting, sensible defaults for fonts, and modern engines like XeLaTeX for robust CJK support. Cross-references, numbering, and page layout are automated, reducing the risk of last-minute formatting errors before submission. Clear build instructions and commented examples help you slot in your content while keeping the required style intact.
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  • AI-powered service management for IT and enterprise teams Icon
    AI-powered service management for IT and enterprise teams

    Enterprise-grade ITSM, for every business

    Give your IT, operations, and business teams the ability to deliver exceptional services—without the complexity. Maximize operational efficiency with refreshingly simple, AI-powered Freshservice.
    Try it Free
  • 5
    Scalaz

    Scalaz

    Principled Functional Programming in Scala

    Scalaz is a foundational functional-programming library for Scala that provides type classes, data types, and syntax to write pure, composable code. It implements classic abstractions such as Functor, Applicative, Monad, Monoid, Foldable, and Traverse, along with powerful transformers (ReaderT, StateT, WriterT, OptionT, and more) to structure effects. The library offers rich data structures—\/ (disjunction), Validation, NonEmptyList, IList, and Free—that help model errors, invariants, and interpretable programs. ...
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  • 6
    RxDart

    RxDart

    The Reactive Extensions for Dart

    RxDart is a reactive programming toolkit that augments Dart’s native Streams with a rich set of Rx-style operators and subjects. It offers composition primitives like map, where, debounce, throttle, merge, combineLatest, and switchMap, enabling expressive pipelines for events and async data. Subjects provide multicast behaviors so multiple listeners can share a single data source while maintaining semantics like replaying the latest value. By emphasizing immutability and one-way data flow,...
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  • 7
    AppDevKit

    AppDevKit

    AppDevKit is an iOS development library

    ...Yahoo’s Taiwan based iOS app development team has been using this development kit for the past three years, and we plan future apps to use AppDevKit as well. Our use of this code in many apps helped improve its stability and utility. We find these libraries help address some incompatibility issues found in different iOS versions, and overall make app development easier and our apps operate more consistently. AppDevKit has five major parts that include command, user interfaces, animations, image view, and list view support libraries. ...
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  • 8
    GraphQL Juniper

    GraphQL Juniper

    GraphQL server library for Rust

    GraphQL is a data query language developed by Facebook intended to serve mobile and web application frontends. Juniper makes it possible to write GraphQL servers in Rust that are type-safe and blazingly fast. We also try to make declaring and resolving GraphQL schemas as convenient as Rust will allow. Juniper does not include a web server - instead it provides building blocks to make integration with existing servers straightforward. It optionally provides a pre-built integration for the...
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  • 9
    Must-Watch CSS

    Must-Watch CSS

    CSS Grid, flexbox, custom variables, performance, frameworks

    Must-Watch CSS is a curated collection of high-quality recorded talks and presentations about CSS, covering both foundational topics (like layout, Flexbox, Grid) and more advanced or modern concerns (such as performance, tooling, CSS variables, animations, and architecture). Rather than being a code library, it serves as a learning resource — a “watch-list” for front-end or UI developers who want to deepen their understanding of CSS beyond basic syntax. The repository organizes talks by year and topic, giving a historical and topical overview of how CSS practices and tools have evolved. For learners, this collection allows seeing how experts think about layout, performance, design trade-offs, and how CSS­-related problems are solved in real projects or at scale. ...
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  • Stop vibe-debugging. Icon
    Stop vibe-debugging.

    Plug Claude into your app's actual errors.

    AppSignal's MCP server hands Claude, Cursor, or Zed your real errors, traces, and the deploy that shipped them. AI writes the fix; you review the diff.
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  • 10
    APNGKit

    APNGKit

    High performance and delightful way to play with APNG format in iOS

    The APNGKit project is a high-performance framework for loading, displaying, and animating images in the UIKit / AppKit ecosystem (iOS, macOS, tvOS), specifically targeting the APNG (Animated PNG) image format. Unlike traditional static PNG images, APNG supports frame-by-frame animation with full-color images and transparency — yielding better visual fidelity than GIFs while remaining efficient. APNGKit offers a friendly API (e.g. APNGImage, APNGImageView) that behaves similarly to the...
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  • 11
    deep-q-learning

    deep-q-learning

    Minimal Deep Q Learning (DQN & DDQN) implementations in Keras

    ...It implements the core logic needed to train an agent using Q-learning with neural networks (i.e. approximating Q-values via deep nets), setting up environment interaction loops, experience replay, network updates, and policy behavior. For learners and researchers interested in reinforcement learning, this repo offers a concrete, runnable example bridging theory and practice: you can execute the code, play with hyperparameters, observe convergence behavior, and see how deep Q-learning learns policies over time in standard environments. Because it’s self-contained and Python-based, it's well-suited for experimentation, modifications, or extension — for instance adapting to custom Gym environments, tweaking network architecture, or combining with other RL techniques.
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  • 12
    Roadmap To Learn Generative AI In 2025

    Roadmap To Learn Generative AI In 2025

    Basic Machine Learning Natural Language Processing Roadmap

    Roadmap To Learn Generative AI In 2025 is a curated learning path focused on contemporary generative AI — covering large language models (LLMs), diffusion-based image generation, prompt engineering, multi-modal AI, fine-tuning techniques, and the practical considerations for deploying generative models. It’s aimed at learners and developers who already have some programming or ML basics and wish to specialize in generative AI, offering a modern, structured plan that reflects the state of the...
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  • 13
    1D Visual Tokenization and Generation

    1D Visual Tokenization and Generation

    This repo contains the code for 1D tokenizer and generator

    The 1D Visual Tokenization and Generation project from ByteDance introduces a novel “one-dimensional” tokenizer designed for images: instead of representing images with large grids of 2D tokens (as in many prior generative/image-modeling systems), it compresses images into as few as 32 discrete tokens (or more, optionally) — thereby achieving a very compact, efficient representation that drastically speeds up generation and reconstruction while retaining strong fidelity. This compact...
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  • 14
    UNO

    UNO

    A Universal Customization Method for Single and Multi Conditioning

    UNO is a project by ByteDance introduced in 2025, titled “A Universal Customization Method for Both Single and Multi-Subject Conditioning.” It suggests a framework for image (or more general generative) modeling where the model can be conditioned either on a single subject or multiple subjects — which may correspond to generating or customizing images featuring specific people, styles, or objects, possibly with fine-grained control over subject identity or composition. Because the project is...
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  • 15
    DreamO

    DreamO

    A Unified Framework for Image Customization

    DreamO is a unified, open-source framework from ByteDance for advanced image customization and generation that consolidates multiple “image manipulation” tasks into a single system, rather than requiring separate specialized models. Built on a diffusion-transformer (DiT) backbone, it supports a diverse set of tasks — including identity preservation, virtual “try-on” (e.g. clothing, accessories), style transfer, IP adaptation (objects/characters), and layout/condition-aware customizations —...
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  • 16
    InfiniteYou

    InfiniteYou

    Flexible Photo Recrafting While Preserving Your Identity

    InfiniteYou is an open-source image-generation and “identity-preserving image editing / generation” framework from ByteDance, designed to generate high-fidelity images that preserve a subject’s identity while allowing flexible editing or re-creation according to textual prompts. Using an architecture built around diffusion transformers (DiTs), InfiniteYou introduces a component called InfuseNet that injects identity features derived from reference images into the generation process — via...
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  • 17
    AI Commits

    AI Commits

    A CLI that writes your git commit messages for you with AI

    AI Commits is a command-line tool that writes your git commit messages for you using an AI model. It works by running git diff to gather your staged code changes, sending that diff to an LLM (originally GPT-3, now configurable), and receiving back a concise, human-readable commit message. The tool is designed to integrate cleanly into a developer’s workflow so that generating a descriptive commit message becomes a single command rather than a chore. It supports configuration via environment variables or config files so you can set your API key, preferred model, message style, and more. ...
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  • 18
    MetricFlow

    MetricFlow

    MetricFlow allows you to define, build, and maintain metrics in code

    MetricFlow is an open-source semantic layer engine designed to help organizations define, manage, and query business metrics in a consistent, governed way. It works alongside a data stack—typically built with dbt—and allows you to express metrics as YAML‐based definitions tied to semantic models and dimension tables, rather than embedding logic ad-hoc across many dashboards or scripts. When a user or tool requests a metric (e.g., “monthly revenue by region”), MetricFlow generates optimized,...
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  • 19
    Zerocopy

    Zerocopy

    Zerocopy makes zero-cost memory manipulation effortless

    Zerocopy is a Rust library designed to make zero-cost memory manipulation both safe and effortless. It allows developers to reinterpret or convert raw byte sequences into structured types—and vice versa—without writing unsafe code directly. The crate provides safe abstractions for transmuting data while preserving Rust’s strict safety guarantees, removing the need for manual memory manipulation. Zerocopy introduces a suite of conversion traits such as TryFromBytes, FromBytes, IntoBytes, and FromZeros for defining how types can be safely constructed from or written to byte sequences. ...
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  • 20
    FairChem

    FairChem

    FAIR Chemistry's library of machine learning methods for chemistry

    FAIRChem is a unified library for machine learning in chemistry and materials, consolidating data, pretrained models, demos, and application code into a single, versioned toolkit. Version 2 modernizes the stack with a cleaner core package and breaking changes relative to V1, focusing on simpler installs and a stable API surface for production and research. The centerpiece models (e.g., UMA variants) plug directly into the ASE ecosystem via a FAIRChem calculator, so users can run relaxations, molecular dynamics, spin-state energetics, and surface catalysis workflows with the same pretrained network by switching a task flag. ...
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  • 21
    Chartkick

    Chartkick

    Create beautiful JavaScript charts with one line of Ruby

    Chartkick is a Ruby gem that makes it easy to generate beautiful charts in Rails and other Ruby applications with minimal code. It provides a high-level API where developers can use simple helpers like line_chart, pie_chart, or column_chart and pass in data arrays or ActiveRecord queries. Under the hood, it works with popular charting libraries such as Google Charts, Chart.js, and Highcharts, but hides their verbose JavaScript APIs behind a clean Ruby interface.
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  • 22
    Enterprise-Scale

    Enterprise-Scale

    The Azure Landing Zones (Enterprise-Scale) architecture

    Enterprise-Scale is a reference architecture and set of landing-zone blueprints for building large, governed Azure estates. It codifies recommended patterns—network topology, identity integration, subscription and resource organization, policy assignment, and guardrails—so enterprises can provision repeatable, secure, and compliant environments for workloads. The project supplies deployable artifacts (Bicep/ARM/Terraform modules, examples, and documentation) to accelerate consistent...
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  • 23
    Optopsy

    Optopsy

    A nimble options backtesting library for Python

    ...The csv_data() function is a convenience function. Under the hood it uses Panda's read_csv() function to do the import. There are other parameters that can help with loading the csv data, consult the code/future documentation to see how to use them. Optopsy is a small simple library that offloads the heavy work of backtesting option strategies, the API is designed to be simple and easy to implement into your regular Panda's data analysis workflow. As such, we just need to call the long_calls() function to have Optopsy generate all combinations of a simple long call strategy for the specified time period and return a DataFrame. ...
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  • 24
    mlforecast

    mlforecast

    Scalable machine learning for time series forecasting

    ...It lets you apply any regressor that follows the typical scikit-learn API, for example, gradient-boosted trees or linear models, to time-series data by automating much of the messy feature engineering and data preparation. Instead of writing custom code to build lagged features, rolling statistics, and date-based predictors, mlforecast generates those automatically based on a simple configuration. It supports multi-series forecasting, meaning you can train one model that forecasts many time series at once (common in retail, demand forecasting, etc.), rather than one model per series. ...
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  • 25
    PaSa

    PaSa

    An advanced paper search agent powered by large language models

    PaSa is an open-source “paper search agent” built around large language models (LLMs), designed to automate the process of academic literature retrieval with human-like decision making. Instead of simply translating a query into keywords and returning a flat list of matching papers, PaSa uses a dual-agent architecture (Crawler + Selector) that can iteratively search, read, analyze, and filter academic publications — simulating how a researcher might dig through citation networks, expand...
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