Search Results for "simple java code" - Page 6
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Showing 382 open source projects for "simple java code"
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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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Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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CUDA Agent
Large-Scale Agentic RL for High-Performance CUDA Kernel Generation
...Its architecture combines large-scale data synthesis, a skill-augmented CUDA development environment, and long-horizon reinforcement learning to build intrinsic optimization capability rather than relying on simple post-hoc tuning. The system operates in a ReAct-style loop where the agent profiles baseline implementations, writes CUDA code, compiles it in a sandbox, and iteratively refines performance. CUDA-Agent has demonstrated strong benchmark results, achieving high pass rates and significant speedups compared with compiler baselines such as torch.compile. -
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Segment Anything
Provides code for running inference with the SegmentAnything Model
Segment Anything (SAM) is a foundation model for image segmentation that’s designed to work “out of the box” on a wide variety of images without task-specific fine-tuning. It’s a promptable segmenter: you guide it with points, boxes, or rough masks, and it predicts high-quality object masks consistent with the prompt. The architecture separates a powerful image encoder from a lightweight mask decoder, so the heavy vision work can be computed once and the interactive part stays fast. A... -
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TraceRoot
Find the Root Cause in Your Code's Trace
TraceRoot.AI is an open source, AI-native observability and debugging platform designed to help engineering teams resolve production issues faster. It consolidates telemetry into a single correlated execution tree that provides causal context for failures. AI agents operate over this structured view to summarize issues, pinpoint likely root causes, and even suggest actionable fixes or draft GitHub issues and pull requests. It offers interactive trace exploration with zoomable log clusters,... -
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DeepDetect
Deep Learning API and Server in C++14 support for Caffe, PyTorch
...While the Open Source Deep Learning Server is the core element, with REST API, and multi-platform support that allows training & inference everywhere, the Deep Learning Platform allows higher level management for training neural network models and using them as if they were simple code snippets. Ready for applications of image tagging, object detection, segmentation, OCR, Audio, Video, Text classification, CSV for tabular data and time series. Neural network templates for the most effective architectures for GPU, CPU, and Embedded devices. Training in a few hours and with small data thanks to 25+ pre-trained models. ... -
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All Agentic Architectures
Implementation of 17+ agentic architectures
All Agentic Architectures is an open educational repository that provides hands-on implementations of modern AI agent architectures. The project acts as a practical learning resource that bridges the gap between theoretical research on autonomous agents and real software implementations. It contains more than a dozen agent architectures implemented using frameworks such as LangChain and LangGraph. Each architecture is explained through runnable notebooks that illustrate how the agent works... -
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llmx.txt hub
The largest directory for AI-ready documentation and tools
llms-txt-hub serves as a central directory and knowledge base for the emerging llms.txt convention, a simple, text-based way for project owners to communicate preferences to AI tools. It catalogs implementations across projects and platforms, helping maintain a shared understanding of how LLM-powered services should interact with code and documentation. The repository aims to standardize patterns for allowlists, denylists, attribution, rate expectations, and contact information, mirroring the spirit of robots.txt for the AI era. ... -
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PPTAgent
PPTAgent: Generating and Evaluating Presentations
PPTAgent is a research system for generating and evaluating slide decks that goes beyond simple text-to-slides. It follows a two-stage, edit-based workflow: first it analyzes reference presentations to infer slide roles and structure, then it drafts an outline and iteratively performs editing actions to produce new slides. The project includes both the generation agent and an evaluation framework, PPTEval, to score content quality, design, and coherence. The repository highlights the EMNLP... -
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Softaworks Agent Skills
A curated collection of skills for AI coding agents
The Softaworks Agent Toolkit is a comprehensive collection of agent skills, commands, and sub-agents designed to augment AI coding assistants like Claude Code, Codex, and Cursor with practical workflow capabilities. It packages broad categories of modular skills that help with development automation, documentation creation, planning, architecture, testing, and soft professional workflows. Beyond simple skills, it also includes agents and CLI slash commands that help developers automate common tasks such as pattern finding, diagram generation, requirement drafting, and daily standup preparation. ... -
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VibeKit
Run Claude Code, Gemini, Codex in a clean, isolated sandbox
Vibekit is an open-source toolkit focused on rapid prototyping and building of AI-driven experiences, particularly those that integrate multimodal inputs, reactive interfaces, and context-aware behaviors. It provides a set of abstractions and utilities that let developers connect generative models to UI frameworks, sensors, event streams, and external services without having to build plumbing from scratch. Instead of treating AI models as black boxes behind simple prompts, Vibekit encourages... -
$300 Free Credits to Build on Google CloudStart your next project with $300 in free Google Cloud credit. Spin up VMs, run containers, query petabytes in BigQuery, or build agents with Gemini Enterprise Agent Platform. Once your credits are used, keep building with 20+ always-free tier products including Compute Engine, Cloud Storage, GKE, and Cloud Run functions. No commitment required—just sign up and start building.
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Animated Drawings
Code to accompany "A Method for Animating Children's Drawings"
AnimatedDrawings is a framework that converts user sketches or line drawings into fully animated 2D motion sequences using learned motion priors. The idea is that you draw a simple static figure (stick figure, silhouette, or contour lines), and the system produces plausible skeletal motion (walking, jumping, dancing) that adheres to the drawn shape constraints. The architecture separates shape embedding (to understand user-drawn geometry) from motion embedding / generation (to produce... -
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Kubeflow pipelines
Machine Learning Pipelines for Kubeflow
Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable. A pipeline is a description of an ML workflow, including all of the components in the workflow and how they combine in the form of a graph. The pipeline includes the definition of the inputs (parameters) required to run the pipeline and the inputs and outputs of each component. A pipeline component is a self-contained set of user code, packaged as a Docker image, that performs one step in the pipeline. ... -
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Poetiq
Reproduction of Poetiq's record-breaking submission to the ARC-AGI-1
poetiq-arc-agi-solver is the open-source codebase from Poetiq that replicates their record-breaking submission to the challenging benchmark suite ARC-AGI (both ARC-AGI-1 and ARC-AGI-2). The project demonstrates a system that orchestrates large language models (LLMs) — like those from major providers — with carefully engineered prompting, reasoning workflows, and dynamic strategies, to tackle the abstract, logic-heavy problems in ARC-AGI. Instead of relying on a single prompt or fixed... -
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LLM Course
Course to get into Large Language Models (LLMs)
...Evaluation is treated as a first-class topic, with examples of automatic and human-in-the-loop methods to catch regressions and verify quality beyond simple loss values. By the end, students have a mental model and a practical toolkit for iterating on datasets, training configs, etc. -
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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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OpenAI Realtime Agents
This is a simple demonstration of more advanced, agentic patterns
This repository demonstrates how to build low-latency, streaming “voice + chat” agents using OpenAI’s Realtime API combined with the OpenAI Agents SDK. The demo shows patterns for connecting a realtime voice stream (audio in/out) with agents that can use tools, maintain state, and orchestrate multi-agent workflows. The SDK offers abstractions such as agent orchestration, event handling, handoffs, state management, and guardrails, tailored to support realtime, conversational systems. The demo... -
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DotVVM
Open source MVVM framework for Web Apps
...Save your time with GridView, FileUpload and other components shipped with the framework. Don't spend the time building an API. Just load data from the database and use data-binding to display them. DotVVM needs less than 100 kB of JavaScript code. It's smaller than other ASP.NET-based frameworks. DotVVM offers a free Visual Studio extension giving you all the comfort you are used to. DotVVM comes with ready-made components you can use in your HTML files. The state and user interactions are handled in view models - C# classes. The controls render simple HTML which can be styled easily. ... -
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deep-q-learning
Minimal Deep Q Learning (DQN & DDQN) implementations in Keras
The deep-q-learning repository authored by keon provides a Python-based implementation of the Deep Q-Learning algorithm — a cornerstone method in reinforcement learning. 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... -
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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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OpenCV
Open Source Computer Vision Library
The Open Source Computer Vision Library has >2500 algorithms, extensive documentation and sample code for real-time computer vision. It works on Windows, Linux, Mac OS X, Android, iOS in your browser through JavaScript. Languages: C++, Python, Julia, Javascript Homepage: https://opencv.org Q&A forum: https://forum.opencv.org/ Documentation: https://docs.opencv.org Source code: https://github.com/opencv Please pay special attention to our tutorials!... -
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Weka
Machine learning software to solve data mining problems
Weka is a collection of machine learning algorithms for solving real-world data mining problems. It is written in Java and runs on almost any platform. The algorithms can either be applied directly to a dataset or called from your own Java code. -
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PyCaret
An open-source, low-code machine learning library in Python
PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive. In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. -
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OculiX
Visual Automation IDE — automate anything you see on screen
OculiX is the evolution of SikuliX, actively maintained with the full agreement of its original creator RaiMan. Automate any desktop application using image recognition (OpenCV) and OCR (Tesseract + PaddleOCR). No access to source code or DOM required — if you can see it, you can automate it. Key features: - Guided step-by-step recorder with live code preview - Image recognition via OpenCV 4.10 - Dual OCR: Tesseract (built-in) + PaddleOCR (neural, high precision) - Local and remote automation via integrated VNC - SSH tunnels via embedded JSch - Cross-platform: Windows, macOS (Apple Silicon M1-M4), Linux - Scripting: Jython, JRuby, Java, PowerShell, AppleScript - Java 17 recommended (Java 8+ supported) - Full CI/CD with automated builds for all platforms Used worldwide for test automation, RPA, and visual regression testing. ... -
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Qwen2.5-Coder
Qwen2.5-Coder is the code version of Qwen2.5, the large language model
...Qwen2.5-Coder, with its long context length of 128K tokens, is ideal for a variety of use cases, from simple code assistants to complex programming scenarios, matching the capabilities of models like GPT-4o. -
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AudioCraft
Audiocraft is a library for audio processing and generation
...Both models operate over discrete audio tokens produced by a neural codec (EnCodec), which acts like a tokenizer for waveforms and enables efficient sequence modeling. The repo provides inference scripts, checkpoints, and simple Python APIs so you can generate clips from prompts or incorporate the models into applications. It also contains training code and recipes, so researchers can fine-tune on custom data or explore new objectives without building infrastructure from scratch. Example notebooks, CLI tools, and audio utilities help with prompt design, conditioning on reference audio, and post-processing to produce ready-to-share outputs. -
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DeepSeek AIO
Access and use all DeepSeek AI models in one program.
DeepSeek AIO is a simple program that allows you to interact with all DeepSeek large language models in one place. It supports text-based chats, data analysis, code generation, language translation, and more. The program is designed to make it easy for users to use DeepSeek's AI tools for different purposes without switching between multiple platforms.
