Search Results for "tasks" - Page 53
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Showing 3767 open source projects for "tasks"
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Claude Code Bridge
Real-time multi-AI collaboration: Claude, Codex & Gemini
Claude Code Bridge is an open-source command-line tool designed to enable real-time collaboration between multiple AI coding assistants within a unified development environment. The system allows developers to coordinate interactions between models such as Claude, Codex, and Gemini so that they can work together on programming tasks. By maintaining persistent shared context between these models, the tool reduces redundant prompts and minimizes token usage while allowing each AI system to contribute specialized capabilities. The architecture functions as a unified launcher that manages communication between multiple AI providers and coordinates their responses within the same development session. ... -
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mllm
Fast Multimodal LLM on Mobile Devices
mllm is an open-source inference engine designed to run multimodal large language models efficiently on mobile devices and edge computing environments. The framework focuses on delivering high-performance AI inference in resource-constrained systems such as smartphones, embedded hardware, and lightweight computing platforms. Implemented primarily in C and C++, it is designed to operate with minimal external dependencies while taking advantage of hardware-specific acceleration technologies... -
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Agentic Context Engine
Make your agents learn from experience
...The system treats context as a dynamic “playbook” that evolves over time through a process of generation, reflection, and curation, enabling agents to refine strategies across repeated tasks. In this workflow, one component generates solutions, another reflects on outcomes, and a third curates useful knowledge so it can be reused in future interactions. This architecture allows agents to gradually build persistent operational memory without requiring additional training datasets or model retraining. -
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Fractals
Fractals is a recursive task orchestrator for agent swarm
Fractals is an experimental open-source framework designed to orchestrate complex tasks using swarms of AI agents organized in a recursive structure. The system takes a high-level goal and decomposes it into a hierarchy of smaller subtasks, forming a self-similar tree that resembles a fractal structure. Each leaf node of this tree represents a specific executable task that can be processed independently by an AI agent. -
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LangGraph.js
Framework to build resilient language agents as graphs
LangGraphJS is a JavaScript framework designed to build stateful AI applications and autonomous agents using graph-based execution models. Developed as part of the LangChain ecosystem, the framework allows developers to represent complex AI workflows as graphs where nodes represent tasks and edges define the flow of execution. This structure makes it easier to implement long-running agents, multi-step reasoning pipelines, and workflows that require persistent state. LangGraphJS supports advanced capabilities such as branching logic, loops, and conditional execution, enabling developers to build sophisticated AI systems that can adapt to dynamic conditions. ... -
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InternLM-XComposer-2.5
InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System
...It incorporates visual understanding modules that allow the model to analyze images and integrate them into coherent narrative outputs. The framework also supports tasks such as image captioning, multimodal reasoning, and layout generation for structured visual documents. By combining language generation with visual composition capabilities, the system enables new forms of content creation that integrate written explanations with automatically generated visual components. -
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LLM Workflow Engine
Power CLI and Workflow manager for LLMs (core package)
...Developers can construct structured workflows using configuration files and integrate them with tools such as Ansible playbooks or custom scripts to automate complex tasks. The engine supports multiple AI providers through a plugin architecture, allowing connections to services like OpenAI, Hugging Face, Cohere, or other compatible APIs. -
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ClaraVerse
Claraverse is a opesource privacy focused ecosystem to replace ChatGPT
ClaraVerse is an open-source private AI workspace designed to give users a unified environment for interacting with large language models, building automations, and managing AI-driven tasks in a self-hosted environment. The platform combines chat interfaces, workflow automation, and long-running task management into a single application that can connect to both local and cloud-based AI models. Users can integrate models from multiple providers such as OpenAI, Anthropic, Google, or locally hosted systems like Ollama and LM Studio, enabling flexibility in how AI capabilities are deployed and managed. ... -
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lms
LM Studio CLI
...LMS is built using the LM Studio JavaScript SDK and integrates tightly with the LM Studio runtime environment. The interface is designed to simplify automation workflows and scripting tasks related to local AI deployment. By exposing model management capabilities through command-line commands, the tool enables developers to integrate local LLM operations into development pipelines and backend services. As a result, LMS acts as a bridge between interactive local AI tools and automated software development workflows. -
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The Alignment Handbook
Robust recipes to align language models with human and AI preferences
...The project focuses on the post-training stage of model development, where models are refined after pre-training to behave more helpfully, safely, and reliably in real-world applications. It provides detailed training recipes that explain how to perform tasks such as supervised fine-tuning, preference modeling, and reinforcement learning from human feedback. The handbook also includes reproducible workflows for training instruction-following models and evaluating alignment quality across different datasets and benchmarks. One of its goals is to bridge the gap between academic research on alignment methods and practical engineering implementation. -
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Deep Search Agent
Implement a concise and clear Deep Search Agent from 0
Deep Search Agent is an experimental demonstration project that showcases an autonomous AI agent designed to perform multi-step research and information gathering tasks. The repository illustrates how large language models can be orchestrated with tools and planning logic to execute complex search workflows rather than single-prompt responses. It typically combines reasoning, retrieval, and iterative refinement so the agent can break down questions, gather evidence, and synthesize structured outputs. ... -
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nb
CLI and local web plain text note‑taking, bookmarking, and archiving
nb is a powerful, flexible, and self-contained note-taking and knowledge management application that empowers users to write, organize, search, and archive plain-text notes, bookmarks, and tasks from the command line or through a local web server. Designed to be portable and vendor-independent, it stores all content as plain text files with optional encryption, which means notes can be versioned, diffed, and synced with Git just like code, giving users fine-grained control over their personal information. The tool supports tagging, linking with wiki-style references, filtering, pinned items, and full-text search, making it easy to build a structured knowledge base or Zettelkasten system that grows over time. ... -
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FireRed-Image-Edit
General-purpose image editing model that delivers high-fidelity
FireRed-Image-Edit is an open-source general-purpose image editing model and toolset designed to deliver high-fidelity, visually coherent edits across a wide range of editing tasks, from simple object modifications to complex enhancements like restoration and style preservation. It is built on a flexible text-to-image foundation model that has been extended with training paradigms including pretraining, supervised fine-tuning, and reinforcement learning to imbue the system with strong instruction following and editing consistency. ... -
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Secure OpenClaw
A personal 24x7 AI assistant like OpenClaw
Secure-OpenClaw is an open-source project designed to transform your everyday messaging apps like WhatsApp, Telegram, Signal, and iMessage into a 24×7 personal AI assistant that can respond to queries, run tasks, remember context, and interact with external services. Unlike a traditional chatbot that lives in a browser or API dashboard, Secure-OpenClaw runs locally or on your chosen infrastructure and listens for messages on channels you already use, giving you an always-on interface to a powerful agent. It leverages Claude and other models to interpret messages and is built to manage persistent memory, scheduled reminders, and integration with hundreds of third-party apps to automate workflows without leaving your chat. ... -
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Inkeep
Create AI Agents in a No-Code Visual Builder or TypeScript SDK
...Agents built with this framework can act as real-time conversational assistants — for example, handling help desk inquiries, providing internal support to teams, or driving in-app experiences — and they can be extended to automate multi-step tasks that interact with external systems like CRMs, knowledge bases, or ticketing systems. The project includes support for designing rich workflows where agents communicate with each other (agent-to-agent communication) and maintain state. -
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Step 3.5 Flash
Fast, Sharp & Reliable Agentic Intelligence
Step 3.5 Flash is a cutting-edge, open-source large language model developed by StepFun-AI that pushes the frontier of efficient reasoning and “agentic” intelligence in a way that makes powerful AI accessible beyond proprietary black boxes. Unlike dense models that activate all their parameters for every token, Step 3.5 Flash uses a sparse Mixture-of-Experts (MoE) architecture that selectively engages only about 11 billion of its roughly 196 billion total parameters per token, delivering... -
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AI Agents Masterclass
Follow along with my AI Agents Masterclass videos
...The project includes structured lessons, code examples, and practical exercises that cover foundational concepts like prompt engineering, chaining agents, tool usage, plan execution, evaluation, and safety considerations. It breaks down how autonomous agents interact with external systems, handle iterative reasoning, and integrate with third-party services or APIs to perform real tasks — for example, web search, browsing, scheduling, or coding assistance. Students of the masterclass can follow written modules or Jupyter notebooks that illustrate concepts step by step and progressively build more capable agents. The content is suitable for both beginners and intermediate developers because it starts with basic principles and escalates to advanced architectures like multi-agent coordination. -
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Context Engineering Template
Context engineering is the new vibe coding
Context Engineering Template is a comprehensive template and workflow repository designed to teach and implement context engineering, a structured approach to preparing and organizing the information necessary for AI coding assistants to complete complex tasks reliably. Instead of relying solely on short prompts, this project encourages developers to create rich, structured context files that include project rules, examples, and validation criteria so that AI systems can act more like informed collaborators and less like general-purpose generators. The repository provides templates such as CLAUDE.md for defining global project rules, INITIAL.md for feature requests, and folders for examples, PRPs, validation scripts, and settings to support systematic prompt generation and execution with tools like Claude Code. ... -
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Step3-VL-10B
Multimodal model achieving SOTA performance
...Despite having only about 10 billion parameters, it delivers performance that rivals or even surpasses much larger models (10×–20× larger) on a wide range of multimodal benchmarks covering reasoning, perception, and complex tasks, positioning it as one of the most powerful models in its class. It achieves this efficiency and strong performance through unified pre-training on a massive 1.2 trillion-token multimodal corpus that jointly optimizes a language-aligned perception encoder with a powerful decoder, creating deep synergy between image processing and text understanding. -
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Menubar Electron
High level way to create menubar desktop applications with electron
Menubar Electron is a lightweight JavaScript library for creating desktop applications with persistent menu bar (Mac) or system tray (Windows/Linux) icons/interfaces, allowing developers to build utility apps that live in the user’s system bar and open a small window or popup when clicked. It serves as a wrapper around frameworks like Electron or Node GUI backends, abstracting common tasks such as positioning the popup window, handling click events, and integrating native tray interactions without writing platform-specific code. The library makes it easy to build cross-platform utilities like clipboard managers, quick note popups, status displays, or background tools that users access from the bar icon without filling taskbars or docks. menubar handles the lifecycle of the UI window, system tray icon, and related events so developers can focus on the application logic rather than boilerplate integration. -
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UCP Python SDK
The official Python SDK for UCP
...This SDK provides Pydantic models for UCP schemas, making it easy for Python developers to construct, validate, and serialize protocol messages and data structures according to the UCP specification. By adhering to the official protocol standards, applications built on this SDK can participate in tasks like capability discovery, checkout flows, order management, and more, while remaining interoperable across different UCP implementations and surfaces. -
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HunyuanOCR
OCR expert VLM powered by Hunyuan's native multimodal architecture
...It’s designed to unify the entire OCR pipeline, detection, recognition, layout parsing, information extraction, translation, and even subtitle or structured output generation, into a single model inference instead of a cascade of separate tools. Despite being fairly lightweight (about 1 billion parameters), it delivers state-of-the-art performance across a wide variety of OCR tasks, outperforming many traditional OCR systems and even other multimodal models on benchmark suites. HunyuanOCR handles complex documents: multi-column layouts, tables, mathematical formulas, mixed languages, handwritten or stylized fonts, receipts, tickets, and even video-frame subtitles. The project provides code, pretrained weights, and inference instructions, making it feasible to deploy locally or on a server, and to integrate with applications. -
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Personal Security Checklist
A compiled checklist of 300+ tips for protecting digital security
Personal Security Checklist is a comprehensive, plain-language checklist for improving personal digital security and privacy across devices, accounts, and everyday workflows. It’s organized so that complete beginners can make quick, high-impact changes, while advanced users can dig into deeper hardening steps. The guidance spans topics like passwords, 2FA, device encryption, browser hygiene, network safety, backups, and incident response planning. Each section breaks recommendations into... -
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30 Seconds of Java
Collection of reusable tested Java 21 compatible code snippets
...Each snippet is designed to be read and understood in under a minute, showing a single idea such as working with streams, handling optionals, manipulating collections, or doing common utility tasks. The repo targets both beginners who want bite-sized learning and experienced developers who just need a ready-made idiom without digging through docs. Because the snippets are standalone, you can copy them directly into your project or adapt them with minimal changes. The collection is organized and documented so you can browse by category and skill level. ... -
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nanochat
The best ChatGPT that $100 can buy
...The repository stitches together every stage of the lifecycle: tokenizer training, pretraining a Transformer on a large web corpus, mid-training on dialogue and multiple-choice tasks, supervised fine-tuning, optional reinforcement learning for alignment, and finally efficient inference with caching. Its north star is approachability and speed: you can boot a fresh GPU box and drive the whole pipeline via a single script, producing a usable chat model in hours and a clear markdown report of what happened. The code is written to be read—concise training loops, transparent configs, and minimal wrappers—so you can audit each step, tweak it, and rerun without getting lost in framework indirection.
