Search Results for "ofn-layer-modes"
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Showing 395 open source projects for "ofn-layer-modes"
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OAGI Python SDK
Python SDK for the Computer Use model Lux, developed by OpenAGI
OAGI Python SDK is a Python client library for the Lux computer-use model that turns Lux into a programmable automation layer for operating human-facing software via vision and actions. It exposes the OAGI API in an ergonomic way, letting you trigger Lux in three main modes: Tasker for precise scripted sequences, Actor for fast one-shot tasks, and Thinker for open-ended, multi-step objectives. The SDK is designed around “computer use” as a paradigm, where the AI actually navigates interfaces, clicks, types, scrolls, and reads the screen through screenshots instead of only calling APIs. ... -
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OCRmyPDF
OCRmyPDF adds an OCR text layer to scanned PDF files
OCRmyPDF adds an optical character recognition (OCR) text layer to scanned PDF files, allowing them to be searched. PDF is the best format for storing and exchanging scanned documents. Unfortunately, PDFs can be difficult to modify. OCRmyPDF makes it easy to apply image processing and OCR (recognized, searchable text) to existing PDFs. -
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Cline
Autonomous coding agent right in your IDE
...Built on Node's readline module, it offers features like history support, auto-completion, and input masking. Cline allows you to define custom commands with argument placeholders, making it versatile for various command structures. Additionally, it supports modes, where different command sets can be dynamically loaded depending on the application's state. -
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OpenAI Codex CLI
Lightweight coding agent that runs in your terminal
...The CLI can scaffold new files, run code in sandboxed environments, install dependencies, and commit changes automatically, streamlining chat-driven development. It supports various approval modes—from suggestion-only to full automation—ensuring safe and controlled code execution. Codex CLI can also handle multimodal inputs like screenshots and diagrams to implement features intelligently. The tool includes built-in sandboxing & security measures, such as network restrictions and directory confinement, to protect your system during code execution. ... -
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5
Forge: Magic: The Gathering Rules Engine
An unofficial rules engine for the world's greatest card game
...Developed collaboratively by a community of contributors, it implements the vast majority of the game’s rules and card interactions, allowing players to experience both casual and competitive gameplay without official clients. The software supports multiple play modes, including single-player against AI opponents, multiplayer sessions, and even adventure-style gameplay modes that expand beyond traditional card duels. Its architecture is highly extensible, enabling developers to script new cards, create custom sets, and modify gameplay mechanics through its API and scripting system. Forge includes tools for deck building, card searching, and automated gameplay assistance, making it accessible to both new players and experienced enthusiasts. -
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Roo Code
Roo Code gives you a whole dev team of AI agents in your code editor
...Designed to work on your terms, Roo Code gives you full control locally while enabling delegation and parallel execution at scale. Its model-agnostic architecture ensures flexibility as AI models and providers evolve, letting you choose or bring your own keys. Role-specific agent modes keep AI focused, reliable, and aligned with real engineering workflows. Open source, secure, and highly configurable, Roo Code fits seamlessly into both individual and team-based development environments. -
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Atomic Chat
Atomic-Chat is an open source alternative to ChatGPT
Atomic Chat is part of the AtomicBot ecosystem and represents a chat-based interface layer for interacting with autonomous AI agents powered by the OpenClaw framework, enabling users to communicate with and control an AI assistant through natural language conversations. The system is designed to go beyond traditional chatbots by allowing the assistant to take real actions across connected tools such as email, calendars, browsers, and file systems, effectively functioning as a task-executing agent rather than a passive responder. ... -
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AirLLM
AirLLM 70B inference with single 4GB GPU
...The project addresses one of the main barriers to local LLM experimentation by introducing a memory-efficient inference technique that loads model layers sequentially rather than storing the entire model in GPU memory. This layer-wise inference approach allows models with tens of billions of parameters to run on devices with only a few gigabytes of VRAM. AirLLM preprocesses model weights so that each transformer layer can be loaded independently during computation, reducing the memory footprint while still performing full inference. As a result, developers can experiment with models that previously required specialized high-end GPUs. -
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MarkFlowy
The AI Markdown Editor
...The application integrates AI assistants that support tasks such as text generation, translation, summarization, and conversational interaction, helping users improve productivity and content quality. It supports multiple editing modes, including both raw Markdown and WYSIWYG interfaces, allowing users to choose their preferred workflow. MarkFlowy also includes a robust file management system with features like global search, drag-and-drop organization, and support for multiple file types beyond Markdown, such as JSON and plain text. Customization is a key aspect of the platform, with support for themes, keyboard shortcuts, and extensibility through plugins and integrations. -
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AIMET
AIMET is a library that provides advanced quantization and compression
Qualcomm Innovation Center (QuIC) is at the forefront of enabling low-power inference at the edge through its pioneering model-efficiency research. QuIC has a mission to help migrate the ecosystem toward fixed-point inference. With this goal, QuIC presents the AI Model Efficiency Toolkit (AIMET) - a library that provides advanced quantization and compression techniques for trained neural network models. AIMET enables neural networks to run more efficiently on fixed-point AI hardware... -
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Model Explorer
A modern model graph visualizer and debugger
Model Explorer is a visual tool for exploring, debugging, and optimizing ML models deployed on edge devices. Developed by Google AI Edge, it offers a browser-based interface to inspect layer-wise performance, memory usage, and inference timing of TensorFlow Lite and other supported models. It’s a powerful utility for developers optimizing models for constrained environments. -
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Pinchtab
High-performance browser automation bridge and orchestrator
...It launches and manages its own Chrome instance while remaining framework-agnostic, so it can be used with any language or agent system. Pinchtab also supports persistent sessions, stealth automation, and both headless and headed operation modes. The project’s goal is to provide fast, cheap, and portable browser control infrastructure for modern AI workflows. -
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EPUB to Audiobook Converter
EPUB to audiobook converter, optimized for Audiobookshelf
...A recent addition is a Gradio-based WebUI, which wraps all configuration options in a graphical interface for users who prefer not to work with the command line. The tool offers advanced options such as controlling chapter ranges, handling paragraph detection via newline modes, removing endnote markers, and using regex-based search-and-replace files to tweak pronunciations. It can be run directly with Python or via Docker. -
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AxonHub
Use any SDK to call 100+ LLMs
AxonHub is an open-source AI gateway platform designed to simplify the process of integrating and switching between different large language model providers. The system acts as a compatibility layer that allows developers to use the same SDK interface while routing requests to various AI services behind the scenes. Instead of rewriting code when switching providers such as OpenAI or Anthropic, developers can simply change configuration settings within the gateway. AxonHub translates requests from one provider’s API format into another, enabling seamless interoperability across different AI platforms. ... -
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Perplexica
Perplexica is an AI-powered answering engine.
...It combines live internet search results with AI models, letting you use local LLMs via Ollama or connect to providers like OpenAI, Claude, Gemini, and Groq. Powered by SearxNG, it aggregates results from multiple search engines while keeping your identity and queries private. Perplexica offers multiple search modes—Speed, Balanced, and Quality—so you can trade off latency and depth depending on the task. It also enhances the experience with widgets and rich search types like images, videos, and domain-limited queries. With local search history, file uploads, and an easy Docker-based setup, it’s built for everyday research without sacrificing control or privacy. -
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Codex Autoresearch
A codex plugin for running optimization loops inside a codebase
...Developers define a goal and verification command, and the agent continuously runs experiments to reach the desired outcome. The framework supports multiple operational modes, including debugging, planning, security auditing, and release validation. It can run unattended for extended periods, producing logs of experiments and improvements. This approach transforms software development into an iterative, evidence-driven optimization process rather than manual trial and error. -
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BoxMOT
Pluggable SOTA multi-object tracking modules for segmentation
...It also includes evaluation tools and benchmarking pipelines that allow researchers to test tracking performance on standard datasets such as MOT17 and MOT20. The system offers different performance modes that balance computational efficiency with tracking accuracy depending on the application requirements. -
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BruteForceAI
Advanced LLM-powered brute-force tool combining AI intelligence
...From a software design perspective, its distinguishing idea is the use of language models as a front-end analysis layer that interprets a target page before the rest of the workflow proceeds. -
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Kimi K2.5
Moonshot's most powerful AI model
...Based on a 1T-parameter Mixture-of-Experts (MoE) architecture with 32B activated parameters, it integrates advanced language reasoning with strong visual understanding. K2.5 supports both “Thinking” and “Instant” modes, enabling either deep step-by-step reasoning or low-latency responses depending on the task. Designed for agentic workflows, it features an Agent Swarm mechanism that decomposes complex problems into coordinated sub-agents executing in parallel. With a 256K context length and MoonViT vision encoder, the model excels across reasoning, coding, long-context comprehension, image, and video benchmarks. ... -
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Poco Claw
A more beautiful and easier-to-use alternative to OpenClaw
...The system operates on a sandboxed runtime, ensuring that tasks executed by the agent are isolated from the host environment, which improves security and reliability. It extends beyond simple chatbot functionality by supporting structured workflows, task planning modes, and multi-step execution pipelines. The platform also allows users to manage files and contexts directly within the interface, enabling more complex interactions with data and projects. It is built to make AI agent systems accessible to a broader audience, including users who may not be comfortable with command-line environments. -
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Gpt-Agreement-Payment
End-to-end protocol replay toolkit for ChatGPT Plus/Team/Pro sub
...It documents and implements controlled workflows around account setup, payment routing, runtime state storage, and operational monitoring. The project includes both command-line and web UI modes, with Docker and manual deployment options. It stores runtime output and logs in a local SQLite-backed structure for review and debugging. Because it touches payment systems, account workflows, and fraud controls, it should be treated strictly as a research and compliance-sensitive project, not as a general-purpose automation product. ... -
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GLM-4.5
GLM-4.5: Open-source LLM for intelligent agents by Z.ai
...The flagship GLM-4.5 model has 355 billion total parameters with 32 billion active parameters, while the compact GLM-4.5-Air version offers 106 billion total parameters and 12 billion active parameters. Both models unify reasoning, coding, and intelligent agent capabilities, providing two modes: a thinking mode for complex reasoning and tool usage, and a non-thinking mode for immediate responses. They are released under the MIT license, allowing commercial use and secondary development. GLM-4.5 achieves strong performance on 12 industry-standard benchmarks, ranking 3rd overall, while GLM-4.5-Air balances competitive results with greater efficiency. ... -
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gse
Go efficient multilingual NLP and text segmentation
...Gse is implements jieba by golang, and try add NLP support and more feature. Support common, search engine, full mode, precise mode and HMM mode multiple word segmentation modes. Support user and embed dictionary, Part-of-speech/POS tagging, analyze segment info, stop and trim words. Support multilingual: English, Chinese, Japanese and others. Support Traditional Chinese. Support HMM cut text use Viterbi algorithm. Support NLP by TensorFlow (in work). Named Entity Recognition (in work). Supports with elastic search and bleve. run JSON RPC service. -
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Attention Residuals (AttnRes)
Drop-in replacement for standard residual connections in Transformers
...In standard transformers, residual connections simply sum outputs from previous layers, which can lead to uncontrolled growth of hidden states and dilution of early-layer information in deep networks. Attention Residuals introduces a learnable softmax attention mechanism that allows each layer to selectively retrieve and weight useful representations from earlier layers, making depth dynamically adaptive rather than uniformly aggregated. This approach improves gradient stability, preserves meaningful signals throughout the network, and enhances performance in reasoning-heavy tasks such as coding, mathematics, and multi-step problem solving. -
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MemMachine
Universal memory layer for AI Agents
MemMachine is a universal memory layer designed for AI agents that provides persistent, rich memory storage and retrieval capabilities so autonomous agent systems can recall context, personal preferences, and long-term interaction history across sessions, models, and use cases. Unlike ephemeral LLM prompt state, MemMachine supports distinct memory types—short-term conversational context, long-term persistent knowledge, and profile memory for personalized facts—persisted in optimized stores (e.g., graph databases for episodic lines of reasoning and SQL for user facts) to support robust, context-aware intelligence in agents. ...