Search Results for "concept-graph.js"
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73 projects for "concept-graph.js" with 2 filters applied:
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Large Concept Model
Language modeling in a sentence representation space
Large Concept Model is a research codebase centered on concept-centric representation learning at scale, aiming to capture shared structure across many categories and modalities. It organizes training around concepts (rather than just raw labels), encouraging models to understand attributes, relations, and compositional structure that transfer across tasks. -
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Strix
Open-source AI hackers to find and fix your app’s vulnerabilities
Strix is an open source agent-driven security platform that uses autonomous AI agents to identify, investigate, and validate vulnerabilities in software applications. The system is designed to mimic the behavior of real attackers by executing dynamic testing and verifying findings through proof-of-concept exploitation. Unlike traditional vulnerability scanners that rely heavily on static analysis, Strix agents actively run code, probe systems, and attempt exploitation to confirm whether vulnerabilities are genuinely exploitable. The platform is intended for developers and security teams that need rapid security assessments without the overhead of manual penetration testing engagements. ... -
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SAM 3
Code for running inference and finetuning with SAM 3 model
...It accepts both text prompts (open-vocabulary concepts like “red car” or “goalkeeper in white”) and visual prompts (points, boxes, masks) and returns high-quality masks, boxes, and scores for the requested concepts. Compared with SAM 2, SAM 3 introduces the ability to exhaustively segment all instances of an open-vocabulary concept specified by a short phrase or exemplars, scaling to a vastly larger set of categories than traditional closed-set models. This capability is grounded in a new data engine that automatically annotated over four million unique concepts, producing a massive open-vocabulary segmentation dataset and enabling the model to achieve 75–80% of human performance on the SA-CO benchmark, which itself spans 270K unique concepts. -
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Text2Code for Jupyter notebook
A proof-of-concept jupyter extension which converts english queries
Text2Code for Jupyter notebook project is a proof-of-concept extension for Jupyter Notebook that allows users to generate Python code directly from natural language queries written in English. The tool is designed to simplify data analysis workflows by enabling users to describe their intended operation in plain language instead of manually writing code. When a user enters a textual command, the extension interprets the request and generates a corresponding Python code snippet that can be inserted into the notebook and executed automatically. ... -
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Deep Search Agent
Implement a concise and clear Deep Search Agent from 0
...It typically combines reasoning, retrieval, and iterative refinement so the agent can break down questions, gather evidence, and synthesize structured outputs. The project is positioned primarily as a proof of concept for deep research agents rather than a production-ready system. Its architecture highlights agent loops, tool calling, and stepwise execution, which are increasingly important patterns in modern AI automation. Overall, the demo serves as a practical reference for developers exploring autonomous research agents and multi-tool LLM orchestration. -
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OmniBox
Collect, organize, use, and share, all in OmniBox
...The project focuses on creating a centralized input field where users can enter commands, queries, or shortcuts that trigger actions across different applications or services. Inspired by the omnibox concept used in modern browsers, the system combines search functionality with command execution so that users can access information and perform tasks without navigating complex menus. The mirrored distribution on SourceForge exists to provide an additional download source and preserve access to the software’s source code independent of its original repository. ... -
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MiGPT-Next
Let the Xiaoai speaker "listen to you"
MiGPT-Next is an upgraded branch of the MiGPT concept focused on customizable responses for Xiaomi XiaoAI smart speakers. It lets developers intercept recognized messages and define how the speaker should respond through programmable handlers. The project can connect to OpenAI-compatible model endpoints and allows configuration of models, API URLs, headers, system prompts, and speaker credentials. -
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Open-XiaoAI
Let the Xiaoai speaker "hear your voice"
Open-XiaoAI is an open-source framework that takes deeper control of compatible Xiaomi XiaoAI smart speakers by accessing their audio input and output paths. It extends the earlier MiGPT concept beyond cloud API responses and enables developers to build custom voice experiences directly around the speaker hardware. Demonstrations include custom wake words, Xiaozhi AI integration, MiGPT integration, Gemini Live API access, and stereo pairing. The architecture uses separate client and server components, with the client running on modified speaker firmware. ... -
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Free LLM API resources
A list of free LLM inference resources accessible via API
...It collects a wide range of resources including hosted free-tier LLM APIs, documentation links, public model endpoints, open datasets useful for training or evaluation, tooling integrations, and examples showing how to interact with these services in real applications. This list helps developers, hobbyists, and researchers quickly find models they can use for prototyping, experimentation, or production proofs-of-concept without needing paid subscriptions, reducing friction for innovation. The repository typically categorizes offerings by provider, type of service (text, embeddings, vision), availability conditions (open without key, free tier with key), and usage examples to make discovery and adoption easier. -
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SuperPrompt
Experimental prompt framework exploring reasoning structures in AI
...It explores structured prompt engineering techniques that combine symbolic expressions, logical constructs, and conceptual frameworks to guide large language models toward deeper reasoning processes. Its main concept revolves around a highly structured prompt format that includes tagged sections for reasoning, analysis, conceptual expansion, and recursive thinking patterns. These sections act as a kind of meta-instruction system intended to influence how an AI model approaches problem solving and conceptual exploration. SuperPrompt also incorporates ideas from mathematics, recursion, abstract algebra, and theoretical reasoning to encourage models to perform multi-step conceptual synthesis rather than simple direct responses. -
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DeepTutor
AI-Powered Personalized Learning Assistant
DeepTutor is an AI-powered tutoring and learning assistant framework designed to automatically teach, explain, and reinforce academic or technical concepts in depth according to a learner’s specific needs. It goes beyond simple Q&A by constructing multi-stage educational narratives, breaking down complex topics into sequenced “lesson steps,” and offering prompts, examples, and exercises that build on each other in a logical curriculum. The core architecture combines LLM-based reasoning with... -
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Beelzebub
A secure low code honeypot framework
Beelzebub is an open-source cybersecurity framework designed to create intelligent honeypot environments for detecting and studying cyber attacks. Honeypots are systems intentionally exposed to attackers in order to capture malicious behavior, and Beelzebub enhances this concept by incorporating artificial intelligence and virtualization techniques. The platform allows organizations and researchers to deploy decoy services that mimic real infrastructure while recording attacker interactions. By using AI models to simulate realistic system behavior, the honeypot becomes harder for attackers to identify, increasing the likelihood that malicious activity can be observed and analyzed. ... -
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Oh My OpenCode Slim
Slimmed, cleaned and fine-tuned oh-my-opencode fork
Oh My OpenCode Slim is a lightweight, optimized fork of the broader oh-my-opencode ecosystem, designed to deliver high-performance multi-agent coding workflows while significantly reducing token consumption and system overhead. It retains the core concept of orchestrating multiple specialized AI agents but streamlines their configuration, execution, and communication to make the system more efficient and practical for everyday use. The framework introduces a structured “pantheon” of agents, each with a defined role such as orchestration, exploration, and execution, allowing tasks to be automatically delegated and completed through coordinated workflows. ... -
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MemOS
AI memory OS for LLM and Agent systems
MemOS is an experimental operating system and runtime built around the concept of memory-centric computing, where memory objects are first-class citizens and program execution is organized around efficient, persistent memory access rather than traditional process and file system boundaries. The project explores rethinking system abstractions by tightly coupling computation with memory objects so that programs can operate on large datasets without expensive serialization or context switching. ... -
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Easy AI
Easy-to-understand AI learning resources for beginners
easy-learn-ai is a beginner-friendly AI learning resource project. It collects and explains AI concepts, prompts, model information, benchmarks, tutorials, and industry updates in a more accessible way. The project is designed for learners, developers, and creators who want to understand modern AI without being overwhelmed by fragmented technical material. It includes visual and interactive explanations for core topics such as tokens, prompts, LLMs, Transformers, and evaluation benchmarks.... -
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Markdown Viewer Agent Skills
Opinionated skills for AI coding agents to create stunning diagrams
...The project emphasizes simplicity and composability, allowing developers to integrate these skills into existing systems without requiring complex infrastructure. Each skill is designed to be reusable and adaptable, making it easy to customize behavior for different use cases. The repository aligns with the broader concept of agent skills, where capabilities are encapsulated into modular units that can be dynamically loaded and executed. It supports workflows that rely heavily on markdown as a primary medium for content and communication. -
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TurboQuant+
Implementation of TurboQuant (ICLR 2026)
TurboQuant Plus is an extended and enhanced version of quantization tooling aimed at improving neural network efficiency through advanced compression and optimization strategies. It builds upon the concept of reducing model precision to accelerate inference while attempting to maintain or recover accuracy through refined techniques. The project explores additional enhancements such as improved calibration, adaptive quantization, and potentially hybrid precision approaches that combine multiple levels of compression. It is designed to be used in conjunction with modern machine learning workflows, particularly those involving large models that require optimization for deployment. ... -
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comfyui-mixlab-nodes
Workflow and speech recognition app
comfyui-mixlab-nodes is a large collection of custom nodes for ComfyUI that turns workflows into interactive apps and adds real-time multimedia, LLM, and TTS capabilities. It introduces a “Workflow-to-APP” concept, where a ComfyUI graph can be transformed into a Web App through an AppInfo node, complete with categories, batch prompts, and editable configurations. The project also brings Real-time Design features like screen capture and floating video nodes, enabling creative pipelines that mix live screen content, generative models, and visual effects. ... -
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TypeChat
Library for building type-safe natural language interfaces with LLMs
...With the rise of large language models, developers can interpret user requests more easily, but they still face challenges related to output reliability, safety, and structured responses. TypeChat addresses these challenges by replacing traditional prompt engineering with a concept called schema engineering. Instead of writing complex prompts, developers define types that represent the intents supported by their applications. It then uses those type definitions to construct prompts for language models and translate user input into structured data that follows the defined schema. -
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JamAI Base
The collaborative spreadsheet for AI
...JamAI Base exposes its functionality through a simple REST API and a spreadsheet-style interface that allows users to manage AI workflows visually. One of the key ideas behind the platform is the concept of generative tables, which allow database columns to automatically populate with AI-generated content. The system also supports action tables and chat tables that simplify the creation of interactive AI features such as conversational interfaces and dynamic workflows. -
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Ralph Wiggum Marketer
A Claude Code Plugin that provides an autonomous AI copywriter
Ralph Wiggum Marketer is a Claude Code plugin that serves as an autonomous AI copywriter tailored for SaaS content marketing, enabling automated generation of marketing copy such as landing pages, taglines, feature summaries, and promotional messaging. It leverages the Ralph Wiggum loop concept — a continuous iteration pattern named after the iconic character that symbolizes persistent, repeated refinement — to let Claude Code keep iterating on content until predefined completion criteria are met, rather than stopping after a single output. This plugin integrates with Claude Code workflows so that marketers or product teams can define high-level goals, seed their product or service context, and then have the AI autonomously draft, refine, and polish marketing materials with minimal human intervention. -
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Build Your Own OpenClaw
A step-by-step guide to build your own AI agent
Build Your Own OpenClaw is a step-by-step educational framework that teaches developers how to construct a fully functional AI agent system from scratch, gradually evolving from a simple chat loop into a multi-agent, production-ready architecture. The project is structured into 18 progressive stages, each introducing a new concept such as tool usage, memory persistence, event-driven design, and multi-agent coordination, with each step including both explanatory documentation and runnable code. It begins with foundational concepts like conversational loops and tool integration, then expands into more advanced capabilities such as dynamic skill loading, web interaction, and context management. ... -
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LOTUS
AI-Powered Data Processing: Use LOTUS to process all of your datasets
...The system provides a declarative programming model that allows developers to express complex AI data operations using high-level commands rather than manually orchestrating model calls. It offers a Python interface with a Pandas-like API, making it familiar for data scientists and engineers already working with data analysis libraries. The core concept of the framework is the use of semantic operators, which extend traditional relational database operations to support reasoning over text and other unstructured data. These operators allow tasks such as semantic filtering, ranking, clustering, and summarization to be expressed directly within data processing pipelines. The LOTUS engine automatically optimizes how language models are used during execution, which can significantly improve performance and reduce computational cost. -
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holaOS
An Open Agent Computer for ANY digital work
holaOSc is an AI-native operating system concept designed to integrate intelligent agents directly into the core of the computing environment. It provides a framework where AI agents manage tasks, workflows, and interactions across applications. The system emphasizes seamless automation, allowing users to interact with their computer through natural language and high-level instructions. -
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Humanoid-Gym
Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real
...Its primary goal is to enable efficient training of humanoid robots in simulation while enabling policies to transfer effectively to real-world hardware without additional training. The framework emphasizes the concept of zero-shot sim-to-real transfer, meaning that behaviors learned in simulation can be deployed directly on physical robots with minimal adjustment. To improve reliability and generalization, the framework also includes sim-to-sim validation pipelines that test trained policies across different physics engines.
