Search Results for "can" - Page 50
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Showing 5017 open source projects for "can"
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cloud_enum
Multi-cloud OSINT tool for discovering public cloud resources
...It works by taking user-provided keywords and generating variations through mutation wordlists, then testing these combinations against common cloud service naming patterns. cloud_enum performs both HTTP probing and DNS lookups to identify resources such as storage buckets, cloud applications, and databases that may be exposed or accessible. cloud_enum uses concurrent processing to speed up scanning, enabling efficient enumeration of large numbers of possible resource names. It can identify both publicly accessible and protected resources, helping security researchers understand the external cloud footprint of an organization. -
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Claude Code Skills & Plugins Hub
270+ Claude Code plugins with 739 agent skills
...These plugins cover a wide range of domains including DevOps automation, security testing, API debugging, infrastructure management, and AI workflow orchestration. The project also includes orchestration patterns and best practices that guide how multiple AI agents or tools can collaborate effectively in software development workflows. Developers can install plugins through a package-style plugin system and integrate them with their Claude Code environment using standardized commands. -
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LongWriter
Unleashing 10,000+ Word Generation from Long Context LLMs
LongWriter is an open-source framework and set of large language models designed to enable ultra-long text generation that can exceed 10,000 words while maintaining coherence and structure. Traditional large language models can process large inputs but often struggle to generate long outputs due to limitations in training data and alignment strategies. LongWriter addresses this challenge by introducing a specialized dataset and training approach that encourages models to produce longer responses. ... -
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xLSTM
Neural Network architecture based on ideas of the original LSTM
...The architecture aims to provide competitive performance with transformer-based models while maintaining advantages such as linear computational scaling and efficient memory usage for long sequences. Researchers have demonstrated that xLSTM models can scale to billions of parameters and large training datasets while maintaining efficient inference speeds. -
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Chat with LLMs Everywhere
Run PyTorch LLMs locally on servers, desktop and mobile
...TorchChat supports running models through Python interfaces as well as integrating them directly into native applications written in languages such as C or C++. The project also demonstrates how modern LLMs like LLaMA-style models can be deployed locally while maintaining good performance across different hardware platforms. -
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LLM Workflow Engine
Power CLI and Workflow manager for LLMs (core package)
...The platform allows users to interact with AI models directly from the terminal, enabling conversational AI access through shell commands and scripts. Instead of focusing solely on chat interactions, the system is built to embed LLM calls into larger automation pipelines where model outputs can drive decision making or trigger additional processes. 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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ai-cookbook
Examples and tutorials to help developers build AI systems
...The repository contains examples that demonstrate how to build AI workflows using modern tools such as large language models, autonomous agents, and external APIs. Developers can learn how to construct applications like intelligent assistants, automation pipelines, and AI-powered data analysis tools through step-by-step tutorials and ready-to-run scripts. The code examples are designed to emphasize practical architecture patterns that are commonly used in production environments, helping developers understand how to integrate AI services into software products. -
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PyTorch-Tutorial-2nd
CV, NLP, LLM project applications, and advanced engineering deployment
...It also introduces practical machine learning techniques such as convolutional neural networks, recurrent networks, and other architectures commonly used in modern AI applications. Each tutorial focuses on step-by-step implementation so learners can understand how theoretical concepts translate into working code. The materials are designed for both beginners and intermediate developers who want to gain practical experience building deep learning models using PyTorch. -
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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. The project is positioned primarily as a proof of concept for deep research agents rather than a production-ready system. ... -
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Fun Audio Chat
Large Audio Language Model built for natural interactions
Fun Audio Chat is an interactive voice-first conversational AI platform designed to let users engage in natural spoken dialogue with large language models in real time, turning speech into context-aware responses while maintaining a smooth back-and-forth experience. It combines speech recognition, audio processing, and AI generation so users can speak simply and receive spoken replies, enabling applications such as virtual assistants, voice bots, and hands-free chat interfaces. The system supports dynamic audio input and output, meaning it can handle different voices, tones, and conversational contexts without forcing users into typed interactions. With real-time streaming, it minimizes latency and delivers responses quickly, making it suitable for applications where responsiveness matters, such as interactive demos, accessibility tools, and conversational games. -
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CineCLI
CineCLI is a cross-platform command-line movie browser
...It connects to popular online movie databases to fetch metadata such as titles, release dates, ratings, genres, casts, posters, and plot summaries, presenting all of that in a concise, text-friendly format suitable for terminals or scripts. Users can search by keyword, year, or exact title and then drill into detailed views for individual films, making it useful for creating watchlists, learning more about films before watching, or integrating movie lookup into shell workflows. CineCLI also supports paginated results and filters so users can navigate large search outputs without overwhelming their screens. ... -
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Sygil WebUI
Stable Diffusion web UI
...It provides multiple UI modes (including a legacy Gradio interface) and focuses on making iterative prompting, parameter tuning, and post-processing accessible without writing code. The UI exposes core generation controls like resolution, CFG guidance, sampling steps, samplers, seeds, and batch generation so users can reproduce results and refine outputs systematically. It also supports jumping between workflows, such as sending an output directly into Image2Image for variations or into an “Image Lab” style area for enhancement and upscaling. Post-processing and enhancement are a major emphasis: the interface can route images through different upscalers and face-enhancement tools, helping users turn raw generations into cleaner, higher-resolution results. -
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ML Sharp
Sharp Monocular View Synthesis in Less Than a Second
ML Sharp is a research code release that turns a single 2D photograph into a photorealistic 3D representation that can be rendered from nearby viewpoints. Instead of requiring multi-view input, it predicts the parameters of a 3D Gaussian scene representation directly from one image using a single forward pass through a neural network. The core idea is speed: the 3D representation is produced in under a second on a standard GPU, and then the resulting scene can be rendered in real time to generate new views interactively. ... -
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JS Analyzer
Burp Suite extension for JavaScript static analysis
...The extension is designed to reduce manual effort when analyzing large or obfuscated JavaScript assets, helping testers find security vulnerabilities and sensitive information faster and more reliably. It also includes UI features such as live search, result filtering, and the ability to export findings in JSON format for further processing. The underlying engine can be used independently in Python, enabling integration into custom workflows or automated pipelines outside Burp Suite. -
15
Context Engineering Template
Context engineering is the new vibe coding
...By using this template, teams can ensure consistency across AI outputs, reduce errors that stem from contextual misunderstandings, and build reusable patterns. -
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Orpheus TTS
Towards Human-Sounding Speech
...The project ships both pretrained and finetuned English models, as well as a family of multilingual models released as a research preview, and includes data-processing scripts so users can train or finetune their own variants. Inference is provided through a Python package that uses vLLM under the hood for high-throughput, low-latency generation, including streaming examples that show how to generate audio chunks in real time. The maintainers provide Colab notebooks, a standardized prompting format, and one-click deployment via Baseten for production-grade, FP8/FP16 optimized inference with ~200 ms streaming latency. -
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screenshot-to-code
Drop in a screenshot and convert it to clean code
...A web interface lets you upload images, tune options, and preview generated results, while a backend service orchestrates the model calls and post-processing. The tool focuses on practical developer outputs—semantic markup, reusable components, and readable classes—so the result is a starting point you can refine, not a throwaway demo. It also supports multi-model backends and local-first options to balance cost, speed, and privacy. Teams use it for rapid prototyping, migrating static mockups to codebases, and exploring design alternatives without hand-coding every pixel from scratch. -
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aws-cli
Universal Command Line Interface for Amazon Web Services
The AWS CLI is the universal command-line interface for managing AWS services, automating tasks, and scripting cloud workflows. It exposes nearly every public API from EC2 and S3 to IAM, Lambda, and beyond, providing parity with the service SDKs in a tool you can run anywhere. Profiles, regions, single-sign-on, and credential helpers make it straightforward to switch contexts securely across accounts and environments. Its output controls and JMESPath querying let you slice, filter, and transform JSON responses directly in the shell, which is essential for automation. Waiters, paginators, and retries handle long-running or large list operations cleanly so scripts are resilient. ... -
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4M
4M: Massively Multimodal Masked Modeling
...The design leans into flexibility and steerability, so prompts and masks can shape behavior without bespoke heads per task. In short, 4M provides a unified recipe to pretrain large multimodal models that generalize broadly while remaining practical to fine-tune. -
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ML Ferret
Refer and Ground Anything Anywhere at Any Granularity
Ferret is Apple’s end-to-end multimodal large language model designed specifically for flexible referring and grounding: it can understand references of any granularity (boxes, points, free-form regions) and then ground open-vocabulary descriptions back onto the image. The core idea is a hybrid region representation that mixes discrete coordinates with continuous visual features, so the model can fluidly handle “any-form” referring while maintaining precise spatial localization. ... -
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Pearl
A Production-ready Reinforcement Learning AI Agent Library
...Tutorials demonstrate end-to-end workflows on OpenAI Gym tasks and contextual-bandit setups derived from tabular datasets, emphasizing reproducibility and clear baselines. Pearl’s design favors clarity and deployability: metrics, logging, and evaluation harnesses are integrated so you can monitor learning, compare agents, and catch regressions. -
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DINOv2
PyTorch code and models for the DINOv2 self-supervised learning
...Pretrained checkpoints cover multiple model sizes so practitioners can trade accuracy for speed and memory depending on their deployment constraints. -
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Tiktoken
tiktoken is a fast BPE tokeniser for use with OpenAI's models
...The repo supports multiple encodings (e.g. “cl100k_base”) and lets users switch encoding names to match different model contexts. It also offers extension mechanisms so that custom encodings can be registered. Internally, it includes the core tokenizer logic (often implemented in Rust or efficient lower-level code), APIs for encoding, decoding, and counting tokens, and binding layers to Python (and sometimes other languages) for easy use. -
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Napkin
An Infinitely Large Napkin
...The coverage spans undergraduate and early graduate topics, designed to show how different areas of math fit together—linear algebra, analysis, topology, number theory, and more—without going deeply into every subtopic. Because it is written in LaTeX (with supporting Asymptote or other tools), readers can compile their own version, and the repository integrates diagrams, flowcharts, and supplementary files. -
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Textual
Textual is a TUI (Text User Interface) framework for Python
...Textual adds interactivity to Rich with a Python API inspired by modern web development. On modern terminal software (installed by default on most systems), Textual apps can use 16.7 million colors with mouse support and smooth flicker-free animation. A powerful layout engine and re-usable components makes it possible to build apps that rival the desktop and web experience. Textual runs on Linux, macOS, and Windows. Textual requires Python 3.7 or above. The addition of [dev] installs Textual development tools. ...
