Search Results for "raw"
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Showing 14 open source projects for "raw"
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OpenHealth
AI health assistant for private, local data-driven insights mgmt
...A strong emphasis is placed on privacy, as the platform can run entirely locally, ensuring that sensitive medical data does not need to leave the user’s environment. OpenHealth also includes a data parsing layer that transforms raw medical inputs into structured datasets, making them usable for analysis and AI-driven insights. OpenHealth separates data ingestion, processing, and AI interaction, enabling flexibility in integrating different models and data sources. -
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Readest
Readest is a modern, feature-rich ebook reader
Readest is a project meant to facilitate reading, studying, or consuming content by integrating reading tools with AI-powered assistance. Although the repository is not as widely documented or popular as some, the idea is that Readest supports features to help with reading comprehension — likely combining OCR / text retrieval, translation, note-taking, or summarization for reading materials (eBooks, articles, PDFs). The goal appears to be to let users feed in arbitrary reading material and... -
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Plannotator
Annotate and review coding agent plans visually, share with your team
...Plannotator integrates with diff views so reviewers can annotate changes line-by-line in git diffs, provide structured feedback, and navigate plans visually rather than through raw text alone. Users can attach and annotate images, save approved plan versions, and automatically export feedback into systems like Obsidian or Bear Notes for documentation purposes. -
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Hyprnote
Local-first AI Notepad for Private Meetings
Hyprnote is an open-source, privacy-first AI notepad app designed for taking notes during meetings—transcribing audio (microphone and system) and generating context-rich summaries using on-device AI models like Whisper and HyprLLM, all without any data leaving your machine.(turn0search7, turn0search1). Listens to your meetings while you write. Crafts smart summaries based on your quick notes. Runs completely offline using open-source models like Whisper or HyprLLM. Use approved third-party... -
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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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LiteParse
A fast, helpful, and open-source document parser
LiteParse is an open-source lightweight parsing library designed to extract structured data from unstructured text using large language models in an efficient and cost-effective manner. It focuses on simplifying the process of turning raw text into structured outputs such as JSON by providing a streamlined interface for prompt-based parsing. The system is designed to minimize overhead, making it suitable for applications where performance and cost are critical considerations. LiteParse supports integration with multiple language models, allowing developers to choose the best balance between accuracy and efficiency. ... -
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PasteGuard
Masks sensitive data and secrets before they reach AI
...It sits between an application and the LLM provider, automatically replacing names, emails, tokens, and other personally identifiable information (PII) with placeholders so that external services never see raw sensitive values, and then optionally unmasking them in the returned output. PasteGuard supports two primary modes: mask mode, which anonymizes data and still uses external APIs; and route mode, which forwards sensitive requests to a local LLM inference engine while sending the rest to the cloud. It can be self-hosted via Docker, works with a wide range of SDKs and tools, and includes a browser extension for automatic protection in everyday AI chats. -
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Rill
Fast SQL-based BI tool for real-time dashboards and analytics
Rill is an operational BI tool that turns raw datasets into fast, interactive dashboards using SQL and a code-first approach. It helps data teams move from data lake to insight quickly, without the complexity of traditional BI systems. With an embedded in-memory database powered by DuckDB or ClickHouse, queries run in milliseconds, enabling real-time exploration and analysis. -
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LLM Scraper
Extract structured data from webpages using LLM-powered scraping
...LLM Scraper integrates browser automation through Playwright, allowing it to load webpages and process their content before sending it to a language model for interpretation. Multiple content processing modes are supported, including raw HTML, cleaned HTML, Markdown, extracted text, screenshots, and custom inputs, making it adaptable to a wide range of scraping scenarios. LLM Scraper also provides streaming output and code generation capabilities that help developers build reusable scraping workflows. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Brokk
Brokk brings code intelligence to AI
Brokk is a code intelligence assistant framework designed to let large language models (LLMs) understand code semantically (not just as raw text) so that they can work effectively on large codebases that don’t fit wholly in a prompt context. It helps bridge the gap between LLMs and real-world engineering code by offering tooling to index, analyze, query, and augment code context, so that AI can meaningfully reason about existing code, suggest edits, and navigate across projects. ... -
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lms
LM Studio CLI
lms is a command-line interface tool designed to interact with and manage local large language models through the LM Studio ecosystem. The tool allows developers to control model execution directly from the terminal, providing programmatic access to features that are otherwise available through graphical interfaces. Through the CLI, users can load and unload models, start or stop local inference servers, and inspect the inputs and outputs generated by language models. LMS is built using the... -
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Framelink MCP for Figma
MCP server enabling AI coding tools to access Figma design data
Figma-Context-MCP is an open source server that connects Figma design data with AI-powered coding tools through the Model Context Protocol (MCP). It allows coding assistants to retrieve structured information from Figma files so they can better translate visual designs into working code. Instead of relying on screenshots or manual descriptions, Figma-Context-MCP accesses layout, styling, and component metadata directly from the Figma API and presents it in a simplified format optimized for... -
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TTS-Vue
Microsoft speech synthesis tool, built with Electron
TTS-Vue is a desktop text-to-speech application built with Electron, Vue, ElementPlus, and Vite, focused on using Microsoft’s official Speech API for high-quality neural synthesis. It wraps the Microsoft TTS WebSocket interface in a clean UI so users can paste or load text, choose voices, tweak parameters, and export audio without touching raw API calls. The app supports SSML (Speech Synthesis Markup Language), letting power users specify fine-grained control over pronunciation, pauses, prosody, and emphasis using XML-like markup. It includes batch conversion: users can select multiple .txt files and convert them into audio in one go, making it handy for large text collections or repetitive tasks. ... -
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node-markov-generator
Generates simple sentences based on given text corpus
This simple generator emits short sentences based on the given text corpus using a Markov chain. To put it simply, it works kinda like word suggestions that you have while typing messages in your smartphone. It analyzes which word is followed by which in the given corpus and how often. And then, for any given word it tries to predict what the next one might be. Here you create an instance of TextGenerator passing an array of strings to it - it represents your text corpus which will be used...
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