Showing 1519 open source projects for "gmail source code"

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  • 1
    agentsview

    agentsview

    Local-first session intelligence and analytics for coding agents

    agentsview is a local-first analytics and session browser for developers using AI coding agents. It indexes conversations from tools like Claude Code, Codex, Gemini CLI, Cursor, OpenHands, and many other agent systems. The project lets users browse, search, and analyze coding-agent activity without creating an account or sending session content to a hosted service. It tracks token usage, cost, models, projects, tools, and session behavior across different agents. Its web interface adds...
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  • 2
    AutoBE

    AutoBE

    AI Vibe Coding Agent of TS backend server

    AutoBE is an AI backend builder that turns natural-language requirements into a working TypeScript backend application. It analyzes what the user wants, creates specifications, designs database structure, defines API documentation, writes end-to-end tests, and generates implementation logic. The project is aimed at moving from prototype to production more quickly while keeping generated code buildable and verifiable. It uses an agentic workflow supported by compiler-friendly checks and test...
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  • 3
    adversarial-spec

    adversarial-spec

    A Claude Code plugin that iteratively refines product specifications

    adversarial-spec is a framework focused on designing and testing systems using adversarial thinking to uncover weaknesses and improve robustness. It encourages developers to define specifications that anticipate failure modes, edge cases, and malicious inputs before implementing solutions. The project emphasizes proactive design, ensuring that systems are built with resilience in mind from the beginning. It provides structured approaches for identifying vulnerabilities and stress-testing...
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  • 4
    SEO Machine

    SEO Machine

    A specialized Claude Code workspace for creating long-form

    SEO Machine is an AI-powered content production system built as a structured workspace for generating long-form, SEO-optimized blog content through automated workflows. It integrates research, writing, analysis, and optimization into a single pipeline, allowing users to produce high-quality articles tailored to search engine performance. The system uses specialized commands and agents to perform tasks such as keyword research, competitor analysis, content drafting, and optimization. It...
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  • 5
    markstream-vue

    markstream-vue

    A Vue 3 renderer specifically built for AI-powered streaming Markdown

    markstream-vue is a specialized Vue 3 rendering engine designed to handle AI-generated streaming Markdown content with high performance and visual stability. It is built specifically for real-time applications such as chat interfaces, AI assistants, and live documentation systems where content is incrementally streamed rather than rendered all at once. The framework focuses on eliminating jitter and reflow issues that commonly occur when rendering progressively generated text, ensuring a...
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  • 6
    ZML

    ZML

    Any model. Any hardware. Zero compromise

    ZML is a high-performance machine learning inference stack designed to run AI models efficiently across heterogeneous hardware environments using a modern systems programming approach. Built with technologies such as Zig, MLIR, and Bazel, it focuses on production-grade deployment where performance, portability, and scalability are critical. The system allows models to be compiled and executed across multiple types of accelerators, including GPUs and TPUs, even when distributed across...
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  • 7
    AI Notes

    AI Notes

    Curated AI engineering notes on LLMs, generative models, and tools

    ai-notes is a curated repository of notes focused on the evolving landscape of artificial intelligence, particularly generative models and large language models. It is designed to help software engineers quickly understand modern AI concepts, tools, and developments through structured documentation and research notes. It functions as a living knowledge base composed of numerous markdown files that organize topics such as text generation, image generation, AI infrastructure, and code...
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  • 8
    TypeAgent Python

    TypeAgent Python

    Structured RAG: ingest, index, query

    TypeAgent Python is an experimental Python implementation of Microsoft’s TypeAgent architecture designed to explore how large language models can interact with structured software systems. The project focuses on implementing structured Retrieval-Augmented Generation workflows that allow agents to ingest information, index it in structured form, and answer queries using language models. Instead of relying solely on free-form prompts, the architecture emphasizes converting natural language...
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  • 9
    GPU Puzzles

    GPU Puzzles

    Solve puzzles. Learn CUDA

    GPU Puzzles is an educational project designed to teach GPU programming concepts through interactive coding exercises and puzzles. Instead of presenting traditional lecture-style explanations, the project immerses learners directly in hands-on programming tasks that demonstrate how GPU computation works. The exercises are implemented using Python with the Numba CUDA interface, which allows Python code to compile into GPU kernels that run on CUDA-enabled hardware. By solving progressively...
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  • 10
    KG-LLM-Papers

    KG-LLM-Papers

    Papers integrating knowledge graphs (KGs) and large language models

    KG-LLM-Papers is a curated academic resource that collects and organizes research papers exploring the intersection between knowledge graphs and large language models. The repository functions as a continuously updated index of scholarly work that investigates how structured knowledge representations can enhance the reasoning, factual accuracy, and interpretability of language models. It includes surveys, benchmark studies, and cutting-edge research that examine topics such as knowledge...
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  • 11
    All Agentic Architectures

    All Agentic Architectures

    Implementation of 17+ agentic architectures

    All Agentic Architectures is an open educational repository that provides hands-on implementations of modern AI agent architectures. The project acts as a practical learning resource that bridges the gap between theoretical research on autonomous agents and real software implementations. It contains more than a dozen agent architectures implemented using frameworks such as LangChain and LangGraph. Each architecture is explained through runnable notebooks that illustrate how the agent works...
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  • 12
    TONL

    TONL

    TONL (Token-Optimized Notation Language)

    TONL is a cutting-edge data platform built around a production-ready serialization format designed to be both compact and powerful, combining human readability with performance features that make it suitable for large-scale applications and AI workflows. It provides a serialization format that significantly reduces token usage compared with traditional JSON, which can result in lower costs and more efficient prompt size utilization in LLM-driven systems. TONL isn’t just a format — it...
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  • 13
    D4RL

    D4RL

    Collection of reference environments, offline reinforcement learning

    D4RL (Datasets for Deep Data-Driven Reinforcement Learning) is a benchmark suite focused on offline reinforcement learning — i.e., learning policies from fixed datasets rather than via online interaction with the environment. It contains standardized environments, tasks and datasets (observations, actions, rewards, terminals) aimed at enabling reproducible research in offline RL. Researchers can load a dataset for a given task (e.g., maze navigation, manipulation) and apply their algorithm...
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  • 14
    llmx.txt hub

    llmx.txt hub

    The largest directory for AI-ready documentation and tools

    llms-txt-hub serves as a central directory and knowledge base for the emerging llms.txt convention, a simple, text-based way for project owners to communicate preferences to AI tools. It catalogs implementations across projects and platforms, helping maintain a shared understanding of how LLM-powered services should interact with code and documentation. The repository aims to standardize patterns for allowlists, denylists, attribution, rate expectations, and contact information, mirroring...
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  • 15
    llm.c

    llm.c

    LLM training in simple, raw C/CUDA

    llm.c is a minimalist, systems-level implementation of a small transformer-based language model in C that prioritizes clarity and educational value. By stripping away heavy frameworks, it exposes the core math and memory flows of embeddings, attention, and feed-forward layers. The code illustrates how to wire forward passes, losses, and simple training or inference loops with direct control over arrays and buffers. Its compact design makes it easy to trace execution, profile hotspots, and...
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  • 16
    PPTAgent

    PPTAgent

    PPTAgent: Generating and Evaluating Presentations

    PPTAgent is a research system for generating and evaluating slide decks that goes beyond simple text-to-slides. It follows a two-stage, edit-based workflow: first it analyzes reference presentations to infer slide roles and structure, then it drafts an outline and iteratively performs editing actions to produce new slides. The project includes both the generation agent and an evaluation framework, PPTEval, to score content quality, design, and coherence. The repository highlights the EMNLP...
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  • 17
    OpenAI Realtime Embedded

    OpenAI Realtime Embedded

    Instructions on how to use the Realtime API on Microcontrollers

    openai-realtime-embedded is a repository that provides resources, SDKs, and example links for using OpenAI’s Realtime API on embedded hardware platforms (e.g. microcontrollers). The goal is to enable low-latency conversational agents (e.g. voice-based assistants) running directly on constrained devices, by leveraging WebRTC and streaming APIs to communicate with OpenAI systems. The repo includes pointers to an ESP32 implementation (maintained as esp32 branch) and documentation that Espressif...
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  • 18
    WebMCP

    WebMCP

    Enabling web apps to get accessed by AI agents

    WebMCP is a proposed web standard that enables web applications to expose their functionality as JavaScript-based “tools” accessible to AI agents, browser assistants, and assistive technologies. It allows developers to define structured, natural-language-described functions directly in client-side code, effectively turning web pages into Model Context Protocol (MCP)-like servers running in the browser. Unlike backend integrations, WebMCP executes tools within the live web page, preserving...
    Downloads: 1 This Week
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  • 19
    GLM-4.6V

    GLM-4.6V

    GLM-4.6V/4.5V/4.1V-Thinking, towards versatile multimodal reasoning

    GLM-4.6V represents the latest generation of the GLM-V family and marks a major step forward in multimodal AI by combining advanced vision-language understanding with native “tool-call” capabilities, long-context reasoning, and strong generalization across domains. Unlike many vision-language models that treat images and text separately or require intermediate conversions, GLM-4.6V allows inputs such as images, screenshots or document pages directly as part of its reasoning pipeline — and...
    Downloads: 1 This Week
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  • 20
    Shadcn UI v4 MCP Server

    Shadcn UI v4 MCP Server

    A mcp server to allow LLMS gain context about shadcn ui component

    Shadcn UI v4 MCP Server is a Model Context Protocol server that enables AI assistants to access, retrieve, and utilize shadcn/ui component libraries within development workflows, effectively bridging UI component systems with AI-driven coding tools. It provides structured access to component source code, demos, metadata, and reusable UI blocks, allowing AI agents to generate accurate and production-ready interface implementations. The server supports multiple frontend frameworks including React, Svelte, Vue, and React Native, making it highly versatile for cross-platform development. It includes smart caching and efficient GitHub API usage to optimize performance and handle rate limits during component retrieval. ...
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  • 21
    Cofounder

    Cofounder

    AI tool that generates full-stack web apps with generative UI systems

    ...Cofounder also provides a dashboard and local API environment that helps manage project generation, iteration, and interaction with the system. Generated projects are stored locally and can be launched using standard development commands, allowing developers to run and modify the code that the system produces. Cofounder relies on configurable nodes and sequences that define how AI operations are executed.
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  • 22
    hls4ml

    hls4ml

    Machine learning on FPGAs using HLS

    hls4ml is an open-source framework that enables machine learning models to be implemented directly on hardware such as FPGAs and ASICs using high-level synthesis techniques. The system converts trained neural network models from common machine learning frameworks into hardware description code suitable for ultra-low-latency inference. This approach allows machine learning algorithms to run directly on specialized hardware, making them suitable for applications that require extremely fast response times and minimal power consumption. ...
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  • 23
    Advanced NLP with spaCy

    Advanced NLP with spaCy

    Advanced NLP with spaCy: A free online course

    ...It also demonstrates how spaCy pipelines work and how developers can extend them with custom components and training data. The course is structured as a hands-on learning environment where students can run code examples, experiment with NLP techniques, and build practical language processing applications. Because spaCy is widely used in production environments, the course emphasizes industrial-strength NLP workflows and best practices.
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  • 24
    HeavyDB

    HeavyDB

    HeavyDB (formerly MapD/OmniSciDB)

    HeavyDB is an open-source GPU-accelerated analytical database designed to perform extremely fast queries on large datasets. The system is built as a SQL-based relational columnar database engine that leverages modern hardware parallelism, including GPUs and multicore CPUs. Its architecture allows users to query datasets containing billions of rows in milliseconds without requiring traditional indexing, pre-aggregation, or sampling techniques.
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  • 25
    seq2seq-couplet

    seq2seq-couplet

    Play couplet with seq2seq model

    ...The repository also points users to an external dataset source and documents vocabulary formatting requirements for custom datasets, showing that it is meant for both experimentation and extension.
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