Search Results for "lightweight programming language" - Page 8
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Showing 344 open source projects for "lightweight programming language"
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Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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Anomaly Detection Learning Resources
Anomaly detection related books, papers, videos, and toolboxes
Anomaly Detection Learning Resources is a curated open-source repository that collects educational materials, tools, and academic references related to anomaly detection and outlier analysis in data science. The project serves as a centralized index for researchers and practitioners who want to explore algorithms, datasets, and publications associated with detecting unusual patterns in data. The repository organizes resources into structured categories such as books, tutorials, academic... -
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ADK Go
Code-first Go toolkit for building, evaluating, and deploying AI agent
ADK-Go is an open source toolkit designed to help developers build, evaluate, and deploy sophisticated AI agents using the Go programming language. It is part of the Agent Development Kit ecosystem and follows a code-first approach that allows developers to define agent behavior, tools, and orchestration logic directly in Go code. ADK-Go applies traditional software engineering principles to agent development, making it easier to structure, test, and maintain complex agent-based systems. ... -
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Dagger
Containerized automation engine for programmable CI/CD workflows
Dagger is an open source automation engine designed to build, test, and deliver software in a consistent and programmable way. It enables developers to define software delivery workflows using code instead of complex shell scripts or configuration files. Dagger executes tasks inside containers, ensuring that automation runs in identical environments across local machines, CI servers, or cloud infrastructure. Dagger provides a core execution engine and system API that orchestrates containers,... -
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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... -
AI-powered service management for IT and enterprise teamsGive your IT, operations, and business teams the ability to deliver exceptional services—without the complexity. Maximize operational efficiency with refreshingly simple, AI-powered Freshservice.
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Nanocoder
A beautiful local-first coding agent running in your terminal
Nanocoder is an open-source, local-first coding assistant that runs in the command line and allows developers to use AI models to assist with programming tasks directly from their terminal environment. The tool is designed as a privacy-focused alternative to proprietary AI coding assistants, allowing users to run local models or connect to external APIs while keeping full control over their data and development workflow. Built with TypeScript and distributed as a CLI application, nanocoder... -
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qxresearch-event-1
Python hands on tutorial with 50+ Python Application
qxresearch-event-1 is an open-source educational repository that provides a collection of lightweight Python applications designed to demonstrate programming concepts and artificial intelligence techniques in simple and accessible examples. The repository contains dozens of small programs, many implemented with minimal lines of code, covering topics such as machine learning, graphical user interfaces, computer vision, and API integration. -
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shimmy
Python-free Rust inference server
The shimmy project is a lightweight local inference server designed to run large language models with minimal overhead. Written primarily in Rust, the tool provides a small standalone binary that exposes an API compatible with the OpenAI interface, allowing existing applications to interact with local models without significant code changes. This compatibility enables developers to replace remote AI services with locally hosted models while keeping their existing software architecture intact. ... -
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PromptBin
A free guide for learning to create ChatGPT3 Prompts
PromptBin is an open-source prompt engineering resource and lightweight web application designed to help users discover, organize, and reuse effective prompts for large language models like ChatGPT. At its core, the project acts as a curated and structured library of prompts across a wide variety of use cases, including writing, brainstorming, debugging, and coding, making it particularly useful for both beginners and advanced users. -
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OpenSandbox
OpenSandbox is a general-purpose sandbox platform for AI applications
OpenSandbox is a general purpose sandbox platform designed to securely run and isolate AI applications and untrusted workloads in controlled environments. The project focuses on providing a unified sandbox API that simplifies the process of executing code safely across different runtime backends. It supports multiple programming languages through SDKs, allowing developers to integrate sandbox capabilities into their systems without building custom isolation layers. The platform is built to... -
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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machine_learning_examples
A collection of machine learning examples and tutorials
machine_learning_examples is an open-source repository that provides a large collection of machine learning tutorials and practical code examples. The project aims to teach machine learning concepts through hands-on programming rather than purely theoretical explanations. It includes implementations of many machine learning algorithms and neural network architectures using Python and popular libraries such as TensorFlow and NumPy. The repository covers a wide range of topics including supervised learning, unsupervised learning, reinforcement learning, and natural language processing. ... -
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rag-search
RAG Search API
rag-search is a lightweight Retrieval-Augmented Generation API service designed to provide structured semantic search and answer generation through a simple FastAPI backend. The project integrates web search, vector embeddings, and reranking logic to retrieve relevant context before passing it to a language model for response generation. It is built to be easily deployable, requiring only environment configuration and dependency installation to run a functional RAG service. ... -
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Open Deep Research
An AI-powered research assistant that performs iterative research
Deep Research is a lightweight AI research agent designed to autonomously investigate complex topics through iterative web exploration and reasoning. The project combines search engines, web scraping, and large language models to progressively refine its understanding of a user’s query and dive deeper over multiple cycles. Its core goal is to provide the simplest possible implementation of a deep research workflow so developers can study and extend agent behavior without dealing with large, opaque codebases. ... -
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DFlash
Block Diffusion for Ultra-Fast Speculative Decoding
DFlash is an open-source framework for ultra-fast speculative decoding using a lightweight block diffusion model to draft text in parallel with a target large language model, dramatically improving inference speed without sacrificing generation quality. It acts as a “drafter” that proposes likely continuations which the main model then verifies, enabling significant throughput gains compared to traditional autoregressive decoding methods that generate token by token. -
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MCP UI
SDK for building interactive UI components over MCP for AI tools
mcp-ui is a software development kit designed to bring interactive user interface capabilities to applications built on the Model Context Protocol (MCP). It enables developers to create rich, dynamic UI components that can be delivered from an MCP server and rendered seamlessly by a compatible client. Instead of returning only text responses, tools can provide structured UI resources such as HTML or remote-rendered components, allowing more engaging and functional interactions. mcp-ui... -
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Gen.jl
A general-purpose probabilistic programming system
An open-source stack for generative modeling and probabilistic inference. Gen’s inference library gives users building blocks for writing efficient probabilistic inference algorithms that are tailored to their models, while automating the tricky math and the low-level implementation details. Gen helps users write hybrid algorithms that combine neural networks, variational inference, sequential Monte Carlo samplers, and Markov chain Monte Carlo. Gen features an easy-to-use modeling language... -
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windsurf.vim
Free, ultrafast Copilot alternative for Vim and Neovim
windsurf.vim is a plugin for Vim and Neovim by Exafunction (formerly part of the Codeium project) that brings in AI-driven code completion and assistance capabilities. The aim is to provide a “free, ultrafast” alternative to other AI code assistants (such as GitHub Copilot) directly within Vim/Neovim. Once installed and configured, windsurf.vim can suggest code completions, generate multi-line snippets based on comments or invitation in code, and make the editing experience more predictive... -
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Embabel Agent Framework
Agent framework for the JVM. Pronounced Em-BAY-bel
Embabel Agent Framework is a JVM-based framework for building advanced AI agent systems that combine structured programming with large language model interactions to execute complex, goal-oriented workflows. The framework introduces a planning-driven approach where agents dynamically determine sequences of actions required to achieve objectives, rather than relying on fixed pipelines or predefined flows. It models agent behavior through concepts such as goals, actions, and conditions, allowing systems to adapt in real time based on changing inputs and outcomes. ... -
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ZML
Any model. Any hardware. Zero compromise
...One of its key strengths is cross-compilation, enabling developers to build once and deploy across various platforms without rewriting code. zml provides example implementations of models and workflows, demonstrating how to run inference tasks such as image classification or large language models. It is designed to handle complex distributed setups, including scenarios where model components are split across devices connected via networks. -
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RAG-Retrieval
Unify Efficient Fine-tuning of RAG Retrieval, including Embedding
...The framework also supports reranking models that refine retrieved results using large language models or lightweight transformer architectures. Additional training techniques such as preference-based supervised fine-tuning and embedding distillation are included to improve retrieval quality. -
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MCP Shrimp Task Manager
Shrimp Task Manager is a task tool built for AI Agents
Shrimp Task Manager is an MCP server that converts natural-language requests into structured development tasks with dependencies, status, and style/format rules—built for agents that reason step-by-step. It emphasizes chain-of-thought and reflection loops, allowing an assistant to plan, refine, and re-prioritize work like a human project assistant. The server exposes typed tools so clients can create tasks, link prerequisites, record progress, and enforce writing or coding standards for... -
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MetaCLIP
ICLR2024 Spotlight: curation/training code, metadata, distribution
MetaCLIP is a research codebase that extends the CLIP framework into a meta-learning / continual learning regime, aiming to adapt CLIP-style models to new tasks or domains efficiently. The goal is to preserve CLIP’s strong zero-shot transfer capability while enabling fast adaptation to domain shifts or novel class sets with minimal data and without catastrophic forgetting. The repository provides training logic, adaptation strategies (e.g. prompt tuning, adapter modules), and evaluation... -
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Alan AI
In-App assistant SDK to build a multimodal conversational UX websites
Quickly add voice to your app with the Alan Platform. Create an in-app voice assistant to enable human-like conversations and provide a personalized voice experience for every user. Alan is a conversational voice AI platform that lets you create an intelligent voice assistant for your app. It offers all the necessary tools to design, embed, and host your voice solutions. A powerful web-based IDE where you can write, test and debug dialog scenarios for your voice assistant or chatbot. Alan's... -
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minbpe
Minimal, clean code for the Byte Pair Encoding (BPE) algorithm
minbpe is a minimal, clean implementation of byte-level Byte Pair Encoding (BPE), the tokenization approach widely used in modern language models. It operates on UTF-8 encoded bytes rather than Unicode characters, which makes it robust to arbitrary text inputs and avoids needing a language-specific character vocabulary. The repository is structured as a teaching-oriented implementation that shows how to train a tokenizer by learning merge rules, then apply those merges to encode text into token IDs and decode tokens back into text. ... -
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Supertonic
Lightning-fast, on-device TTS, running natively via ONNX
Supertonic is a lightning-fast, on-device text-to-speech system built around ONNX Runtime for maximum speed and portability. It focuses on running entirely locally, eliminating the need for cloud APIs and providing low latency and strong privacy guarantees, even on constrained devices like Raspberry Pi boards and e-readers. The core model is highly compact at around 66 million parameters, yet benchmarks show it can generate speech up to 167× faster than real time on modern consumer hardware... -
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MiniMax-M2
MiniMax-M2, a model built for Max coding & agentic workflows
MiniMax-M2 is an open-weight large language model designed specifically for high-end coding and agentic workflows while staying compact and efficient. It uses a Mixture-of-Experts (MoE) architecture with 230 billion total parameters but only 10 billion activated per token, giving it the behavior of a very large model at a fraction of the runtime cost. The model is tuned for end-to-end developer flows such as multi-file edits, compile–run–fix loops, and test-validated repairs across real repositories and diverse programming languages. ...
