Search Results for "main"
Sort By:
94 projects for "main" with 2 filters applied:
-
Stop vibe-debugging.AppSignal's MCP server hands Claude, Cursor, or Zed your real errors, traces, and the deploy that shipped them. AI writes the fix; you review the diff.
-
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
1
NeuralNote
Audio Plugin for Audio to MIDI transcription using deep learning
...The software functions as an audio plugin that can be used inside digital audio workstations as well as a standalone application for music production and analysis. Its main purpose is to perform audio-to-MIDI transcription, allowing musicians to record a performance and automatically transform it into editable MIDI notes. NeuralNote supports polyphonic transcription, meaning it can detect multiple notes played simultaneously, making it useful for instruments such as piano or guitar. The system relies on neural network models to analyze audio signals and infer pitch, timing, and other musical attributes that can be represented as MIDI data. ... -
2
ZeroGPU Router
Cut inference costs without dumbing down your agent
...Its main value is letting agents reserve expensive frontier models for complex reasoning while offloading routine language tasks to cheaper specialized routes. -
3
Read Frog
Open Source Immersive Translate
...Instead of forcing learners to switch between translation tools and the original text, the extension displays translations alongside the source language, making comprehension immediate and continuous. The system automatically extracts the main content of an article using intelligent parsing techniques, allowing users to focus on the most relevant text without distractions. AI models are used to generate summaries, introductions, and explanations for words, phrases, and sentences based on the learner’s language level, making the experience personalized and adaptive. -
4
OpenMythos
A theoretical reconstruction of the Claude Mythos architecture
...The project explores the idea that instead of stacking hundreds of unique transformer layers, a smaller set of layers can be reused iteratively during inference to achieve deeper reasoning without increasing parameter count. It divides computation into three main stages, including a pre-processing phase, a looped recurrent reasoning block, and a final output refinement stage, creating a structured pipeline for inference. The architecture incorporates advanced techniques such as mixture-of-experts routing, adaptive computation time, and multiple attention mechanisms to dynamically allocate compute where needed. ... -
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.
-
5
AirLLM
AirLLM 70B inference with single 4GB GPU
AirLLM is an open source Python library that enables extremely large language models to run on consumer hardware with very limited GPU memory. The project addresses one of the main barriers to local LLM experimentation by introducing a memory-efficient inference technique that loads model layers sequentially rather than storing the entire model in GPU memory. This layer-wise inference approach allows models with tens of billions of parameters to run on devices with only a few gigabytes of VRAM. AirLLM preprocesses model weights so that each transformer layer can be loaded independently during computation, reducing the memory footprint while still performing full inference. ... -
6
No Cost AI
80+ free AI services for chat, image, video, voice & APIs
...It is useful for students, developers, creators, and experimenters who want to test AI tools with minimal cost. The repository also warns that free AI services can change, disappear, rate-limit users, or become unreliable over time. Its main value is discovery, not guaranteed infrastructure, so it works best as a resource map rather than a production dependency. -
7
Gajae-Code
Gajae Code MVP
...It includes tmux-backed execution options for coordinating parallel workers when a task is large enough to benefit from them. Gajae-Code is useful for developers who want coding agents to behave less like single-prompt assistants and more like disciplined project collaborators. Its main value is giving AI coding work a clearer loop for interviews, plans, goals, execution checks, and evidence. -
8
LazyCodex
The one and only agent harness for complex codebases
...LazyCodex also emphasizes verified completion, which means the workflow is built around checking whether tasks are actually finished rather than only generating code. Its main value is turning Codex into a more disciplined coding agent environment for larger and more demanding repositories. -
9
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. -
Compliant and Reliable File Transfers Backed by Top Security CertificationsStop relying on non-certified, legacy file transfer tools that creak under the weight of modern security demands. Get full audit trails, advanced access controls and more supported by an award-winning team of experts. Start your free 25-day trial today.
-
10
React Native ExecuTorch
Declarative way to run AI models in React Native on device
...It helps React Native developers use local AI capabilities without needing deep native programming or machine learning infrastructure expertise. The library is especially relevant for privacy-first apps, offline experiences, and mobile products that need low-latency inference. Its main value is bridging React Native with native AI execution so apps can run powerful models directly on user devices. -
11
Easy AI
Easy-to-understand AI learning resources for beginners
...It includes visual and interactive explanations for core topics such as tokens, prompts, LLMs, Transformers, and evaluation benchmarks. It also organizes AI-related works, tutorials, and daily updates so users can explore both fundamentals and current developments. Its main value is making AI knowledge easier to browse, understand, and apply. -
12
Alook
The collaboration layer for your AI workforce
...It is designed for developers, solo founders, and teams that want multiple AI agents to handle development, operations, research, and routine work with clearer structure. Alook also emphasizes memory and traceability, so agents can remember past decisions, learn preferences, and keep a record of instructions and replies. Its main value is turning separate AI coding tools into an organized, always-on operating system for agent-based work. -
13
TokenSpeed
TokenSpeed is a speed-of-light LLM inference engine
...It builds on ideas and components from the broader open-source inference ecosystem while presenting its own execution stack. TokenSpeed is useful for developers building local or server-side LLM infrastructure for agents, coding systems, and high-volume AI applications. Its main value is providing an inference layer optimized for fast token generation under practical agent workloads. -
14
Google Antigravity SDK
Python library for building agents that leverages Google Antigravity
...It supports streaming responses, stateful sessions, custom Python tools, MCP server integration, hooks, policies, and event-driven triggers. The package relies on a compiled runtime binary distributed through platform-specific PyPI wheels, so installation from PyPI is required for normal use. Its main value is giving developers a structured Python framework for creating local, tool-using, multimodal, policy-controlled AI agents. -
15
improve
Use your most capable model to audit your codebase
...Its core idea is to use a stronger model for understanding, judgment, and planning, then hand the actual implementation to cheaper or separate execution agents. The skill does not modify code by default, because its main output is a self-contained plan. It can run full, quick, deep, security-focused, branch-scoped, and feature-suggestion audits. It maps repository conventions, identifies findings, writes ordered markdown plans, and records verification steps such as lint, build, and test commands. It is useful for teams that want AI-assisted technical planning without letting the planning model directly change production code. -
16
Karpathy Skills
A single CLAUDE.md file to improve Claude Code behavior
...Its principles encourage the agent to think before coding, prefer simple implementations, make surgical changes, and define verifiable success criteria. It can be installed as a Claude Code plugin or copied directly into a project as reusable instructions. Its main value is not automation, but behavioral alignment that helps coding agents produce cleaner, smaller, and more deliberate changes. -
17
a-stock-data
Full-stack China A-Share data toolkit for AI coding assistants
...It is designed to work with tools such as Claude Code, Codex, and OpenClaw through context-injected Markdown and embedded Python. The repository emphasizes tested endpoints and practical usability for agent-driven financial analysis workflows. Its main value is turning fragmented A-share data sources into a unified toolset for AI-assisted research and coding. -
18
Sinas
Open-source platform for building AI agents and serverless automation
...Sinas can be managed through a web console or declarative YAML configuration, making it suitable for both interactive administration and GitOps-style workflows. Its main value is combining agents, functions, permissions, storage, and automation into one self-hosted AI application platform. -
19
butterbase
Open-source backend-as-a-service
...It is especially useful for founders, builders, and “vibe coders” who want to move quickly without spending time on DevOps or backend setup. Butterbase also emphasizes open building blocks and reduced lock-in through a Postgres-backed architecture. Its main value is giving AI-assisted development workflows a ready backend that agents can operate directly. -
20
Google Gen AI SDK
Google Gen AI Python SDK provides an interface for developers
...It is intended to replace older Gemini Python SDK patterns with a more unified and actively maintained API surface. The project is useful for application developers, data scientists, AI engineers, and backend teams building Gemini-powered features. Its main value is providing a supported, production-ready Python interface for Google’s current generative AI ecosystem. -
21
WhatsApp MCP Server
WhatsApp MCP server enabling AI access to chats and messaging
whatsapp-mcp is an open source Model Context Protocol (MCP) server that enables AI agents to interact directly with a user’s WhatsApp account through a structured interface. It acts as a bridge between WhatsApp and large language models, allowing controlled access to messages, chats, and contacts. whatsapp-mcp is composed of two main components: a Go-based bridge that connects to the WhatsApp Web API and stores data locally, and a Python-based MCP server that exposes tools for AI interaction. All message data is stored in a local SQLite database and is only accessed when explicitly requested through defined tools, giving users control over how their data is used. ... -
22
React Native RAG
Private, local RAGs. Supercharge LLMs with your own knowledge base
...It is also extensible, allowing teams to implement their own interfaces when they need custom model or storage behavior. React Native RAG supports multiple integration styles, including a simple hook, a class-based API, and direct component usage. Its main value is giving React Native apps a privacy-friendly way to combine local knowledge retrieval with on-device AI workflows. -
23
Dia2
TTS model capable of streaming conversational audio in realtime
...Dia2 provides 1B and 2B model checkpoints along with inference code for research and experimentation. It currently focuses on English generation and supports up to two minutes of generated audio. Its main value is enabling low-latency, dialogue-oriented TTS workflows where timing, turn-taking, and natural conversation matter. -
24
Stop Slop
A skill file for removing AI tells from prose
...It also includes a scoring rubric that rates drafts across dimensions such as directness, rhythm, trust, authenticity, and density. The skill is useful for drafting, editing, polishing, and quality-checking prose before publication. Its main value is giving writers and AI assistants a practical checklist for making text feel less synthetic and more intentional. -
25
LLMSurvey
A Survey of Large Language Models
...The project is closely associated with the academic survey titled “A Survey of Large Language Models,” which provides a comprehensive overview of the development, architecture, capabilities, and societal implications of modern LLMs. The repository organizes hundreds of research papers into thematic sections that reflect the main areas of LLM research, including model architectures, training strategies, evaluation benchmarks, alignment techniques, and downstream applications. By structuring the literature in a navigable format, LLMSurvey allows researchers and practitioners to quickly explore important publications in the field without manually searching through multiple databases.
