Search Results for "libhamlib-4"
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Showing 17 open source projects for "libhamlib-4"
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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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AutoGPT
Powerful tool that lets you create and run intelligent agents
AutoGPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. This program, driven by GPT-4, chains together LLM "thoughts", to autonomously achieve whatever goal you set. As one of the first examples of GPT-4 running fully autonomously, AutoGPT pushes the boundaries of what is possible with AI. -
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TencentDB Agent Memory
TencentDB Agent Memory delivers fully local long-term memory for AI
TencentDB Agent Memory is a local long-term memory system for AI agents. It uses symbolic short-term memory and layered long-term memory instead of storing everything as flat vector fragments. For active tasks, it offloads heavy logs into external files and keeps a compact Mermaid canvas in the agent context. For personalization, it organizes memory from raw conversations into atoms, scenarios, and persona-level knowledge. The design keeps high-level memory inspectable while preserving a... -
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Agent Skills
Production-grade engineering skills for AI coding agents
Agent Skills is a collection of production-oriented engineering workflows designed for AI coding agents. It packages senior software-development practices into structured Markdown skills that can be loaded into many agent systems. The collection contains 24 skills spanning project definition, planning, implementation, testing, debugging, review, security, documentation, and delivery. Each workflow includes explicit steps, verification gates, and safeguards against skipping important checks.... -
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SWE-agent
SWE-agent takes a GitHub issue and tries to automatically fix it
SWE-agent turns LMs (e.g. GPT-4) into software engineering agents that can resolve issues in real GitHub repositories. On the SWE-bench, the SWE-agent resolves 12.47% of issues, achieving state-of-the-art performance on the full test set. We accomplish our results by designing simple LM-centric commands and feedback formats to make it easier for the LM to browse the repository, and view, edit, and execute code files. -
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MetaGPT
The Multi-Agent Framework
The Multi-Agent Framework: Given one line Requirement, return PRD, Design, Tasks, Repo. Assign different roles to GPTs to form a collaborative software entity for complex tasks. MetaGPT takes a one-line requirement as input and outputs user stories / competitive analysis/requirements/data structures / APIs / documents, etc. Internally, MetaGPT includes product managers/architects/project managers/engineers. It provides the entire process of a software company along with carefully orchestrated SOPs. -
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Sepia
De-AI writing skill for any Agent Skills-compatible agent
...Fiction processing emphasizes narrative architecture before sentence-level polishing. Professional modes apply different rules to release notes, technical articles, pull request replies, postmortems, tickets, and other workplace formats. Four workflows are provided: writing from scratch, diagnostic review, minimal refactoring, and complete recreation. The skill follows the standard Agent Skills format and can therefore be installed across many compatible assistants. Native packaging is also provided for selected environments such as Claude Code, Codex, Grok Build, Antigravity, and QwenPaw. -
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Griptape
Python framework for AI workflows and pipelines with chain of thought
The Griptape framework provides developers with the ability to create AI systems that operate across two dimensions: predictability and creativity. For predictability, Griptape enforces structures like sequential pipelines, DAG-based workflows, and long-term memory. To facilitate creativity, Griptape safely prompts LLMs with tools (keeping output data off prompt by using short-term memory), which connects them to external APIs and data stores. The framework allows developers to transition... -
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Self-Operating Computer
A framework to enable multimodal models to operate a computer
The Self-Operating Computer Framework is an innovative system that enables multimodal models to autonomously operate a computer by interpreting the screen and executing mouse and keyboard actions to achieve specified objectives. This framework is compatible with various multimodal models and currently integrates with GPT-4o, o1, Gemini Pro Vision, Claude 3, and LLaVa. Notably, it was the first known project to implement a multimodal model capable of viewing and controlling a computer screen.... -
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native-feel.skill
An Agent Skill for designing cross-platform desktop apps
...The skill helps AI assistants reason about when to use native code, when to share web-based interface layers, and how to avoid the common failures of Electron-style or WebView-based products. It includes eight architectural tenets, a four-layer architecture model, WebKit and WebView2 guidance, and a large ship audit checklist. The project is useful when an agent is advising on desktop apps that need global shortcuts, system tray behavior, native windows, fast interaction, and polished platform conventions. Its main value is turning hard-won desktop architecture knowledge into reusable agent guidance. -
$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
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Reef
Continual learning infra for self-improving agents
...It connects live inference, feedback collection, learning, evaluation, and versioned deployment in one lifecycle. The system can improve either underlying model weights or the surrounding agent harness, including prompts, skills, and rules. Its core loop follows four stages: serve requests, observe feedback, grow candidate improvements, and commit accepted updates. Weight-training workflows can integrate with systems such as Slime and SGLang, while harness optimization can run without local training GPUs. Versioned artifacts let deployments stay operational while new candidates are evaluated and released. ... -
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OpenAI Realtime Agents
This is a simple demonstration of more advanced, agentic patterns
...It also supports a “Chat-Supervisor” pattern where a lightweight realtime chat agent handles user interactions and delegates more complex reasoning or tool usage to a stronger textual model (e.g. GPT-4). Because realtime agents are still a beta feature, the code and API surface are subject to changes and may evolve. -
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Shinkai: Local AI Agents
Shinkai allows you to create advanced AI (local) agents effortlessly
...Key Features: - No-Code Agent Creation - Build specialized agents (trading bots, sentiment trackers, etc.) with simple descriptions - Multi-Agent Collaboration - Agents work together to solve complex problems - Crypto Integration - Built-in support for decentralized payments and transactions - Flexible AI Models - Choose from cloud models (GPT-4, Claude) or run locally - Universal Compatibility - Works with Model Context Protocol (MCP) for cross-platform integration - Local Security - Crypto keys and computations stay on your device Shinkai transforms AI from single-task tools into collaborative, autonomous systems that can operate in decentralized networks while maintaining privacy and security. -
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iX
Autonomous GPT-4 agent platform
IX is a platform for designing and deploying autonomous and [semi]-autonomous LLM-powered agents and workflows. IX provides a flexible and scalable solution for delegating tasks to AI-powered agents. Agents created with the platform can automate a wide variety of tasks while running in parallel and communicating with each other. -
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AI-Agent-Host
The AI Agent Host is a module-based development environment.
The AI Agent Host integrates several advanced technologies and offers a unique combination of features for the development of language model-driven applications. The AI Agent Host is a module-based environment designed to facilitate rapid experimentation and testing. It includes a docker-compose configuration with QuestDB, Grafana, Code-Server and Nginx. The AI Agent Host provides a seamless interface for managing and querying data, visualizing results, and coding in real-time. The AI... -
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PyGOAPng
Python Goal Oriented Action Planning (GOAP) library
A library for implementing GOAP in an AI agent. Based on pygoap v3 by Leif Theden et al. Updated code to work without having pygame installed, bug-fixed functions to make them implement the behaviors that were expected, and implemented desired behaviors so that the Pirate demo works properly for all known actions and goals. -
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Statistical Casting Daemon
SCASTD (Statistical Casting Daemon)
SCASTD (Statistical Casting Daemon) is a lightweight, high-performance statistics collection and analytics engine we designed specifically for streaming media infrastructure. Unlike traditional streaming servers, we built SCASTD to be the definitive monitoring solution that bridges legacy and modern streaming protocols, providing unified analytics across your entire streaming ecosystem -
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BindAI
Open-source Python framework for AI agents and agentic workflows.
BindAI is an open-source, opinionated Python framework for building, composing, and running AI agents and agentic workflows. It provides a structured application architecture for tools, memory, knowledge and RAG, model providers, integrations, APIs, and project-based organization. BindAI is designed for Python developers building AI applications, agent systems, and multi-step workflows.
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