A first-use tutorial for MaxAgent, the AI assistant for Alibaba Cloud’s MaxCompute platform, walks through activating the standard edition, checking account and project permissions, asking a product-knowledge question and verifying a key answer against official documentation. It explains that Credits are shared across a tenant and subject to tenant and individual limits. The lesson also distinguishes MaxAgent usage from MaxCompute engine charges for SQL queries. Its example requires no business data and does not create tables, execute SQL or change configuration.
What problem it solves
Go applications need client libraries for interacting with Google Cloud Platform APIs. The project provides packages for those services and supports common authentication flows for local development and Google Cloud Platform environments.
Key features
Google Cloud API client packages — The repository contains Go packages for Google Cloud Platform services, including storage,…
What problem it solves
AI agents need relevant design context from Figma files to generate code that follows a design system and reflects design intent. This project documents how to connect the Figma MCP server to supported clients, use its tools and skills, and apply practices for more consistent results.
Key features
Connect supported MCP clients — Setup instructions cover the Figma MCP server …
What problem it solves
Coding agents need a structured way to create, manage, and build from Google Stitch designs while integrating with the Stitch MCP server. The project organizes design, code generation, and supporting workflows into skills that can be installed with supported coding agents.
Key features
Design workflows — The design plugin covers converting frontend code into Stitch designs, …
What problem it solves
Selecting an open-source language model requires accounting for available memory, processor capabilities, GPU configuration, model size, quantization, and context requirements. The project addresses the difficulty of determining which models can run comfortably on a particular machine and comparing fit, speed, quality, and context before deployment.
Key features
Hardware aut…
What problem it solves
Many AI applications send messages to remote servers, where they may be logged and analyzed outside the user's view. This project addresses that concern by running language models, conversations, voices, and tools on the device without requiring an account or internet connection.
Key features
On-device language model chat — Run GGUF language models fully offline, including G…
What problem it solves
Developers, researchers, teams, and students need practical examples to understand and evaluate AI agent architectures. The collection organizes production examples, tutorials, and working code by framework and industry to support implementation and learning.
Key features
Runnable agent examples — Each project under agents/ is presented as a self-contained implementation wit…
What problem it solves
Building full-stack web applications in Rust often requires coordinating server rendering, client reactivity, routing, assets, and reusable UI components. Topcoat aims to simplify and streamline that work by combining these capabilities in a unified framework.
Key features
Server rendering with client reactivity — Components render asynchronously on the server and can access…
The most direct way to give an Agent long-term memory is to write history into a database and retrieve a few relevant records when a new question arrives. This pipeline soon encounters two different requirements: a personal assistant needs to remember specific people, events, times, and preferences; a task Agent needs to learn from past actions what to do next. The former fears losing details to c…
What problem it solves
AI applications need isolated environments where agents can execute code, run commands, manage files, and operate browsers or desktops. Teams also need a consistent way to run these environments locally with Docker or on Kubernetes without switching between separate interfaces.
Key features
Agent working environments — Provide agents with isolated environments for command ex…
What problem it solves
Building automated trading strategies requires exchange-facing interfaces, bot lifecycle management, configuration handling, and monitoring. Hummingbot addresses these needs through a modular framework that supports multiple strategy styles and exchange connector types.
Key features
Command-line bot operations — The `hbot` CLI runs, controls, and monitors trading bots non-in…
What problem it solves
Turning videos and image sequences into 3D Gaussian Splats involves frame preparation, camera reconstruction, model training, and publishing the result. OOOSplat aims to make this workflow accessible through a desktop interface while keeping source material and generation work on the local computer.
Key features
One-click local generation — Select a video or image sequence, …
What problem it solves
Investigating a phone number requires combining basic number details with configured scanners, external APIs, phone books, search engines, and reputation sources. The project aims to assist this research while making its limits clear: it does not provide verified data, track a phone in real time, reveal a precise location, or hack a phone.
Key features
Phone number validatio…
What problem it solves
LLM-powered software must become reliable and maintainable enough for production customers, rather than relying on a generic prompt, tool loop, and framework-driven rewrite. The project addresses the need for engineering principles that give builders deep control while allowing small, modular agent concepts to fit into existing products.
Key features
A factor-based design gu…
What problem it solves
People need a unified way to browse files kept across different storage services. The project provides a file list and WebDAV interface for working with multiple storage backends from one place.
Key features
Multiple storage backends — The project supports local storage, cloud drives, object storage, network protocols, and other storage services behind a common file list.
So…
What problem it solves
AI-generated interfaces can produce output that is difficult to control or render reliably. The framework addresses this by defining component catalogs, schemas, and actions that constrain generation while supporting progressive rendering across multiple platforms.
Key features
Guardrailed component catalogs — Define the components and actions available to a model, then gene…
What problem it solves
People need a clear way to inspect network activity, understand traffic patterns, and identify the programs and remote hosts involved in connections. The project addresses this with a cross-platform application for monitoring, analyzing, and inspecting observed traffic.
Key features
Traffic monitoring and inspection — Choose a network adapter, apply filters to observed traff…
What problem it solves
Ardia provides a Mac browser with a sidebar-first interface, split panes, and an integrated extension and synchronization workflow. It focuses on keeping Chromium behavior while adding privacy controls, profile organization, and a polished native macOS experience. The project also documents how to build the browser from its patched Chromium engine and application shell.
Key …
What problem it solves
Coding agents need visual and spoken evidence from videos before they can answer questions about them. Watch transforms a URL or local video into frames, transcripts, and timestamped visual evidence that an agent can inspect.
Key features
Process URLs and local videos — Accept a remote video URL or a local file and turn it into timestamped frames and a transcript for the age…
What problem it solves
Developers need a unified way to build semantic search applications, ground language models with knowledge sources, and connect models into larger workflows. They also need support for different data types, models, deployment modes, and application interfaces without assembling each capability separately.
Key features
Unified embeddings database — The core component combines…
What problem it solves
Learners need a practical path from AI engineering fundamentals to tested systems rather than isolated API examples. The curriculum is structured to make model internals, retrieval pipelines, agent runtimes, evaluation, and failure inspection understandable through runnable code and retained evidence.
Key features
Structured AI engineering curriculum — The curriculum spans m…
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