pi-autoresearch is an autonomous experiment loop for optimizing various targets like test speed, bundle size, LLM training, or build times. Inspired by karpathy/autoresearch, it utilizes a skill-extension architecture, allowing domain-agnostic infrastructure paired with domain-specific knowledge. The core workflow involves editing code, committing changes, running experiments, logging results, and either keeping or reverting the changes – a cycle that repeats indefinitely. Key components include a status widget, a detailed dashboard, and configuration options for customizing behavior. It persists experiment data in `autoresearch.jsonl` and session context in `autoresearch.md` for resilience and reproducibility.
This guide walks new users through the fundamentals of GitHub Actions, the CI/CD and automation platform built into GitHub. It explains what Actions are, how workflows are defined with YAML, and the key concepts of events, runners, jobs, and steps. The article provides a hands‑on example of creating a simple workflow that automatically labels new issues, covering the necessary permissions, environment variables, and use of prebuilt Marketplace actions. Readers learn how to test, debug, and manage their workflows from the Actions tab, and are encouraged to explore further capabilities such as building, testing, and deploying code.
This article introduces `install.md`, a proposed standard for creating installation instructions that are easily understood and executed by LLM-powered agents. The core idea is to provide a structured markdown file that details the installation process in a way that an agent can autonomously follow. This contrasts with traditional documentation geared towards human readers and allows for automated installation across various environments. The standard includes sections for product description, action prompts, objectives, verification criteria, and step-by-step instructions. Mintlify now auto-detects and generates `install.md` files for projects, offering a streamlined approach to agent-friendly documentation.
WebMCP is a new technology that allows AI agents to interact with web pages more directly. It works by turning web pages into MCP (Model Context Protocol) servers via a Chrome extension. This enables agents to understand and manipulate web content in a structured way, potentially improving efficiency and user experience.
The technology, backed by Google and Microsoft, is designed to work alongside human users, allowing them to ask agents questions about the page they are viewing. WebMCP uses a Declarative API for standard actions and an Imperative API for more complex tasks. Early experiments demonstrate the ability to query web pages and receive structured data back.
The technology, backed by Google and Microsoft, is designed to work alongside human users, allowing them to ask agents questions about the page they are viewing. WebMCP uses a Declarative API for standard actions and an Imperative API for more complex tasks. Early experiments demonstrate the ability to query web pages and receive structured data back.
This article discusses the recent wave of AI-driven layoffs in the tech industry, with companies like Atlassian and Block citing AI automation as a key reason. It explores the growing debate between the Model Context Protocol (MCP) and APIs for connecting AI agents, with some developers favoring APIs for their simplicity and efficiency. The piece also highlights the increasing trend of using Mac Minis as dedicated hosts for AI agents, and the rapid growth of platforms like Replit and Claude, indicating a shift in how software is developed and deployed with the aid of AI.
CoPaw is a personal AI assistant designed for easy installation and deployment, whether on your local machine or in the cloud. It supports multiple chat applications and offers easily extensible capabilities. Core features include broad channel support (DingTalk, Feishu, QQ, Discord, iMessage, and more), user control over memory and personalization, and built-in skills with the ability to create custom ones.
CoPaw enables various use cases, from social digests and productivity tools to creative writing and research assistance. It's a versatile teammate for your digital life, aiming to be a helpful "co-paw" by your side.
CoPaw enables various use cases, from social digests and productivity tools to creative writing and research assistance. It's a versatile teammate for your digital life, aiming to be a helpful "co-paw" by your side.
Sowbot is an open robotics platform designed to scale regenerative agriculture by providing accessible, lightweight robotics to researchers and farmers. It aims to bridge the "prototype gap" in agricultural robotics with a Reference Hardware Design and a Production-Ready Software Stack. The system comprises an 'Open Core' compute unit with dedicated boards for control/safety and perception/AI, powered by open-source software like Lizard, RoSys, and DevKit ROS. Various platforms like Sowbot, Sowbot Mini, and Sowbot Pico cater to different development stages, all emphasizing modularity, open-hardware, and a commitment to sustainability.
GitHub Agentic Workflows are built with isolation, constrained outputs, and comprehensive logging. Learn how our threat model and security architecture help teams run agents safely in GitHub Actions.
This post explains how we built Agentic Workflows with security in mind from day one, starting with the threat model and the security architecture that it needs. It details the defense in depth approach using substrate, configuration, and planning layers, emphasizing zero-secret agents through isolation and careful exposure of host resources. It also highlights the staging and vetting of all writes using safe outputs, and comprehensive logging for observability and future information-flow controls.
This post explains how we built Agentic Workflows with security in mind from day one, starting with the threat model and the security architecture that it needs. It details the defense in depth approach using substrate, configuration, and planning layers, emphasizing zero-secret agents through isolation and careful exposure of host resources. It also highlights the staging and vetting of all writes using safe outputs, and comprehensive logging for observability and future information-flow controls.
Júlio Falbo argues that integrating AI into engineering organizations is hampered by complex connection methods, proposing a solution centered around “SKILL.md” – Markdown files defining tool usage – and “AI Gateways” for centralized orchestration. This combination fosters an “AI-native architecture” prioritizing ease of use, governance, and scalability over bespoke integrations. Ultimately, this approach shifts the focus from complex coding to clear documentation, democratizing AI tool access and boosting productivity.
* Simplifies AI integration via Markdown-based "skills."
* Utilizes AI Gateways for centralized control and security.
* Promotes a convention-over-configuration approach for AI systems.
* Simplifies AI integration via Markdown-based "skills."
* Utilizes AI Gateways for centralized control and security.
* Promotes a convention-over-configuration approach for AI systems.
One CLI for all of Google Workspace — built for humans and AI agents. Drive, Gmail, Calendar, and every Workspace API. Zero boilerplate. Structured JSON output. 40+ agent skills included.
