Robo Staking
◈ CuratorHome Assistant |Chat Status| Open source home automation that puts local control and privacy first. Powered by a worldwide community of tinkerers and DIY enthusiasts. Perfect to run on a Raspberry Pi or a local server. Check out `home-assistant.io `__ for `a demo `__, `installation instructions `__, `tutorials
🦞 OpenClaw — Personal AI Assistant EXFOLIATE! EXFOLIATE!
Cross-platform ecosystem that brings iMessage to Android, Windows, Linux, and web browsers. Requires a Mac server but enables full iMessage functionality including media, reactions, and group chats across all platforms.
An open-source Python library that enables AI agents to interact with web browsers through natural language commands. Automates complex web tasks like form filling, shopping, and data extraction using LLMs to control browser actions.
A Chrome extension that makes AI-generated emails sound more human by removing robotic phrases, adding natural typos, and eliminating telltale AI writing patterns. It integrates directly into Gmail to help your emails pass as authentically written by humans.
You are not using AI wrong because you haven't found the right model. You are using AI wrong because you haven't built the right environment. There is a reason some teams are shipping a million lines of code with three engineers while others are struggling to get a consistent refactor out of their agent pipeline. The difference is not GPT-5 versus Claude Opus. The difference is not the temperature setting or the max tokens. It isn't even the prompt, though everyone loses months of their life arguing about prompts. The difference is the harness. This article is about what that word actually means, technically and philosophically, because the industry has developed a bad habit of using it loosely. A harness is not a system prompt. It is not a wrapper around an API call. It is not an eval framework or a prompt template or a chatbot with memory. A harness is the complete designed environment inside which a language model operates, including the tools it can call, the format of information it receives, how its history is compressed and managed, the guardrails that catch its mistakes before they cascade, and the scaffolding that allows it to hand off work to its future self without losing coherence. When you look at what Anthropic built to make Claude Code actually work, what OpenAI built to ship a million lines of code through Codex with zero manually-written code, and what the Princeton NLP group published in their landmark SWE-agent paper about agent-computer interfaces, you start to see the same pattern emerging from every serious team working in this space. The model is almost irrelevant. The harness is everything. This is a detailed technical breakdown of how that idea became the defining insight of applied AI engineering in 2025 and 2026. It covers the research, the real implementations, the failure modes that motivated the design decisions, and the patterns that repeat whether you are building a coding agent, a research agent, or a long-running autonomous software
A web scanner that analyzes websites for compatibility with AI agents by checking for emerging standards like MCP, agent skills, OAuth, and other agent-friendly protocols. Helps developers optimize their sites for the growing ecosystem of AI agents that browse and interact with web content.
A Claude Code skill that generates production-quality SVG and PNG technical diagrams from natural language descriptions. Supports 8 diagram types, 7 visual styles, and specialized knowledge of AI/Agent domain patterns like RAG, Multi-Agent systems, and tool call flows.
A Claude Code skill and plugin that makes AI agents communicate like cavemen, reducing token usage by ~75% while maintaining full technical accuracy. Transforms verbose AI responses into concise, direct answers that save money and increase response speed.