Zapier is a cloud-based automation platform that lets users connect and automate workflows between thousands of web services without coding. Recently, Zapier announced support for Anthropic's MCP standard. MCP is an open protocol that allows AI models to interact with external tools, data sources, and services, and it's now also supported by OpenAI, Microsoft, and Amazon. In this post, I share my experiences with Zapier's MCP server.

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AI enables administrators to adapt complex open-source projects easily to their environment. If the project is not hosted on GitHub or other version control platforms, it becomes tedious to merge your changes into the open-source software whenever the maintainer updates their project. The Bash script I discuss in this post uses Git to automate merging your code changes into a new version provided by the maintainer of an open-source project lacking version control.

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In my previous post, I explained how to use the OpenAI library to augment your AI-powered terminal. Today, we will build on this enhancement and encourage two AIs to collaborate on IT tasks in the Warp terminal. Warp includes strong AI capabilities, though it currently only supports models lacking search features. As a result, you're using models with outdated IT knowledge. With a few tweaks, you can make Warp AI prompt GPT-4 Search to bypass the cutoff date of the Warp models.

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The GPT-4o Search model offers search engine functionality similar to ChatGPT Search or Perplexity when accessed via the OpenAI API. With the free OpenAI Python Library, you can enhance your terminal AI by adding online search features, overcoming the limitations of LLM cutoff dates, and eliminating the frustration of outdated instructions. This post will guide you on installing the OpenAI Python library and using it conveniently in Windows PowerShell, Warp, or any other terminal.

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In this guide, I’ll walk through installing ShellGPT (shell_gpt) – a command-line AI assistant – on PowerShell using a local Ollama LLM. This approach lets you use AI in your terminal without relying on cloud APIs, which is great for privacy. This review highlights the use of ShellGPT on Windows through PowerShell. If you are using Mac or Linux, check out our earlier review of ShellGPT.

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Manus AI, a Chinese general AI agent, has created significant buzz in the AI community. Some perceive it as the next DeepSeek moment, while others minimize its significance, viewing it merely as a wrapper for Anthropic's Sonnet 3.5. Exploring the open-source tool WebUI helps understand one of the underlying technologies of Manus AI, the open-source framework Browser Use, which is key to Manus AI's web capabilities.

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Lindy.ai is a no-code platform that enables users to build custom AI assistants to automate various business workflows without requiring coding skills. Users can create AI agents to handle tasks such as email management, meeting scheduling, customer support, or sales outreach. The platform integrates with various business tools, such as Google Workspace, HubSpot, Slack, and Zendesk.

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OpenAI has greatly enhanced the Work with Apps functionality in the ChatGPT app. Users can now incorporate the outputs from these apps as context for their prompts in ChatGPT and implement code changes in Cursor, VS Code, and various other IDEs. This feature positions the ChatGPT app as a competitor to Cursor and GitHub Copilot in VS Code. Furthermore, outputs from well-known terminal applications such as Warp and iTerm2 can be used as context within ChatGPT.

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Zach Lloyd, a former Principal Engineer at Google, introduces new features at warp speed to the best terminal app in the galaxy, with the latest Warp release introducing cutting-edge AI technologies from The Next Generation. The standout addition is the Dispatch mode, which I prefer to call reasoning mode, enabling administrators to plan complex automation tasks with AI assistance. Prompt Suggestions might better be described as big brother or creepy mode since the AI continually observes your actions and intervenes when it believes you need assistance. Additionally, AI memory functions similarly to the personalization feature in ChatGPT, allowing you to save preferences as Knowledge across sessions.

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We have reviewed Warp's Mac and Linux versions multiple times, as it stands out as the top terminal application. The newly released Windows edition introduces AI-driven automation for PowerShell administrators. Even if you have no use for natural language automation because PowerShell is your primary language anyway, you should give Warp a try, as no terminal app can compete with it on Windows.

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When I first watched Anthropic's demo of Claude Code, I was thrilled because I believed this tool could be a valuable asset as an AI-driven CLI assistant akin to Warp's AI agent. After all, who even codes in a terminal anymore?

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A few weeks back, I reviewed the new AI features of Warp, the best terminal app out there. Now, Warp has taken the next step, transforming the terminal into a fully autonomous AI agent.

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In my previous post, I detailed the process of importing open-source models, such as DeepSeek R1 from Hugging Face, into Amazon Bedrock, a fully managed service offered by AWS that gives users access to foundational models from top AI firms and Amazon. This post will explain how to access the AI models through the Bedrock API from third-party apps like VS Code or Cursor. Specifically, when using AI models like Claude Sonnet 3.5 for coding, accessing it through Amazon Bedrock offers enhanced security compared to Anthropic or model hub APIs such as OpenRouter.

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In a previous post, I demonstrated how to run DeepSeek R1 on your Mac or PC. Today, I will guide you through importing the Chinese open-source model that has outperformed OpenAI’s top models in various benchmarks from Hugging Face—the GitHub equivalent for hosting, sharing, and deploying open-source AI models. Hugging Face provides the hf-bedrock-import tool for this purpose, which is more straightforward than the aws bedrock create-model-import-job command.

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To keep pace with Cursor, Microsoft has integrated several valuable features from the VS Code fork into its GitHub Copilot extension. The February update introduces three new features: Agent Mode, Vision, and Next Edit Suggestions (NES). You'll appreciate these latest additions if you prefer VS Code over Cursor for some reason.

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In a prior article, I showcased automating PowerShell management in natural language using the VS Code extension Cline. Roo Code (formerly Roo Cline) is a Cline fork that provides extra valuable features for the AI-enhanced IT pro. Roo Code remains comparable, and the insights I shared about working with MCP servers in Cline also apply to Roo Code. The main difference is that Roo Code serves system administrators more effectively, as it provides a mode more suitable for natural language use in a CLI. Additionally, Roo Code's support of VS Code's Code Actions feature makes it perfect for understanding and modifying a colleague's scripts.

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In my previous posts, I introduced Anthropic's Model Context Protocol (MCP) and explained how to install an MCP server for the Claude Desktop app. Today, I show you something more relevant for admins. My introductory article about Cline mentioned that the VS Code extension supports MCP. You will see that installing an MCP server is easier with the assistance of an AI. In this post, I will explain how to install AWS MCP, an MCP server that allows you to manage AWS resources with the help of an AI. If you don't want to be left behind, I highly recommend getting started with learning how to use AI to get your job done. This tech revolution is advancing faster than any before it. And we are only at the very beginning.

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In my last post, I introduced Anthropic's new Model Context Protocol (MCP), which enables AI agents to access and manipulate external data sources. In today's article, I will illustrate the MCP concept by installing MCP Server Fetch, which allows the Claude desktop app to retrieve webpages and convert them to Markdown. I believe that managing MCP servers will likely be one of the main tasks for AI ops admins.

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The Model Context Protocol (MCP) is an open standard developed by Anthropic to facilitate secure, two-way connections between AI models and external data sources, such as databases, business tools, and development environments. It enables AI agents to make informed decisions to perform tasks autonomously. Given that Anthropic's announcement is just a few weeks old, the number of available MCP servers is impressive. In the near future, installing and configuring MCP servers will likely be one of the primary tasks for AI Ops admins responsible for deploying AI agents

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