What if you could hand your social media accounts over to an AI agent and let it network on your behalf? Not just post, but actually search for the right people, filter posts, even DM them to set up a coffee chat. The problem is, most social platforms don't give you a CLI or an MCP to do that. RedNote, for instance, has no API that lets an AI agent query posts or send messages. But you can build your own CLI tool that connects your AI agent to any app you use day to day, and that's exactly what I'm going to show you.
The tool we're using is called Printing Press. It's a CLI factory. You feed it a website or an app, and it spits out a command-line interface that your AI agent can use to perform actions on your behalf. No more waiting for an official API. No more wrestling with screenshots and accessibility trees. Just text in a terminal, the way LLMs like it.
What even is a CLI and why should your AI agent care?
CLI stands for command-line interface. It's a way to control a computer or software by typing text commands instead of clicking buttons. Simple example: you want to create a folder on your desktop. Normally you'd right-click and hit "New Folder." In a CLI, you open a terminal, type mkdir new-folder, and it's done instantly. Same action, no graphical interface needed.
Now, there's already a way to let an AI agent operate your software visually. Tools like "computer use" take a screenshot of your screen, feed it into the AI, and then the AI moves the cursor, clicks buttons, types text, exactly like you would. The problem is that screenshots are images. Images eat up a ton of context tokens. More tokens means higher cost, and as the context window fills up, the AI's accuracy drops. The author of Printing Press even calls this out directly: it costs so many tokens.
A CLI keeps everything in text. No screenshots, no downloading entire websites for the accessibility tree. Just clean, structured text commands. That's token-efficient.
Then there's MCP. You might ask, why not just use an MCP? Two reasons. First, MCPs tend to consume more tokens for the same task. Second, MCPs don't filter data well. When an API returns a big block of text, an MCP will often dump the entire thing into the AI's context window and let the AI sort it out. A CLI tool, on the other hand, can use commands like grep to filter that data before it ever hits the AI. The AI only sees what's relevant.
And here's the part that really matters: CLI tools load on demand. You don't preload every possible tool into the context window. The tool only gets pulled in when the AI actually needs it. That saves a massive number of tokens upfront.
There's one more advantage that often gets overlooked. The large language models we're using today already have a deep, native understanding of common developer tools like git, docker, aws, grep, and so on. They were trained on that stuff. You don't have to teach them how to run those commands every single time. They know it right off the bat. So when you give an AI agent a CLI, you're handing it a tool it already speaks fluently.
Printing Press: the CLI factory that turns any app into a terminal tool
So how does Printing Press actually work? Head over to printingpress.dev and you'll see two ways to get a CLI for any app.
The first way is the factory skill. You give it a URL, tell it what you want the CLI to do, and it generates a custom CLI tool for that site. The second way is to search the library. There are over 300 pre-built CLIs already in there. You type in the name of an app you use, like Instagram, and you'll find a CLI for Instagram business metrics. Use Linear a lot? There's a Linear CLI. Want to turn your entire school community into a CLI so your AI agent can pull information on your behalf? You can do that too.
Before you build anything from scratch, always check if an official CLI already exists. Many companies ship their own. Firecrawl has an official CLI. Stripe has one. Google Workspace has a CLI that gives your AI agent access to Gmail, Calendar, Drive, Sheets, and more. If you can use an official, open-source CLI, do it. It'll save you a ton of time. Only build your own when nothing else exists.
Building a Rednotes CLI to network with AI folks (step by step)
Let me walk you through a real example. I wanted to network with people on Rednotes, which is basically the Chinese version of Instagram. People post all kinds of things there, and I was looking for folks in the AI space. I wanted to search for relevant posts, maybe leave a comment or DM the creator, and ask for a coffee chat. But Rednotes has no CLI, no MCP, no API for that. So I built one.
Step 1: Install Printing Press
I use Codex as my AI agent. If you're on Claude Code, there's a separate install option. I went to the Printing Press repository, copied the curl command, and told Codex to install it. Once that was done, I restarted my Codex session so everything was fresh.
Step 2: Generate the CLI from a URL
Printing Press has a skill command that converts any website into a CLI. I gave it the URL for Xiaohongshu (that's the actual name of Rednotes) and told it to generate the CLI. It created a plugin right inside Codex. The plugin showed up with capabilities like search, read, write, and social actions. I clicked install and it was ready to go.
Step 3: Authenticate with a QR code
The CLI uses your local browser cookies, not a headless browser or Puppeteer. To log in, it generated a barcode. I scanned it with my phone, and just like that, my AI agent was authenticated to my Rednotes account.
Step 4: Search and let context engineering do the heavy lifting
Now came the fun part. I pasted a search link from Rednotes into Codex. The agent first verified that it had access and was logged in. Then, instead of blindly running a search, it gathered context about me. On this channel, we talk a lot about context engineering. The more context you give your AI, the smarter its decisions become. Codex pulled from what it already knew about my goals, my interests, and my networking preferences. It proposed specific options: what keywords to target, what outcome I wanted from the outreach, how to filter the results. I gave my input, and it went to work.
Step 5: Automated outreach
In the background, Codex searched across multiple keywords, pulled up relevant posts, and identified people I could connect with. It didn't just dump a list of links. It gave me actionable next steps based on my context. From there, I could have it draft comments or DMs to start conversations.
A quick word about documentation (and a sponsor)
Before I show you what else you can do with this, I want to mention the sponsor of this video, Document 360. Most teams don't have one documentation problem. They have three. Internal knowledge lives in Notion. Developer docs end up in Gitbook or Mintlify. The help center sits somewhere else entirely, and support keeps getting the same tickets because nobody finds the right answer fast enough.
Document 360 is an AI-powered knowledge base built to replace all three. Unlike Notion, it's built for structured publishing with version control and a proper public site. Unlike Gitbook, non-technical teams like support and product get the same editor experience as engineering. Unlike Mintlify, it covers SOPs, user manuals, and help centers alongside API documentation.
For non-technical teams, the workflow runs through a dedicated portal with custom approval flows, role-based permissions, and analytics. For engineering teams, the developer documentation experience is built separately but connected. You import your OpenAPI or Swagger spec, and endpoints, parameters, authentication flows, and response codes auto-populate into a structured reference. Developers get code samples in multiple languages and a "try it out" API playground inside the same knowledge base.
The AI layer is where Document 360 separates from every other tool. Eddie AI powers an AI search that delivers synthesized answers from your docs, not just a list of links. The AI chatbot embeds on your knowledge base site and answers user questions in real time from your actual content. Writers get AI-powered drafting, rewriting, and auto-generated glossaries. The support ticket deflector surfaces relevant articles when a ticket arrives, cutting volume before it reaches your team.
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Beyond Rednotes: turning your school community, Fiverr, and anything else into a CLI
Rednotes was just one example. I also built a CLI for my own school community using the same Printing Press factory skill. My school community doesn't have an MCP or a CLI. But now I have a tool that lets my AI agent engage with the community, pull member lists, check how members are doing, extract courses in markdown format, look at calendars, create posts, update content, and even delete things when needed.
Think about what that unlocks. If you're part of multiple school communities, you can ingest all that data into your AI agents and analyze it. You can track activity, spot trends, and automate engagement across every group you're in.
And you can take it even further. Let's say you're in HR and you want to hire the right people. You could convert Fiverr into a CLI, have your AI agent browse freelancer profiles, filter by skills, and initiate conversations. If you're a freelancer yourself, you could use the same approach to talk to multiple clients, all through a terminal interface your agent controls.
In my school community, I have a course inside the Hermas agent track called "Apps to Her Agent Tool." It walks through exactly how to build these CLIs and connect them to your AI agents, whether you're using Hermas, Codex, or something else. The whole point is to stop waiting for platforms to give you an API and start building the interface yourself.
Key Takeaways
- A CLI keeps AI agent interactions in text, which is far more token-efficient than screenshot-based approaches like computer use.
- MCPs often consume more tokens and don't filter data before feeding it to the AI; CLI tools can use commands like
grepto return only what's relevant. - CLI tools load on demand, so you don't bloat the context window with unused tools.
- LLMs already have a deep native understanding of CLI commands from their training data, so they can use them immediately without extra instruction.
- Printing Press is a CLI factory that converts any website or app into a CLI tool your AI agent can use.
- Always check for official CLIs first (Stripe, Google Workspace, Firecrawl, etc.) before building your own.
- Context engineering makes the difference: the more your AI knows about your goals, the better its decisions when using these tools.
- You can build CLIs for social platforms, community groups, freelance marketplaces, and practically any web app you use daily.
The whole idea behind this is simple. Instead of waiting for every app to build an integration for your AI agent, you take control. You build the CLI once, and from that point on, your agent can operate that app the same way you'd operate a terminal: fast, cheap, and with full context. If you want to go deeper, my school community has the full roadmap, from automations to AI agents to productizing your skills. Jump in, start building, and I'll see you there.