Unlock the Power of Automation Platforms

💼 In Today’s 5-min AI Brief

• Your Everyday AI Toolkit: Learn why Make.com became the most powerful no-code AI automation platform

• Prompt of the Week: Map your toolchain with this Automation Platform prompt

• AI News You Can Use: Explore 3 levels of AI powered automation, Moonshot AI’s impressive agentic intelligence, and a Meta x Oakley release

• What I’m Learning: An MIT research study shows AI can sharpen or dull thinking based on how it’s used, while industry leaders say the next edge is managing agents, not just prompts

Ever feel like your essential tools operate in silos? When apps work in isolation, workflows often stall. This leads to too many open tabs, endless copy-pasting, and disconnected handoffs.

Automation platforms give your tech stack structure. They move data, trigger actions, and keep everything in sync behind the scenes so your systems can actually flow.

In today’s AI Brief, I’ll break down key automation platforms, when to use them, and how tools like Make.com can transform your disconnected apps into seamless, AI-powered systems.

🛠️ Your Everyday AI Toolkit

Meet Make.com, your no-code control center for AI workflows

If you’ve ever wished your tools could talk to each other and follow logic, Make.com is worth exploring. It’s a drag-and-drop automation platform that lets you build custom workflows without writing code. It handles far more complexity than most give it credit for.

Where tools like Zapier follow a straightforward path ("if this, then that"), Make lets you build branches, loops, filters, error handling, and conditionals - ideal for real-world, multi-step use cases. This capability allows it to orchestrate sequences of actions, turning smart tools into full systems.

What you can do with Make:

• Trigger automations from emails, forms, databases, or calendars

• Route tasks based on logic (e.g., customer industry, deal size, response time)

• Integrate tools like Notion, Airtable, Slack, Google Sheets, and, yes, ChatGPT

• Insert AI at just the right moment (e.g., summarize, rewrite, draft, classify)

• Monitor or retry failed steps automatically, building resilience into your workflows

Example:

New lead books a call → Info added to Airtable → Auto-draft intro email using ChatGPT → Route to the right sales rep based on industry → Post a deal summary in Slack

Try this today:

1. Go to Make.com and create a free account

2. Choose “+ Create a new scenario”

3. Start with a basic 3-step flow:

• Trigger: Google Calendar event 

• Action: Use ChatGPT to summarize meeting agenda 

• Output: Send summary to Slack or email

Once you see how easy it is to connect tools and insert AI into the flow, you’ll start spotting automation opportunities everywhere.

💭 Prompt of the Week

You can leverage this prompt when you’re unsure whether to use Zapier, Make, LangChain, or another automation platform, and want a fast recommendation tailored to your workflow.

This prompt works especially well inside ChatGPT or Claude when paired with a real example like “Send client intake forms to Notion and auto-create a Slack thread.”

Give it a try with one recurring task this week and test the tool it suggests. This is the fastest way to go from scattered apps to a structured system.

💡 AI News You Can Use

Why Workflow Automation Is the Next Must-Have Skill

Microsoft’s CEO recently said:

If you’re just prompting ChatGPT and copy-pasting results, you might be the human bottleneck. The real productivity gains come when AI is part of the system, not a sidekick.

Here’s a quick look at the 3 levels of AI-powered automation, using a common task: turning meeting notes into Asana tasks.

Level 1: Basic Workflows (Rule-Based)

Manual rules + simple logic.
Example: You tag action items in a Google Sheet → Zapier creates Asana tasks → Slack update is posted.

Level 2: AI Workflows (Intelligent Automation)

Same flow, but AI handles the heavy lifting.
Example: AI transcribes the meeting → extracts tasks → adds owners/estimates → Auto-creates tasks in Asana.

Level 3: AI Agents (Autonomous)

Goal-driven systems that plan, act, and adapt.
Example: An AI agent joins your meeting, detects priorities, assigns tasks, and follows up - without step-by-step instructions.

👉 Check out my 90-second video that covers 3 levels of AI automation to help you evolve from knowledge worker to Agent Manager!

Last Week in AI

• Kimi-Researcher sets a new benchmark and raises the bar for agents. Meanwhile, Moonshot AI is pushing the envelope where it arguably matters most: agentic intelligence. Their new research agent, Kimi-Researcher, just scored a record 26.9% on Humanity’s Last Exam - surpassing both Gemini and OpenAI’s Deep Research. 

  • It’s a reminder that while Big Tech is busy playing chessboard acquisitions, innovation is also happening in unexpected corners. I’m intrigued by how these next-gen research agents could change how we think, write, and solve.

• Meta x Oakley debut rugged AI glasses for the active crowd. Despite internal talent turbulence, Meta’s Ray-Ban smart glasses have been one of the few hits in the AI hardware space. Now, Meta is teaming up with Oakley to launch a $399 “performance” version built for durability and water resistance. These upgraded glasses pack 3K video capture, 8-hour battery life, and seamless access to Meta’s onboard AI assistant.

📖 What I’m Learning

If you’re interested in what I’ve been digging into… check out:

1. ChatGPT May Be Eroding Critical Thinking Skills, According to a New MIT Study: A new MIT study making the rounds shows that when people relied entirely on ChatGPT to write essays, their brain activity, recall, and sense of ownership dropped significantly. But the same study also found that when participants used AI more strategically, like refining their own ideas or building on drafts, it actually enhanced learning and engagement. 

   • The takeaway isn’t that AI makes you dumber, but that misusing it can dull your thinking, while thoughtful use can sharpen it.

2. Scale AI: The Data Engine Behind Top AI Models – By delivering high-quality labeled data and evaluation services to giants like Meta and OpenAI, Scale has become critical for companies building LLMs. Alexandr Wang (CEO) tells YC that the next must-have skill is managing swarms of AI agents, instead of just writing prompts.

Final Thoughts

If AI is the engine, automation is the steering, and you’re becoming the driver, the architect, and the manager of agents and systems.

This shift from AI tools to AI systems is about learning how to build and manage workflows where AI does real work at scale, across your stack.

Start by mapping one repeatable process you own.

Plug in the right automation layer.

Then ask: Could an agent run this for me next?

Thaddeus