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Where do you think AI agents can make the biggest impact in a real business? 🤖
I'm exploring AI agents and automation through projects with n8n, Python, RAG and APIs.
I'm curious to hear from other builders and founders:
What would you automate first if you could remove one repetitive task from your business?
Lead management? Customer support? Internal operations? Something else?
Would love to hear your ideas 👇
#AIAgents #AIAutomation #n8n #AI #Automation
I'd automate invoice intake first: extract fields, validate totals and tax against the source document, then route mismatches to a reviewer. That removes repetitive data entry without silently accepting an incorrect invoice.
That's a great one. Invoice intake is exactly the kind of repetitive process where AI + automation can have a measurable impact. I especially like the validation step - having the workflow flag uncertain invoices instead of blindly processing them.
Yeah, I agree with this. Keeping existing clients happy should definitely come first. And then AI can help with the lead follow-up side without making everything so manual.
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Marlowe Mobile Detailing
Solo-owner mobile car detailer, Austin, TX (serving Round Rock, Cedar Park, and Lakeway)
Marlowe Mobile Detailing was losing about 1 in 5 bookings to no-shows and running behind on nearly every job. This build replaces a vague "can you detail my car?" text with a fully scoped, weather-aware booking flow that sets an accurate time and price upfront, captures access instructions before he arrives, and automates rain-day rescheduling so he never has to chase a customer again.
He works alone and travels to the customer. Rain cancels outdoor jobs. Customers under-describe how dirty their cars are, so jobs run long. About 20% of bookings were no-shows because people forgot he was coming to them.
AetherOS is a web desktop for AI agents. The entire project sits inside a single HTML file. It starts with a boot screen. Then it opens into a dark workspace. The background has black animated particles. Everything runs on plain CSS and vanilla JavaScript. The site loads zero external dependencies and zero sound files.
The bottom dock holds seven apps. These tools manage four AI agents: Atlas, Sage, Bolt, and Vega. The Agents app shows them chatting and passing tasks. Files lets you browse a drive. Memory shows a live graph of what the system knows. Users can also modify a history timeline. Studio lets you test prompts. Flows builds node pipelines, and Cost tracks token usage.
Every window can be moved, resized, minimized, or maximized. The top menu bar has a model switcher. It routes tasks to Claude Opus 4.7, GPT-5.2, Gemini 3 Pro, DeepSeek V4, or Llama 4. The OS also has Spotlight search, sound toggles, and local API key inputs. The interface uses dark glass panels with blue highlights.
Building the node graphs in one single HTML5 file was tough. At first, the physics nodes pushed apart too hard. They floated right off the screen. Dragging extra lines while getting zoomed in also threw off the precise positions. I fixed the center gravity math. Then I divided mouse positions by the zoom factor. That stopped the unnecessary movement and kept the OS stable.😊
