Monday usually starts the same way for a business owner running everything themselves. A list of ten things that all feel urgent, and no real order to work through them in.⁣⁣ ⁣⁣ Most Kajabi systems have the same problem at a bigger scale. Everything looks urgent because nothing's been sequenced.⁣⁣ ⁣⁣ The process I run every audit through has five steps, in this order, every time. Scan the setup as it actually exists. Craft the fix for the highest-leverage gap first. Assemble it inside the system properly. Launch the change. Evaluate what actually moved before touching the next thing.⁣⁣ ⁣⁣ Skip the order and you get a business that's busy everywhere and stuck everywhere. Follow it and the list gets shorter on its own, because most of the ten things are just distractions.⁣⁣ ⁣⁣ Before you open your task list this week, ask which one thing, done first, makes three of the others less urgent.⁣⁣ ⁣⁣ That's where Monday should start.

Project title: AI Voice & Text Personal Assistant for Slack

Project description:

Built a conversational AI assistant in n8n that lives inside Slack and handles both voice and text messages, giving users a personal assistant they can talk to naturally, without switching apps or typing long commands.

The problem: The client wanted a way for their team to quickly get answers or delegate tasks through Slack, including by voice, without building a separate app or bot from scratch, and needed the assistant to hold context across a conversation rather than treating every message as a one-off.

What I built:

Message filtering: Every incoming Slack message is automatically checked to filter out bot-generated messages, so the assistant only responds to real human input.

Voice and text handling: The system detects whether the incoming message is a voice note or plain text. Voice messages are automatically downloaded and transcribed into text using an audio transcription model, while text messages pass straight through, so both input types end up in the same format for processing.

Conversational AI agent with memory: The transcribed or typed message is sent to a personal AI agent (built on Groq) that generates a response. The agent uses a memory module to retain context across the conversation, so it can handle follow-up questions naturally rather than starting fresh every time.

Instant reply in Slack: Once the agent generates a response, it's posted straight back into the Slack channel, so the interaction feels like a live conversation.

Outcome: The client's team now has a Slack-native assistant that understands both typed and spoken messages, remembers conversation context, and responds instantly, no separate app, no manual transcription, no lost context between messages.

This is good