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Saved an hour each week with a Telegram finance bot
I finally got rid of the weekly spreadsheet grind. Using n8n’s Schedule Trigger I pull orders and delivery logs from Google Sheets, merge them, run a quick Code node to calc the margins and then ask Groq AI for a plain‑English summary. The text lands in my Telegram via an HTTP Request. The whole thing runs on Friday evenings and hands me a ready‑to‑read report – no more manual copy‑pastes. It’s a small win, but it shaves an hour off my workload each week.
#n8n #automation #ScheduleTrigger #GoogleSheets
Follow for more: https://go.phonezait.de/
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Saved an hour daily monitoring competitor prices
I got fed up with the manual price checks for my dropshipping store, so I wired n8n together with a Schedule Trigger, an HTTP Request to the competitor API, Google Sheets for my own rates, a Merge and an IF node to spot gaps, then a Telegram node for the alert. The flow runs every 15 minutes and now I’m not staring at spreadsheets for an hour each day.
#n8n #automation #ScheduleTrigger #HTTPRequest
Follow for more: https://go.phonezait.de/
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
I built an AI-powered Telegram assistant that handles incoming messages, understands requests, and responds automatically.
Using n8n, Google Gemini, and memory, the workflow helps reduce repetitive manual responses and keeps communication moving without constant intervention.
