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A few months ago I shipped my first production RAG pipeline. Simple idea: upload a document, ask questions, get answers with citations.
The part nobody warns you about is everything after "it works on my laptop." ChromaDB breaking on cloud deployment. Secrets formatting silently failing. API timeouts I had to tune by hand.
That gap between a working demo and a deployed system is where most of the actual engineering happens. It's also the part most tutorials skip entirely.
Since then I've built an AI-powered browser extension that automates form-filling inside an enterprise ERP system, and I'm currently building an AI equity research tool for Pakistan Stock Exchange companies using LangGraph.
I'm an AI Engineering student, and I'm now taking on freelance work in RAG systems, LLM integrations, and AI automation. If you're building something in this space, or know someone who is, I'd love to talk.
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Your AI agent just did 9 things: pulled 412 orders, matched them to Stripe, caught a duplicate £248 charge, drafted a ledger entry, opened a ticket. Whole run cost $0.011.
Now the real design question — what does the user actually SEE?
A — The transcript. A paragraph of text and a collapsed "Used 3 tools".
B — The run canvas. Nine colour-coded rows: what ran, what it cost, what got flagged, and one step that stops and waits for you before money moves.
Same agent. Same run. Two completely different products.
Vote A or B — and tell me why. I build the B version for a living.
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Wow! So beautiful and professional work!👌
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
🚀 AI Workflow Hack: Edit Videos 10x Faster!
I built a custom video tutorial showcasing an automated workflow for editing video content. By combining AI-powered tools, this setup helps creators remove dead air, cut down silence, and structure their video files seamlessly. I designed it to help solo creators clean up their timeline and edit up to 10x faster.