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Cover image for Case Study: How One AI Agent Saved a Founder Weeks of Resear...
Case Study: How One AI Agent Saved a Founder Weeks of Research Hey founders! A few days ago, a client reached out with a new requirement. He said: “I need a full due diligence report Agent. Research everything news, social media, the whole internet and give me a clean PDF.” (for same report Messari.io (https://messari.io/) charge $4000/year) No big research team. No weeks of waiting. I built a smart AI agent that handled the entire job automatically. Here’s exactly what happened: The client simply uploaded the white paper. The AI read it carefully, then went out and searched the internet, news sites, and social media for real-world insights, risks, and opportunities. It pulled everything together and delivered a professional, ready-to-read PDF report. All done in hours instead of weeks. I used the latest AI tools behind the scenes a simple chat interface on Slack, multiple smart agents working together, and powerful search capabilities. But the founder didn’t need to know any of that. He just uploaded the document and got his report. This kind of AI is perfect when you have a consistent workflow things you do again and again, like due diligence, market checks, competitor reviews, or investment research. It gives you accurate, thorough results every single time without hiring extra people or spending endless hours. For non-tech founders, this is game-changing. You stay focused on building your business while the AI does the heavy research lifting. Would you use an AI like this for your next big decision? Drop a comment I’d love to know what kind of research you hate doing manually! 👇
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Cover image for I implemented an automated transaction
I implemented an automated transaction reconciliation system using Python, designed to match platform transactions with bank records, developed as part of a larger Admin Portal for financial operations. What this system supports: • Automated matching of bank & platform transactions • Keyword-based and rule-driven reconciliation logic • Batch processing for large transaction datasets • Configurable matching rules per account or merchant • Clear linking between matched records for auditing • Scheduled reconciliation via cron jobs running on Render • Designed to integrate with existing APIs and databases This project focused heavily on: ✨ Reducing manual reconciliation time within the Admin Portal ⚡ Performance-focused processing for large datasets 🧠 Clear, maintainable Python logic developed iteratively using Cursor
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