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This work showcases a structured outreach and pipeline system I built for a client to bring clarity and consistency to lead management.
I organized the full lifecycle, from sourcing to closing, by defining clear stages, tracking follow-ups, and assigning ownership at each step. The goal was to eliminate gaps in communication and ensure no leads were lost in the process.
This reflects my approach to operations: creating simple, structured systems that improve visibility, accountability, and execution.
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I designed and developed a custom operations management system for a social association, covering administrative workflows, reporting, statistics, logs, attendance records, services, care-related documentation, benefits, support tracking, and operational overviews. The goal was to create a structured and practical platform that improves transparency, documentation, and day-to-day administration across the organization.
I recently got over my fear of doing outreach 😅
Not sure if I’m the only one, but the hardest part wasn’t writing emails… it was figuring out how to track everything. Who did I contact? When? Did I follow up?
Notion? Excel? Random notes?
Didn’t feel right.
So I did the most developer thing possible…
opened my terminal and typed: laravel new 🧠
I ended up building my own CRM from scratch.
It took me a couple of weeks (and a bit of obsession), but now I have a system that helps me manage leads, follow-ups and pipeline without friction.
I even added a radar using Google APIs to discover local businesses automatically 📍
Way more fun than spreadsheets.
Curious…
who else ends up building their own tools instead of using existing ones? 👀
Built a tool that turns Discord discussions into structured UMA data 🚀
Just finished a Python pipeline that connects Discord threads with UMA voting data.
What it does:
— collects messages
— extracts market_id
— links with UMA results
— cleans & structures data
— exports to CSV / SQLite
The goal: turn messy discussions into something analyzable.
Had to deal with inconsistent data, edge cases, and linking off-chain with UMA.
Now it runs end-to-end and produces clean datasets ready for analysis.
Pretty cool to see chaos → structured data.
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