Live automations, dashboards, and tools — built from scratch while learning AI and programming. Each project here started with a real problem and ended with something that actually runs.
Things I built to solve problems in my own life. Each one taught me something that carried forward into the next project.
An automated daily reminder that sends a personalised HTML email to my inbox every morning at 8:00 AM — with three prompts: soak millet for Ambali, a motivational quote, and a reminder to eat roasted seeds. Built entirely in Make.com with no coding.
When mornings get busy, small habits slip. Instead of relying on memory or phone alarms, I built a system that delivers a structured nudge automatically — every day, without me touching it.
How to connect a schedule trigger to a Gmail send action in Make.com, map dynamic content into HTML email format, and build a scenario that runs reliably without any manual input. This was my first ever automation — the one that made everything else feel possible.
Trigger-based automation logic, HTML email formatting, Make.com scenario structure, data field mapping, and debugging failed runs using execution history.
Tools built to solve real problems in my day-to-day work — tracking requests, processing meeting notes, and cutting down on manual admin.
An automated request tracking system that watches Gmail every 15 minutes and logs every email with "Request" in the subject line into a Google Sheet — capturing date, sender name, sender email, subject, and a default status of "New".
Manually copy-pasting email details into a tracker is time-consuming and error-prone — especially across multiple active projects. This automation captures every request the moment it arrives, creating a clean audit trail with zero human effort.
This mirrors real-world workflows — stakeholder request tracking, escalation logging, and ticket intake. The watch-filter-log pattern is the same one that powers JIRA automation rules and helpdesk ticketing systems.
How to connect Gmail to Google Sheets using Make.com, build a filter condition that keeps data clean, and design column structure before building the automation — a habit that directly mirrors how I approach tracker setup in professional projects.
An end-to-end AI pipeline that watches a Google Drive folder and automatically processes any meeting transcript uploaded to it. Drop a .docx file in — Google Apps Script reads it, sends the content to the Claude API, and instructs the AI to extract all action items, owners, and deadlines. The results are logged as individual rows in Google Sheets and a formatted follow-up email is created as a Gmail draft, ready to review and send.
After every meeting, reading through transcripts to pull out tasks, assign owners, log deadlines, and draft a follow-up can take 20 to 40 minutes. Across multiple meetings a week, across multiple projects, that time adds up fast. This automation reduces the entire process to one step: upload the file.
Post-meeting follow-up — extracting actions, assigning owners, logging tasks, communicating next steps — is one of the most time-consuming parts of project coordination. This automates it completely, and shows how AI can be embedded into structured PM workflows, not just used as a chat tool.
How to build a multi-step automation pipeline entirely in Google's cloud using Apps Script — no local machine, no paid third-party tools. How to call an external AI API, parse structured JSON responses, and route output to multiple destinations simultaneously. I also debugged a significant real-world issue: a missing MIME type filter caused the trigger to process all file types, generating thousands of duplicate files. Diagnosing and fixing this taught me how critical defensive coding and trigger hygiene are — skills that transfer directly to managing automated workflows in any environment.
Not all of these will be PM tools. Some will be experiments. Some will solve personal problems. All of them will be real.
Scans the action item tracker daily and sends reminder emails to task owners who are approaching or past their deadline — without me having to chase anyone manually.
Every Friday, pulls open tasks from the tracker, summarises project status using AI, and emails a formatted report to stakeholders automatically.
A personal tracking dashboard — what it tracks is still being decided. Could be habits, projects, reading, or something else entirely. Building it to learn frontend and database skills.
A small standalone app — purpose not finalised yet. The goal is to go beyond automations and build something with a real interface. Learning as I go.
I'm actively looking for my next role where I can bring both international delivery experience and an automation-forward mindset to a high-impact team.