Every skill I have was a checkpoint on the way here. The route runs from
first principles to the systems I build today — and it keeps going.
01 · FOUNDATION · 2022–2024
Foundations
Learned to program, then to reason about programs — algorithms, data structures, and relational database design behind real project databases.
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Where the route begins: problem decomposition, fluency across languages, complexity analysis, and schema design. The groundwork that later made graph-coloring research and production database work possible.
02 · DISCOVERY · 2024
Research
DOST-SEI Scholar; conducted graph-coloring research — hypothesis, experiment, honest measurement.
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A DOST-SEI scholarship and graph-coloring research reframed building as a scientific act: form a hypothesis, test it, measure honestly, and let the measured result decide what ships.
03 · VELOCITY · 2024–2025
Hackathons
Top-5 regional finish, then Honorable Mention (top 6 of 70+ teams) at the international APRU Tech Policy Hackathon 2025.
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Compressing the whole arc — idea to demo — into a weekend. A Top-5 finish at the Regional Smart Communities Exposition, then an Honorable Mention at the APRU Tech Policy Hackathon 2025 in Google Bangkok — top 6 among 70+ teams from across Southeast Asia. Both taught more about scope, teamwork, and shipping than any amount of unhurried planning could.
04 · VENTURE · 2025
Startup Building
Validating startup concepts with real users — problem interviews and prototypes before code.
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Treating ideas as hypotheses about people: problem interviews and prototypes with real users before committing to code, and learning that the hardest engineering is often deciding what not to build.
05 · CRAFT · 2025–2026
Software Engineering
Shipped production systems (Lab IMS, AiGRI) — maintainable code, reviews, and cloud deployment.
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Beyond making it work — making it maintainable, reviewable, and collaborative. Real projects (Lab IMS, AiGRI, BiteSpot on AWS) where the code has to outlive the moment it was written, and where deployment, cost, and reliability become engineering decisions too.
06 · FIELD WORK · 2026
Internship — MYT SoftDev Solutions
Production engineering at MYT SoftDev Solutions Inc. (Cebu City) — ERP performance work and a ticketing notification system for paying clients.
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First professional deployment: inside MYT SoftDev Solutions' live codebases in Cebu City — profiling and optimizing two client ERP systems (~40% faster loads on the heaviest modules) and closing the notification loop in a support ticketing system. Full field reports in Featured Expeditions: Production ERP Systems and the Support Ticketing System.
07 · HORIZON · 2026 →
Future Expeditions
Open to engineering roles and collaboration — harder problems, new domains, real impact.
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What's next is uncharted on purpose. New domains, harder problems, and technology that helps people navigate the real world. Still exploring.
Field Reports
Featured Expeditions
Each project written up as a case study: the problem, the approach,
and what it changed.
Hackathon — Google Bangkok · 2025
Project AiGRI
Drone and computer-vision pipeline that turns crop damage into a submittable insurance claim — Honorable Mention (top 6 of 70+ teams), APRU Tech Policy Hackathon 2025, Google Bangkok.
Outcome — Top 6 of 70+ teams from economies across Southeast Asia — Honorable Mention at the APRU Tech Policy Hackathon 2025 (Google Bangkok), for a working pipeline connecting field imagery to a submittable, LGU-reviewed claim that cut damage-assessment turnaround from weeks to days in pilot runs.
FIG. E-01 · placeholder — drop captures in the images folder
Report E-01 — Filed from Google Bangkok
Mission
Give smallholder farmers a faster, evidence-backed path to an insurance payout after crop damage, instead of a slow manual assessment.
Problem
Manual damage assessment is slow and inconsistent, and smallholder farmers often lack the documentation insurers require to process a claim at all.
Approach
Combined drone imagery with computer vision to run before/after damage analysis, packaging the results for LGU review and direct submission to insurance companies as a claim request.
Lessons Learned
The hard part wasn’t the vision model — it was designing output that a non-technical institutional reviewer (the LGU) and an insurer could both act on directly.
Internship — MYT SoftDev, production
Production ERP Systems
Performance and feature work across two production ERP systems serving paying clients — software engineering internship.
Outcome — ~40% faster page loads on the heaviest modules, on systems paying clients use daily.
PHP, CodeIgniter 3 & 4, MySQL, Hestia CP
Proprietary — live client systems, MYT SoftDev Solutions
40% faster loads
10.3157°N 123.8854°E
Field report
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Report E-02 — Filed from Cebu City
Screenshot pending
FIG. E-02 · placeholder — drop captures in the images folder
Mission
Keep two clients’ ERP systems fast and reliable as their data volume and usage kept growing.
Problem
Data-fetching and page load were slowing down on live client systems as scale increased — a slowdown client staff felt every working day.
Approach
Profiled and optimized data-fetching and load performance, fixed existing bugs, and shipped client-requested features across two separate, actively-used ERP codebases.
Lessons Learned
Optimizing inside someone else’s production codebase is a different discipline than building from zero — every change has to respect existing architecture and can’t break what’s already shipped.
Internship — MYT SoftDev, production
Support Ticketing System
Ticketing upgrade with automated two-way notifications for clients and sales reps — same internship, second production system.
Outcome — Same-day first response on client tickets, down from next-day, once notifications closed the loop for both clients and the internal sales team.
PHP, CodeIgniter 4, MySQL, Hestia CP
Proprietary — live client system, MYT SoftDev Solutions
Same-day response
10.3157°N 123.8854°E
Field report
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Report E-03 — Filed from Cebu City
Screenshot pending
FIG. E-03 · placeholder — drop captures in the images folder
Mission
Make sure the right person is notified the moment a ticket needs their attention, instead of relying on someone remembering to check a dashboard.
Problem
Tickets logged without a notification loop just sat until someone manually checked — slow for clients and slow for the sales team responsible for the account.
Approach
Shipped email notifications to both the client and the relevant salesperson per project whenever a ticket was issued — in both directions — alongside data-load optimization and bug fixes.
Lessons Learned
A notification system has two audiences with different needs (client vs. internal sales) — the same event had to be communicated differently to each.
Mobile app — campus adoption
Klaro
Mobile grade tracker that reads a syllabus PDF or screenshot and builds the semester’s grading breakdown automatically.
Outcome — 100+ students across the college adopted it within its first semester.
FIG. E-04 · placeholder — drop captures in the images folder
Mission
Keep students aware of exactly where they stand in a course while there’s still time to do something about it.
Problem
Grading breakdowns live buried in a syllabus PDF students read once and forget; nobody recomputes “what do I need on the final” by hand until it’s too late.
Approach
Built parsing that extracts grading weights directly from an uploaded syllabus — PDF or screenshot — turning a static document into a live per-semester tracker instead of something read once and closed.
Lessons Learned
Real syllabi are inconsistent — different formats, different weighting language — so parsing had to be built for that messiness from the start.
Web platform — AWS-staged
BiteSpot
Community-driven food discovery platform for Tacloban, staged on AWS for a real production deployment.
Outcome — 50+ local food spots cataloged during the pilot; started as a school project, now being evaluated for a real production launch.
Laravel, PHP, MySQL, AWS EC2, AWS RDS
50+ spots listed
11.2543°N 125.0000°E
Field report
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Report E-05 — Filed from Tacloban City
Screenshot pending
FIG. E-05 · placeholder — drop captures in the images folder
Mission
Give small, local Tacloban food spots — the kind that never show up on national delivery apps — a way to be found by their own community.
Problem
Local vendors have no shared discovery layer; word of mouth doesn’t scale past a neighborhood, and there’s no dedicated place for the community to surface and rate the spots they know.
Approach
Built a platform where vendors showcase their establishment and users rate, visit, and add new “bitespots,” then took it past the classroom by staging a simulated production deployment on AWS (EC2, RDS) to validate it as more than a school project.
Lessons Learned
Standing up real cloud infrastructure — even simulated — surfaced deployment and scaling questions a local dev environment never would have.
Desktop system — UP Tacloban
General Laboratory IMS
Desktop inventory system for UP Tacloban’s General Laboratory, replacing pen-and-paper borrow/return tracking.
Outcome — 400+ equipment items across multiple departments, tracked in the lab's day-to-day operations.
Java, MySQL, JDBC
400+ items tracked
11.2543°N 125.0000°E
Field report
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Report E-06 — Filed from UP Tacloban
Screenshot pending
FIG. E-06 · placeholder — drop captures in the images folder
Mission
Remove the lost paperwork and guesswork from lab equipment borrowing, and hold both students and equipment accountable.
Problem
Paper-based tracking meant no reliable record of what left the lab, what condition it came back in, or who was responsible.
Approach
Built a desktop application where students log borrows and returns with condition proof, and admins get a searchable, real inventory instead of a drawer of forms.
Lessons Learned
Designing for accountability meant the data model had to capture condition and proof alongside quantity — a different problem than a standard inventory CRUD app.
Web app — organic launch
Salin
AI-powered expense tracker for college students — 30+ registered users in its first week, fully organic.
Outcome — 30+ registered users within the first week of launch with zero paid promotion, and roughly 40% still logging expenses weekly a month in.
FIG. E-07 · placeholder — drop captures in the images folder
Mission
Help college students actually keep tracking their money past week one, without the manual-entry friction that gets most budgeting apps abandoned.
Problem
Students already record spending somewhere — a notes app, a text to themselves — just never in the tracker itself, because re-entering everything by hand is the first habit to slip.
Approach
Built a mobile-first tracker that reads free-form notes and auto-fills transactions, so tracking happens where students were already writing things down instead of in a separate form. Added multi-card support and savings goals so the app models how a student’s money is actually split up.
Lessons Learned
Removing one point of friction — manual entry — moved retention more than any feature added after it.
See more on GitHub ↗
including work in progress like AcadSort, a desktop app that auto-organizes academic files.
Field Notes
Research & Innovation
The areas I'm studying right now. Each one feeds directly into at
least one of the projects above.
Active — current focus
Ongoing — continuing work
FIG. R-01
R-01
Active
Computer Vision
Damage quantification from drone imagery — classification pipelines built on pre-trained models (ResNet-50) and OpenCV, applied to real agricultural insurance claims rather than benchmark datasets.
PROJECT AIGRI
FIG. R-02
R-02
Ongoing
Artificial Intelligence
How AI systems earn trust in institutional settings — designing model output that non-technical reviewers and decision-makers can act on directly.
PROJECT AIGRI
SALIN
FIG. R-03
R-03
Ongoing
Machine Learning
Transfer learning and fine-tuning pre-trained models for domain-specific classification — making models work under small, messy, real-world datasets instead of curated ones.
PROJECT AIGRI
FIG. R-04
R-04
Active
Graph Algorithms
Graph coloring — heuristic and exact approaches, and where the tractability boundary actually sits in practice. Hypothesis, experiment, honest measurement.
FIELD STUDY · GRAPH COLORING
FIG. R-05
R-05
Ongoing
Optimization
Performance under real constraints — algorithmic complexity treated as a budget, query tuning, and load-time profiling measured on live production systems.
ERP SYSTEMS
FIG. R-06
R-06
Active
AI Applications
Free-form text into structured data — expense notes into transactions, syllabi into live grading models. Parsing built for how people actually write — inconsistent, abbreviated, messy.
SALIN
KLARO
FIG. R-07
R-07
Ongoing
Software Engineering
What keeps production systems maintainable as they grow — architecture that survives other people's changes, and performance work measured on codebases that are already shipped.
ERP SYSTEMS
TICKETING SYSTEM
Instruments
Engineering Toolkit
FIG. T-00 · Compass
Primary instruments — proven in shipped work
Python
Computer-vision pipelines and model training
Project AiGRI
PHP
Production ERP and client systems at scale
Production ERP Systems · Support Ticketing System · BiteSpot
MySQL
The workhorse database behind four shipped systems
Production ERP Systems · Support Ticketing System · BiteSpot · General Laboratory IMS
