Projects2026

Schrollar

Overview

An AI-powered research-discovery platform that turns natural-language queries into curated academic-paper feeds. Shipped end-to-end in a 24-hour hackathon — placed 3rd of 22 teams and won PHP 10K at GDG Loyola Hackfest 2026.

Role: Full-stack developer
Context: GDG Loyola Hackfest 2026
Delivery: 24-hour build
Result: 3rd of 22 teams · PHP 10K prize

Results at a glance

140+ Papers aggregated
16+ Research streams
3rd / 22 Hackfest placement

Problem

Researchers — especially students new to a field — spend hours filtering paper databases trying to phrase the right query. Discovery tools are tuned for exact keyword search, not the messy way real research questions get asked in plain language.

Approach

Schrollar accepts a natural-language research query and returns a curated, ranked feed of relevant papers. Queries are mapped to active research streams so a user describing what they care about gets back a feed shaped around that interest, not a literal keyword match. We shipped the full pipeline — query understanding, paper aggregation, relevance ranking, and the reading interface — end-to-end during a 24-hour hackathon.

Technology

Next.js · TypeScript · Python · Tailwind CSS

Technical decisions

Natural-language → research-stream mapping

Rather than embedding every query against the entire paper corpus, queries are first mapped to a smaller set of active research streams. Streams are aggregated lazily and reused across users. This kept latency tolerable and the cost predictable inside a hackathon budget.

24-hour scope discipline

We deliberately cut features that didn't directly support the demo path: query → ranked feed → readable result. Auth, persistence, and personalization were pushed past v1. The discipline is what got us shippable in 24 hours and on stage at Loyola Hackfest 2026.

Tradeoffs

Stream-first architecture vs. open-ended search

Mapping queries to a curated set of streams trades expressiveness for relevance and latency. It works well for early-stage exploration; users with very specific paper-level queries are better served by a keyword tool.

Hackathon shipping vs. production hardening

No retry/backoff, minimal error states, and best-effort caching. Good enough for a demo; the next iteration would put these in.

Outcomes and lessons

Shipped a complete query-to-reader demo in 24 hours, covering 140+ papers across 16+ research streams.
Placed 3rd of 22 teams and won a PHP 10K prize at GDG Loyola Hackfest 2026.
A production release would add authentication, durable user libraries, retries, and relevance evaluation.

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