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collaboration95/README.md

Hey, I'm Guru.

I build things in Singapore. Some start with a small annoyance on my Mac. Others start in a room full of people, with a deadline and an idea I hadn't planned for.

My website · LinkedIn · Say hello

Here are a few things I've been making. Shelf rearranged October 2026.

A room that builds itself

The Commons: a cartoon of the makeathon room, with a kopi cup, otters and other wished-for creatures walking among the tables.

At 10:01, the organisers started explaining the makeathon. By 10:04, I'd dropped all ten ideas I'd prepared.

I ended up building The Commons: scan a QR code, wish for a creature, and watch it walk into a cartoon of the room you're sitting in. The agent writes its behaviour, tests it, fixes its bugs and draws it. Solo build; Best Use of Tech at Common Grounds.

The build and how it works →

My desktop used to be a graveyard of screenshots

Hundreds of little mysteries I had to open one by one. So I made Screenshot Declutterer.

Before: a batch waiting for review. After: five queued for Trash, with Undo still there. These are synthetic demo screenshots; click either to look closer.

Before: the local screenshot board has 21 unsorted screenshots and a batch of five selected for review. After reviewing the batch: five screenshots are queued for Trash, sixteen remain unsorted, and Undo and Clean up 5 are available.

Keep, rename, compare similar images, change your mind. Nothing goes to macOS Trash until you confirm. Optional naming runs on the Mac too.

Run it locally →

How much of a map do you actually need?

Plotline keeps what a forest-map screening rule needs, then checks that the smaller representation gives the same answers.

Plotline retained proof: 744,732 cells checked for each of two screening contracts, with zero mismatches between original layers and the compact representation. Scope: the pinned region, input versions and written rules.

744,732 cells per contract. Zero mismatches. A bounded hackathon experiment, with the rule and retained evidence open to inspection.

Read the experiment · Inspect the proof

An agent bought an article. Now what?

For ResearchAgent, our team kept coming back to three questions: is it worth buying, did the right evidence arrive, and can someone follow what happened?

1. Set a budget → 2. Check the evidence → 3. Keep the receipt.

Step 1: ResearchAgent asks a question and sets a spending budget. Step 2: the answer view shows passages, sources and a proof-check trail. Step 3: an itemised testnet receipt records the evidence purchase.

SingHacks finalist, built with my team. On 10 October 2026, I took ResearchAgent to the main stage at AI Tinkerers Singapore. The live demo ran on Google Cloud Run and placed among the top three Cloud Run demos, earning US$100 in cloud credits.

ResearchAgent’s answer and spending budget projected beside a presenter at the Singtel podium during AI Tinkerers Singapore.

Photo: event attendee, via the AI Tinkerers Singapore event gallery. The slides from the talk →

Fictional articles and XRPL Testnet, with challenges and refunds in the demo. The Cloud Run deployment has been retired; the walkthrough is still there.

Follow the walkthrough →

Two tabs I keep around

yald started because I wanted to know where my coding-agent tokens were going. Now it's a tab that's always open. Read-only local reports; the cost chart estimates API prices, not my subscription bill. Public preview.

Then there's talktoit, for asking questions of an Apple Health export. The interesting bit turned out to be the importer: one recorded M1 benchmark went from about 26 minutes to 9.77 seconds. The measurement, without anyone's health records.

Earlier, I was teaching a camera where to look

DetectT's public prototype shows the camera feed beside a mirrored view with pose landmarks overlaid.

Fitback was our capstone team's two-photo sizing project. The public DetectT prototype shows the pose-guidance and capture part; getting two model runtimes to cooperate meant isolating the older runtime in a worker.

Our team took first place in Design AI at InspireCon 2025. Here's the camera code.

A couple of older saves

Sushi Shakedown: a pixel-art sushi counter with waiting customers and ingredients to prepare. Our Breakout game's brick-framed menu, with quick game, load game, high score and settings buttons.

Sushi orders on one side, bouncing bricks on the other. Sushi Shakedown is a team Unity game (upstream); I worked on backgrounds and NPCs. Breakout is an earlier team course project in C and SDL2. Saves, scores, sound, and a lot of bouncing.


There are other threads too: Rewind, our group time-capsule prototype; FlickPond, our course team's video pipeline; and uno-dos-tres, a phone camera reading Uno cards.

Further along the trail: provenance-aware permissions with the MandateFlow team, Handoff's evidence-to-invoice experiment, StyleMind's fashion conversations, a Raspberry Pi looking for parked cars, and an educational Raft-style booking simulation.

Project screenshots, not stock art. Image sources and context.

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