Suyog Bhoye

what it is

Gen Z students in India graduate with certificates, hackathon wins and side projects that no recruiter has time to parse. Résumés flatten all of it into bullet points. The platform needed a single signal a recruiter could trust in ten seconds — and students could actually move.

the fun bit

Made the score a ring, not a bar

The first build used a progress bar with a percentage. It tested badly: a bar implies a finish line, and students read 62% as failure. Switching to a 0—1000 ring with an animated delta reframed the same data as position rather than completion — the number moves, the ring never ends. The delta animation (+18 since last week) became the thing users opened the app for.

what it is

Campus social apps die from two causes: outsiders flooding in, and a feed that stalls the moment it gets popular. The product needed hard verification at the door and a swipe deck that stays at 60fps while the backend runs on free-tier economics.

the fun bit

Prefetched the deck instead of paginating it

The obvious build is a paginated feed: fetch 20, swipe, fetch 20 more. That puts a network round-trip in the middle of a gesture. Instead the client holds a rolling window of candidates and refills in the background at a low-water mark, while the Go service precomputes candidate sets per user rather than ranking on read. Swipes never wait on the network.

what it is

The brand's whole value is provenance: ingredients sourced from a women's self-help group, processed in small batches. On the shelf, that story is invisible — it competes with mass-produced jars that look identical. The client needed the supply chain to become the marketing.

the fun bit

Anchored batch records to a ledger, kept the read path off-chain

A traceability claim nobody can verify is a marketing claim. Writing every batch on-chain makes it verifiable but makes page loads slow and costly. The build hashes each batch record to the ledger for tamper-evidence, while the consumer page reads from MongoDB — so verification is available on demand and the trail still loads instantly on a 3G phone in a shop aisle.

what it is

Actuarial teams model products as thousands of interdependent variables, then override slices of them per workspace. Doing this in spreadsheets is slow and untraceable; doing it in Prophet means hand-translating formulas. The engine had to hold the variable model, run simulations locally, and emit Prophet-compatible formulas.

the fun bit

Product/workspace override resolution instead of copied models

The naive model copies a product's full variable set into each workspace so it can be edited. That multiplies storage, and a change to the product never reaches its workspaces. Instead workspaces store only overrides and resolve against the product at read time — one lookup layer, one source of truth. Changing a product variable propagates to every workspace that hasn't explicitly overridden it.