A B.Tech student in Delhi, Aryan Nishad, has built Bribes.fyi, a free platform that lets citizens anonymously report bribery demands at government offices, and it has since gone viral, collecting 616 reports from 251 cities.
What makes this notable from a builder’s perspective isn’t just the idea, which isn’t new, but the execution speed: Nishad used AI-assisted coding to build the entire platform solo, going from concept to a live, functioning product with real usage in a matter of weeks.
That’s a meaningful data point for anyone evaluating what a single motivated builder can now ship without a team, a budget, or institutional backing — AI-assisted development has materially lowered the cost of building and shipping a working civic-tech product.
The platform’s design choices are also worth studying: reports are anonymised and aggregated rather than individually verified, which trades investigative rigor for speed and low friction, letting the product surface patterns (which departments, which cities) rather than adjudicate individual claims.
That’s a deliberate scope decision, and a smart one for a solo-built MVP: rather than trying to build a verification system that would require far more resources, the platform focuses on what a lightweight, crowdsourced dataset can do well, which is reveal where complaints cluster.
For founders evaluating what’s now buildable solo, Bribes.fyi is a useful proof point: the bottleneck for shipping a genuinely useful public-interest product has shifted from engineering capacity to picking the right, narrow problem and shipping fast.
The idea follows a broader tradition of crowdsourced anti-corruption reporting tools that have surfaced periodically in India over the years, though Bribes.fyi has stood out for the speed with which it was built and its rapid early adoption.
The platform’s low-friction, minute-long reporting process is widely seen as a key reason for its quick early adoption compared to more formal complaint mechanisms.
Photo by Tirza van Dijk, Wikimedia Commons, CC0