How Parth Built LokaLinc with AI and Reported 500+ Users Within Three Weeks

Parth had lived in Germany for eight years when he began building LokaLinc. As a systems engineer, he brought experience solving problems, but his professional background was outside computer science. The problems he chose were familiar to his community: temporary accommodation, travel support and finding useful local information.

An experience during his mother's visit helped make the need personal. He had looked for travel assistance and could not find a suitable solution. Instead of waiting for someone else to build it, he began experimenting with an AI app builder on a weekend.

Parth describes getting an initial prototype running in approximately three hours and showing it to his wife. In the following days, he gathered feedback from friends and surveyed potential users. Those conversations helped him decide whether the idea deserved more work.

The prototype and the launch were separate milestones. He says the app was mostly ready after about 15 days, then developer friends helped him test it and work through bugs. Registrations, administrative work and store preparation followed. His estimate for the full journey to the App Store and Google Play was about two and a half months.

As the project developed, he learned about notifications, analytics and manual code changes. He credits both friends and Newly's support team with helping him work through unfamiliar parts of app development.

LokaLinc brings three activities into one app: sublet listings, travel support and community discussions. Users can describe accommodation, add photos and filter listings by city and budget. Travel posts help people find companions or arrange small delivery assistance. Community discussions let members ask questions and share experience.

Parth describes in-app contact as a way to let people discuss arrangements without immediately sharing personal contact details. The product was free to use at the interview, and his focus was on solving the community's problem before choosing a revenue model.

The audience existed before the app. Parth and his collaborators began by discussing the problem in community groups and on social media. After store approvals, they announced the platform, shared its website and explained what people could expect. Once it launched, they made videos showing how to use it.

He reports passing 500 users in approximately two and a half weeks. During the interview he also reads out 217 Google Play installs and 391 App Store downloads, then discusses a separate PostHog dashboard. Store downloads, registered users and analytics users are different measures, so the 500+ figure here is his account of the early audience, not a combined or independently audited total.

His approach was to show specific jobs the app could help with, such as finding accommodation or offering travel assistance. He also acknowledged that a community of 500 people could not yet replace every existing group or guarantee a match.

LokaLinc connects a personal problem with a reachable audience. The first prototype made the idea tangible; feedback, testing and targeted distribution turned it into a product people could try.

For someone considering a community app, the useful starting question is who already has the problem and where those people talk about it. Parth's story provides one example of that process, rather than a promise that every app will achieve the same growth.