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Lattecino
Finally, permission to say hello in the café you're in
The person you should meet today is probably twenty feet away, in a café you have not found yet. Lattecino shows you both. Check in and see who is open to a hello. Mutual opt-in, declines stay invisible, only that room, never "nearby". And because a hello starts with trusting the room, we measure cafés by hand: real speed tests, coffee rated in person, the espresso machine written down. 488 cafés, exactly 3 earned a 5.0. Nobody can buy a rating. The room is the network.
I built Lattecino because of something I believe: In a world where AI does more of our work and more of our talking, human connection becomes the scarcest resource. It is the new capital.
Almost every way to meet people asks you to spend money, travel somewhere and plan ahead. Connection becomes a project. Cafés ask none of that. You are already there, already lingering. The person worth meeting is twenty feet away. The only missing piece is permission.
So the endgame is simple: you sit down with a flat white, open the app, and see who else in the room is open to a hello. Proximity instead of planning.
But I hit a wall immediately. You cannot send people to the right room if nobody knows which room is right. Every café everywhere is a 4.7 star mystery, and no app could tell me whether the WiFi would survive a video call or whether the coffee was worth crossing the street for.
So I did the unscalable thing. I walk in, run a real speed test at the table, rate the coffee in person, and write down which espresso machine is on the bar. 488 cafés so far across Bali and Hamburg, 99 speed tests, 74 bars with the equipment logged.
The part I think this crowd will appreciate is that the honesty is enforced in the database, not in a policy page:
The rating formula is a Postgres trigger. Work-fit factors like WiFi and desk space can drag a score down but can never lift it above the coffee quality.
Imported listings are hard-capped at 4.7 until a human physically verifies them. No café can buy its way past that.
When I improved the WiFi curve so full marks require 100 Mbps instead of 50, 51 cafés dropped a tenth. I shipped the drop.
The connection layer is built and deliberately quiet about privacy: only people checked in at that café can see each other. Never "nearby", never a map of strangers. It is early and most rooms are still empty, which is the honest state of a two-city app on day one.
Under the hood: Next.js 16 and Supabase with the integrity rules in SQL, Cloudflare's speed test engine running in the browser, both Mapbox and Google Maps shipped side by side so users pick the winner with an analytics event instead of me picking with an opinion, and an AI operations layer that writes the café copy from photos, renders the social cards from live rows, and cuts the video ads. I am one person. The only thing I refuse to automate is walking into the café.
AI can now generate almost anything. It still cannot generate the feeling of a real conversation with a real person who was sitting twenty feet away the whole time. That is what all the measuring is for.
Ask me anything, especially the hard stuff: chicken-and-egg, how a solo founder verifies at scale, or why I think the dataset itself is the business.
About Lattecino on Product Hunt
“Finally, permission to say hello in the café you're in”
Lattecino was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #14 on the daily leaderboard. The person you should meet today is probably twenty feet away, in a café you have not found yet. Lattecino shows you both. Check in and see who is open to a hello. Mutual opt-in, declines stay invisible, only that room, never "nearby". And because a hello starts with trusting the room, we measure cafés by hand: real speed tests, coffee rated in person, the espresso machine written down. 488 cafés, exactly 3 earned a 5.0. Nobody can buy a rating. The room is the network.
On the analytics side, Lattecino competes within Productivity, Travel and Data & Analytics — topics that collectively have 707.8k followers on Product Hunt. The dashboard above tracks how Lattecino performed against the three products that launched closest to it on the same day.
Who hunted Lattecino?
Lattecino was hunted by Michael Hensel. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Lattecino including community comment highlights and product details, visit the product overview.
Michael Hensel
Hi Product Hunt, Michael here, solo founder.
I built Lattecino because of something I believe:
In a world where AI does more of our work and more of our talking,
human connection becomes the scarcest resource. It is the new capital.
Almost every way to meet people asks you to spend money, travel somewhere and plan ahead.
Connection becomes a project. Cafés ask none of that. You are already there, already lingering. The person worth meeting is twenty feet away. The only missing piece is permission.
So the endgame is simple: you sit down with a flat white, open the app, and see who else in the room is open to a hello. Proximity instead of planning.
But I hit a wall immediately. You cannot send people to the right room if nobody knows which room is right. Every café everywhere is a 4.7 star mystery, and no app could tell me whether the WiFi would survive a video call or whether the coffee was worth crossing the street for.
So I did the unscalable thing. I walk in, run a real speed test at the table, rate the coffee in person, and write down which espresso machine is on the bar. 488 cafés so far across Bali and Hamburg, 99 speed tests, 74 bars with the equipment logged.
The part I think this crowd will appreciate is that the honesty is enforced in the database, not in a policy page:
The rating formula is a Postgres trigger. Work-fit factors like WiFi and desk space can drag a score down but can never lift it above the coffee quality.
Imported listings are hard-capped at 4.7 until a human physically verifies them. No café can buy its way past that.
When I improved the WiFi curve so full marks require 100 Mbps instead of 50, 51 cafés dropped a tenth. I shipped the drop.
The connection layer is built and deliberately quiet about privacy: only people checked in at that café can see each other. Never "nearby", never a map of strangers. It is early and most rooms are still empty, which is the honest state of a two-city app on day one.
Under the hood: Next.js 16 and Supabase with the integrity rules in SQL, Cloudflare's speed test engine running in the browser, both Mapbox and Google Maps shipped side by side so users pick the winner with an analytics event instead of me picking with an opinion, and an AI operations layer that writes the café copy from photos, renders the social cards from live rows, and cuts the video ads. I am one person. The only thing I refuse to automate is walking into the café.
AI can now generate almost anything. It still cannot generate the feeling of a real conversation with a real person who was sitting twenty feet away the whole time. That is what all the measuring is for.
Ask me anything, especially the hard stuff: chicken-and-egg, how a solo founder verifies at scale, or why I think the dataset itself is the business.