When
@WhenMarkets
Fully onchain, verifiable prediction markets for timing & ranges on @Solana
We are live. WHEN is a new kind of prediction market built for uncertainty not yes/no outcomes. Today we’re launching our first markets: Motherlode range predictions for • GODL • ORE Predict in ranges not binaries. when.markets The future isn’t binary. It’s a
Some people have been asking how to use WHEN. So here is a quick walkthrough of our Motherlode range Prediction for $GODL and $ORE
Motherlode showed how ranges can change interaction with outcomes. What gets interesting is when ranges stop being a single product and become a primitive creators can use.
Most systems try to predict exact outcomes. On WHEN, we’re more interested in where outcomes tend to land. This difference changes everything.
SOVs are probabilistic by nature. You don’t know WHEN the hit comes. You only know it lands somewhere. That’s exactly why SOVs like @OREsupply and @Godlsupply made sense for our first range based product.
We didn’t build WHEN on @solana just for one market. Motherlode prediction is our first product, not the destination. Range based predictions open the door to new ways of interacting with outcomes. $ORE $GODL
We’ve been answering a lot of questions about overlapping ranges on our ML predictions. They don’t overlap. Final hit lands in one range. Transparent settlement onchain. Appreciate all the feedback so far. when.markets $ORE
Small but important update: We’ve added verification links to the ML UI so users can independently check the count and hit.
Most prediction markets force the future into a YES/NO box. WHEN doesn’t. Our first live product - Motherlode hits prediction lets you predict in ranges not binary with everything verifiable on-chain. Because forcing precision is the mistake. It always lands somewhere.
First Motherlode range just resolved on $GODL. Not a demo. Not a test. A real prediction by real users, real settlement and real payout. All verifiable onchain. Small stakes today. Bigger markets next. #WHEN
Most people don’t lose because they’re wrong. They lose because they were too precise.
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