A prediction product on CryptoPrediction.com could help people compare forecasts before deciding how much attention to give them. I would begin with an information-organizing tool: a place to record who made a claim, when it was published, which horizon it covers and what would count as an outcome. That is a concrete product job. It avoids making an early software business depend on a claim that its own model can see the future.
Choose a comparison that can be made fairly
The first design challenge is deciding which claims belong beside one another. A year-end price outlook and a probability of a particular event are different objects. Even two price forecasts may use different reference markets, currencies or timestamps. A good comparison screen would expose those differences instead of hiding them behind a tidy ranking. The product brief should name the first narrow class of forecasts it will support.
For an early prototype, I would enter a small set manually with the publisher's permission where needed. Each record would contain the original wording, a source link, a publication timestamp, the stated horizon and a note about missing information. If a forecast cannot be interpreted consistently, the product should say why. That exercise tests the data model before the team commits to collection automation or a large subscription database.
Make the record explainable
A forecast record needs a history. When a publisher updates a target, users should be able to distinguish the original claim from the revision. If an input source changes, the product should show what changed and when. Version identifiers, edit notes and a visible last-checked date are ordinary software details that become important when the subject is a moving market narrative.
The same care applies if the tool includes models of its own. A model version should be tied to its inputs and the date it ran. A later improvement should not overwrite the output that an earlier user actually saw. The interface can help by separating the saved output from the current model. That makes later review possible without implying that a stored result was accurate or suitable for a financial decision.
Design around questions, not a leaderboard
A comparison view could let a reader filter by horizon, source type and available assumptions. Clicking through would reveal the original context. A missing assumption should appear as missing, rather than as a neutral score that looks measured. Readers might save a small watchlist of claims to revisit. The value would be the reduction in manual organization and the ability to inspect what was said.
I would be cautious about an overall accuracy score in a first release. Any evaluation rule needs to be defined before the outcome is known, and it must explain which records are excluded. Broad statements can be hard to evaluate at all. A product can begin by helping users compare the structure of claims, then publish a clear evaluation method if that becomes a justified feature. Attractive charts are not a replacement for that method.
Plan the data work
The ongoing burden would include source access, permissions, broken links, timestamp handling and review of ambiguous claims. A founder should estimate the time for those tasks separately from interface development. Automated extraction can make a mistake that is easy to miss in a dashboard. A review queue could ask a person to confirm fields that matter, especially horizons, units and qualifications in the original wording.
A useful first customer might be a small editorial or research team already maintaining its own spreadsheet. Interviews should ask what that spreadsheet contains, who updates it and which steps take the most effort. The prototype can then test one saved workflow. A recurring team subscription might make sense if the tool replaces enough repeated work, but willingness to pay should be tested with those users rather than inferred from interest in crypto.
Earn distribution through a clear demonstration
A public example could follow one hypothetical claim through entry, revision and review. It would show the product's mechanics without advertising a winning forecast. Short demonstrations in relevant research communities could lead to structured trials. Trial feedback should focus on whether users can find the source, understand the comparison and return to the record later. Those are observable product outcomes that do not depend on market performance.
CryptoPrediction.com would give this tool a direct subject identity. A precise product subtitle would explain the comparison job so visitors understand what they can do on arrival. The team would still need to build reliable records, a sustainable collection process and trust with users. Before investing in a full platform, sketch the record format and test ten entries with a prospective customer. If the workflow has a clear purpose and the domain suits it, the pricing inquiry is a natural next step.