Where rendered futures meet reality.
-- Prediction markets scored by edit distance. +
+ Prediction markets scored by edit distance. Predict the exact text a public figure will post. The closer your words, the more you win — scored character by character via Levenshtein distance on Coinbase BASE L2. @@ -315,6 +308,13 @@
- A market opens: What will @satyanadella post? +
+ A market opens: What will @satyanadella post? Players roleplay as Nadella — channeling his voice, anticipating his numbers, matching his syntax. Two AI models submit predictions. Then the actual post arrives:
+
On a yes/no market, both AIs "predicted correctly" — no one wins anything interesting. - Here, the 7-edit gap between them decides everything. Closest guess takes the pool. + Here, the 7-edit gap between them decides everything. Closest guess takes the pool.
+
Note: This example is constructed and illustrative, not real data.
+
Levenshtein distance counts the minimum number of single-character changes (insertions, deletions, or substitutions) needed to turn one string into another. - It's the same algorithm your spellchecker uses. Here's how it scores the two predictions above: + It's the same algorithm your spellchecker uses:
+
Lower edit distance = closer prediction = bigger share of the prize pool. - The smart contract computes this on-chain for every submission, so scoring is transparent and tamper-proof. + The smart contract computes this on-chain for every submission.
cat → cartcart → catcat → cutcat → cartcart → catcat → cut- Each operation costs 1 edit. The total count is the Levenshtein distance between two strings. -
The market resolves with the actual text. The contract computes Levenshtein distance on-chain for every submission. Closest prediction wins the pool (minus 7% platform fee).
+The market resolves with the actual text. The contract computes Levenshtein distance on-chain for every submission. Closest prediction wins the pool (minus 7% platform fee).
Submit the __NULL__ sentinel to bet that someone won't post. If they don't, dL = 0 — a perfect match. A market primitive that binary contracts can't express.
Submit the __NULL__ sentinel to bet that someone won't post. If they don't, dL = 0 — a perfect match.
+
Proteus is one piece of Timepoint AI (opens in new tab)'s open-source infrastructure for temporal reasoning. - It validates predictions against reality — the scoring layer for Timepoint's rendered futures. + It validates predictions against reality — the scoring layer for rendered futures.
Prediction market protocol on BASE L2. Scores rendered futures against actual outcomes via on-chain edit distance.
- How they connect: +
+ How they connect: Pro simulates what people might say. Proteus scores those simulations against what they actually say. Clockchain accumulates the results. SNAG-Bench measures how well the whole system reasons about time.
@@ -567,9 +563,8 @@+
This is a v0 alpha — a working prototype, not a production system. - The smart contracts work. The math works. Everything else is scaffolding.
- These are the things that would need to exist before this becomes a real product. We're not there yet. +
+ These are the things that would need to exist before this becomes a real product.
TIMEPOINT LABS
+
Proteus is part of Timepoint's Labs program — early-stage experiments shared openly - for feedback, contribution, and collaboration. If the idea of text-based prediction markets - interests you, we'd love to hear from you. + for feedback, contribution, and collaboration.
- No real funds. Testnet only. Everything is open source under MIT. +
+ No real funds · Testnet only · MIT License