Compares spreads across DraftKings, FanDuel, Polymarket, and Kalshi on identical sports markets to isolate how counterparty identification versus maker competition affects pricing. Finds PM prices are 100-300 bps better on liquid markets but long-tail markets on prediction markets have 10-50% spreads. Argues sportsbooks tighten spreads through counterparty-aware pricing while PMs compensate through maker competition and order book transparency.

Follow-up to functionSPACE's V1 discretisation analysis, splitting Polymarket's 18,863 multi-market events into continuous (price brackets, weather ranges, margin percentages) versus categorical (teams, candidates) and re-running the pathology tests. Both types concentrate 90% of volume in the top 5-6 markets, but ghost markets turn out to be largely a categorical phenomenon: continuous events distribute volume more evenly across buckets and survive the liquidity cliff longer at high N. With continuous events overtaking categorical by event count in 2026Q1, the case for a continuous-distribution primitive applies to a growing share of the platform.

Launch post for sdk.markets, a toolkit for creating parimutuel prediction markets on arbitrary questions, built on Base. Argues CLOB infrastructure does not fit thin community markets and that parimutuel pools are simpler and fairer when there is no natural counterparty. Details design choices that address parimutuel's classic 'wait and see' sniping problem (short answer windows, snapshot locking, DPM-style pricing) and three resolution modes: single admin, multi-admin consensus, and an AI oracle that resolves from arbitrary URLs.

Argues that central limit order books fixed the capital destruction problem of early Polymarket AMMs but introduced a new pathology: passive participation is impossible, and only professional market makers can quote. Kalshi reportedly has 23 active market makers with the top three providing 70% of election-contract liquidity, meaning any market those firms ignore is dead on arrival. Positions this as the reason prediction markets remain concentrated in politics and sports while entertainment, science, and culture verticals stay empty, and makes the case for peer-to-peer architecture that lets the first participant seed liquidity for the second.

Argues that prediction market TAM should include the supply side: as the cost of producing real-time probability estimates collapses, the addressable market extends beyond trading volume to every decision that benefits from better forecasts. Presents an ordered liquidity formation path from entertainment to information to institutional demand, and contends that scaling to $1T requires massive breadth in long-tail markets rather than concentrated depth in a few high-volume categories.