helloiamvu
Show HN: Natural language search across Kalshi and Polymarket (API and MCP)
Hi HN, we built a better search for Kalshi and Polymarket. You can use it on the web, via API, or through MCP so AI agents can query it too.There are about 80k active contracts across both platforms right now. Sports, weather, crypto, politics. Tonight's NBA games, BTC up or down in 5 mins. You name it. We trade prediction markets everyday and needed a smarter way to search across all of...
Show HN: Natural language search across Kalshi and Polymarket
Hi HN, we built a better search for Kalshi and Polymarket.There are about 80k active contracts across both platforms right now. Real-time probability estimates for everything from tonight's NBA games to whether Bitcoin will be up in 5 minutes. But the data is fragmented with no unified way to search it.We trade prediction markets everyday and needed a smarter way to search across all of them....
The NFL Edge Index tracks how teams perform against Polymarket odds, compounding into a rating across seasons.
HOW IT WORKS: Edge function pulls Polymarket's API for NFL game markets. Then we capture last valid price before kickoff (filtered 5-95% to exclude illiquid noise). After games finalize, it compares outcomes to pre-game odds. Each team starts at a baseline rating, and if they beat expectations as an underdog, rating climbs. Lose as a favorite, it drops. The rating compounds into a trajectory across seasons.
THE FINDING - NFL FAVORITES ARE OVERPRICED: 80%+ implied: 75 games, market expected 85.1%, actual 70.7% 70-79% implied: 161 games, market expected 74.7%, actual 67.1% 60-69% implied: 201 games, market expected 64.5%, actual 53.7%
Heavy favorites underperform by 14 percentage points. "Any given Sunday" is measurable.
For comparison, I ran the same analysis on 5,600 NBA games. Basketball markets are almost perfectly calibrated - 80%+ favorites win at ~82%. The NFL gap suggests something structural: parity, injury variance, weather, or thinner markets.
TEAM-LEVEL FINDINGS: Best underdog performers - Kansas City: 76.5% win rate as underdogs (13-4). Market priced them at 31% average.
Detroit Lions: 65% win rate as underdogs (13-7). Market consistently undervalued them.
Worst as favorites - Carolina Panthers: Lost 87.5% of games when favored. 14 losses in 16 favored games. New York Giants: 76.9% loss rate as favorites.
Biggest upset - Dallas beat NYG at 7.5% implied odds (Nov 2023). Market gave NYG 92.5%.
EDGE ACCURACY: 70.9% accuracy when model had 70%+ confidence (161/227 games).
Live dashboard: https://attena.xyz/nfl
If you're trading on Polymarket, I'd be curious if you find this data valuable.
Show HN: I analyzed 627 NFL games on Polymarket. Here's what I found
Show HN: How NBA teams perform vs. prediction market expectations
Hi HN, we built this.The NBA Edge Index uses pre-game win probabilities from Polymarket (real-money prediction markets). After each game finalizes, we compare the outcome to the pre-game odds. Beating expectations moves a team's rating up; underperforming moves it down. Each team starts at 2000, and ratings accumulate game-by-game throughout the season. Updates happen automatically after games...
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It converts named layers, layer groups, basic layer effects, layer masks, vector shapes, text layers and more.
Let me know your thoughts. I hope you will like it.
The converter actually works pretty well even with large and complex designs. You should really try it out. There are still some features that we're working on.