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Prediction markets are a (poorly understood) multi-billion dollar industry. This Law of Code episode is a multi-hour deep dive on prediction markets, from conclave betting in 15th century Rome to proposed rulemaking from the CFTC earlier this month. My goal: the internet's most comprehensive explainer on prediction markets. I...

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E174: Tarek Mansour - Launching the First Regulated Perps In The U.S and building the next generation of financial markets Tarek Mansour is the co-founder and CEO of Kalshi, the first regulated prediction market exchange in the US, valued at $22 Billion. He grew up in Lebanon with a single mom, studied at MIT, worked at Citadel, and spent 6 years years building a company most people ignored before it finally took off. We talk about what it actually takes to not give up, why markets are better at finding truth than experts, why Kalshi is launching the first regulated Perps in the US, and how his team is building what he calls the next generation of financial markets. Timestamps: 0:00 Intro 1:54 Urgency 3:11 Anime 4:53 Who is Tarek? 7:02 Mathematics & Certainty 9:10 Tarek's chip on the shoulder 13:33 Partnerships: Trezor Bitwise 15:19 Resilience 16:31 Entrepreneurship is Therapy 18:23 The startup emotional rollercoaster 22:16 Showing up for 2000 days with no results 24:45 First time Founder advantage 26:40 When Kalshi almost made it, but did not 29:54 Partnerships: KAST 31:19 Focus Inputs, Not Results 33:16 The Kalshi beginnings story 36:00 The True Innovation Of Prediction Markets 38:28 How Prediction Markets Revolutionize The Media 41:37 Prediction Markets and Hedging explained simply 44:55 Leverage In Prediction Markets 47:16 Partnerships: Jupiter Ethena 48:00 Insider Trading 51:19 Insider Trading Rules enforcement: who is responsible? 55:26 How Kalshi spots Suspicious Behavior 56:59 Tarek's Honest View On Crypto 59:41 Launching the first Regulated Perps In The U.S 1:00:30 What Does Regulated Perps Mean? 1:02:22 Was Kalshi perps launch inspired by Hyperliquid? 1:03:41 Competition 1:05:58 Kalshi Endgame 1:07:06 What is Kalshi doing with the billions of $ they raised 1:08:31 Happiness and engagement 1:13:08 One Thing Tarek Should Let Go Of 1:14:18 Closing Thoughts

MR SHIFT 🦁

591,936 Aufrufe • vor 1 Monat

Over a trillion dollars worth of perps are traded every month, yet 99% people have never heard of them. This Law of Code episode is a multi-hour deep dive on perps, starting from the history of grain futures in Chicago to Friday's historic CFTC announcements. It took me months to put this together. My goal: the internet's most comprehensive explainer on perps. You'll hear from the world's leading experts on the legal layer of perps; Jake Chervinsky and Brad Bourque of Hyperliquid Policy Center, Brett Harrison of Architect, Katherine Kirkpatrick Bos, Ryne Miller 🇺🇸, Mike Frisch and David Shafer of Coinbase 🛡️. By the end of this episode, I promise you'll be in the top percentile for understanding perps, regardless of where you're starting from. (You just might need to listen twice. There's a lot here.) Timestamps: 0:00 Intro 4:04 What is a perp Brett Harrison 7:18 Why futures contracts exist 8:15 Liquidity fragmentation 11:01 History of U.S. futures Ryne Miller 🇺🇸 17:08 Richard Nixon, the gold standard and financial futures 21:27 Birth of the CFTC 24:27 Robert Shiller's 1992 paper Katherine Kirkpatrick Bos 30:09 Price convergence 32:00 The funding rate 43:41 Oracles and manipulation risk 47:39 Are perps swaps or futures? 52:44 A Mike Selig clip on perps 54:02 The DCM framework 59:16 DCMs, DCOs and FCMs explained 1:04:55 History of crypto perps (BitMEX) 1:13:00 How Hyperliquid works 1:25:41 CFTC's historic announcements on May 29, 2026 1:35:00 Fireside with Jake Chervinsky and Brad Bourque of Hyperliquid Policy Center Nothing in this podcast is legal or investment advice.

Jacob Robinson

154,217 Aufrufe • vor 1 Monat

Inside the mind of an ex-SIG quant trader who can't turn off the EV brain - even for his kid's school choice Andrew Courtney (Andrew Courtney) ran the International ETFs Trading Desk at Susquehanna International Group for ~15 years before leaving in 2023. He now runs Kalshionomics (Kalshinomics), a prediction markets analytics tool, and writes the Whirligig Bear, one of the sharpest prediction markets Substacks out there. "I think of everything as a bet. I kind of don't understand how you talk to normal people — they do not do that." SIG trains their junior traders with poker, spending 2hrs/day turning over cards after every hand, justifying every decision quantitatively AND qualitatively. 15 years later, Andrew views prediction markets the same way: read who's on the other side, size accordingly, fold when the whale comes back at you 10x. We cover: - Why SIG pays junior traders to play poker for 2hrs/day — & what happens after every single hand - The "one eye on the market, always" attention tax that destroys most people's careers - How to find edge in prediction markets by asking: who am I actually trading against? - Why meme-heavy, overhyped markets (Taylor Swift at the Super Bowl) might be the juiciest trades - The insider trading debate in prediction markets — & why it's "socially corrosive" - Floor trading vs. upstairs quant: why the transition saved his career - 40 connections after ~15 years at one of the world's best firms — the hidden cost of prop trading - Why he doesn't have collision insurance on his car (& the EV math behind it) Thank you so much Andrew Courtney for coming on the pod! Timestamps: 00:00 Intro 05:00 Floor trading vs. electronic trading 06:28 What makes an upstairs trader 10:16 Poker as trader training 13:00 Thinking in bets as a mental framework 15:11 Decision trees in real life 16:40 Where prediction markets actually have edge 19:00 Why the LLM forecasting layer falls short 19:40 Liquidity incentives and trading low-volume markets 22:00 Limiting downside even when the model is wrong 24:32 Executing in illiquid markets 25:44 Fair value vs. directional conviction 27:11 Bayesian updating when liquidity responds 28:40 Fading hype and crowded narratives 31:07 Longshot bias vs. fanbase bias 34:20 How to judge whether you really have edge 36:40 Building analytics tools for prediction markets 38:20 The temporary edge for smart amateurs 40:35 Where prediction markets fit best 41:20 Markets that shouldn’t exist 43:20 Why insider trading corrodes incentives 46:52 Are prediction markets a net good or bad 50:47 Minimizing degeneracy and maximizing signal 53:32 A simple EV mindset anyone can use

Ethan Kho

436,074 Aufrufe • vor 5 Monaten

Debunking Coffeezilla’s prediction market claims In his recent video “prediction markets aren’t just gambling,” Coffeezilla made the three untrue or misleading claims below: 1) “The only way you get the news early, by the way, is if it’s insider trading.” 2) “All these prediction markets are doing is aggregating sentiment on the news.” 3) “The only way you can get something not in the news from these markets is if someone with non-news information trades, which is AKA inside information.” I want to start by saying that I’ve really enjoyed Coffeezilla’s content in the past, and I appreciate him blacking out my name in the tweet he screenshared. I also watched the full video and agreed with parts of it (and disagreed with other parts). That said, these three claims are egregiously incorrect, and I want to correct the record. I hope Coffeezilla reads this and reconsiders these points 1) “The only way you get the news early, by the way, is if it’s insider trading.” Merriam-Webster defines insider trading as “the illegal use of information available only to insiders in order to make a profit in financial trading.” This claim is wrong because it is clearly possible to get information early in entirely legal ways. For example, in CPI inflation markets, someone might notice prices rising on goods they regularly buy, or a sophisticated trader might aggregate pricing data across many products and form a forecast before the CPI release. Journalists may later report on inflation, but the information existed beforehand. Markets also react faster to sudden events, like a Trump Truth Social post or an earthquake, than journalists do. Traders are financially incentivized to react in seconds; journalists are not. Recent high-profile examples include Nobel Peace Prize, Spotify, and Time POTY markets, where traders had information before the news broke. In the Nobel case, there was disinformation claiming insider trading, but as far as most observers can tell, the information was obtained legally via web-scraping. Even if the Nobel Committee disliked it, legally obtained information is fair game. 2) “All these prediction markets are doing is aggregating sentiment on the news.” This is easy to debunk. Prediction markets do aggregate information, but not merely sentiment or headlines. That’s likely why CNN and CNBC partnered with Kalshi. News is filtered through editors, incentives, and bias. Taking headlines at face value is not a winning trading strategy. Savvy traders treat news as one input among many variables. If a headline says “Poll X shows Clinton up 10 points,” markets may adjust, but they don’t blindly price the headline. They factor in other variables. I’d argue markets are often smarter than the news. Domer❤️‍🔥 has even argued that Fed markets on Kalshi are more accurate than CME due to traders like himself making them more efficient, and I think he’s right. 3) “The only way you can get something not in the news from these markets is if someone with non-news information trades, which is AKA inside information.” Merriam-Webster defines insider information as “information not known to the public that one has obtained by virtue of being an insider.” You can obtain non-news information without being an insider. This overlaps with point one, but here’s a concrete example. For the recent TN-07 special election, I traveled to TN-07 and spoke with voters leaving early-voting sites and with everyday residents. I learned how little awareness there was that a special election was even happening, and how voters were thinking about the race. That information wasn’t in the news, but it informed how I traded. I’m not an insider. This was “alpha hunting” through firsthand observation. Almost every serious prediction market trader has similar stories. This is certainly not "insider trading." I’m genuinely curious to hear your thoughts, Coffeezilla, and hope for a good-faith dialogue. I hope you are doing well!

Benjamin Freeman

80,021 Aufrufe • vor 7 Monaten

🚨 we’re back with the superteam india podcast! we’ve all heard of prediction markets but how do prediction markets with precision look like? on the first episode back, this episode of the superteam india podcast, Harkirat Singh sits down with Anam, blockchain engineer at Trepa — a precision prediction market built on solana (Colosseum hackathon winners by the way) where you don’t just bet yes or no, you predict exact numbers and get rewarded based on accuracy. we go deep into the technical architecture: why they store predictions in logs instead of on-chain, how rpc providers can silently truncate your program logs at 12kb, the emit cpi fix that saved them, why they rebuilt v1 from scratch over missing reserved bytes in their pdas, and the real cost challenges of sponsoring gas fees for a consumer app on solana. ⏱️ timestamps: 00:00 - cold open 00:38 - intro & guest welcome 01:08 - anam’s journey: mlh fellow → solana foundation → helius → trepa 03:04 - working at solana foundation & helius 04:20 - what are precision prediction markets? 05:03 - trepa vs polymarket: how payouts work 06:05 - the three factors: accuracy, stake & time 06:56 - what if no one else joins the pool? 08:09 - liquidity & market making challenges 09:17 - how prediction precision & steps work 10:37 - why they rebuilt v1 → v2 (reserved bytes) 12:28 - onchain storage optimization & rent reclaim 13:39 - security audit with adware labs 15:12 - the 12kb rpc log truncation discovery 15:59 - emit cpi fix: storing data as instruction data 16:33 - on-chain vs off-chain architecture deep dive 17:17 - why trepa sponsors gas (and the cost implications) 20:45 - top open source solana contracts to learn from 22:12 - what’s next: flash pools (2-minute prediction cycles) 23:38 - team size & hiring at trepa 25:50 - advice for aspiring solana smart contract devs 27:10 - outro

Superteam India

12,312 Aufrufe • vor 5 Monaten

E159: Hyperliquid: Housing all of Finance jeff.hl came back on the When Shift Happens Podcast to talk about the Hyperliquid journey since the TGE and what the future holds for one of the most loved and prolific protocols in the space Hyperliquid Timestamps 0:00 Intro 2:01 Singapore 2:27 Reminiscing on the Token Launch 5:00 Was This Scale Of Wealth Expected? 6:28 Doing The Right Thing In Crypto 9:07 The Responsibility that comes with Billions of $ 11:10 Jupiter KAST 11:51 Bringing Hyperliquid to the masses 15:21 Pre TGE and Post TGE: Operational difference 20:13 Choices on what to build Internally vs Externally 22:05 How to build a reliable team 24:51 Did the Team celebrate the HYPE wealth Generation event? 26:45 How to test talents for High Integrity 28:31 How much does the Hyperliquid team sleep? 30:05 Employee Vesting Fears 31:41 Dealing with FUD 32:28 How Does Jeff Personally Handle FUD 35:02 Token "Buybacks" critics 37:20 Why Hyperliquid can't have Discretionary "Buybacks" 39:04 HyperEVM, explained Simply 40:00 Paradex Zodl (fka Zashi) 40:41 HyperEVM: Success so Far? 44:05 HIP-3, explained Simply 47:44 What makes Hyperliquid's approach different 48:19 Why Should People Care? 51:33 Bring All Finance On Chain 52:08 Why Is The Hyperliquid Approach Better? 53:47 Key Numbers showing that Hyperliquid Is Doing it right 59:01 What Has the Unit team demonstrated with spot trading on Hyperliquid in 2025 1:03:29 HIP-4: Outcome Markets 1:08:01 Trezor Sui 1:08:58 What does "Housing All Of Finance" mean? 1:10:51 Why Hyperliquid is not a crypto company 1:12:23 Why Does Hyperliquid have A Stablecoin USDH (Native Markets) 1:14:39 What Is Kinetiq & Why Does It Matter? 1:16:15 Why Is What HyperLend Is Building Important For HyperLiquid 1:23:39 Where did Fairness cost the most? 1:24:47 What should Hyperliquid be Remembered for? 1:25:24 Why should people stay in Crypto when there's an AI brain drain? 1:28:10 Closing Thoughts

MR SHIFT 🦁

577,130 Aufrufe • vor 5 Monaten

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Sean O'Malley

118,091 Aufrufe • vor 2 Jahren