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Moon Dev
The $88M Short That Broke Hyperliquid: Using Python to Trace the World’s Smartest Money

becoming an ai expert starts with watching a single wallet siphon eighty eight million dollars out of the market while everyone else is sleeping. while most traders were getting liquidated into oblivion last night one mysterious address was shorting nine figures of bitcoin minutes before the entire market collapsed

this was not just luck or some random gut feeling because the precision was so surgical it felt like someone had a cheat code to the universe. i started digging through the transaction history to find out who this whale was and where the money came from but what i found in the wallet history changed how i view the entire game of trading

the sheer size of the position was enough to make anyone sick to their stomach. we are talking about ninety two million dollars in profit within twenty four hours on a platform like hyperliquid where every single move is tracked on the public ledger. someone knew exactly when the trump tariffs were going to hit and they positioned themselves perfectly to catch the falling knife

as i pulled up the hyperscan data i realized this account was brand new which usually screams insider or a massive fund moving under the radar. the wallet moved eighty million dollars in from a bridge just forty six hours ago and immediately went to work with high leverage shorts on both bitcoin and ethereum. it makes you wonder if there is a group of people who simply see the future or if they are the ones actually creating it

the trail lead me straight into the rabbit hole of ether scan and arbitrum scan trying to find the original source of the funding. tracking money through bridges is like trying to follow a ghost through a maze because the funds get split and mixed across multiple chains to hide the origin. i noticed a suspicious amount of activity coming from exchanges that are not even accessible in the us like bybit and bingx which adds another layer to the mystery

there was one specific address that kept popping up in the internal transfers that felt like a major clue. it was a contract that seemed to be a splitter or a multi send wallet that was distributing eth to several different high profile names. i had to open up a loop with my own curiosity because if these people are connected to the whale then we might have just found the source of the smartest money in crypto

the investigation took a weird turn when i found a name associated with one of the bridge relayers called leon fish. seeing a personal name attached to a multi million dollar bridge contract is rare and it made me pause the entire search to figure out if this guy was the mastermind or just a community manager caught in the crossfire. if this bridge was private and only used by a few whales then we just stumbled upon a secret highway for institutional capital

the manual digging was starting to take too long and that is when i realized i was doing it all wrong. instead of clicking through thousands of transactions on a web browser i decided to build a custom ai agent to do the detective work for me while i watched the charts. this is where the real power of being a coder comes into play because i can turn a three hour investigation into a three second script

i fired up the terminal and started writing python scripts to interface with alchemy and scrape data across every major chain simultaneously. the goal was to find every address this whale ever interacted with and rank them by frequency to see who their closest associates were. within minutes the ai was pulling records from ethereum and base and arbitrum to build a comprehensive profile that no human could ever compile by hand

the results were startling because the ai found that this wallet was actually part of a much larger network of circular funding. it discovered that the whale had received over twenty thousand eth from a set of wallets that were likely belonging to a major exchange or a massive [...]

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