Portfolio
Ryan Garner / imkharn
Primary architect of Augur protocol, an ex-top-10 cryptocurrency and the world’s first subjective consensus protocol. It is still the only trustless subjective oracle. The key innovation, Market Cap Adjudicated Universe Forking, is capable of giving any oracle a security margin far higher than its market cap -- theoretically infinite security:
https://github.com/AugurProject/whitepaper/releases/download/v2.0.6/augur-whitepaper-v1.pdf
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Ajna Game Theory Audit and simulation of interest rate controller:
https://docs.google.com/document/d/1bqw356AJnjVowdDIwk5unToRfnHYwchCyOTRncEpazY
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Tren Finance funded research to evaluate 10 DeFi liquidation mechanisms and provide recommendations for leverage, insurance, and social loss.
https://docs.google.com/document/d/1ChN0QTVy3c2Uwzc8TCNU-I-jFueI77hJmdiWk31HJTA/
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Working for Mycelium, I designed a way to discover the optimal (revenue maximizing) spread for an AMM without volatility measurements or formulas.
https://x.com/imkharn/status/1892398473640632492
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Prime Intellect hired me to design a consensus protocol for machine learning on untrusted computers.
Existing protocols had 10%+ overhead and required a trusted party to validate. I found a solution to remove trusted parties. I also discovered a mechanism that reduces overhead to a fraction of what was considered required by prior analysis. The prevention of convergence can be made byzantine for only 3% overhead and a bond sized at a week of income. This is far less than the overhead cost of zkML, opML, TEE ML, and deterministic learning. It is under NDA, so here is a censored abstract:
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Decentralized general purpose insurance whitepaper from 2016. Can insure anything unlike Nexus Mutual; though prices would be bad for anything you can't verify easily over the internet:
https://docs.google.com/document/d/1Re558BAos0PNcgVhVBJt-ZlBPNZ4wVpx7ljHoUlGbt0
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A paper I wrote on the Impossible Trinity and the basic truths of creating a derivative whose goal is correlation with another asset. I present a novel method that might be the first form of leverage without borrowing.
https://docs.google.com/document/d/17TyY49GyIdt-SX_1MA0kz7dKZyZQaeu_mFZA6AQ3l5Q
This is patented:
https://patents.google.com/patent/AU2022201218A1/en?q=(MANE+DEVELOPMENT+pool+%22BLOCKCHAIN+SYSTEMS+AND+METHODS%22)&oq=MANE+DEVELOPMENT+pool+%22BLOCKCHAIN+SYSTEMS+AND+METHODS%22
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Ajna struggles to explain their protocol in non technical terms. Their ELI5 was quite technical, often using the same wording as the whitepaper. So this I wrote for fun, it explains it so simply that even people clueless about crypto can understand how Ajna works and why its great:
https://docs.google.com/document/d/1gUCVpdi-qlJVf7erUsIPtVKDVnKwCD3vRQEZo4BwZDo
A half finished paper on Game Theoretic Mechanism Design written 2017. Vitalik helped me write this definition for DAO:
https://docs.google.com/document/d/1Oghfq1VFfGvScxzNWD14vNg_fEAC8HVrfVVVU3Al-gA
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Design for a median-commodity based stablecoin protocol:
https://twitter.com/imkharn/status/1710427619173306791
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Deep dive on each AMM to create the History of AMM Innovation:
https://twitter.com/imkharn/status/1708247207797785044
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An attempt at trustless offchain leveraged trading that in optimistic ways avoids using Ethereum. This was invented before L2s were popular, yet still there may be use for interacting with L1 liquidity with L2 costs. Cow-swap is quite similar except for the oracle, but close enough, so this is no longer needed.
https://docs.google.com/drawings/d/1QPHnpiVzrogWNZ1l08XYiXZnlWzb_jqp2RArX-ICAh4
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Improvements that could be applied to almost all existing Perpetual Swaps (price on target instead of slightly above, permissionless market creation, investible insurance funds)
https://docs.google.com/document/d/127VbyOKUIX8bf70GvmTfDLgPvAEwxl2nO7c1kTDalvg
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I helped research and write the related works section of TOPLOC. Until this paper, verifying a non-deterministic inference required the same or more computation. This reduces the cost of verifying inferences from an untrusted provider to as little as 1% the original inference.
https://arxiv.org/pdf/2501.16007