Crazed

Ethereum is for AI.

I’m sorry, but I just can’t stand it anymore. This massive, outrageous, pork-filled Congressional spending bill is a disgusting abomination. Shame on those who voted for it: you know you did wrong. You know it.

Who are the biggest $PENGU believers on X? Drop your SOL wallet below 👇

Thank you to everyone who took the time to thoughtfully respond to my post on transparent markets. I understand that the thesis is controversial and that Hyperliquid is at a new frontier as the first fully transparent order book venue of its scale. I could well be mistaken, and welcome the continuous dialogue on market structure innovation. However, many criticisms I saw stemmed from misunderstandings, with some points actually supporting transparent systems like Hyperliquid. Market structure is notoriously counterintuitive, and novel approaches often challenge established paradigms, leading to understandable skepticism. For example, Hyperliquid pioneered protocol-level cancel prioritization, which has since been implemented by new DEXs and even inspired novel transaction ordering ideas on other blockchains. But at the time, it was considered controversial because it went against traditional market design. I hope that transparent trading will follow a similar path to adoption. I may have been too ambitious trying to cover a complex argument in a single post. Given the specific patterns in criticisms, I'd like to take this opportunity to zoom in on nuances that were missed in the high level summary. What follows is an argument for the final state of efficient markets, with the understanding that Hyperliquid is far from fully efficient today. However, inefficiency is opportunity for those hungry to act. Hopefully this post can also be a call to action for traders, market makers, and builders to translate transparent markets into the highest quality execution venue for all. -- Before delving into specific concerns, let’s crystallize some counterintuitive principles that can form a helpful mental model for market structure: 1. Counterparty principle: Benefits of counterparty curation are misattributed to privacy. Users ultimately care about execution. As studies have shown though, privacy sells. Alternative trading venues often market privacy as the causal feature for improving execution. In reality, the primary source of benefit for users is the screening of counterparties allowed to participate on the venue. Hyperliquid’s market design provides these same benefits more directly and effectively than patchwork solutions. Hyperliquid’s solution also democratizes access, improving execution for all traders large and small. Note that transparency does not mean doxxing. Of course, the exact identity of some traders will fundamentally change the value of the asset. But those traders need not dox themselves, e.g. Warren Buffet can buy BTC and benefit from transparent markets, without tying his identity to his address. 2. Competition principle: Maximizing competition is key to improving execution. Many traders who want to execute in size have some form of alpha. However, the group of informed medium/long term traders in aggregate is difficult to distinguish even over yearly timeframes, as their realized sharpe is too low for statistical significance. It is challenging to distinguish between a trader with solid medium term alpha and a degenerate gambler who got lucky. Therefore, while the desire to minimize market impact and alpha leakage is natural, it’s usually outweighed by the improved liquidity from transparent markets. Traders therefore see improved execution despite revealing their strategy, as market makers are bound to provide liquidity to the entire range of flows in the market. Competition is the bedrock of capital markets and economics. As an example, the Hyperliquid order books support an onchain TWAP. Such a broadcasted intent to trade is in fact a reasonable proxy for optimal execution. Market makers will immediately fill some size so that the earlier TWAP orders receive worse execution, but will also compete to fill the remaining flow. The competition between market makers ensures near optimal overall execution over the course of the TWAP. Any inefficiency in execution is an opportunity for another market maker to undercut the others. 3. Repeated games principle: Execution improves when one-time games become repeated games. Market makers evaluate each decision from a game-theoretical framework, as they are in the business of making positive expectancy bets. On Hyperliquid, every account placing more than one order is playing a repeated game. Repeated games have dramatically different optimal strategies from the one-time games of private venues, and the resulting equilibrium is better execution for everyone other than toxic extractors. Competition is essential for the optimal market marker strategy to benefit the end user, which is amplified by the next principle. 4. Full transparency principle: Benefits from transparency are non-linear and only manifest when transparency is at the system level. When optimizing for execution, “the system knows” > “no one knows” > “some people know.” The worst of the three states is where some insiders have privileged information. Those insiders can act exploitatively to extract profit from end users. Because L3 books are not transparent in tradfi, the “darker” venues often implement systems to unilaterally apply counterparty-specific filtering to trades. Hyperliquid achieves the same effect on a lit venue and therefore maintains the benefits of efficient order book execution. -- Common criticisms to the initial post, and my responses [I’ve bracketed references to the different principles]: 1. Many large desks in tradfi trade OTC, which is evidence that public venues cannot support large size. Response: This point actually supports Hyperliquid. In tradfi's L3 books, there is no reliable way to broadcast your identity trustlessly to all counterparties. Using an OTC desk is a compromise, telling a small set of professional counterparties that you are non-toxic. Like trading on an L4 order book, trading OTC is a repeated game where the OTC desk is quick to ban any counterparties that adversely select a small fraction of quotes, or engage in otherwise toxic behavior [repeated games principle]. The OTC desks offer quotes where their own algorithmic execution/hedging costs are below the markup, which is only possible when their fills’ immediate markouts are positive. A Hyperliquid whale who places an onchain TWAP order is effectively routing their flow to every "OTC desk" plugged into Hyperliquid. When OTC counterparties expand from a select few to all market makers, the competition improves execution for the user compared to the bespoke OTC quote [competition principle]. In summary, execution on Hyperliquid incorporates the efficiency of lit venues with the counterparty signaling of OTC. This high quality execution is available to all users equally. 2. A large percentage of tradfi volume happens on dark pools, retail internalizer systems, etc. Response: This argument also supports Hyperliquid. The basic idea behind dark pools is that two large whales with a "coincidence of wants" can match immediately and bypass the spread that lit markets charge. Until such a match exists, orders are attempted to be kept private to reduce market impact. While a neat idea at first glance, the privacy of dark pools is unlikely to meaningfully protect intentions or improve execution. For example, sophisticated actors participate in dark pools themselves. At a minimum, their fills are a strong signal on the supposedly private flow. This shares many parallels with the insider information discussed in the following section. Information that will be deduced anyway is better made public [full transparency principle]. As another argument against the effectiveness of privacy properties, dark pools rely heavily on participants having identities known to the pool operator [repeated games principle]. This is necessary because the private information is easily leaked. There are strict requirements for participation, e.g. high fill rate, minimum order size, and negative short term markouts. Offenders with toxic behavior are banned or deprioritized [counterparty principle]. Like OTC desks discussed above, transparent L4 books on Hyperliquid incorporate and improve upon many of these positive properties of dark pools within an open, systematic framework. 3. Public data allows hunting of liquidations/stops. Response: Most would agree that unlike size information, preserving margin privacy is beneficial for the end user. Perhaps a ZK privacy implementation can accomplish this in the future. However, until then, users are less likely to be successfully hunted if everyone knows liquidation and stop prices than when only the exchange operator knows [full transparency principle]. Two reasons: a. On CEXs, your position information is far from private. Based on empirical data of insider trading leading up to listings, one should assume that liquidations and stops are also vulnerable to misuse. This can be despite best efforts from management: it is extremely difficult to completely control large organizations from leaking information. When insiders hunt stops and liquidations, there is no public data for other market makers to understand the source of the temporary dislocation. This decreases the required capital to successfully push the price. b. In the game theoretical equilibrium of transparent data, stop and liquidation hunting are likely unprofitable endeavors on average. Whales are protected by the entire system of market participants acting rationally. People trying to hunt liquidations and stops will be counteracted by people trying to trick them into the hunting. For example, someone who wants to open a large long position can execute half of their position on high leverage, bait the hunters to short, then increase collateral and enter the remaining desired position at a more favorable price. As long as some profit seeking “anti-hunters” exist, all whales benefit from the cover. While point (b) will take time to play out, markets are ultimately efficient. Even before this equilibrium is reached, the full transparency principle in point (a) suggests Hyperliquid's model offers more robust protection for whales. Liquidity is generally deeper when lit venues are more transparent [competition principle], which further increases the cost of liquidation and stop hunting. 4. Some users have alpha and will not benefit from transparency. Response: The users that are disadvantaged by Hyperliquid’s system are a very small set of “toxic” participants. These are the same adversarial traders that dark pools, OTC desks, and other solutions try to avoid. A small number of professional HFT firms have alpha on this timescale, and it’s a failing of traditional market structure that these toxic takers have the ability to tax all other users of the system. As an aside, short term alpha and toxicity is a continuous spectrum, so I’m oversimplifying for sake of argument. For example, there are intraday quantitative strategies that can realize significant sharpe ratios, whose flow could be a reliable momentum signal for market makers. The technical reason this is not a problem is that cost to rotate accounts is proportional to fee sensitivity of the strategy, which is inversely proportional to the time it takes for others to detect the strategy with statistical significance. In other words, the more execution matters to a quant strategy, the less the burden of obfuscation. Regardless, the vast majority of users on Hyperliquid do not fall remotely close to this category of quantitative, toxic alpha. Note that “toxic” does not mean “informed,” but rather traders who profit non-constructively from slight infrastructural or other structural advantages such as latency. Hyperliquid's cancel prioritization and L4 order book essentially boost the short term liquidity available to non-toxic small and large orders, respectively. As a conservative lower bound, as long as market maker counterparties on Hyperliquid can hedge in time on other venues, the trader benefits from Hyperliquid’s system. -- I know I’ve missed other points, but will stop here to keep this post digestible. Thanks again to everyone for their thoughtful feedback, especially those who took time to review an earlier version of this post. I look forward to continuing this discussion!

bullish?

链上记录是我的朋友圈，钱包里资产是生活的照片，我和群友们在玩一个叫 PVP 的社交游戏，每一次的买入和卖出，都在彰显智慧、审美和价值观……

These uncut GEMS on hl. are about to giga rip! 50 Hype floor incoming Get them while they are cheap guys!! hWo

bitches hate to see a nigga minding his own biz

People w/ their needs met can be friends w/ whoever Anyone who has less sex, money, or emotional support than they need will always try to satisfy their needs using the people around them - regardless of whether or not those people want to satisfy it The guy pretending to be friends with your gf to sleep w/ her is never Gigachad - he already has everything he needs. (Also, the last time Gigachad slept with a girl who neglected to tell him about her bf, he had like 3 months of drama and woke up to the bf pointing a gun at him, so he's very opposed to repeating that) It's always the floppy lonely guys that befriend girls for sex The floppy weasel man is the least trustworthy male in existence bc he himself doesn't know why he's doing anything. He says and thinks he's fine just being friends, but he gets observably bitchy over time if his female friends don't sleep w/ him Most guys trying to intimately befriend girls are floppy weasels. There's a lot more of them and Gigachad already has all the friends he needs Beware the floppy weasel-man

Fun fact: There's only 1111 gems for 466,546 users NFA

hyperliquid

No. I don’t think I will turn down the volume. You know what? Ima turn it UP

hypio.coded() if devs.has_executed(): mind_virus.expand() join_or_be_infected (signal) --- RT to spread the virus and post your @HypioHL to get infected. 👇

Gib GEM on hl

新年快乐！

✅ HyperFlasks are now tradeable on netprotocol! netprotocol.app/app/bazaar/hyp…

@primiciascl trabajoredos euso ahhf🤓

Our friends @Meta are taking part in the 8VC Llama Stack Innovation Challenge! First challenge: 3/7-4/19. Use Llama Stack to build desktop, field, and edge AI applications that run locally for privacy and performance. Join now: llamastackchallenge.com

🎥 Today we’re premiering Meta Movie Gen: the most advanced media foundation models to-date. Developed by AI research teams at Meta, Movie Gen delivers state-of-the-art results across a range of capabilities. We’re excited for the potential of this line of research to usher in entirely new possibilities for casual creators and creative professionals alike. More details and examples of what Movie Gen can do ➡️ go.fb.me/kx1nqm 🛠️ Movie Gen models and capabilities Movie Gen Video: 30B parameter transformer model that can generate high-quality and high-definition images and videos from a single text prompt. Movie Gen Audio: A 13B parameter transformer model that can take a video input along with optional text prompts for controllability to generate high-fidelity audio synced to the video. It can generate ambient sound, instrumental background music and foley sound — delivering state-of-the-art results in audio quality, video-to-audio alignment and text-to-audio alignment. Precise video editing: Using a generated or existing video and accompanying text instructions as an input it can perform localized edits such as adding, removing or replacing elements — or global changes like background or style changes. Personalized videos: Using an image of a person and a text prompt, the model can generate a video with state-of-the-art results on character preservation and natural movement in video. We’re continuing to work closely with creative professionals from across the field to integrate their feedback as we work towards a potential release. We look forward to sharing more on this work and the creative possibilities it will enable in the future.