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nobody

@nikkokra

Katılım Haziran 2023
781 Takip Edilen276 Takipçiler
nobody
nobody@nikkokra·
The new bottlenecks are: 1. Curiosity 2. Asking the right questions to efficiently satisfy that curiosity
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nobody
nobody@nikkokra·
This implies that Coinbase views USDH as a threat, which means that Hyperliquid is a threat, which is an admission that Coinbase's own exchange is complete and utter ass
Hyperliquid@HyperliquidX

Coinbase has announced its plan to activate AQAv2 on USDC as the treasury deployer, with Circle serving as the technical deployer responsible for CCTP and native cross-chain infrastructure. Both Coinbase and Circle have committed to stake HYPE to activate AQAv2. As part of this transition, Native Markets has agreed to terms granting Coinbase the right to purchase the USDH brand assets. With Coinbase, in its role as treasury deployer, sharing the vast majority of reserve yield revenue with the protocol, USDC will become the most aligned stablecoin on Hyperliquid. As a result, canonical outcome (HIP-4) markets will use USDC as the quote asset in a future network upgrade. User and builder feedback has been consistent that fragmentation leads to degraded experience; now, the community no longer needs to choose between liquidity and protocol alignment. The pioneering work of Native Markets in launching USDH as the first production-scale stablecoin sharing yield directly with a protocol in a purely onchain implementation made AQAv2 possible. The learnings and mechanics pioneered by USDH will live on in AQAv2. The Hyper Foundation will give grants to eligible HIP-3 deployers, HIP-1 deployers, and builders who integrated USDH, supporting teams through migration over the next months. These grants reflect an ongoing commitment to teams who choose to build on Hyperliquid and align with the protocol. USDH markets are fully functional but will sunset over time. USDH remains fully backed, with feeless conversions to USDC and fiat available to users during this transition.

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nobody
nobody@nikkokra·
Video games
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nobody
nobody@nikkokra·
What do you do when you don't want to do anything?
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nobody
nobody@nikkokra·
The government steals from the lower class via inflation, and then, when the lower class lashes out, blames the rich and takes their money via taxation
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nobody
nobody@nikkokra·
AlphaFold is a throughput optimization on drug discovery, not a latency optimization. You still need to go through phase 1/2/3 clinical trials, but the number of drugs that enter clinical trials + pass all of them increases
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nobody
nobody@nikkokra·
@DannyNemer @ns123abc Buying equity in a company and transferring charity funds into it is self dealing
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Danny Nemer
Danny Nemer@DannyNemer·
Why the hell does Greg have to inform Musk of his position in Cerebras? Musk has nothing to do with Cerebras. It's completely unrelated. Separately, it is not self-dealing. Cerebras is phenomenal; one of two viable accelerators for inference leveraging specialized silicon (the other is Groq). Of course it makes sense to invest if given the personal opportunity. And of course it makes sense for every frontier lab to want to acquire them. So Greg and Sam can never invest in companies in which they have expertise due to their job?
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NIK
NIK@ns123abc·
🚨 BOTH ALTMAN AND BROCKMAN SELF-DEALING ON CEREBRAS >Greg Brockman acquires personal Cerebras ownership in 2017 >Altman, separately, invests in Cerebras >Brockman pushes OpenAI to merge with Cerebras that same month >Brockman never discloses his Cerebras ownership to Musk >December 2025: OpenAI signs $10 billion Cerebras deal + loans Cerebras $1 billion >February 2026: Cerebras valuation triples from $8B to $23B on OpenAI commitments >April 2026: OpenAI commitment expanded to $20+ billion through 2029 >April 2026: Cerebras files IPO at potential $26.6 billion valuation Brockman, under oath today: Q: When you were having discussions about a financial transaction between OpenAI and Cerebras, you were actually an owner of Cerebras, weren't you? Brockman: "There was some overlap between discussions and being an investor in Cerebras. Yes." Q: Can you point to an email in which you told Elon you were an owner of Cerebras at the same time you were advocating that OpenAI do this transaction with Cerebras? Brockman: "I do not believe an email that says that exists." Q: How about a chat? Brockman: "I did not." Q: A text? Brockman: "No." Q: And yet you stood to gain personally if there was a transaction between OpenAI and Cerebras. Brockman: "I suppose so, but it wasn’t something on my mind " Both co-founders. Both fiduciaries of a 501(c)(3) charity. They directed OpenAI to commit $20+ billion to a company in which they both hold personal undisclosed equity. Cerebras valuation tripled. The IPO is the cash-out. California charitable-trust law calls this self-dealing.
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nobody
nobody@nikkokra·
What are the fundamental metrics that crypto improves
Elon Musk@elonmusk

@teslaownersSV Any technology invention can be assessed in terms of how it improves fundamental metrics. It is impossible to become a multiplanet civilization without reusable rockets, just as it would have been impossible to colonize America with expendable boats.

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nobody
nobody@nikkokra·
For takers: clob > dark pool > rfq For makers: rfq > dark pool > clob Makers love rfq because they get an opportunity to reject executions. Markets which inherently disadvantage makers (ones with lots of adverse selection, eg prediction markets) are best for rfq
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Dwarkesh Patel
Dwarkesh Patel@dwarkesh_sp·
There's a quadrillion-dollar question at the heart of AI: Why are humans so much more sample efficient compared to LLM? There are three possible answers: 1. Architecture and hyperparameters (aka transformer vs whatever ‘algo’ cortical columns are implementing) 2. Learning rule (backprop vs whatever brain is doing) 3. Reward function @AdamMarblestone believes the answer is the reward function. ML likes to use pretty simple loss functions, like cross-entropy. These are easy to work with. But they might be too simple for sample-efficient learning. Adam thinks that, in humans, the large number of highly specialised cells in the ‘lizard brain’ might actually be encoding information for sophisticated loss functions, used for ‘training’ in the more sophisticated areas like the cortex and amygdala. Like: the human genome is barely 3 gigabytes (compare that to the TBs of parameters that encode frontier LLM weights). So how can it include all the information necessary to build highly intelligent learners? Well, if the key to sample-efficient learning resides in the loss function, even very complicated loss functions can still be expressed in a couple hundred lines of Python code.
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nobody
nobody@nikkokra·
Prediction markets let you bet only on what you know. Asset markets force you to take on exposure to everything else too. Hence prediction markets have more capacity for speculation, assuming bet sizes are proportional to confidence
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nobody
nobody@nikkokra·
@ChairmanSelig @CFTC @SDNYnews Why is everyone so against insider trading? Letting insiders trade leads to faster information dissemination, banning them just shifts value accrual from insiders to low latency event based HFTs which eat up all the liquidity after announcements. Non-insiders lose either way
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Mike Selig
Mike Selig@ChairmanSelig·
I’ve been crystal clear: anyone who engages in insider trading in any of our markets will face the full force of the law. Today, the @CFTC took parallel action with @SDNYnews to charge an individual with insider trading involving event contracts. The @CFTC won’t tolerate insider trading in our markets, and our Division of Enforcement will continue to vigilantly police our markets for any illegal actions. Read more ⬇️ cftc.gov/PressRoom/Pres…
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nobody
nobody@nikkokra·
@Osint613 Prediction markets need to be auctioned so that there's a market mechanism to select markets with decentralized resolution
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nobody
nobody@nikkokra·
@systematicls The issue is that you can't backtest LLM-based signals, and you can't measure generalizability without a backtest
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nobody
nobody@nikkokra·
@a16z Cameras dont shoot out photons
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a16z
a16z@a16z·
Waymo CEO Dmitri Dolgov explains the benefits of lidar, radar, and cameras for self driving: "They're very complementary." "The frequencies are very different. Laser gives you very high resolution. Think of it as a laser beam that goes out, spins around, and shoots out millions of these laser pulses per second. Then each one comes back and you can sample the 3D structure of the world with very high resolution." "Radar has much lower resolution, but because of the physics of it, it degrades much better in adverse weather conditions. So—fog, snow, heavy rain." "If it's a nice, bright, sunny day, cameras are very valuable. If it's pitch dark, or you have the sun in your face, or you're blinded by the headlights from an oncoming car, then the camera will degrade." "It's a combination of the sensors. Each one is noisy. How the noise characteristics show up in different environments is different, but it's not like we switch from one to another." "They all go into the system that gives you, jointly, the best view of what's happening in the world." @dmitri_dolgov with @collision
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