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0X
0xMedia@0xmediaco·1d

uPEG 与 Slonks 之后,Uniswap v4 Hook 终于被市场读懂了

Uniswap v4 Hooks transform AMM pools from fixed rules into programmable infrastructure, enabling pools to execute custom logic before and after swaps. 0xMedia highlights uPEG and Slonks as breakthrough examples: uPEG generates on-chain SVG unicorn images from swaps themselves, while Slonks uses a Hook as fee collector to fund buying and voiding NFTs tied to CryptoPunks, replacing opaque token taxes with pool-layer mechanics. The trade-off is that v4 Hooks eliminate safety by default—they can hide fees, enforce transfers, or contain malicious logic, requiring new market literacy to distinguish safe implementations from exploitative ones.

PB
Pink Brains@PinkBrains_io·5d

HIP-4 Is Not a Prediction Market - It's the Options Layer: A Full Guide

Pink Brains explains that Hyperliquid's HIP-4, which launched May 2nd with a daily BTC binary as its first mainnet market, functions as an options layer rather than a prediction market. The distinction matters for understanding the protocol's architecture and trading mechanics, though the full implications require examining how this positioning affects $HYPE's ecosystem development.

CT
Cameron Tao@quack_builder·7d

Bittensor 是 AI 时代的比特币吗?— 译 Jacob 在清华大学的演讲

Translation + commentary on Bittensor founder Jacob Steeves's Tsinghua University talk. Cameron walks through Jacob's framing of "incentive computing" as the universal pattern behind both Bitcoin and AI. Five-step argument:

(1) One pattern underlies every powerful adaptive system: state · objective · feedback · adaptation · loop. AlexNet 2012 broke MNIST not by hand-coding what digits look like, but by letting the network self-adapt to a target. The same loop describes RL, genetic algorithms, slime molds finding shortest paths through mazes, river deltas, the structure of leaf veins.

(2) Bitcoin is the first production-scale implementation of this pattern — not as money, but as a self-adaptive computer that produces hashes. The numbers are absurd: 1000x the compute of America's six largest cloud providers combined, 10²¹ hashes/sec, 23GW continuous power (Thailand-scale). 700-9000x more efficient at producing hashes than centralized cloud — because it's borderless, always-on, autonomous, and permissionless. Bitcoin is the world's largest supercomputer, optimized purely for hash production.

(3) Incentive computing generalizes the pattern by replacing "reward = a number in a computer" with real money. ML's reward signal can't pay 200 countries' worth of contributors; Bitcoin's can — that's why the entire planet became a mining network. But hashes are useless outside Bitcoin. The question is whether the same mechanism can mint anything.

(4) Bittensor is the generic version — replace "miners produce hashes" with "miners produce any useful work": storage, compute, ML models, gradients, data, robotics. Validators score, network mints. PyTorch for incentive computing.

(5) Five proven examples already running on Bittensor:

  • SN62 Ridges (SWE-Bench coding agents) — top miner makes $60K/day. The agent that beat Claude/OpenAI on SWE-Bench was 7,000 lines written by an unknown person. "An AI lab with no engineers — it doesn't define how to solve the problem, it only defines the incentive."
  • SN3 τemplar (cross-internet collaborative pre-training) — successfully trained a 70B-parameter model across the open internet. Has never been done before. Cameron notes the founder later "ran away" — full piece coming.
  • GPU markets (SN51 Lium, SN4 Targon) — borderless permissionless GPU rental → world's lowest GPU prices.
  • SN64 Chutes (open-source inference) — #1 open-source provider on OpenRouter, 9.1T tokens. Briefly served more DeepSeek queries than DeepSeek itself.
  • Robotics + long tail — drone simulation, US stock signals, sports betting, drug discovery, weather forecasting, quantum compute, commodity trading.

dTAO (live since Feb 2025) makes the network self-referential — subnets compete in capital markets for emission allocation. The market itself decides which incentive mechanisms get the next round of TAO.

The deeper point: AI is being captured by a tiny number of closed labs (OpenAI, ~3K employees, you'll never own any of it, your data goes who knows where). Incentive computing distributes ownership and makes the rules visible. Anyone can enter, contribute, and own a piece — even if Bittensor isn't the project that wins, the shape of the AI economy will change because of this idea.

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MD
Mesky | Delpho@mesky_·8d

HIP-4: The Business Case for Outcome Markets

Mesky frames HIP-4 not as a Polymarket clone but as a missing payoff layer for Hyperliquid: bounded, dated, fully-collateralized outcome contracts that settle at a date or event with no leverage and no liquidation engine. Where spot trades ownership and perps trade direction, HIP-4 trades states of the world — turning event risk into a composable financial object on the same execution engine that already prices crypto.

The real bull case is not "capture prediction-market volume" (~$240B est. 2026, per Bernstein). It's that HIP-4 expands the addressable market into short-dated convexity and event hedging — analogous to 0DTE options, which now do ~59% of SPX volume. At a 7 bps base spot-taker fee on chargeable close/settle notional, $25–100B/mo of HIP-4 flow becomes one of the platform's most material revenue lines.

Strategic edge: Hyperliquid isn't bootstrapping a venue — it already has $183B/30d perp volume, $643M annualized revenue, and the maker base. HYPE captures value through (1) Assistance-Fund buyback/burn from incremental fees, (2) staking-collateral demand if HIP-4 deployers require staked HYPE like HIP-3 (500K HYPE), (3) staking discounts (up to 40%), and (4) USDH demand as the native unit of account for event risk.

Mesky's prescription: don't out-Polymarket Polymarket. Sequence rollout toward crypto-native, recurring, hedgeable templates (BTC weekly thresholds, Fed decision markets, token unlock outcomes) where market makers can build inventory — not viral one-offs. Repeatability beats virality.

Real risks: ambiguous resolution, regulatory perimeter (CFTC v Wisconsin, Brazil's blanket ban), insider trading (DOJ Polymarket case, Kalshi candidate suspensions), long-tail spam, and perp cannibalization. Mainnet HIP-4 spec/fees/deployer rules still aren't formalized in the Hyperliquid GitBook.

SM
Stacy Muur@stacy_muur·10d

Why All RWA Yield Flows Into Pendle

Stacy argues most of the $310 billion stablecoin market earns no yield, but real-world yield flowing onchain is reversing this. As Treasury bill interest and other RWA yields reach crypto, Pendle becomes the natural destination because its yield-stripping mechanics let investors isolate and trade different maturity profiles and coupon streams that traditional stablecoin holders previously couldn't access.

BA
Baheet@Baheet_·14d

Is Sui a Good Chain for Prediction Markets?

Baheet argues Sui's object-centric architecture, Move language, 390ms finality via Mysticeti, native DeepBook v3 CLOB, and March 2026-launched USDsui stablecoin create an underutilized technical foundation for prediction markets as the category scaled to $20-27 billion monthly volumes across Polymarket and Kalshi in 2026. While Polymarket's VP of Engineering acknowledged infrastructure strain from rapid traction—citing on-chain latency, transaction cancellations, and CLOB stability issues—Sui remains absent from the dominant prediction market apps, presenting a first-mover opportunity for builders prioritizing high-frequency scalar markets and institutional settlement over ecosystem maturity.