本周精选交互项目:Mavryk Network上线Kaito;Sentient新Galxe任务;Zama公会任务

Odaily星球日报Pubblicato 2025-08-21Pubblicato ultima volta 2025-08-21

原创 | Odaily 星球日报(@OdailyChina

作者 | Asher(@Asher_ 0210

Mavryk Network:RWA 板块的 Layer 1

参与理由

Mavryk Network 是一个专门做 RWA 板块的 Layer 1,旨在简化 RWA 代币化和 DeFi 集成,使数字资产所有权更加容易获得。通过简化资产的代币化流程,MavrykNetwork 使现实世界的资产(如房地产、债券、奢侈品等)能够以代币形式在区块链上交易,提供即时流动性和去中心化金融的整合。其核心目标是通过区块链技术连接传统金融(TradFi)与去中心化金融(DeFi),推动资产的民主化和可访问性。

今年 2 月末,Mavryk Network 宣布完成 520 万美元融资,Ghaf Capital、Big Brain、MetaVest Capital、Cluster Capital、Collective Ventures 和 Atlas Fund 领投。

8 月 19 日,Mavryk Network 上线“嘴撸”平台 Kaito。

交互教程

STEP 1. 在谷歌商城中下载 Mavryk Wallet(链接:https://chromewebstore.google.com/detail/mavryk-wallet/cgddkajmbckbjbnondgfcbcojjjdnmji),并创建一个新账号。

STEP 2. 输入注册好的钱包地址,领取测试币(链接:https://atlasnet.faucet.mavryk.network/),同时领取 MVRK、USDT 与 MVN 测试币。

STEP 3. 进入交互网站(链接:https://atlasnet.mavenfinance.io/loans/earn),将 MVRK 与 USDT 测试币存入该平台。此外,选择“质押”栏目,完成 MVN 质押。

STEP 4. 完成 Galxe 上的任务(链接:https://app.galxe.com/quest/YxQGYqYQcCGDcfX7KZMU8j/?sort=Trending),多为社交类任务。

交互教程Sentient:开源 AI 开发平台

参与理由

Sentient 是一个开源 AI 项目,对标 GROK 和 ChatGPT,可简单理解 Sentient 是 Web 3 版的 ChatGPT。值得一提的是,Sentient 的核心贡献者之一是 Polygon 联合创始人 Sandeep Nailwal。Sentient 的顾问是 EigenLayer 创始人兼 CEO Sreeram Kannan。

2024 年 7 月 2 日,Sentient 宣布完成 8500 万美元种子轮融资,Peter Thiel 的 Founders Fund、Pantera Capital 和 Framework Ventures 共同领投,Ethereal Ventures、Robot Ventures、Symbolic Capital、Delphi Ventures、Hack VC 参投。

交互教程

Sentient 今晨在 Galxe 上线新任务,25 日截止(链接:https://app.galxe.com/quest/5pH5qUCoxjtXdLh2XMpRKT/GCdGDt6py9),为社交类任务,但需要 X 账户注册时长超 1 个月并且粉丝数超 200 个。

Zama:致力于推动全同态加密技术发展

参与理由

Zama 是一家公司专攻全同态加密(FHE)技术,该技术算是隐私计算领域最硬核的创新之一。简单来说,它允许直接对加密数据进行计算而无需解密。Zama 的使命是打造高效、易用的 FHE 工具,推动该技术在 Web 3 及其他领域的广泛落地。

根据 ROOTDATA 数据显示Zama 目前已完成两轮融资,总金额高达 1.3 亿美元,其中:

2024 年 3 月 7 日,Zama 宣布完成 7300 万美元 A 轮融资,Multicoin Capital 和 Protocol Labs 共同领投;

2025 年 6 月 26 日,Zama 宣布完成 5700 万美元 B 轮融资,Blockchange Ventures 和 Pantera Capital 领投。

交互教程

完成公会任务(链接:https://guild.xyz/zama)。

Letture associate

From Suppliers to Shareholders: The Big Three Memory Chip Giants Jointly Invest in Anthropic, AI Supply Chain Power Structure Undergoing Reshuffle

For the first time, memory chip giants Micron, Samsung, and SK hynix have jointly invested in the same AI company, Anthropic, as part of its massive $65 billion Series H funding round. This strategic move, positioning the three rival HBM suppliers as "strategic infrastructure partners," highlights a fundamental shift in the AI industry's power dynamics. With HBM (High Bandwidth Memory) being a critically scarce resource essential for AI model training and inference, securing a stable supply has become a key competitive differentiator. By making these chipmakers shareholders, Anthropic aims to lock in this vital component for its rapid expansion, which includes securing major compute commitments from Amazon, Google, and others. For the memory trio, this investment represents a strategic bet on defining the future of AI hardware. Each company gains: SK hynix reinforces its dominant position in the NVIDIA supply chain; Samsung diversifies its client base beyond NVIDIA; and Micron leverages its geopolitical significance as the sole US-based HBM maker. Their collective move signals that competition in AI is evolving beyond model capability to encompass control over the entire compute supply chain—from chips and memory to power and networking. This vertical integration trend, where infrastructure providers become direct stakeholders in AI firms, marks the industry's maturation as AI transforms from a research project into essential global infrastructure, setting the stage for a new era of ecosystem competition.

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From Suppliers to Shareholders: The Big Three Memory Chip Giants Jointly Invest in Anthropic, AI Supply Chain Power Structure Undergoing Reshuffle

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Investment Philosophy of Gavin Baker, an Early Nvidia Investor: Long AI Infrastructure Bottlenecks, Short Overall Market Risk

Gavin Baker, an early investor in Nvidia and founder of Atreides Management, outlines his investment philosophy: going long on AI infrastructure bottlenecks while hedging against broader market risk. He argues AI is not a bubble but a supercycle driven by constraints in power, wafers (semiconductors), and compute efficiency (tokens per watt). True alpha, he believes, lies not in application-layer companies like OpenAI but in "picks and shovels" providers—companies solving physical bottlenecks in GPU connectivity (e.g., Astera Labs), memory (Micron), inference chips (Cerebras, Positron), advanced manufacturing (TSMC, ASML), and energy supply. His portfolio reflects this barbell strategy: concentrated bets on key infrastructure players alongside a significant put position on the QQQ ETF to hedge overall market downside. Baker contends this cycle differs from the dot-com bubble because demand is fueled by the strong balance sheets of hyperscalers (Google, Meta, Amazon, Microsoft), not debt, and physical supply constraints (e.g., chip manufacturing capacity) prevent runaway overinvestment. He highlights the growing importance of inference (vs. pre-training), vertical/small language models, sovereign infrastructure deployment speed, and the convergence of energy and space (e.g., orbital compute). His long-term view is that performance-per-watt and token cost reduction will dictate winners as AI scaling hits fundamental physical limits.

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Investment Philosophy of Gavin Baker, an Early Nvidia Investor: Long AI Infrastructure Bottlenecks, Short Overall Market Risk

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Apple Re-invented Image Compression with AI: Same Quality, One-Third the File Size

Apple’s PICO: An AI-Powered Image Codec That Cuts File Size by Two-Thirds at Equal Perceived Quality In 2025, JPEG AI became the first international standard for learned image compression. However, it, like most codecs, still prioritizes mathematical metrics like PSNR over true perceptual quality—what the human eye finds pleasing. Apple researchers have introduced PICO (Perceptual Image Codec), a neural codec designed to optimize for human perception. It tackles key practical challenges: 1) Speed: A novel "one-shot context model" accelerates entropy encoding without sacrificing compression efficiency. 2) Artifacts: A dedicated TextFidelity loss preserves text clarity, and a TilingArtifact loss eliminates color seams between image tiles processed in parallel. 3) Control: It avoids the "hallucinations" common in GAN-based perceptual models. In a large-scale human evaluation (74,925 comparisons), PICO achieved the same perceived quality as standards like AV1, VVC, and JPEG AI while using only 30-43% of the bitrate. It also outperforms other learned perceptual codecs by 20-40%. Remarkably, it runs in 230ms (encode) and 150ms (decode) on an iPhone 17 Pro Max. While less efficient on synthetic graphics, PICO represents a significant shift from optimizing mathematical scores to directly targeting human visual experience, making high-quality perceptual compression practical for consumer devices. The work builds on expertise from WaveOne, whose team joined Apple and previously advanced neural video compression.

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Apple Re-invented Image Compression with AI: Same Quality, One-Third the File Size

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127 Totale visualizzazioniPubblicato il 2025.02.21Aggiornato il 2025.03.21

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