崛起与幻灭：friend.tech从爆火到崩盘的戏剧性反转

coinvoicePubblicato 2024-09-10Pubblicato ultima volta 2024-09-10

原文作者：Karen，Foresight News

转载：Daisy，火星财经

昔日之辉煌，映照今朝之落寞。friend.tech 的兴衰轨迹犹如一场戏剧性的反转，令人唏嘘不已。

北京时间 9 月 8 日，friend.tech 官推发布了一则意味深长的公告，将其管理和所有权参数设置为 0x000...000，名义上是为了防止将来对其费用或功能进行任何更改，实则是对智能合约控制权的全面放手。部分社区用户将此解读项目开发的停滞与终结。

尽管 friend.tech 试图安抚市场情绪，强调此变更不会影响 Web 客户端的正常运行，且开发团队不会从智能合约或平台中抽取任何费用。但社交媒体上「friend.tech 已死」、「软 Rug」的言论蔓延开来，透露出用户对于 friend.tech 未来的忧虑与失望。

尤为值得注意的是，自 8 月份以来，friend.tech 官方推特的内容变得异常单调与机械，每日仅发布当天最热门的 5 个 Clubs 信息，除此之外再无其他新鲜内容或互动。这种变化不仅让人感受到项目团队的疲惫与无力感，或许这也正是 friend.tech 团队内心挣扎与最终放弃智能合约控制权决心的前兆。

昔日翘楚如今落寞

追溯往昔，friend.tech 的崛起之路曾是何等辉煌。起初，得益于 Paradigm 的加持、重量级 KOL 的积极参与、一码难求的设计、潜在空投预期以及货币化 KOL 价值和 IP 的新颖玩法，friend.tech 在短短两周之内便实现从 0 到 10 万 + 用户的斐然成绩，累计用户超过 91 万。

然而，时过境迁，今日的 friend.tech 却已风光不再。日活跃用户数量在最近两个月已下滑至个位数、两位数，Key 的日交易量也从昔日的 2000 万美元高点降至如今的数千美元，曾经的繁荣景象仿佛在几个月之间化为泡影。

更令人唏嘘的是，FRIEND 代币的价格也经历了断崖式下跌。从今年 5 月份的近 3 美元高点一路滑落至当前的 0.06 美元，跌幅超过 98%，市值仅剩 500 万美元。黄立成曾斥巨资购入 FRIEND 代币，如今却面临巨额浮亏，此前 1560 万美元买入的 FRIEND 现在仅价值 57 万美元，至少浮亏 1500 万美元。这也成为了 friend.tech 衰落的一个缩影。

回顾 friend.tech 的发展历程，团队内部与外部合作的不确定性也为其衰落埋下了伏笔。从与 Base 关系的微妙变化到 Friendchain 愿景的夭折，再到最终放弃将 FRIEND 代币迁移至其他链的决策，friend.tech 在战略层面的摇摆不定进一步加剧了其困境。

5 月底，friend.tech 联合创始人 Racer 暗示团队与 Base 关系不稳定有意迁出协议，6 月份， friend.tech 随即宣布与 Conduit 合作开发基于 Base 的社交专用链 Friendchain，Gas 代币为 FRIEND。之后，friend.tech 删除了此条推文，放弃 Friendchain 愿景，并遵循社区反馈决定不将 FRIEND 迁移至其他链。

同时，friend.tech 关闭 BunnySwap（内置 Swap）、Clubs 和 v1 智能合约的所有协议费用，100% 费用将归于社区内的交易者、流动性提供者和 Club Chair。此前在 friend.tech V1 中，对每笔交易收取 10% 的费用，其中一半为协议收入。

DefiLlama 数据显示，截至今日，friend.tech 共产生 6338 万美元的费用，收入达到 3166 万美元。如下图所示，friend.tech 在去年 10 月份辉煌时期曾创下单日 100 万美元以上的费用，而在 V2 版本发布后，friend.tech 费用增长基本处于停滞状态，最近一个月单日费用仅为几百美元。

智能合约

来源：DefiLlama

friend.tech 因何沉寂？

friend.tech 的沉浮，如同一面镜子，映照出 Web3 社交在追求快速扩张的同时，也需警惕泡沫风险。

friend.tech 的沉寂背后，交织着多重复杂原因。首要的是其产品所蕴含的高投机性，这在一定程度上吸引了大量追求短期利益的投资者，却难以构建稳固的长期用户基础，使得用户忠诚度难以维系。其次，friend.tech 在探索市场定位的过程中，似乎未能精准找到与其核心价值相契合的细分市场，导致产品与用户需求的脱节。此外，此前过度的 PUA 策略也在一定程度上损害了 friend.tech 的声誉，使得用户对其信任度大打折扣。在团队建设与外部合作方面，friend.tech 也面临着诸多不确定性，这不仅影响了项目的执行效率，也加剧了市场对项目前景的担忧。更为关键的是，friend.tech 在战略层面的频繁摇摆不定，使得项目方向频繁调整，难以形成有效的市场影响力和用户粘性。

Web3 社交何在？

而另一边 Web3 社交领域的龙头项目 Farcaster 近期同样遭遇了用户增长与平台活跃度的双重困境。Farcaster 在年中时候每日发帖数和互动数一度在 14 万以上，这一良好势头在近一个月内出现了显著放缓，DAU 滑落至大约 10 万的水平，标志着 Farcaster 增长动能的减弱。

智能合约

来源：Dune（@pixelhack）

更为严峻的是，Farcaster 用户获取方面也遇瓶颈。自 2 月初以来，Farcaster 的每日新用户增长从高峰时期的 15,366 人骤降至最近两周的日均五六百人。这一数字的变化不仅揭示了新用户吸引力的大幅下滑，也映射出平台在保持用户增长动力上面临的严峻考验。除此之外，Farcaster 生态里的 Meme 也遭受重创，其中，Degen 自 5 月底触及历史高点以来，其价值已下跌超过 95%。

智能合约

来源：Dune（@filarm）

这些数据不仅揭示了市场信心的动摇，也预示着 Farcaster 生态系统正经历着前所未有的压力与调整期。

反观 Telegram 通过与 TON 生态的协同发展和小程序游戏的爆发，在 Web3 社交领域的优势更加明显。而 Web3 原生社交项目要想大规模扩展，需要更加注重精准定位、降低 Web2 到 Web3 社交迁移成本、优化用户体验以及增强用户体验和信任建立等方面的工作。

声明：本内容为作者独立观点，不代表 CoinVoice 立场，且不构成投资建议，请谨慎对待，如需报道或加入交流群，请联系微信：VOICE-V。

来源：火星财经

Letture associate

Google CEO Admits Lagging Behind in Coding

Google CEO Sundar Pichai acknowledged in a recent interview that Google's Gemini AI models are currently "lagging behind" in coding capabilities, particularly for complex, long-horizon tasks requiring advanced developer expertise. He noted the field is advancing at an "unprecedented" pace, where 30-60 days now brings changes equivalent to five years in the past. Pichai expressed that achieving Artificial General Intelligence (AGI) now seems closer than previously imagined due to rapid progress. While highlighting strengths in text, multimodal, and reasoning tasks, Pichai admitted competitors like Anthropic and OpenAI have focused more intently on coding. He emphasized Google's commitment to catching up, citing internal tools like Antigravity 2.0 and the newly released Gemini 3.5 Flash, which aims to address previous shortcomings. Regarding Google Search's AI-driven overhaul, Pichai stated changes will be gradual to align with user needs, not disrupt the core search experience or its advertising model. He addressed public AI anxiety as understandable, given the technology's potential to reshape jobs and society, but remained optimistic about AI augmenting human capabilities and creating new opportunities. Pichai stressed the need for broad societal dialogue and responsible development as AI approaches more advanced, potentially recursive self-improvement stages. He affirmed Google's long-term commitment to leading in AI while navigating its profound implications responsibly.

marsbit13 min fa

Google CEO Admits Lagging Behind in Coding

marsbit13 min fa

The Paradox of Automation: The Stronger the AI, the Busier Humans Become

The Paradox of Automation: The more powerful AI becomes, the more work humans have to do. This article, based on observations from AI-heavy company Every, argues that while AI agents automate tasks like coding, writing, and customer service, they don't eliminate human jobs. Instead, they transform work and create *more* demand for human expertise. AI commoditizes "yesterday's human capabilities" by cheaply generating code, text, and images from past data. This leads to an abundance of similar, generic outputs. Consequently, what becomes scarce and valuable is human judgment in the present moment: knowing *what* is worth doing, *why*, and *how* to do it well. The article identifies two collaboration models: "Agent employees" for delegated tasks and "human-AI collaboration" within tools like Claude Code for complex work. In both cases, humans are essential to set direction, judge quality, and maintain systems. As AI makes execution cheap, human roles shift from executors to designers, reviewers, and meaning-makers. The author addresses "benchmark anxiety" by explaining that AI excels within specific, human-defined problem "frames." As AI masters one frame (e.g., code rewriting), new, more complex frames emerge (e.g., deciding *when* to rewrite). This creates an ongoing cycle where AI chases the frames, but humans remain the "framers." Even with advanced AGI, this dynamic may persist as long as AI lacks true human-like agency and self-directed purpose. The core paradox holds: automation amplifies the need for the very human judgment it seems to replace.

marsbit1 h fa

The Paradox of Automation: The Stronger the AI, the Busier Humans Become

marsbit1 h fa

a16z: 7 Charts to Understand How Tokenization is Changing the Nature of Assets

"a16z: 7 Charts on How Tokenization is Changing the Nature of Assets" Tokenized Assets (or Real-World Assets - RWA) are transforming asset forms, liquidity, and financial system construction. The market recently surpassed $30 billion, stabilizing around $34 billion (excluding stablecoins), representing a tenfold increase in less than two years, driven by clearer regulations, mature institutional infrastructure, and increased financial institution adoption. The primary driver of recent growth is tokenized U.S. Treasury bonds. These offer investors efficient, flexible digital access to yield-bearing assets and improve institutional operations like settlement and collateral management. Other asset classes show varied growth: asset-backed credit leads, followed by niche financial assets (e.g., reinsurance, mining notes), while venture capital took longer to scale. Market segmentation shows high concentration. In commodities, tokenized gold dominates (~$5 billion), as its standardized, storable nature fits tokenization well. Bonds are the largest category ($15.2B), but only ~5% are used in DeFi protocols. Conversely, smaller niches like reinsurance tokens see high (~84%) on-chain utilization, highlighting a core industry divide: most current tokenized assets are merely digitized records for easier holding/transfer, lacking the "composability" (free combination/interaction) that is key to blockchain-native finance. The ecosystem is distributed across multiple blockchains, with Ethereum hosting over half the value ($15.7B), followed by BNB Chain, Solana, and others. Future market size predictions vary widely (e.g., $2-$30 trillion by 2030+), but all indicate massive potential from the current small base. Tokenized assets currently represent minuscule fractions of their global counterparts (e.g., 0.01% of global bonds). The current phase focuses on digitizing straightforward assets. The next challenge is to bring more complex financial components on-chain and deeply integrate tokenized assets into composable, internet-native financial infrastructure.

链捕手2 h fa

a16z: 7 Charts to Understand How Tokenization is Changing the Nature of Assets

链捕手2 h fa

a16z: How Tokenization is Transforming the Nature of Assets in 7 Charts

"Tokenized Assets: How Tokenization Changes the Nature of Assets" by a16z Crypto The market for tokenized assets, excluding stablecoins, has grown from under $3 billion two years ago to over $340 billion today. US Treasury bonds are the primary growth driver, allowing investors to hold yield-bearing assets digitally and enabling more efficient settlement. Other key sectors include private credit (growing fastest), commodities (dominated by gold), and niche financial assets. However, the market remains concentrated in tokenized US Treasuries and gold. A critical insight is that most tokenized assets currently lack "composability." While the total market is large, only a small fraction is actively used within DeFi protocols. For instance, only about 5% of tokenized bonds and a low percentage of tokenized gold are utilized on-chain. In contrast, assets like reinsurance and private credit tokens show much higher on-chain usage rates (84% and 33%, respectively). This highlights a divide: many tokenized assets are merely digital records on a blockchain without enabling new, programmable financial applications. The Pantera Capital Token Native Index indicates over 70% of tokenized assets have minimal on-chain native functionality. Ethereum remains the dominant blockchain for tokenized assets (over $150B), but the ecosystem is diversifying across chains like BNB Chain, Solana, and Stellar, based on factors like cost and compliance. Major institutions forecast massive future growth, with predictions for the tokenized asset market ranging from $2 trillion to over $30 trillion by the early 2030s. However, compared to the global financial system (e.g., ~$140T bonds, multi-trillion dollar gold market), tokenized assets currently represent a tiny fraction (0.01% or less). The conclusion is that while tokenization has begun by digitizing and streamlining settlement for simpler assets, the next phase involves bringing more complex financial instruments on-chain and deeply integrating them into composable, internet-native financial infrastructure.

Odaily星球日报2 h fa

a16z: How Tokenization is Transforming the Nature of Assets in 7 Charts

Odaily星球日报2 h fa

The Revived Codex, Carrying OpenAI's Hopes for IPO

This article analyzes the intense recent development of OpenAI's Codex, positioning it as a crucial component for OpenAI's impending IPO. Over the past two months, Codex has seen a rapid series of major updates focused on integrating into real enterprise workflows. Key new features include enhanced context capture (Appshots, file previews, built-in browser), long-running task execution ("Goal Mode"), remote operation (phone control, lock-screen access), and enterprise management tools (plugin sharing, access tokens, automated risk review). These updates aim to make Codex a comprehensive AI workbench that can "see the scene, push tasks, and manage risks." The author argues that while ChatGPT proves OpenAI's massive user base and API provides foundational revenue, Codex represents OpenAI's clearest path to demonstrating tangible, high-value commercial viability. It targets developers and engineering teams—a segment already accustomed to paying for efficiency gains in costly software development cycles. This is critical because, despite higher overall revenue, OpenAI's adjusted operating margins remain deeply negative, highlighting the challenge of outrunning immense compute costs. The pressure is amplified by competitor Anthropic's success with Claude Code, which has shown that a focused approach on high-value enterprise and developer workflows can lead to a path toward profitability. Codex's aggressive evolution is thus seen as OpenAI's strategic move to capture a similar enterprise-ready, revenue-generating narrative essential for its market debut. In essence, "ChatGPT proved OpenAI has users. Codex needs to prove OpenAI is a business that can make money."

marsbit3 h fa

The Revived Codex, Carrying OpenAI's Hopes for IPO

marsbit3 h fa

Trading

Spot

Futures