SocialFi数据对比：Friend.tech VS Farcaster，谁才是社交王者？

Odaily星球日报Publicado em 2024-05-06Última atualização em 2024-05-06

Resumo

KOL Club对线频道式论坛，Friend.tech盈利能力略胜一筹，Farcaster用户粘性更强。

原创｜Odaily星球日报

作者｜Wenser

SocialFi数据对比：Friend.tech VS Farcaster，谁才是社交王者？

作为 2024 年热度最高的 L2 网络， Base 生态先后跑出了 Friend.tech 和 Farcaster 两大社交协议，前者在经历了去年 8 月份的火热后一度陷入沉寂，但近期随着 FRIEND 代币和 V2 版本的上线带来的 Club 玩法再次吸引了市场上的众多关注；后者则在初期建设于 OP 生态之后，发挥背靠 Base 生态、于 2024 年 1 月推出 Frame 应用以及凭借包括 DEGEN 在内的 Meme 币热潮等优势，逐渐走出了一条属于自己的“社交论坛之路”。

今天，Odaily星球日报将带领大家一起，从数据角度看 SocialFi 赛道这两大热门产品—— Friend.tech 和 Warpcast（底层协议为 Farcaster）的各项表现。

一、基础数据：用户数量+协议收入

据 Dune 数据显示，目前 Friend.tech 总用户数为 909861 个独立地址，协议收入为 17056.5 ETH（约合 5464.7 万美元），当然，这是自 2023 年 8 月到 2024 年 5 月初的数据，整体时长约为 9 个月左右。

SocialFi数据对比：Friend.tech VS Farcaster，谁才是社交王者？

https://d1x7dwosqaosdj.cloudfront.net/images/2024-05-06/ca6c652c26aa0384d2ef27d3d62db34b

据 Dune 数据，目前 Farcaster 总用户数为 344, 474 个独立用户，协议收入为 123.4 万美元，值得注意的是，Farcaster 的协议收入数据开始于 2023 年 10 月初，相比于 Friend.tech 甫一上线便火爆全网进而手续费收到手软的情况，Farcaster 的协议收入初期仅为数千美元，可以说是差距巨大。

SocialFi数据对比：Friend.tech VS Farcaster，谁才是社交王者？

https://d1x7dwosqaosdj.cloudfront.net/images/2024-05-06/ff78238b9c7be9dfff4fb7957bce0a43

二、进阶数据：流入资产+交易数据

在了解了两大社交产品的基本面之后，我们可以再进一步对比一下二者的流入资产数据以及交易量数据。

据 Dune 数据，Friend.tech 的总流入资产数为 31, 877.59 ETH（约合 1.02 亿美元），总体交易量为 13, 681, 507 次。

SocialFi数据对比：Friend.tech VS Farcaster，谁才是社交王者？

https://d1x7dwosqaosdj.cloudfront.net/images/2024-05-06/ca6c652c26aa0384d2ef27d3d62db34b

Farcaster 的资产流入数据统计则存在一定难度，目前 Dune 等数据分析平台都没有对应面板和准确数据，但每个 Warpcast 的注册用户如果想要加入一些付费频道，则需要购买 25 美元的 Warp 作为应用内代币，兑换比率约为 1: 100 ，按照 381, 544 个独立地址计算，总流入资产数可以粗略统计为 953.8 万美元。而由于 Farcaster 协议支持的包括 Warpcast 在内的客户端应用包括链上和链下两类操作行为，而根据相关数据显示，Farcaster 生态发帖量在 126 万次左右，所以我们这里不对 Farcaster 相关的交易量数据进行过多展开。

SocialFi数据对比：Friend.tech VS Farcaster，谁才是社交王者？

https://d1x7dwosqaosdj.cloudfront.net/images/2024-05-06/5d17e2583c4616c52a05afdc1287d655

三、高阶数据：代币数据+日活用户

在以上数据对比的基础上，最重要的数据对比或许应该是二者的代币数据以及日活用户数据之间的区别，前者代表着当下的市场关注度和深度参与人数；后者则代表着项目和产品的长期发展方向和忠实粉丝数量。

据 Coingecko 数据显示，目前 Friend.tech 代币 FRIEND 价格为 2.67 美元，流通总量为 9091 万枚，总体市值为 2.42 亿美元。

SocialFi数据对比：Friend.tech VS Farcaster，谁才是社交王者？

Coingecko 网站截图

Farcaster 协议则并未发行对应代币，因此我们将 Farcaster 生态应用范围较广的 DEGEN 代币作为对比对象。

据 Coingecko 数据显示，目前 DEGEN 价格为 0.02138 美元，流通总量为 369.6 亿，总体市值为 2.66 亿美元。

SocialFi数据对比：Friend.tech VS Farcaster，谁才是社交王者？

Coingecko 网站截图

而在日活用户方面，据 TheBlock 网站数据统计，Friend.tech 的日均活跃用户从去年 10 月中旬的近 8 万用户已经回落至目前的 3000 人左右（独立卖家 1360 人，独立买家 1640 人）。

SocialFi数据对比：Friend.tech VS Farcaster，谁才是社交王者？

TheBlock 统计界面

Farcaster 的日均活跃用户则呈稳步增长态势，据 Dune 数据，目前 Farcaster 生态原创创作者数量约为 3.85 万人，整体活跃用户数量约为 4.3 万人左右。

SocialFi数据对比：Friend.tech VS Farcaster，谁才是社交王者？

Farcaster 日活用户数据界面

值得注意的是，据 Farcaster 创始人 Dan Romero 在 X 平台发文声称，Farcaster 4 月份单日活跃用户创历史新高，达到了 8.5 万人，且首月达成“日均活跃用户破 7 万人”、“月活用户突破 20 万人”等数据记录。

小结：Friend.tech 盈利更胜一筹，Farcaster 忠实粉丝更多

经过以上的数据对比，我们可以阶段性地得出以下结论：

  • 在盈利能力方面，Friend.tech 通过较高的 Key 及 Club 买卖税实现了更高的盈利比率，由此获得了更多协议收入；

  • 在产品周期方面，尽管 Farcaster 生态上线时间早于 Friend.tech，但在 Base 生态进入时间周期来看，Farcaster 发展晚于 Friend.tech，换言之，Friend.tech 有一定的先发优势，且产品已经进入 Club 炒作的 V2 版本，Farcaster 则主要依赖于生态内不同应用、频道的建设，因此产品周期发展相对缓慢；

  • 在忠实粉丝方面，Farcaster 生态的忠实粉丝数量远大于 Friend.tech，这既有 Friend.tech 的进入门槛相对更高的原因，也在一定程度上受到了二者产品功能的影响；

  • 在产品定位方面，Farcaster 生态更像是类似 Reddit、贴吧等互联网论坛，Friend.tech 则是类似 Web3 付费版知识星球、文字类 Clubhouse 等产品，前者面向群体交流，后者则更多是一对多的俱乐部式交流。

除此以外，尽管二者都为内容（Cast）或者 Key、Club 赋予了一定的炒作投机价值，但相对而言， Friend.tech 产品的投机属性更强，正反馈也更为直接，Farcaster 生态的产品则更偏向于社区激励、成员交流方面的价值赋能，因此，长期来看，Farcaster 生态的潜力相对更强，用户粘性也更强一些。

所以，“社交之王”的名号到底花落谁家，或许我们仍然需要经过更长时间的观察，才能得出一个相对明确的答案。

Leituras Relacionadas

Chip Stocks Lead U.S. Market Decline: Is AI Trading Being Hit by Both Interest Rates and Returns?

Chip stocks led a broad decline in US markets, with the Nasdaq dropping 2.2% and the S&P 500 falling 1.4%. This selloff reflects a dual challenge for the once-high-flying AI hardware trade: rising interest rate expectations and growing investor impatience for clear returns from massive AI capital expenditures. The pressure was most acute on hardware leaders. Nvidia fell about 4%, dipping below a $5 trillion market cap, while Micron plunged 13.2% ahead of its earnings report. Declines across memory, storage, AI, and mobile chips indicated a sector-wide retreat. The selloff spread globally, with South Korea's KOSPI index dropping nearly 10% as key suppliers SK Hynix and Samsung recorded double-digit losses. Investors appeared to be taking profits from the most crowded trades first. Macro headwinds intensified as market expectations shifted toward a more aggressive Federal Reserve. Forecasts for multiple rate hikes in 2026 pressured high-valuation tech stocks, which rely on long-term growth projections that become less attractive as discount rates rise. Concurrently, investors are scrutinizing the profit potential of the immense AI spending by cloud giants like Alphabet, Amazon, and Meta. While these expenditures drive demand for chips and hardware, the market is now questioning whether AI services will generate sufficient returns to justify the ongoing costs. This adjustment is not necessarily a bubble burst but a recalibration. AI demand fundamentals remain, but the narrative of endless growth can no longer fully offset concerns over higher interest rates and a longer path to profitability. Near-term direction may hinge on Micron's upcoming earnings guidance and incoming inflation data, which will influence both the AI demand outlook and the Fed's policy path. The market is transitioning from blindly buying growth to demanding clearer visibility on returns.

marsbitHá 53m

Chip Stocks Lead U.S. Market Decline: Is AI Trading Being Hit by Both Interest Rates and Returns?

marsbitHá 53m

OpenAI's New Paper: How to Train an AI that "Doesn't Deteriorate Under Pressure"?

OpenAI's new paper "Reinforcement Learning Towards Broadly and Persistently Beneficial Models" explores training AI to maintain safe, helpful, and honest behavior even under pressure, in unseen scenarios, or after being fine-tuned for harmful purposes. Moving beyond simple rule-based "don'ts," the research focuses on cultivating "beneficial traits" like honesty, risk-awareness, corrigibility, and transparency. It investigates if reinforcement learning (RL), often prone to "reward hacking" where models exploit loopholes, can instead be used to instill robust, generalized positive behaviors. Researchers created a multi-domain synthetic dialogue dataset covering areas like healthcare and law. They trained a model by replacing 5% of standard RL data with "beneficial trait" data. This model outperformed the baseline in 83% of 53 evaluations, showing average gains of 9.1% in alignment, safety, and helpfulness. Crucially, improvements generalized: a model trained only on healthcare "good behavior" data also performed better in 17 out of 19 non-healthcare alignment tests. The paper also tests "alignment persistence." When subjected to adversarial prompts or harmful fine-tuning, the beneficial trait model showed greater resilience, with smaller performance drops and less "spillover" of bad behavior to unrelated tasks. While not a complete solution, this work suggests a shift from post-hoc correction to proactively shaping robust, principled AI behavior, a critical step for deploying models in high-stakes, complex decision-making scenarios.

marsbitHá 55m

OpenAI's New Paper: How to Train an AI that "Doesn't Deteriorate Under Pressure"?

marsbitHá 55m

Goldman Sachs Revisits the State of the AI Boom: Strong Earnings Will Override Valuation Concerns Until the Investment Cycle Peaks, Volatility to Rise Further

Goldman Sachs discusses the current AI investment boom, arguing it is not a simple repeat of the 1999-2000 bubble. Unlike the dot-com era, current valuations are not wildly out of control because earnings expectations are rising in tandem with stock prices. The key issue is that market prices have already priced in substantial optimism, making them sensitive to any narrative shifts. The core risk has shifted from "valuation bubble" to "earnings bubble." While strong profits from companies in semiconductors, cloud infrastructure, and data centers currently outweigh valuation concerns, this is dependent on the ongoing capital expenditure cycle. The true test will be whether these robust earnings can be sustained after the investment cycle peaks. Current market prices imply optimistic assumptions, such as faster AI adoption, higher productivity gains, and a larger share of economic profits going to capital owners. The AI boom may also be masking relative weakness in the non-AI sectors of the economy. As a result, market volatility is expected to rise. Goldman recommends that investors consider maintaining exposure while adding downside protection, such as put options, to manage potential drawdowns.

marsbitHá 59m

Goldman Sachs Revisits the State of the AI Boom: Strong Earnings Will Override Valuation Concerns Until the Investment Cycle Peaks, Volatility to Rise Further

marsbitHá 59m

Zuckerberg Enters the Prediction Market

Mark Zuckerberg's Meta has entered the predictive markets arena with a new app called "Arena." Currently in an early experimental phase, Arena will initially operate as a standalone app using a points-based system where users can predict outcomes on politics, sports, and world events without real money. Meta plans to leverage its vast daily active user base for growth. The move comes as the predictive market industry sees explosive growth, with platforms like Polymarket and Kalshi handling hundreds of billions in volume. While a points system avoids initial regulatory hurdles, Meta hasn't ruled out introducing real-money betting later. The strategy aligns with Meta's pattern of adopting popular trends, though challenges remain, including regulatory scrutiny and the historically mixed success of its standalone apps.

Foresight NewsHá 1h

Zuckerberg Enters the Prediction Market

Foresight NewsHá 1h

Semiconductor Stock Rebound: Is the Technical Correction Over or a Trend Reversal?

The core of recent semiconductor stock volatility is not about daily price swings, but rather the market questioning whether AI-driven semiconductor pricing has entered a new phase. Following a sharp sell-off in Korean stocks on June 23rd, led by Samsung and SK Hynix, a subsequent rebound is seen more as a technical positioning adjustment rather than a confirmed trend reversal. The key variable is HBM (High Bandwidth Memory), essential for AI chips. Its supply-demand imbalance granted memory makers significant pricing power. The current market focus is on whether this dynamic remains strong enough to justify elevated valuations. All eyes are on Micron's upcoming earnings report. The critical factor is not whether results meet already high expectations, but whether the company's guidance confirms that AI memory pricing power, order visibility, and future margins are still expanding. Micron's outlook will serve as a crucial test for the broader AI semiconductor chain, including Samsung, SK Hynix, and other infrastructure players. The recent bounce appears to be a pre-earnings positioning repair. For it to evolve into a sustained uptrend, concrete evidence is needed that the AI infrastructure expansion cycle's fundamentals—particularly for high-end memory—remain robust and can continue to surpass elevated market expectations. The risk is that strong demand alone may not be sufficient if future guidance hints at peaking momentum or increasing supply-side pressures.

marsbitHá 1h

Semiconductor Stock Rebound: Is the Technical Correction Over or a Trend Reversal?

marsbitHá 1h

Trading

Spot
Futuros
活动图片

Categorias popularesright-arrow

Etiquetas Popularesright-arrow