brc20-swap上线，详解其发展历程、产品模式及未来预期

Odaily星球日报Publicado em 2023-11-02Última atualização em 2023-11-02

Resumo

brc20-swap 利用 BRC-20 协议作为底层资产基础，实现 Ordinals 生态第一个原生交易。

原文作者：雾海，PANews

由于 Ordinals 生态资产的交易机制几乎全部处于挂单售卖模式，其流动性也一直以来成为生态的重点关注。2023 年 7 月，集铭刻、交易市场和钱包于一身的 Unisat 宣布即将推出 brc 20-swap，以期改善生态资产的流动性，该产品也于 10 月 30 日上线测试。

本文 PANews 将为你详解 brc 20-swap 的发展历程、产品模式及未来预期。

brc 20-swap 发展历程

2023 年 9 月 20 日，Unisat 向 620 个地址发放 brc 20-swap Early Access 邀请铭文。筛选条件为

1） 7 月 16 日至 9 月 20 日期间在 UniSat Marketplace 上交易至少 1 BTC 的活跃社区成员。

2） 7 月 16 日至 9 月 20 日期间通过 UniSat 铭刻铭文累计至少 500 UniSat 积分的活跃社区成员。

3）Unisat OG PASS 持有者（快照区块高度为 808559）

2023 年 10 月 10 日，UniSat 官方发布 brc 20-swap 主网上线时间表等细节。其逐步向社区用户免费分发名为 brc 20-swap Prime Access 的纪念铭文，持有该纪念铭文可参与 brc 20-swap 产品的早期测试。其通过官方 discord 频道和推特用户抽奖分发。分发总量为 654 个，目前二级市场地板价 0.0067 BTC。

结算和同步交易到比特币主网的工作由排序器完成。交易费率方面 brc 20-swap 参考 UniSwap 当前使用的费率标准，向所有参与交易的用户收取 0.3% 的服务费。该费用中大约 1/6 ( 0.05% ) 由 UniSat 收取，其余 5/6 ( 0.25% ) 则分配给各个交易对的所有流动性提供商。不同的是，brc 20-swap 以 brc 20 资产「sats」作为手续费。

2023 年 10 月 30 日，UniSat 官方宣布了 brc 20-swap 初期支持的 14 种资产。其筛选标准为过去 30 天内在 UniSat 市场上有至少 15 天交易活动的，并且过去 30 天内交易量中位数不为零的资产。此公告发出后，其中涉及到的资产也因利好预期均有明显涨幅。

brc20-swap上线，详解其发展历程、产品模式及未来预期

产品模式

产品细分为交易、流动池和数据一览，与 EVM 链上的 dex 形式大致相同。用户在进行交易前需要首先通过“inscribe TRANSFER”将资产充值到 brc 20-swap 模块中，其充值确认过程为 3 个区块。

brc20-swap上线，详解其发展历程、产品模式及未来预期

brc 20-swap 通过模块化扩展实现，每个模块独立于 brc 20-swap 存在。该模式的优势是：

1）无许可开发，使得为 brc-20 开发新模块是一个无需许可的过程；

2）隔离执行，保证任何单一模块的执行出现问题时，不会影响核心协议和其他模块的运行；

3）共识升级，当大多数索引器认可并实现特定模块的索引时，它可以从当前的黑色模块过渡到白色模块，成为 brc-20 协议的组成部分。

但黑色模块的扩展模式有一个缺点：用户无法实现自由提取资产。Unisat 通过设计动态调整的形式改善，当其他人充值资产大于所要提取的资产数额时可提取。这样做的好处是避免了如 EVM 链上随意增发的模式，不足之处是用户损失了一定的资产流动性和便利。

brc20-swap上线，详解其发展历程、产品模式及未来预期

当黑色模块的行为被用户理解和执行，逐步变得可靠，逐渐被更多索引者接受后，产品从黑色模块过渡到白色模块，达成共识升级。用户也就可以自由充提资产。

brc20-swap上线，详解其发展历程、产品模式及未来预期

此外，因为 brc 20 协议乃至整个 Ordinals 生态依然处于早期，Unisat 占据较大影响力和声誉，其为协议提供了完整的交易和余额查询等索引服务，有一家独大的中心化风险。其模块化运行的架构，使得更多服务商可以参与进来，从而实现索引更加去中心化。

未来预期

经常可以在 Ordinals 生态的社群里看到用户抱怨流动性低，可入场的资金容量低，导致 EVM 链的大户不得不观望。brc 20-swap 利用 brc 20 协议作为底层资产基础，实现 Ordinals 生态第一个原生交易。不断优化流动性，也就是在不断扩大入场资金的容量。因此，brc 20-swap 的推出于整个生态来说也是一个进步。

同时，Unisat 官方公布将 brc 20-swap 的手续费的 2% 捐赠给 brc 20 协议开发者 domo 参与的 L1 F 基金会，推动 brc 20 的开发和标准进一步优化。此外，UniSat 将开源 brc 20-swap 的完整解释和验证源代码，以促进索引器的早期支持。

种种迹象都表明了合作的态度，而非一家独大、独享生态利益。Ordinals 生态发展周期相对较短，需要生态内各方一起做大蛋糕，实现共赢。

Leituras Relacionadas

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

Silicon Valley investor and "Godfather of Startups" Steve Hoffman warns that combining Web3 with AI is likely a trap, not a promising venture. In an interview, Hoffman argues that while AI is a foundational technology touching all industries, Web3 adds complexity, friction, and regulatory risk without solving mainstream consumer or business needs. He advises founders to focus on deep, specialized applications where startups can out-iterate giants, rather than on generic features easily replicated by large tech companies. Hoffman observes that Silicon Valley will lead foundational AI research, while China excels at rapid, large-scale application and commercialization, particularly in robotics. He stresses that AI-driven autonomous agents capable of collaborative, multi-step tasks are 2-4 years away, which will cause significant job displacement. The solution is not to slow AI but to redesign business models around human-AI collaboration and reform social systems like education and retraining. For startups, Hoffman recommends focusing on vertical, expertise-heavy domains to build defensibility. He sees major opportunities in AI fraud detection and cybersecurity. Key founder mindsets include systemic thinking over feature-focus, relentless customer centricity, building adaptive teams, and deeply understanding AI's capabilities and limits. Hoffman is also leading a non-profit initiative to establish university centers aimed at training future leaders in responsible, human-value-aligned AI innovation.

marsbitHá 14m

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

marsbitHá 14m

Token Inefficient, Economy Tokenless

The article "Tokens Aren't Economical, Economics Aren't Tokenized" analyzes a pivotal shift in the AI industry from a technology-driven narrative to one dominated by capital efficiency. It highlights two concurrent trends: a severe capital shortage due to the exorbitant and recurring costs of compute (e.g., OpenAI's high burn rate) and a wave of corporate spin-offs where major tech companies are separating their AI units (like Kuaishou's Kling and Baidu's Kunlunxin). The core argument is that AI's "anti-internet" business model, where user growth increases costs rather than profits, has created a disconnect between high valuations and actual cash flow. Spin-offs address this by allowing AI assets to be valued independently. Within a parent company, they are seen as cost centers, but as standalone entities, they are priced based on their growth potential and scarcity in the primary market, leading to massive valuation premiums (e.g., Kling's estimated value tripling post-spin-off). The industry is at an inflection point, moving from "model worship" to "value realization." The competition is evolving from a pure compute (GPU) race to a broader focus on systemic efficiency and full-stack engineering (involving CPUs and orchestration) to achieve viable commercialization. The year 2026 is framed as a critical moment where the industry must definitively answer how to economically translate AI capability into tangible business value, reshaping the sector's future power structure.

marsbitHá 19m

Token Inefficient, Economy Tokenless

marsbitHá 19m

Crossing the 'Memory Wall': The Wafer-Level Revolution and Computing Power Routes in the AI Inference Era

In 2026, a historic shift occurred in AI as major cloud providers' inference spending surpassed training spending for the first time, signaling a move from "building large models" to "using large models." This shifts the core challenge from computing power to the "memory wall"—the bottleneck of data movement (model weights, activations, KV Cache) between external DRAM and processors, where energy and latency from data transfer far exceed computation itself. Companies like Nvidia face GPU idle time due to bandwidth limits. In contrast, Cerebras Systems adopts a radical "wafer-scale" approach with its Wafer-Scale Engine (WSE). Instead of cutting a silicon wafer into many chips, Cerebras uses almost the entire wafer as one massive chip (WSE-3). This design provides 44GB of on-chip SRAM, delivering memory bandwidth thousands of times higher than traditional HBM (e.g., 21 PB/s vs. Nvidia B200). For LLM inference, weights are streamed layer-by-layer from external MemoryX storage to the chip, avoiding HBM bottlenecks. This results in token generation speeds 1.5–5 times faster than Nvidia's B200 in some models and significant advantages in first-token latency and long-context tasks. Additionally, Cerebras's architecture offers much lower interconnect power consumption (0.15 pJ/bit vs. GPU's ~10 pJ/bit). However, Cerebras faces challenges: SRAM scaling has slowed with advanced nodes, limiting future capacity gains; the chip requires specialized liquid cooling and custom software stacks; and its external I/O bandwidth (150 GB/s) is low compared to NVLink, hindering multi-system scaling for very large models. Competition is intensifying. Major players are pursuing three paths: 1) Developing proprietary inference ASICs (e.g., Google TPU, Microsoft Maia), 2) Leveraging advanced packaging (e.g., TSMC's SoW) to democratize wafer-scale-like integration, potentially eroding Cerebras's process advantage within a few years, and 3) Exploring optical interconnects for ultimate bandwidth. Commercially, Cerebras is transitioning from a hardware vendor to a service provider, facing the immense challenge of building high-power, specialized data centers to meet large contracts (e.g., 250MW/year from 2026–2028). In conclusion, the AI inference era presents a fundamental architectural trade-off. Cerebras opts for extreme physical optimization for low-latency, single-task performance, while Nvidia prioritizes versatility and massive cluster throughput. The path forward remains uncertain, with technology and business models still evolving in the race toward advanced AI.

marsbitHá 25m

Crossing the 'Memory Wall': The Wafer-Level Revolution and Computing Power Routes in the AI Inference Era

marsbitHá 25m

Has Bitcoin's 'Rebound Ended', Officially Entering the Late Bear Market Phase?

**Title: Has Bitcoin's Rebound Ended, Entering the Late Bear Market Phase?** **Summary:** Bitcoin's price has declined by 13% this week, signaling a potential return to late-stage bear market conditions. The price fell to around $67k, positioned between the Realized Price and Realized Cap Weighted Average. For the first time since early 2022, the Short-Term Holder cost basis has dropped below this key average, confirming a hallmark of late-cycle bear markets. Profitability metrics have collapsed sharply. The 7-day average of the Realized Profit/Loss ratio plummeted from a local high of 3.16 to 0.29, mirroring the February panic sell-off. Critically, the 90-day average never breached the threshold of 2, indicating the recent rally to $82k was a bear market bounce, not a structural shift. Realized losses surged to $1.35 billion daily, with $770 million coming from Long-Term Holders selling at a loss. This accelerating redistribution of supply from weak to strong hands is a necessary but ongoing process for a market bottom. The rally stalled almost precisely at the aggregate cost basis (~$83k) of US spot Bitcoin ETF investors, turning that level into strong resistance and leaving the average ETF holder underwater again. Spot market flows have turned decisively negative, showing sellers are dominating order books despite the price drop. While a significant futures long liquidation event cleared over $400 million in leverage, providing a potential reset, sustained spot demand is yet to materialize. Options markets continue to price in higher future volatility (Implied Volatility) than recent price action (Realized Volatility) has shown, with a persistent skew towards put options, indicating ongoing demand for downside protection. In conclusion, multiple metrics point to a fragile market structure. Resistance at the ETF cost basis, accelerating realized losses, dominant spot selling, and cautious options pricing all suggest the bear market trend persists. A sustainable recovery likely requires a resurgence of spot demand, ETF holders returning to profit, and a clear reduction in selling pressure.

marsbitHá 25m

Has Bitcoin's 'Rebound Ended', Officially Entering the Late Bear Market Phase?

marsbitHá 25m

TechFlow Intelligence Agency: Anthropic Calls for Global Pause in AI Development While Preparing for Trillion-Dollar IPO; SpaceX IPO Roadshow Heats Up, But S&P 500 Rejects Fast-Track Inclusion

In today's TechFlow Intelligence Briefing, several major tech stories highlight a growing theme of trust and credibility gaps across AI, crypto, and finance. AI company Anthropic has publicly called for a global pause in AI development, citing risks from Claude's "recursive self-improvement." Ironically, this coincides with reports the company is preparing for a massive IPO targeting a near $1 trillion valuation. This perceived hypocrisy, coupled with widespread user complaints about Claude's declining performance, is sparking debate over whether the safety warning is genuine or a competitive tactic. Meanwhile, in a substantive security move, Anthropic open-sourced a framework for AI-powered vulnerability discovery. In the crypto market, Bitcoin's price drop below $61,000 triggered over $1.16 billion in liquidations, flipping the market into a state where more BTC is held at a loss than at a profit, a historical bearish signal. On the corporate front, SpaceX's highly anticipated IPO is generating immense Wall Street excitement, with Goldman Sachs projecting 100x revenue growth by 2030. However, the S&P 500 has refused to fast-track the company's inclusion post-IPO, potentially limiting immediate institutional demand. Separately, ByteDance's AI app Doubao lost over 6 million monthly active users after introducing a subscription model, highlighting the challenges of AI monetization. Other notable developments include Nvidia certifying HBM4 memory from Samsung, SK Hynix, and Micron; Cloudflare's acquisition of front-end tooling company VoidZero; and its CEO warning that bot traffic now exceeds human traffic online. The underlying narrative connects these events: a trust crisis. From AI firms' contradictory actions and crypto volatility to the clash between SpaceX's hyped narrative and institutional rules, a pattern is emerging where stated intentions and actual practices are increasingly misaligned.

marsbitHá 40m

TechFlow Intelligence Agency: Anthropic Calls for Global Pause in AI Development While Preparing for Trillion-Dollar IPO; SpaceX IPO Roadshow Heats Up, But S&P 500 Rejects Fast-Track Inclusion

marsbitHá 40m

Trading

Spot
Futuros
活动图片

Categorias popularesright-arrow

Etiquetas Popularesright-arrow