比特币生态大爆发：RGB、BRC 2.0、SAT 20谁将引爆下一波财富效应？

marsbitPublished on 2025-08-11Last updated on 2025-08-12

近期，比特币生态总体活跃度上升，许多开发时间较长的项目也都进入了交付或新里程碑阶段，例如，RGB、sat 20、BRC 2.0 等。Odaily 星球日报将在本文中简要梳理相关项目的进展，供生态玩家自行挖掘财富密码。

RGB 协议：主网首个代币将于 8 月 14 日再次开放申领

RGB 协议于 8 月 7 日上线比特币主网，同步发行了主网上第一个代币 RGB，总量 2100 万枚，采用公平申领模式，但每次申领需要支付约 4 美元的费用。

按照社区估计 RGB 代币申领完毕需要约 24 小时，然而实际情况是，截止目前（历时 5 天）RGB 代币申领进度为 60%。主要原因或在于活动太过火爆，网站流量及申领队列过长，Bitlight Labs 多次进行服务器升级、暂停申领以升级防御措施打击机器人等行动。

BRC 2.0

根据 官方公告，Bitlight Labs 已基本完成针对机器人申领的防御措施，并计划于 2025 年 8 月 14 日 17:00 发布 Bitlight 钱包 v 1.1.3 版本和重新开放 RGB 代币申领，但此次仅开放 10%的代币作为申领测试，若测试结果满意，将陆续重新开放 RGB 代币剩余进度。

虽然 RGB 协议此次上线主网并未达到用户预期甚至遭受大量 FUD。但从申领过程来看，Bitlight Labs 仍在持续开发改进并积极打击机器人申领行为，一定程度上维护了申领的公平性和代币筹码分散度，不至于造成大量筹码集中在少数人手中的情况，有利于后期社区发展。

即使暂未有官方交易市场，但已分发的 RGB 代币可直接通过钱包转移，目前单张 RGB（50 枚）价格在 10-13 美元，相比申领成本已实现 2-3 倍收益回报。因此，8 月 14 日申领重新开放后，或许申领竞争将进一步加大。关于 RGB 协议的更多介绍及申领教程可阅读 Odaily 此前文章。（相关阅读：苦等两年，RGB 协议上线主网就这？）

BRC 2.0 升级：第一阶段推迟至 9 月 2 日

BRC 2.0 是 Best in Slot 推出的一项对 BRC 20 协议的升级，旨在为所有 BRC 20 代币启用与 EVM 兼容的智能合约功能。Best in Slot 原计划于区块高度 909969（约 8 月 14 日）在主网进行 BRC 2.0 第一阶段升级，启用 6 字符 BRC 20 代币并推出 launchpad，但因未准备充分，将升级推迟至区块高度 912,690 进行，约在 9 月 2 日。

或许因为升级日期的推迟，Pre-BRC 2.0 资产市值也整体下跌。第一个 BRC 2.0 概念 NFT Adderrels 当前地板价为 0.0076 BTC（约合 900 美元），较高点下跌 60%。不过 Adderrels 项目方仍在运营当中，更新了质押 NFT 获取代币空投的细节，空投将分三个赛季分发，分别释放的总供应量的 8%、9%、10%， 第一季度代币空投在 9 月 2 日 BRC 2.0 上线时分发。

另一个自称为首个在 Ordinals 上铸造的 Pre-BRC 2.0 代币项目 LIQUID 则走向了社区 CTO，此前 LIQUID 原项目方拟向出售整个项目，引起了一波恐慌，目前 LIQUID 的价格 0.00006 BTC，接近铸造成本。

BRC 2.0

关于 BRC 2.0 升级的更多介绍，可阅读 Odaily 此前文章。（相关阅读：主网倒计时，BRC 2.0 升级引爆 BTC 生态，龙头 NFT 月涨百倍｜BTC 生态）

SAT 20 协议：SatoshiNet 主网上线

SAT 20 协议是一个开发 2 年之久的 BTC 原生资产发行和流通协议，其核心特征是资产绑定聪，跟随聪自由流动。SatoshiNet（聪网）是基于 SAT 20 协议建设的比特币原生 Layer 2，基于闪电通道+平行 BTC 网络，其存在的目的就是为了拓展 BTC 主网原生资产的流动性，支持 Ordinals、Runes、OrdX、BRC 20 等多种协议资产。2025 年 8 月 8 日 SatoshiNet 主网正式上线。

SatoshiNet 的核心在于资产发射合约（LaunchPool）、资产穿越合约（Transcend）、AMM 交易合约（Swap）和限价交易合约（LimitOrder）这四个合约能力上，这些能力使聪网具备即时结算、极低手续费和兼容比特币资产的功能。

目前 SatoshiNet 部署了四个穿越合约，分别为 BTC、pearl 和 rarepizza（ordx 资产）、DOG•GO•TO•THE•MOON（Runes 资产），ordx 是 SAT 20 协议的原生资产发行，其为一个强化版本的 Ordinals 协议，发行的资产称为聪资产(SAT 20 ASSETS)，资产绑定在聪上，具有聪的属性。Pearl 是 ORDX 协议的第一个代币，也成为 SatoshiNet 网络的治理代币。

SAT 20 协议公开的团队成员包括市场运营兼英文区业务拓展 huige 和技术主管 尖岗山。SATSWAP 是 SatoshiNet 当前第一个 DEX，具有代币发射、交易市场、限价单等功能。

尽管 SAT 20 协议坚持建设了两年并且已经形成了一个坚实的社区，但却没有形成足以破圈的财富效应吸引更多人关注。

比特币原生 Layer 2 Spark：Launchpad 启动

Spark 自称是专为支付和结算打造的比特币原生 L 2，但不支持智能合约，有自己的代币发行标准 LRC 20.8 月 11 日，Spark 协议唯二的节点运营商之一、Spark 浏览器 sparkscan Flashnet 在 Spark 上进行 LRC 20 AMM 功能测试，但测试并不顺利，估计待正式推出 DEX 还需要一些时间。

同时，8 月 1 日比特币生态代币启动平台 Luminex 宣布与 Spark 官方合作推出 Launchpad 也使生态玩家短暂兴奋，但目前仍未推出。但 8 月 11 日，另一个 LRC 20 Launchpad utxo.fun 于 Flashnet 的 AMM 测试同步上线，不过仍然遇到了严重问题，平台处理了用户退款并暂时禁止了发行代币。

但尽管如此，LRC 20 的龙头代币 FSPKS(b 55 e 结尾)单张地板价仍有 100 美元，与 2 美元的初始铸造成本相比，仍有 50 倍的收益。关于 Spark 协议的更多内容，可以阅读 Odaily 此前的文章（相关阅读：详解 Spark 及生态：a 16 z 支持、PayPal 帮创立的新比特币 L 2）

BitVM 2 比特币桥 Fiamma：主网上线

Fiamma 是一个比特币财富管理平台，核心产品为一键赚取 BTC 收益的非托管超级应用 Fiamma One 和基于 BitVM 2 构建的 Fiamma Bridge。8 月 6 日，Fiamma 正式上线，可将 BTC 转移到以太坊、Arbitrum、Aptos、BNB Chain、Base 等在内的 11 条链上。

同时为了鼓励用户参与，Fiamma 在 8 月 8 日上线了积分任务，Mint 0.00001 FIABTC 可获得 1 羊驼积分、持有 0.00001 FIABTC 每月可赚取 3 羊驼积分、将 FIABTC 存入 DeFi 协议中每月赚取 12 羊驼积分。FIABTC 1:1 原生锚定 BTC，采用 BitVM 2 技术做到信任最小化，同时结合 Fiamma Bridge 和 Fiamma One，Fiamma 有望在保护 BTC 持有者资金安全的同时为其带来更多链上收益。

Related Reads

Three Years Later: Looking Back at My Predictions About ChatGPT in 2023

Three Years Later: Revisiting My 2023 Predictions on ChatGPT In March 2023, shortly after ChatGPT's launch, I made 20 predictions about its future. Now, in mid-2026, I've used AI agents to fact-check each one against the latest data. Overall, most major directional forecasts were correct, with only one outright error (incorrectly stating GPT-4 had 100 trillion parameters). Key successes included predicting that RAG and retrieval architectures would become the standard for handling knowledge and hallucinations, that natural language interfaces (LUI) would create a massive new industry layer beyond the models themselves, and that China would develop viable large language models, significantly closing the performance gap with Western counterparts within about three years. Predictions about the absence of mass unemployment, the rise of a new "robot network" for agent communication, and ChatGPT not possessing consciousness also held true in their core arguments. However, the "devil was in the details." Errors frequently involved specific numbers, timelines, or overlooking distributional effects. I tended to overestimate the speed of adoption (e.g., for agent networks) while underestimating the ultimate scale of capabilities or costs (e.g., AI winning IMO gold without tools, or the extreme capital required for frontier models). Other misjudgments included: underestimating how AI would reinforce, not dissolve, information filter bubbles; incorrectly assuming AI-generated content would easily circumvent copyright (it has instead triggered record-breaking settlements); and misidentifying where value would be captured (it accrued overwhelmingly to the compute layer, like Nvidia, not just the application or model layers). Key lessons from reviewing these predictions are: 1) Directional and mechanistic insights are far more reliable than precise numbers or absolute statements. 2) There's a consistent bias to overestimate short-term speed but underestimate long-term magnitude. 3) Errors often lie in missing distributional impacts within a generally correct aggregate trend. 4) Predictions phrased with nuance and caveats aged the best. 5) Some fundamental debates (e.g., on machine consciousness or the ultimate value chain) remain unresolved even after three years. This exercise is less about scoring the past and more about establishing rules for clearer thinking about the next three years of AI.

marsbit2h ago

Three Years Later: Looking Back at My Predictions About ChatGPT in 2023

marsbit2h ago

Three Years Later: Looking Back on My 2023 Predictions for ChatGPT

Looking Back After Three Years: Revisiting My 2023 Predictions on ChatGPT In March 2023, shortly after ChatGPT's debut and before GPT-4's release, I made over twenty predictions about AI's future based on limited information and intuition. Now, in May 2026, I revisited those forecasts using an AI-driven analysis with 41 Opus 4.8 agents to cross-reference them with the latest data. The assessment used symbols: ✅ Correct, 🟢 Mostly Correct, 🟡 Partially Correct, ❌ Incorrect. Overall, the directional judgments held up well, with only one major factual error regarding GPT-4's rumored parameter size (incorrectly cited as 100T). However, nuances and degrees of accuracy revealed more. **What Was Largely Correct:** Predictions about mechanisms and directions proved accurate. The rise of RAG (Retrieval-Augmented Generation) as the standard architecture for combating AI hallucination was confirmed, as was the transformative potential of LUI (Language User Interface) in creating a new industry layer atop GUIs. The emergence of "robot networks" (agent-to-agent communication protocols) and China's rapid catch-up in developing capable large models (closing the performance gap with top models to ~2.7%) were also on point. The analysis affirmed that LLMs lack consciousness and that the Turing Test merely measures perceived intelligence. **What Was Off Target:** Errors often involved specific numbers, over-optimistic timelines, or misjudged distributions. The prediction that value would primarily accrue to the application layer was half-right but missed NVIDIA's dominance as the profitable infrastructure layer. Forecasts about AI circumventing copyright issues and fostering a "global common ground" by averaging human viewpoints were incorrect; instead, major copyright settlements occurred and AI personalization is increasing. Estimates for model training costs ("$5-10 billion cap") were significantly off, underestimating frontier costs and overestimating replication costs. The notion that LLMs could never do complex math without tools was disproven by later models winning IMO gold. **Key Patterns from the Review:** 1. **Direction over precision:** Judgments about mechanisms and trends were more reliable than specific numbers or definitive statements. 2. **Timing bias:** There was a tendency to overestimate short-term speed but underestimate long-term magnitude and transformation. 3. **The distribution blind spot:** Aggregate-level correctness often masked uneven impacts (e.g., on young professionals' employment). 4. **The value of qualifiers:** Predictions framed with caution (e.g., "reportedly," "for now," "prototype in 2-3 years") aged better. 5. **Some debates continue:** Issues like the nature of "emergent abilities" or machine consciousness remain unresolved. This three-year review highlights that while seeing the big picture is crucial, humility regarding specifics, timelines, and disparate impacts is essential for future forecasting.

链捕手4h ago

Three Years Later: Looking Back on My 2023 Predictions for ChatGPT

链捕手4h ago

AI Bubble Warning: AI Investments Are Negative Returns for Most Tech Giants

The article issues a stark warning about a potential AI investment bubble. It notes that while the AI boom shares similarities with the TMT bubble of the late 1990s, its scale is vastly larger, currently driving 93% of U.S. GDP growth. Major hyperscale cloud providers like Microsoft, Alphabet, Amazon, Meta, and Oracle are planning to invest trillions in AI data centers over the coming years. However, calculations based on analyst projections for 2025-2030 reveal a concerning math problem: expected capital expenditure growth far outpaces projected revenue growth. Even under an extremely optimistic scenario of zero costs, the implied return on investment for most of these tech giants (except Amazon) is deeply negative. This suggests that the current trajectory could lead to one of history's largest shareholder value destruction events. The piece outlines two potential escapes: AI generating vastly more revenue than currently anticipated—a near-impossible task—or a significant cutback in the planned investment splurge. The latter scenario could trigger a domino effect, severely impacting the entire tech supply chain (from Nvidia to TSMC), potentially pushing the U.S. economy into recession, and causing a major stock market downturn. The author suggests upcoming high-profile IPOs by companies like OpenAI and Anthropic might represent a transfer of risk from early investors to public market participants. While the peak of the hype cycle might sustain investment through 2026, the fundamental financial dilemma remains unresolved, setting the stage for a potential market correction in 2027 or 2028, similar to the years following Alan Greenspan's "irrational exuberance" warning.

marsbit5h ago

AI Bubble Warning: AI Investments Are Negative Returns for Most Tech Giants

marsbit5h ago

From Tokens to Machine Labor: AI is Shifting from Tool to "Worker"

The article "From Token to Machine Labor: AI is Evolving from Tool to 'Worker'" argues that the business model for AI is shifting beyond simply selling computational resources (tokens, GPU hours) or model access. Instead, a new "machine labor market" is emerging, where the core economic transaction is the purchase of economically useful work directly performed by software. The central thesis is that AI pricing will evolve through four stages: 1) raw tokens, 2) standardized LLM capabilities (e.g., text generation), 3) industry-specific labor markets (e.g., legal review, radiology), and finally 4) a programmable results market where tasks like resolving a support ticket are bid on and priced based on outcome. In this future, buyers will care less about *which* model or GPU completes a task and more about whether the work meets specified standards for accuracy, latency, and cost. This transition reframes the impact of AI on human labor. Rather than simple replacement, it suggests a re-coordination where machines handle standardized, verifiable work, freeing humans for roles involving oversight, context management, responsibility, and final judgment. In some cases, this "last 1%" of human input becomes more valuable as it enables the other 99% to be automated. Furthermore, as AI reduces the cost of work, demand may expand, creating larger markets (e.g., 24/7 customer service) rather than just cheaper versions of existing ones. The article concludes that while infrastructure (GPUs, models, tokens) remains crucial upstream, the market is converging on a simpler, tradeable unit: machine labor that can be defined, measured, priced, and procured based on contractible specifications.

marsbit5h ago

From Tokens to Machine Labor: AI is Shifting from Tool to "Worker"

marsbit5h ago

Xiaomi MiMo's 99% Price Cut is Not Marketing! Luo Fuli Posts on X to Refute Critics

The price of Xiaomi's MiMo-V2.5 series API has been permanently reduced by up to 99%, specifically for the "Input (Cache Hit)" cost, which covers users re-reading historical context in long conversations. MiMo's head, Luo Fuli, published a detailed technical blog to clarify that this drastic price cut stems from genuine engineering breakthroughs, not a marketing stunt or a simple price war. The core of the achievement lies in six key engineering optimizations. First, the model architecture adopts a Hybrid Sliding Window Attention (SWA), reducing the memory footprint (KVCache) to 1/7th of a traditional model. Second, a dual-pool memory management system actually utilizes these savings, allowing a single GPU to handle over 5 times more concurrent users. Third, an upgraded prefix caching mechanism achieves a cache hit rate of 93-95% for repeated reads, meaning most such requests bypass GPU computation entirely. Fourth, a self-developed distributed cache (GCache) utilizes idle SSD space on existing GPU servers, eliminating additional storage costs. Fifth, an intelligent scheduling system (LLM-Router) efficiently routes requests to maximize cache reuse and performance. Sixth, Multi-Token Prediction (MTP) accelerates the model's text generation ("output") side. Together, these systemic optimizations dramatically lower the real computational cost per request, enabling the 99% price reduction for cached inputs while reportedly maintaining positive gross margins. Luo Fuli's disclosure aims to shift the narrative from "price war" to a demonstration of substantive AI engineering progress.

marsbit7h ago

Xiaomi MiMo's 99% Price Cut is Not Marketing! Luo Fuli Posts on X to Refute Critics

marsbit7h ago

Trading

Spot
Futures

Hot Articles

What is $BITCOIN

DIGITAL GOLD ($BITCOIN): A Comprehensive Analysis Introduction to DIGITAL GOLD ($BITCOIN) DIGITAL GOLD ($BITCOIN) is a blockchain-based project operating on the Solana network, which aims to combine the characteristics of traditional precious metals with the innovation of decentralized technologies. While it shares a name with Bitcoin, often referred to as “digital gold” due to its perception as a store of value, DIGITAL GOLD is a separate token designed to create a unique ecosystem within the Web3 landscape. Its goal is to position itself as a viable alternative digital asset, although specifics regarding its applications and functionalities are still developing. What is DIGITAL GOLD ($BITCOIN)? DIGITAL GOLD ($BITCOIN) is a cryptocurrency token explicitly designed for use on the Solana blockchain. In contrast to Bitcoin, which provides a widely recognized value storage role, this token appears to focus on broader applications and characteristics. Notable aspects include: Blockchain Infrastructure: The token is built on the Solana blockchain, known for its capacity to handle high-speed and low-cost transactions. Supply Dynamics: DIGITAL GOLD has a maximum supply capped at 100 quadrillion tokens (100P $BITCOIN), although details regarding its circulating supply are currently undisclosed. Utility: While precise functionalities are not explicitly outlined, there are indications that the token could be utilized for various applications, potentially involving decentralized applications (dApps) or asset tokenization strategies. Who is the Creator of DIGITAL GOLD ($BITCOIN)? At present, the identity of the creators and development team behind DIGITAL GOLD ($BITCOIN) remains unknown. This situation is typical among many innovative projects within the blockchain space, particularly those aligning with decentralized finance and meme coin phenomena. While such anonymity may foster a community-driven culture, it intensifies concerns about governance and accountability. Who are the Investors of DIGITAL GOLD ($BITCOIN)? The available information indicates that DIGITAL GOLD ($BITCOIN) does not have any known institutional backers or prominent venture capital investments. The project seems to operate on a peer-to-peer model focused on community support and adoption rather than traditional funding routes. Its activity and liquidity are primarily situated on decentralized exchanges (DEXs), such as PumpSwap, rather than established centralized trading platforms, further highlighting its grassroots approach. How DIGITAL GOLD ($BITCOIN) Works The operational mechanics of DIGITAL GOLD ($BITCOIN) can be elaborated on based on its blockchain design and network attributes: Consensus Mechanism: By leveraging Solana’s unique proof-of-history (PoH) combined with a proof-of-stake (PoS) model, the project ensures efficient transaction validation contributing to the network's high performance. Tokenomics: While specific deflationary mechanisms have not been extensively detailed, the vast maximum token supply implies that it may cater to microtransactions or niche use cases that are still to be defined. Interoperability: There exists the potential for integration with Solana’s broader ecosystem, including various decentralized finance (DeFi) platforms. However, the details regarding specific integrations remain unspecified. Timeline of Key Events Here is a timeline that highlights significant milestones concerning DIGITAL GOLD ($BITCOIN): 2023: The initial deployment of the token occurs on the Solana blockchain, marked by its contract address. 2024: DIGITAL GOLD gains visibility as it becomes available for trading on decentralized exchanges like PumpSwap, allowing users to trade it against SOL. 2025: The project witnesses sporadic trading activity and potential interest in community-led engagements, although no noteworthy partnerships or technical advancements have been documented as of yet. Critical Analysis Strengths Scalability: The underlying Solana infrastructure supports high transaction volumes, which could enhance the utility of $BITCOIN in various transaction scenarios. Accessibility: The potential low trading price per token could attract retail investors, facilitating wider participation due to fractional ownership opportunities. Risks Lack of Transparency: The absence of publicly known backers, developers, or an audit process may yield skepticism regarding the project's sustainability and trustworthiness. Market Volatility: The trading activity is heavily reliant on speculative behavior, which can result in significant price volatility and uncertainty for investors. Conclusion DIGITAL GOLD ($BITCOIN) emerges as an intriguing yet ambiguous project within the rapidly evolving Solana ecosystem. While it attempts to leverage the “digital gold” narrative, its departure from Bitcoin's established role as a store of value underscores the need for a clearer differentiation of its intended utility and governance structure. Future acceptance and adoption will likely depend on addressing the current opacity and defining its operational and economic strategies more explicitly. Note: This report encompasses synthesised information available as of October 2023, and developments may have transpired beyond the research period.

363 Total ViewsPublished 2025.05.13Updated 2025.05.13

What is $BITCOIN

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of BTC (BTC) are presented below.

活动图片

Top Questions