Meilin Zhiyue

區塊鏈的感覺就像你最喜愛的遊戲主機，而不是你的開發沙盒。這就是 Somnia 的目標：一條不僅為 DeFi 和機構用戶而建的 L1 鏈，而是為數百萬希望獲得樂趣、遊戲、NFT 和社交體驗的日常用戶而建的。讓我們深入探討。
背景 — 為什麼是 Somnia？
區塊鏈世界直到最近，主要服務於開發者、DeFi 協議和金融用例。但大眾消費者的採用——遊戲、娛樂、社交應用——一直看似觸手可及，卻因摩擦而受阻：高昂的交易費用、緩慢的確認時間、糟糕的用戶體驗、錢包複雜性、薄弱的遊戲開發工具等。

Real-time financial data isn’t a luxury — it’s the bedrock of trust for DeFi, institutional finance, and hybrid systems that blur both worlds.Pyth Network aims to provide exactly that: high-fidelity, ultra-fast, institution-grade data on-chain without middlemen.
Background
Smart contracts are powerful — but their world is blind. To make decisions (price things, enforce liquidations, settle trades), they need accurate external data. Traditionally, oracles have tried to fill that gap. But many oracles rely on data aggregators or third-party nodes, which introduce latency, risk, lack of transparency, and cost.Pyth Network was built to address those limitations via a first-party data oracle model. That means Pyth wants the source of price data to be market participants themselves (exchanges, market makers, trading firms) who are already in the data creation process. The idea: get closer to raw data, reduce intermediaries, improve accuracy & timeliness, align incentives more cleanly, and push that data onto various blockchains with minimal friction.Pyth originally launched on Solana, but has since expanded (or aims to expand) its reach into many blockchains, many asset classes (crypto, equities, commodities, FX), and many use cases (DeFi protocols, derivative platforms, synthetic assets, etc.).
Main Features
Here are the key design choices and technological features that distinguish Pyth:
1. First-Party Publishers
Data comes directly from entities that see trades, orders, quotes: exchanges, market-making firms, trading desks. That increases fidelity, reduces trust needed in aggregators. For example, Nomura’s Laser Digital became a data provider.
2. Pull Oracle Design
Instead of continuously pushing data to every target chain (costly & sometimes wasteful), Pyth uses a "pull" model: the data is streamed off-chain (with signatures, confidence intervals etc.), and only when someone needs a current price on a chain do they “pull” it onto chain (via their transaction). This reduces gas/costs and scales better.
3. Low Latency / High Frequency Updates
For many feeds, updates occur every few hundred milliseconds off-chain; the ability to pull updates quickly makes it possible to support latency-sensitive apps (derivatives, perp markets, automated liquidations). Also, Pyth introduced Lazer (new oracle offering) aimed specifically at latency-sensitive applications, with customizable update frequencies, possibly as fast as ~1 millisecond.
4. Wide Asset Coverage & Multi-Chain Distribution
Pyth supports many types of assets: cryptocurrencies, equities, FX, commodities, ETFs, etc. It also distributes feeds across many blockchains (EVM chains, Solana, Hedera, etc.). Developers on many chains can consume the same feed instead of each building their own.
5. Staking, Governance, Data Fees, and Accountability
There is a token (PYTH) and governance structure. Publishers are required to stake, there are delegators (who stake on publishers or feeds), and there is a mechanism for data fees: consumers can optionally pay to get protection or for usage of the feeds. If publishers produce bad or inaccurate data, stakes can be slashed in some cases. Delegators get rewards for staking. This “skin in the game” model helps with trust.
6. New Oracle Lazer
Introduced in early 2025, Lazer is a low-latency side of Pyth’s offerings, designed for users needing very frequent updates, e.g. high frequency trading, derivatives, perpetuals, etc. This enables update intervals as fast as 1 ms in some configurations.
7. Partnerships with Traditional & Hybrid Finance
Pyth has been forming key partnerships with tokenized asset providers (e.g. Ondo Finance), with financial institutions (Laser Digital, Revolut) to expand data sources and legitimacy.
Benefits
These features translate into real advantages for protocols, developers, and users:
Accuracy & Reliability: First-party data providers reduce latency, reduce risk of manipulation or data poisoning via intermediaries. Confidence intervals help identify when data is less certain.Cost Efficiency: Pull model means you only pay gas when you need to update; avoid constant pushes to every chain. This matters especially for expensive chains and in times of congestion.Scalability and Reach: Because feeds are pulled across many chains, the same infrastructure supports many ecosystems. More developers can access high-quality data without duplicating oracle builds.Suitability for Sensitive Use Cases: Derivatives, perpetuals, margin trading, synthetic assets, or any application where outdated or wrong price data leads to loss, want minimal lag and reliable data — Pyth is built with those in mind.Better Incentives / Accountability: Because publishers are on the line (stake, slashing, rewards), there is more incentive for reliability. Delegators also participate.Bridging TradFi & DeFi: By bringing in providers like Revolut and Laser Digital, Pyth helps build trust and compliance bridges between traditional finance and blockchain finance. It also expands into equity data & real-world asset tokenization.
Limitations & Challenges
Even with strong design, Pyth faces non-trivial challenges and trade-offs:
1. Publisher Centralization & Trust Risk
While first-party data is high quality, if only a few large institutions dominate many feeds, risk from collusion, downtime, or misreporting remains. Ensuring diversity among publishers is key.
2. Latency and On-chain Pull Delays
The pull mechanism still requires on-chain transactions when data is needed. That introduces dependency on chain congestion, gas fees, block times. In times of network stress, the practical latency may degrade. For applications needing sub-millisecond guarantee, that may not suffice.
3. Regulatory & Licensing Risk
Since feeds include equities / FX / real asset data, sometimes from regulated providers, there may be licensing and legal issues over data rights, usage, intellectual property, cross-jurisdiction rules. Also with increasing partnerships with TradFi (e.g. Revolut), regulatory oversight might grow.
4. Fee Model Balance
Deciding how much consumers pay, how data fees are structured, how rewards/slashing work, balancing incentives vs cost is always delicate. If fees are high, small protocols may be priced out; if too low, sustainability suffers.
5. Competition
Other oracles (Chainlink Enterprise, Band, etc.), especially ones offering premium data, might compete heavily. Pyth’s differentiation (latency, first-party, asset coverage) must continue improving to stay ahead.
6. Infrastructure Costs & Complexity
Operating off-chain streaming, maintaining data quality, managing many publishers, cross-chain message delivery, stakes/slash logic, etc., is complex. Bugs or misconfigurations may impact reliability.
Recent Developments (2024-2025)
Here are what’s new (as of mid/2025) — recent partnerships, product launches, metrics, etc.:
Lazer Oracle Launch (early 2025)
Pyth introduced Lazer, a low latency oracle solution targeted at high frequency, latency-sensitive use-cases.
Ondo Finance Partnership
In July 2024, Pyth partnered with Ondo Finance (a provider of tokenized real-world assets) to provide a USDY/USD feed across more than 65 blockchains. This helps bring real-world yield assets into DeFi protocols broadly.
Integral Partnership
In May 2025, Pyth partnered with Integral (a currency technology provider for institutions) to allow their clients to become data publishers on Pyth. This expands the first-party data network.
DWF Labs Partnership
DWF Labs, a Web3 investment firm & market maker, joined Pyth to supply high quality crypto market data as a publisher, and also to integrate Pyth data into their workflows.
Blue Ocean ATS Partnership
Blue Ocean ATS (an overnight US equities venue) partnered with Pyth to deliver on-chain US equity data during overnight trading hours (8:00PM-4:00AM ET) for global off-hours access. This helps fill a blind spot when regular US markets are closed but demand remains, particularly from global markets/regions.
Revolut Joins as First Banking Data Publisher
Revolut became a data publisher for Pyth, contributing its banking/crypto quote and trade data. This is a meaningful step because banking-fintech data brings different credibility, user base, and regulatory linkage.
Feed & Chain Expansion, Asset Coverage
The number of price feeds has been growing (400+ feeds reported in some sources), along with expanding coverage to more blockchains and more asset classes. For example, Pyth supports 400+ real-time price feeds across crypto, equities, FX, and commodities.
Partnership with TradFi Data Providers
The addition of Laser Digital (Nomura’s asset for digital assets) as a publisher strengthens the bridging with regulated finance.
Future Plans & What to Watch
Here are upcoming directions, opportunities, and risk factors to keep an eye on:
1. More Latency Improvement & Customization
Further optimizing update intervals, especially for latency-sensitive applications. More customization in update frequency vs cost tradeoffs. Enhancing Lazer or similar offerings.
2. Broader Geographic & Equity / Real-Asset Data
Expansion into Asian equity markets (Japan, Korea, Hong Kong etc.), more non-US/European markets, commodities, real asset feeds. As tokenization of real assets grows, those data feeds will be in demand.
3. Improved Governance / Decentralization
Strengthening decentralization among data providers, building out delegator/staking models with broad participant base. Evolving governance over fees, slashing, product listings.
4. Regulatory & Compliance Engagement
As Pyth brings in banks, fintechs, equity data, and TradFi participants, regulatory oversight increases. Ensuring compliance with data licensing agreements, privacy, securities law will be increasingly important.
5. New Products Beyond Price Feeds
For instance, randomness services (on-chain entropy), economic & macro data, specialized feed types (options/implied volatility, liquidity metrics), oracles for prediction markets. Some sources mention development of “Entropy V2” for randomness.
6. Fee & Incentive Model Optimization
Adjusting data fee structures to balance affordability for small protocols vs sufficient incentive for data providers. Managing token inflation, unlocks, staking rewards.
7. Integration & Adoption by DeFi / TradFi Hybrids
More partnerships with tokenized real-world asset platforms, DeFi protocols for derivatives/perps, synthetic assets, lending/borrowing. The “real world” demand will test robustness (latency, reliability, cost).
Conclusion
Pyth Network is among the most interesting oracle projects in the Web3 / DeFi ecosystem. Its first-party publisher model, low-latency and frequent update architecture, pull-oriented design, and growing asset & chain coverage make it well-positioned for both current and future DeFi demands. By reducing the trust and cost overhead associated with oracles, Pyth helps unblock more ambitious finance applications (derivatives, tokenized assets, cross-chain stuff).That said, it doesn’t yet solve every problem. Trade-offs remain: on-chain pull latency, regulatory risk, publisher concentration, cost vs accessibility for smaller apps, and competition are real. The success of Pyth will depend heavily on execution: how well it manages its governance, how reliable its data is in “stress events,” how it expands feed coverage, and how it balances incentive economics.If you are building something that needs high-fidelity, real-time or near-real-time data — liquid derivatives, synthetic assets, cross-asset hedging, tokenized real world assets — Pyth is definitely one to consider, and to watch closely as its roadmap unfolds.

@Pyth Network #PythNetwork
$PYTH

Imagine an internet where every dataset, every model tweak, and every agent action is recorded, attributed, and monetized — without hiding behind opaque corporate gates. OpenLedger calls that future the AI Blockchain: a purpose-built, EVM-compatible network that turns data and models into liquid, auditable on-chain assets. From provenance and proof-of-attribution to marketplaces for specialized models and incentives for contributors, OpenLedger aims to solve AI’s twin problems of centralized control and missing economic incentives. This deep dive walks through what OpenLedger is, how it works, why it matters, the obstacles it will face, recent progress (2024–2025), and where it could take AI and Web3 next.
Background — why we need an “AI Blockchain”
Modern machine learning depends on two valuable but fragile things: high-quality data and specialized models. Today those are concentrated inside a handful of companies. Contributors (data curators, labelers, niche domain experts) rarely capture value commensurate with their inputs. Models themselves are often black boxes: we don’t know which datapoints shaped behavior, who owns what, or how outputs were produced.OpenLedger’s thesis is straightforward: use blockchain primitives (provenance, tokenization, transparent economics) to make data, models, and agent work traceable and marketable. In other words, convert contribution and utility into on-chain value. That promises better attribution, fairer rewards, and new business models for AI — especially for specialized, domain-specific models that are otherwise uneconomical to build centrally.
Core design & how OpenLedger works
OpenLedger’s stack blends familiar blockchain building blocks with AI-native components. The high-level elements are:
1. Datanets — community datasets as first-class assets
Datanets are tokenized, curation-driven datasets. Contributors can submit, label, and enrich data; the chain records timestamps, provenance, and contributor identities so downstream model creators can verify and compensate contributors fairly. Datanets are intended to be composable: models can declare which datanets trained them, enabling chain-native attribution.
2. Proof of Attribution / Verifiable Impact
OpenLedger emphasizes mechanisms that trace how much a datapoint or contributor influenced a model’s behavior. This can be done via influence-tracking techniques (e.g., Shapley-style attribution adapted for on-chain accounting) and cryptographic receipts that link model weights or evaluation artifacts back to source data and training runs. The aim is credible, auditable credits and payouts.
3. ModelFactory & OpenLoRA — model tooling on chain
OpenLedger promotes tools that let builders train, fine-tune (LoRA-style), and deploy lightweight specialized models using datanets — with training metadata and checkpoints recorded on chain. No-code or low-code interfaces (e.g., ModelFactory) are intended to broaden participation beyond ML engineers.
4. Agent Marketplace & Runtime
Agents — the autonomous processes that perform tasks (chatbots, data collectors, monitoring agents) — can be deployed, audited, and monetized on the network. Developers can license agent behavior, collect usage fees, and reward contributors based on observed utility. Runtime telemetry and usage logs are anchored on chain for accountability.
5. Tokenomics: $OPEN as the economic backbone
OPEN serves as the unit of exchange for dataset bounties, model payments, staking for reputation, governance, and potentially compute-credit markets. Project launches and listings have drawn market attention, and exchanges have begun listing OPEN tokens.
6. EVM compatibility & Optimism-stack alignment
OpenLedger has been built to interoperate with Ethereum tooling and various L2 ecosystems so wallets, smart contracts, and developer frameworks integrate with minimal friction. Some launch materials indicate the chain targets the Optimism stack for performance and composability.
Main features — what sets OpenLedger apart
On-chain provenance for AI artifacts. Every dataset upload, model training run, checkpoint, and reward is written to the ledger — making attribution auditable. This reduces disputes about who contributed what.Monetization primitives for contributors. Datanet contributors and model builders receive tokenized rewards proportional to measurable impact, enabling microeconomic incentives that can unlock vast swaths of previously unused or under-shared data.Specialized model marketplace. Instead of massive foundation models only, OpenLedger emphasizes smaller, specialized models — domain experts can build profitable niche models that are cheaper to train and more useful for specific tasks.Developer tools & lower barriers to entry. ModelFactory, OpenLoRA, and other tooling aim to let non-ML specialists participate (curate data, run fine-tuning jobs, deploy agents).Verifiability & accountability. Proofs of training runs, cryptographic hashes of weights, and performance benchmarks recorded on chain create a trust fabric that is often missing in centralized ML pipelines.
Benefits — what builders and users gain
1. Fairer incentive alignment. Contributors no longer need to donate data or accept opaque terms — they can be paid when their inputs demonstrably improve models. That broadens participation and unlocks rare or localized datasets.
2. Lower cost for specialized AI. Specialized models trained on high-quality, labeled datanets can often outperform huge general-purpose models for narrow tasks — and cost far less to train and run. OpenLedger’s marketplace makes those projects economically viable.
3. Transparency for enterprises and regulators. On-chain records offer audit trails that enterprises or regulators can inspect, which may lower compliance frictions when AI is used in sensitive domains (healthcare, finance, public sector).
4. Composability & new business models. Tokenized datasets and models become financial primitives — license markets, fractional model ownership, and royalties on model usage are all possible. That enables entrepreneurs to design novel AI businesses native to blockchain economics.
Limitations & challenges
OpenLedger is ambitious; the path to widescale impact includes meaningful hurdles:
Measuring true attribution is hard. Quantifying how much any single datapoint influenced a model (especially in large models) is technically challenging and often computationally expensive. Approximate methods exist, but they may be contested or gamed if economic rewards are at stake.Data privacy & legal constraints. On-chain transparency helps auditability but clashes with privacy needs (HIPAA, GDPR). Techniques like private computation, on-chain commitments with off-chain secret handling, or zero-knowledge proofs will be necessary to reconcile openness with confidentiality.Compute & cost: Training models (even LoRA-style fine-tuning) and running agents costs money. OpenLedger needs robust mechanisms to fund compute (market for compute credits, cloud integrations, staking) without making participation prohibitively expensive.Trust & legal enforceability. Tokenized ownership and attribution are powerful, but real-world legal claims (who owns the original data, contractual rights) still depend on off-chain legal frameworks and trusted custodians. On-chain accounting doesn’t automatically resolve legal disputes.Economics & token design risks. The OPEN token underpins incentives—careless design (inflation, concentration, unlock schedules) can lead to misaligned incentives, speculative behavior, or governance capture. Early market volatility after listings shows price sensitivity.
Recent developments & traction (2024–2025)
OpenLedger’s public materials and coverage indicate rapid activity in 2025:
Mainnet launch & ecosystem rollout. OpenLedger launched its public presence in 2025 with blog content, tooling (ModelFactory, OpenLoRA), and community drives emphasizing datanet creation and agent development. The project’s website and blog contain detailed feature posts and guides.Funding & backers. The project lists notable backers and community supporters; public statements and social channels reference support from well-known crypto investors and ecosystem partners. Binance research and other market platforms have published project summaries.Token launch & exchange listings. OPEN token listings and airdrops have driven market attention and trading activity; press and market analysis recorded price surges following Binance and other exchange interest. That indicates investor appetite — but also brings volatility.Community incentives & campaigns. OpenLedger has run community programs (e.g., Yapper Arena) and prize pools to reward engagement, dataset contributions, and ecosystem building — common, useful tactics to bootstrap supply and demand for datanets and models.Third-party analyses & coverage. Research writeups and platform deep dives (TokenMetrics, CoinMarketCap, Phemex) provide independent overviews and use cases, which helps external stakeholders evaluate the protocol’s prospects.
Real-world examples & early use cases
Domain-specific models. Healthcare triage assistants, legal-document summarizers, or niche industrial monitoring models — use cases where small, high-quality datasets and clear provenance matter — are a natural fit for OpenLedger’s model marketplace. Project blogs and community posts highlight potential vertical apps. Micro-paid data contributions. Citizen scientists and crowdsourced labelers get micropayments when their contributions improve models’ performance — an economic model appealing for socially beneficial datasets (environmental, public health, cultural heritage).Agent economies. Autonomous agents that collect price signals or perform monitoring tasks can be monetized: owners earn fees when agents are used and contributors to the agent’s training pipeline are rewarded proportionally. This opens novel agent-as-a-service markets.
Expert & industry perspectives
Analysts broadly view OpenLedger as part of a wave of projects attempting to decentralize AI infrastructure by focusing on data provenance and contributor economics. Observers note:
The value proposition is compelling: data is under-monetized and contributors under-compensated. On-chain attribution could unlock a massive economic layer if implemented credibly.The technical and legal hurdles are nontrivial: attribution accuracy, privacy compliance, compute costs, and off-chain legal enforceability remain open problems. Success depends as much on practical tooling and legal partnerships as on token mechanics.The market signal (listings, exchange interest, community engagement) shows investor appetite — yet early token volatility suggests the token and governance design will be actively scrutinized.
Future outlook — paths to meaningful impact
OpenLedger’s potential rests on executing across several dimensions:
1. Make attribution robust & cost-efficient. Better, scalable methods for quantifying contribution will be a key technical differentiator and will reduce disputes.
2. Privacy-preserving contributions. Integrate on-chain commitments with off-chain private compute and zero-knowledge primitives so sensitive datasets can participate without leaking private information.
3. Compute markets & partnerships. Building credible compute marketplaces (GPU/TPU partners, serverless model execution) and commercial partnerships will lower the entry cost for model training and inference.
4. Legal & enterprise integrations. Strong legal wrappers, custodial partnerships, and enterprise onboarding (enterprises want SLAs and legal recourse) will be necessary to attract regulated data owners.
5. Ecosystem growth & liquidity. More datanets, more models, active agent markets, and developer-friendly tooling will create the flywheel that sustains long-term usage and value capture.If OpenLedger can make datanets and models truly liquid, and if legal/regulatory concerns are handled pragmatically, the network could become a central marketplace for specialized AI infrastructure. If not, it may be a powerful experiment that nevertheless struggles to attract real enterprise adoption.
Conclusion — why OpenLedger matters (and where it can trip)
OpenLedger isn’t just another blockchain play; it’s an attempt to rewrite the economic underpinnings of AI by making data and model contributions traceable, tradable, and fairly compensated. That idea aligns with larger decentralization goals — returning control and value to many rather than a few — and has real technical and social appeal.However, the road is rocky: proving attribution, protecting privacy, making compute affordable, and ensuring legal enforceability are each hard problems. The early product launches, community campaigns, and exchange listings show momentum — but momentum alone won’t guarantee that datanets become as liquid or as valuable as their proponents hope.For builders, researchers, and investors interested in where AI and blockchain converge, OpenLedger is a must-watch. If the team and ecosystem navigate the technical, economic, and regulatory gauntlets effectively, OpenLedger could help unlock an economy where data contributors and model creators finally capture their fair share — and where AI development grows more open, accountable, and diverse.

@OpenLedger #OpenLedger
$OPEN

如果 DeFi 要像金融一樣運作，它需要與金融速度相匹配的數據。”這就是 Pyth Network 的承諾 — 直接提供金融市場數據，延遲最小且完整性高 — 在價值鏈中沒有中介。
背景 — 為什麼 Pyth 重要
區塊鏈智能合約對執行的要求是嚴格的，但對外部事務卻是盲目的。要做任何依賴現實世界的事情 — 價格源、股票、外匯匯率、衍生品、預言機 — 您需要可靠的外部數據。傳統預言機面臨的問題：延遲、對數據聚合器或抓取器的信任、成本、中介風險，以及難以擴展高頻、多鏈使用。

在過去的幾年中，去中心化金融（DeFi）大幅擴展了加密貨幣的可能性：借貸、衍生品、自動化市場做市商、收益農業、合成資產。然而，最大的未開發前沿之一是現實世界資產（RWAs）——如房地產、私募信貸、碳信用、證券、特許權使用費、商品等。這些資產的價值很大，但以安全、合規、流動、互操作的方式將它們引入鏈上證明是具有挑戰性的。Plume（通常被寫作“Plume Network”或簡稱“Plume”）是一個專門設計來解決這個問題的區塊鏈項目。它的目標是建立模塊化、專用的基礎設施，以便RWAs可以被代幣化、管理、交易、產生收益，並以機構級別的方式輕鬆集成到DeFi中，方便開發者和用戶。它是EVM兼容的，努力融入合規性，並正在組建工具、合作伙伴和生態系統，使RWA DeFi超越小衆。

Intro If decentralized finance (DeFi) is the engine, then high-quality, lightning-fast market data is the fuel. Pyth Network is one of the projects trying to be the global fuel supplier — a specialized oracle designed to stream real-time, institution-grade financial market data on-chain with sub-second latency. This article walks through what Pyth is, why it matters, how it works, recent developments (2024–2025), the challenges it faces, and where it could go next — told in a clear, human voice so the engineering, economics, and drama behind the tech are easy and thrilling to follow.
Background — why Pyth exists
Traditional blockchains are isolated from real-world price data. Early oracle solutions solved the “last-mile” problem by bringing off-chain prices on-chain, but many oracles prioritized decentralization and deep historical security over speed. For financial use cases — margin markets, real-time derivatives, tokenized equities and ETFs — latency, update frequency, and high-quality publisher relationships are critical.Pyth’s niche is high-frequency, first-party market data: instead of aggregating solely from third-party crawlers, Pyth ingests price streams directly from professional market participants (exchanges, trading firms, liquidity providers) and publishes them on-chain rapidly so latency-sensitive applications can rely on live reads. That design aims to make on-chain finance behave more like modern trading systems while preserving blockchain verifiability.
How Pyth works — the tech and the data pipeline
1. First-party data publishers. Pyth’s data comes from firms that produce market feeds in production trading environments — market makers, exchanges, and institutional desks. Those publishers push high-frequency updates into Pyth’s aggregation layer rather than relying only on secondary aggregators.
2. Off-chain aggregation + on-chain publication. Pyth aggregates and processes updates off-chain to produce compact price messages. These are then posted on chains (or made available via bridges) in a way optimized for low verification cost and fast consumption by smart contracts.
3. Multi-chain distribution. Pyth is designed to be chain-agnostic: its feeds can be consumed across many blockchains and L2s. That enables a single canonical feed (e.g., BTC/USD) to be used by many protocols without duplicated publisher setups.
4. Specialized products for latency-sensitive apps. Recognizing that not all consumers have the same needs, Pyth launched offerings (like the “Lazer” oracle) targeted at ultra low-latency consumers and introduced primitives for on-chain randomness and specialized equity/ETF feeds.
Main features & product highlights
Sub-second and high-frequency updates: Pyth emphasizes extremely low latency and frequent updates tailored for finance use cases (market making, liquidations, synthetic assets).Wide publisher network: Pyth works with large institutional data providers and trading firms, positioning itself as a bridge between traditional market infrastructure and blockchains.Cross-chain accessibility: Pyth’s feeds are available to many chains and rollups; documentation and integrations list dozens of consumer chains.Dedicated financial feeds: Beyond crypto asset prices, Pyth has expanded into ETFs, tokenized stocks, and traditional FX and equities data, making it attractive for tokenized real-world asset (RWA) applications.Developer tooling & focused oracles: Tools like “Lazer” provide latency-sensitive oracles; on-chain randomness engines (e.g., Entropy upgrades) and SDKs improve developer UX.
Benefits — why builders pick Pyth
Realtime decisioning: Sub-second updates reduce stale-price risk in liquidations, derivatives settlement, and automated market-making.Institutional signal quality: First-party publisher links improve feed integrity and can reduce susceptibility to simple manipulation vectors.Economies of scale: A shared feed across blockchains lowers duplication of effort and can centralize quality checks and SLAs.Broader financial instrument coverage: Real-time ETF and equities feeds open DeFi to on-chain versions of traditional products.
Limitations and risks
Concentration of trust in publishers. Pyth’s model trades some decentralization for data quality: relying on first-party feeds raises questions about publisher diversity and governance if a small set of entities provide a large fraction of updates. Careful publisher management and on-chain governance are essential.Competitive landscape. Chainlink and other oracle projects compete fiercely. Pyth differentiates via latency and publisher relationships, but market share battles, differing verification models, and enterprise trust can shift dynamics.Regulatory surface area. As Pyth brings traditional equities, FX, and ETF data on-chain — sometimes sourced from regulated entities — the project increases its exposure to securities and market-data regulation. That can be an advantage (enterprise trust) or a compliance burden depending on jurisdiction.Bridging & cross-chain security. Delivering a canonical feed across many blockchains requires secure bridging or native integrations; bridge vulnerabilities remain a general ecosystem risk.
Recent developments (2024–2025) — what’s new and why it matters
Rapid ecosystem growth & product expansion. Throughout 2024–2025 Pyth expanded its coverage beyond crypto assets into ETFs, tokenized stocks, and traditional FX data. That push positions Pyth to serve tokenized real-world financial products that require real-time benchmarking.New latency-focused oracle products. Pyth launched offerings (e.g., “Lazer”) aimed at latency-sensitive applications to compete directly in use cases where milliseconds matter. This is a strategic move to capture derivatives and AMM infrastructure that cannot tolerate stale quotes.Measured adoption & scale metrics. Independent analyses and platform summaries show Pyth’s continued growth: ecosystem writeups indicate rising total value secured/coverage metrics and increased feed update volumes — signs the network is being consumed by more protocols and integrating new data sources. (See industry reports and Q2/Q3 summaries for precise numbers.Expanding publisher & chain base. Pyth’s public materials and support pages report participation from 100+ data publishers and consumption across hundreds of protocols and dozens of chains — evidence of cross-ecosystem traction.
Real numbers & signals (what to watch)
Publisher & consumer counts. Public figures show Pyth supported 100+ publishers and was integrated by 350+ protocols across 80+ blockchains (figures reported by platform documentation and support channels). These numbers indicate broad developer interest and distribution, though raw integrations don’t always mean active or high-volume usage.Market share trends. Industry writeups point to Pyth increasing its market share slice among oracle consumers in early 2025 (for example, analyses citing an increase from ~10–13% in some oracle metrics), signaling competitive gains in specific niches like low-latency market data. Adoption by financial infrastructure. Pyth’s on-chain ETF price feeds and partnerships to bring bank FX and Hong Kong stock prices on-chain are concrete signs it’s being taken seriously by both crypto and traditional finance actors. These product launches matter because they expand the set of DeFi apps that can offer tightly-coupled, real-time financial services.
Expert/industry perspective
Analysts and industry blogs generally place Pyth in the “high-frequency market data” niche among oracles. The project’s strength is its publisher relationships and low latency; its primary strategic questions are around decentralization tradeoffs, governance, and the ability to maintain neutrality as it brings in regulated, off-chain market participants. Observers note that if Pyth can maintain publisher diversity and strong cryptoeconomic guardrails while scaling, it will be one of the central middle-layers for real-time DeFi.
Future outlook — three paths forward
1. Infrastructure dominance in latency-sensitive finance. If Pyth continues to win integrations with derivatives, lending/liquidation engines, and tokenized-asset vaults, it could become the de-facto price layer for trading primitives that demand live quotes.
2. Enterprise & regulated adoption. By onboarding traditional financial data (FX, ETFs, equity markets), Pyth could attract regulated institutions building custody, settlement, or tokenization rails — but this will require mature compliance and legal frameworks.
3. Interoperability & standardization leader. If leading L1s/L2s and enterprise providers adopt common feed formats and verification libraries from Pyth, the network could help standardize how market data is packaged and consumed on-chain — reducing fragmentation and duplication.Risk case: regulatory pressure, publisher concentration, or better technical alternatives (e.g., lower-cost aggregated solutions that are “good enough”) could slow Pyth’s adoption curve. Still, current signals — product launches, publisher partnerships, cross-chain integrations — make its growth trajectory credible.
Conclusion — should you care?
If you’re building anything on-chain that relies on up-to-the-second market information — automated liquidation engines, synthetic asset pricing, or tokenized ETFs — Pyth is one of the first oracle choices worth evaluating. Its first-party publisher model and low-latency focus make it a particularly good fit for financial applications where milliseconds and feed fidelity matter. That said, architecture and governance tradeoffs (publisher concentration, regulatory exposure) are real and should factor into design and risk assessments.Pyth has taken an ambitious path: marrying market-quality data providers with blockchain primitives and pushing real-world financial instruments on-chain. Whether it becomes the price layer for a new generation of DeFi — or one of several specialized layers — depends on its ability to scale while keeping data integrity, decentralization, and compliance in balance.
Key sources & further reading
Pyth Network official site and media room (product announcements, integrations). Industry analysis and Q2/Q3 2025 reports summarizing Pyth adoption metrics. Deep-dive explainers on Pyth’s financial-market focus and architecture.

@Pyth Network #PYTH
$PYTH

Intro — Imagine a world where blockchains stop redoing the same heavy computations a thousand times, where complex on-chain logic runs with the speed and cost profile of a modern web service, and where anyone can buy, sell, or run verifiable compute like a commodity. Boundless aims to make that world real. Built around a decentralised prover marketplace and RISC Zero’s zkVM technology, Boundless decouples expensive proof generation from block production, turning computation into verifiable, tradable work that any chain, rollup or dApp can tap into. This article walks through how Boundless works, its key features, recent milestones, practical trade-offs, and where it might take blockchains next.
Background — why verifiable compute matters
Blockchains inherit a classic trade-off: full decentralization demands each validating node re-execute transactions and smart-contract logic to reach consensus. That guarantees correctness, but at the cost of throughput and expense. Zero-knowledge proofs (ZKPs) flip the equation: heavy computation runs once (off-chain), a succinct cryptographic proof is produced, and every node verifies the proof cheaply on-chain. The result is the same correctness guarantee with far less redundant work — perfect for scaling rollups, cross-chain services, and compute-heavy applications (ML inference, cryptographic operations, privacy layers). Boundless sits squarely in this space as a shared verifiable-compute infrastructure that aims to make proof generation scalable, affordable, and interoperable.
Core architecture & how it works
1. zkVM compute layer (RISC Zero)
Boundless leverages a zkVM architecture (originating from RISC Zero) that can execute standard programs and emit ZK proofs attesting to their correct execution. Rather than custom circuits per app, a general-purpose zkVM lets developers write normal code and obtain proofs of its execution. This lowers developer friction dramatically.
2. Decentralized prover marketplace
Boundless creates a market linking requesters (chains, rollups, dApps) with provers — independent nodes that perform the heavy execution and produce proofs. Provers stake, bid on tasks, and are rewarded for valid proofs. This market model spreads compute load across many providers and introduces competition and specialization (GPU provers, CPU provers, high-latency/low-cost nodes).
3. Proof aggregation & on-chain verification
Provers produce succinct proofs that can be verified on target chains. Boundless focuses on keeping verification on-chain (cheap) while shifting computation off-chain (expensive). It supports multi-chain verification so a proof can be recognized by different blockchains and rollups without each network building bespoke proving infra.
4. Incentive layer: Proof of Verifiable Work (PoVW)
To align incentives, Boundless introduced a Proof of Verifiable Work (PoVW) mechanism that rewards provers for useful computations and ensures economic security of the marketplace. PoVW designs are intended to prevent spam, reward correctness, and bootstrap prover participation.
Main features — what sets Boundless apart
General-purpose zkVM: runs arbitrary programs (not just precompiled circuits), which drastically reduces engineering overhead for builders.Shared, cross-chain proving layer: a single proving marketplace that multiple chains can use, avoiding duplicated infrastructure and reducing overall ecosystem costs.Decentralized prover marketplace: competitive market for proof generation with staking and bidding, making compute resources fungible and market-priced. Token & economic primitives: a native unit (ZKC in the live deployments) supports staking, rewards, governance, and marketplace economic flows. Listings and token events have already begun in 2025.Tooling & developer UX: SDKs, integration docs, and prover node tooling aim to make plugging into Boundless straightforward for rollups and dApps. Community adopters and automated prover setups have started appearing (community guides and GitHub repos exist for prover nodes).
Benefits — immediate wins for chains & dApps
Lower gas + higher throughput: by verifying proofs rather than re-executing logic, chains can avoid gas spikes and scaling limits.Faster developer iteration: a general zkVM reduces the need to design bespoke circuits or low-level ZK code.Interoperability: the same proof can validate computations across multiple chains, easing cross-chain application design.Economic efficiency: computation becomes market priced — projects can shop for cheaper provers or specialized hardware.New application classes: heavy workloads like ML inference, off-chain compute markets, privacy mixers, and on-chain gaming mechanics become more practical.
Limitations and challenges
No single technology is a silver bullet. Boundless faces several meaningful challenges:
Latency vs. synchronous consensus needs: some applications require near-instant finality; waiting for external prover bids and proofs may add latency compared with pure on-chain execution. Designing UX and hybrid flows is nontrivial.Prover decentralization & censorship resistance: if prover concentration occurs (few large provers), censorship or withholding attacks become possible. The marketplace and staking rules must enforce diversity and reliability.Economic attack surfaces: a marketplace introduces new economic vectors (griefing via bogus tasks, manipulation of bids, staking exploits). Mechanism design must be robust.Compatibility & verification standards: different chains have different VM/verification constraints (gas limits, proof verification costs). Creating portable proofs that verify cheaply on many chains is technically challenging.Tooling maturity: while zkVMs simplify development, debugging, profiling, and integrating ZK proofs into complex systems still require new practitioner skills and tools.
Recent developments & hard milestones (what’s new)
Boundless has moved quickly through 2024–2025:
zkVM foundation: RISC Zero released production-ready zkVM primitives that form the technical backbone. Proof of Verifiable Work (PoVW) announced: an incentive mechanism to reward provers for useful, verifiable computation was introduced in mid-2025.Mainnet Beta and launch activity (mid-Sep 2025): Boundless ran a Mainnet Beta and pushed into mainnet activity in September 2025, reporting significant early adoption metrics and listings of its native token on multiple exchanges. Specific launch dates and beta numbers were published around mid-September 2025.Ecosystem traction: thousands of provers and many developer participants joined test programs and collaborative development tracks during the beta period; community tooling and prover node guides began circulating on GitHub.
(These items are sourced from project announcements and ecosystem writeups; dates are precise in the cited material.)
Real-world examples & numbers
During its Mainnet Beta in mid-2025, Boundless reported large community participation metrics and a rapid prover onboarding cadence (project announcements and coverage noted multiple thousands of provers and hundreds of thousands of participants during beta events). These early numbers are promising but still represent an immature marketplace relative to major L1/L2 user bases.
Expert views & ecosystem positioning
Commentators and research pieces position Boundless as one of the leading attempts to commodify verifiable compute — alongside other projects pursuing prover marketplaces and zkVMs. Analysts emphasize that Boundless’ biggest technical advantage is leveraging a general zkVM (reducing per-app engineering), while its biggest business challenge is building a reliable, censorship-resistant supply of provers and aligning incentives at scale.
Future outlook — where Boundless could lead
1. Verifiable compute as infrastructure: if the marketplace scales, we may see verifiable compute become a cloud-like commodity used pervasively by rollups, L1s, and dApps.
2. Cross-chain primitives: standardized proof formats could power trustless cross-chain data and computation bridges with minimal trust assumptions.
3. New business models: compute marketplaces could spawn dedicated service providers (GPU prover farms, private provers for regulated workloads) and financial instruments tied to verifiable compute capacity.
4. Performance & tooling advances: continued optimization of zkVMs, prover acceleration (GPUs/TPUs), and developer workflows will be necessary to achieve mainstream adoption.
5. Regulatory and economic maturity: as tokens and staking become real economic backbones, regulatory clarity will matter — Boundless and similar projects will need strong compliance and transparent governance to engage enterprise users.
Bottom line — who should watch this space?
Rollup builders & L2 teams looking to offload re-execution costs.dApp teams with heavy off-chain computation (AI inference, privacy tools, gaming backends).Infrastructure providers (prover operators, cloud providers) who can supply compute and earn rewards.Investors & researchers tracking the evolution of ZK marketplaces and tokenized compute economies.Boundless isn’t the only project imagining a verifiable compute market, but its combination of a general zkVM, a marketplace model, and rapid 2024–2025 rollout activity places it among the most ambitious efforts to make ZK proofs a practical, widely available utility. The idea — delegate heavy work, verify cheaply — is elegant. The hard part will be building a resilient, decentralized market and the developer tooling that makes it painless to adopt.
Closing thought
The move from bespoke, chain-specific proving systems to shared verifiable compute is one of the clearest routes toward blockchains that feel like modern applications: fast, cheap, and composable. Boundless proposes a market-driven way to get there. If the project can sustain decentralization, secure its incentives, and continue improving developer ergonomics, it could be a major piece of the next wave of blockchain scalability.
Sources (key references)
RISC Zero / zkVM technical foundation and timeline.Boundless announcements, PoVW and Mainnet Beta coverage (September 2025).Ecosystem analyses, token & marketplace descriptions. Prover node docs and community guides (GitHub, setup/operation notes).
If you’d like, I can:
convert this into a publish-ready blog post or Medium article (with a punchy headline and social intro);

@Boundless #Boundless
$ZKC

Imagine a decentralized financial world where every contract, perp desk, and on-chain hedge fund can query the same high-fidelity market price the instant it changes — no intermediaries, no stale feeds, no opaque aggregation. That’s the vision Pyth Network is delivering: a real-time price layer that brings first-party market data on-chain so builders can design faster, safer, and more sophisticated financial products.
Background — origin story and why it matters
Pyth began as a collaboration between trading firms, exchanges, and market-making shops that wanted to publish market data directly for blockchains to consume. Rather than rely on third-party node operators or generalized aggregators, Pyth aggregates first-party feeds — price data published by the people who actually see markets — and distributes them across chains. That model reduces latency, increases transparency, and gives DeFi protocols access to data that looks more like what professional traders use.
Since launch, Pyth has expanded beyond its Solana roots into a cross-chain price layer used by dozens of ecosystems, positioning itself as a backbone for real-time financial infrastructure.
Main features — the toolkit that sets Pyth apart
First-party, high-fidelity publishers
Pyth’s feeds come from institutional publishers — exchanges, trading firms, OTC desks — that report observed prices directly. This reduces layers of potential manipulation and makes provenance auditable. Developers can inspect the publisher set for any feed, improving trust and traceability.
Real-time, low-latency updates
Pyth is built for speed. Feeds are updated at high frequency and are available via streaming and on-chain pull, enabling near real-time use in latency-sensitive products like perpetuals, AMMs, and liquidation engines. Layered delivery models mean you get updates fast off-chain and can pull the on-chain value when needed.
Confidence intervals & provenance metadata
Each price comes with a confidence or error bound, plus metadata about contributing publishers and timeliness. That lets protocols program defensible guardrails (e.g., widen spreads, pause liquidations) when uncertainty spikes.
Cross-chain distribution and bridges
Pyth’s architecture distributes price feeds across many blockchains via cross-chain infrastructure (notably Wormhole), so the same canonical feed can be used by apps on different L1s/L2s without bespoke integrations. This simplifies multi-chain development and reduces divergence between chains.
Broad asset coverage
Beyond crypto tokens, Pyth now covers equities, FX, commodities and other traditional markets — expanding the kinds of financial primitives DeFi can build (structured products, on-chain ETFs, tokenized equities). Its aim is to be “the price of everything.”
Benefits — what builders and institutions gain
Accuracy & market fidelity: First-party sources mean on-chain prices resemble professional market data, reducing arbitrage windows and manipulation vectors. Speed for new primitives: Faster feeds unlock aggressive trading strategies and low-latency liquidation systems that were previously too risky on slower oracles. Cross-chain consistency: One canonical feed across chains prevents fragmented pricing and simplifies cross-chain composability. Institutional credibility: Pyth’s publisher roster and product expansions make it easier for regulated entities to trust and adopt on-chain price infrastructure.
Limitations & challenges — the hard parts
Competition and incumbency
The oracle market is crowded. Big players (Chainlink, Band, DIA) and bespoke rollup solutions compete on latency, decentralization, and data breadth. Pyth must continue differentiating on first-party sources and breadth of markets.
Cost vs. freshness tradeoffs
Ultra-high-frequency feeds can be expensive to persist on-chain. Pyth’s hybrid streaming + on-chain pull model helps, but protocols must still design when and how often to pull to balance gas costs and freshness.
Publisher governance & integrity
Relying on institutions introduces new governance questions: how to onboard/offboard publishers, how to handle misreported data, and what economic or reputational levers ensure long-term honesty. Robust monitoring, slashing mechanisms, or legal contracts may be necessary for certain enterprise uses.
Regulatory and legal exposure
As Pyth moves into publishing macroeconomic and traditional financial data, regulatory scrutiny and contract/licensing complexities grow — especially if governments or official agencies begin to adopt on-chain distribution models.
Latest developments — signals that Pyth is scaling beyond DeFi
Government & institutional adoption: U.S. Department of Commerce partnership
A major recent milestone: in August 2025 the U.S. Department of Commerce selected Pyth to verify and distribute official economic data (starting with GDP and potentially expanding to employment and inflation metrics) on-chain. This is a watershed moment — public-sector use of a decentralized price layer signals institutional trust and opens new classes of civic Web3 use cases.
Growing protocol metrics & TVS gains
Pyth reported growth in usage and Total Value Secured (TVS) in mid-2025: Messari’s Q2 2025 state report noted TVS rising to about $5.31 billion and strong feed-update activity, underscoring expanding real-world reliance on its data. Those are tangible adoption signals for an oracle infrastructure.
Layer & ecosystem expansion: Layer N and 500+ feeds
Pyth’s price feeds launched on Layer N (an Ethereum StateNet), delivering 500+ real-time feeds to developers there — an example of how Pyth scales to new execution environments and supports non-Solana ecosystems.
Tokenomics & protocol economics
Pyth has a native token (PYTH). Official tokenomics show a max supply of 10,000,000,000 PYTH with planned vesting schedules and locked allocations aimed at long-term alignment; specifics (initial circulating supply, unlock cadence) are published in Pyth’s tokenomics documentation. Token design will influence governance and incentives as Pyth expands into paying publishers or funding infrastructure.
Use cases & real-world examples
Perpetuals & derivatives — exchanges and DEXs can use Pyth’s low-latency feeds for funding rates and mark-price calculations, reducing exploitable oracle latency windows.Cross-chain DeFi — a lending protocol on Chain A and a DEX on Chain B can both reference the same Pyth feed (via Wormhole), ensuring consistent collateral valuations across ecosystems. On-chain macro & civic data — with the Commerce Dept partnership, official macro stats can be published on-chain for transparent, verifiable use in foreign-aid contracts, hedging instruments, or policy-triggered DAOs.
Expert views & market sentiment — cautious optimism
Analysts frame Pyth as a pragmatic “price layer” that sits between raw markets and smart contracts: the first-party model is attractive to institutions and advanced DeFi builders, but Pyth will be judged on uptime, the integrity of publisher sets, and whether tokenomics sustain publisher participation and governance. Market commentary after the Commerce Dept announcement showed strong interest across interoperability projects (e.g., Wormhole) and a spike in related on-chain activity.
Future outlook — signals to watch
1. More institutional data & subscription products: Pyth is actively productizing “Pyth Pro” for institutional customers; growth here would create recurring revenue lines and deeper TradFi ties.
2. Governance maturation: How PYTH token governance evolves — e.g., staking, fee distribution to publishers, and protocol treasury use — will determine long-term decentralization and incentives.
3. Resilience & defenses: As usage grows, Pyth must harden publisher onboarding, anomaly detection, and fallbacks so that a misbehaving feed cannot cascade into systemic liquidations.
4. Cross-chain & Layer2 proliferation: Expect broader integrations across L2s and sovereign rollups; Pyth’s utility will scale with how frictionlessly it can serve heterogeneous execution environments.
Conclusion — why Pyth matters today (and tomorrow)
Pyth Network sits at a critical intersection: professional market data, decentralized distribution, and cross-chain accessibility. Its first-party publisher model, real-time feeds, and growing institutional footprint make it a compelling candidate to be the default price layer for the next generation of financial and civic Web3 use cases. The Commerce Department partnership and rising TVS are strong signals — but the real test will be continued reliability, robust governance tooling, and the ability to monetize sustainably without compromising open access.For builders and institutions, Pyth lowers the practical barrier to building advanced financial products on-chain. For the broader ecosystem, it offers a way to import the precision of TradFi markets into decentralized systems — bringing us closer to programmable, auditable finance that can operate at institutional speed and transparency.

@Pyth Network #PYTH
$PYTH

想象一下你最喜歡的虛構角色明天醒來並主持一個直播——回答問題，唱歌，進行品牌交易，甚至在一個社區DAO中投票——同時你（以及可能數百萬其他人）擁有他們知識產權的一部分。這就是Holoworld AI所追求的承諾：一個生態系統，在這裏，AI角色（稱爲代理）不僅僅是聰明的聊天機器人，而是可交易、可創造和可盈利的數字存在，通過Web3工具擁有和管理。Holoworld融合了AI創造力、創作者經濟和區塊鏈支持的所有權，將“虛擬角色”轉變爲現實世界（以及鏈上）的文化資產。接下來是一個詳細的導覽：它是如何運作的，爲什麼重要，新的是什麼，面臨的挑戰，以及它重塑創作者經濟的可能性有多大。

在去中心化金融（DeFi）、衍生品、預測市場以及任何金融產品中，擁有新鮮、可靠、高保真的價格數據至關重要。過時或被操控的價格信息可能會讓協議損失數百萬。傳統的預言機往往會引入延遲、中介或不透明——這些都帶來了風險。進入Pyth Network：一個去中心化的第一方預言機，旨在以速度、透明度和最小的中介直接在鏈上提供實時金融數據。如果它成功了，可能會重塑DeFi甚至傳統金融（TradFi）如何橋接到Web3：降低信任障礙、減少延遲風險，並使以前難以構建的新金融工具成爲可能。

想象一下，如果你的比特幣能夠工作 — 而不僅僅是作爲“數字黃金”靜靜放着。你質押它，它爲安全性做出貢獻，但它也繼續產生收益 — 不僅僅一次，而是分層收益。這就是BounceBit的核心承諾：一個基於CeDeFi（集中式+去中心化金融）框架的本地比特幣再質押鏈。
一些關鍵支柱：
這是一個雙代幣權益證明（PoS）的一層鏈，由BTC（以包裝或託管形式）和其本地代幣BB保障。用戶將BTC（或穩定幣）存入受監管的託管中，獲得流動性託管代幣（LCTs）（例如BBTC，BBUSD），這些代幣保持流動性和可交易性，同時其基礎資產產生收益。這些LCTs可以在鏈上進一步用於質押、再質押、收益農場等。收益來自多個來源：驗證者獎勵（網絡安全）、交易策略（例如套利或基差交易）和通過代幣化保險庫與現實世界資產（RWAs）的集成。BounceBit連接離線（CeFi託管、機構市場）和在線（DeFi、智能合約）世界 — CeDeFi中的“混合”。所以實際上：你的比特幣從未閒置。它可以同時幫助保護網絡，產生利息，流入DeFi策略，並參與現實世界收益產品。

Pyth Network 是一個去中心化的第一方金融預言機，旨在實時提供鏈上的市場數據——價格信息、交易所變化、訂單簿信號——直接來自於生產數據的人和機構（交易所、做市商、交易公司）。Pyth 不依賴於不透明的中介節點來獲取和“猜測”價格，而是將原始的、高頻率的數據安全地傳輸到鏈上，以便智能合約能夠做出更快、更安全的決定。

去中心化平臺如何運作（人性化技術）

有絲分裂不僅僅是另一個去中心化金融協議。它是一個金融工程層，將流動性——去中心化金融的生命線——變得可編程、可組合和更智能。目前在去中心化金融中，流動性經常被困在孤立的池中（自動做市商、借貸金庫、質押農場）。這是可行的，但它是片段化的、低效率的，並迫使用戶面臨“非此即彼”的選擇。有絲分裂通過將流動性位置視為可編程的構建塊來顛覆這一劇本。你的流動性可以在實時中解鎖、重新打包並在多種策略中優化，而不是靜態池。

Somnia是一個與EVM兼容的Layer-1區塊鏈，建立的目的不僅是為了金融極客或硬核DeFi用戶——而是為了大眾。它的DNA專為遊戲、娛樂和消費者應用而調整。可以把它想像成下一波數位文化展演的舞台。當大多數L1專注於吞吐量和DeFi工具時，Somnia則在問：“什麼能讓數百萬普通人上鏈？”他們的答案是：無摩擦的遊戲、社交應用和娛樂平台，讓人感覺有趣、快速和熟悉。

OpenLedger是原生於AI的區塊鏈，旨在為我們時代最有價值但未被充分利用的資產之一解鎖流動性：數據、AI模型和自主代理。OpenLedger將整個AI生命周期上鏈——從訓練到部署——使其透明、可互操作和可貨幣化。
在大多數區塊鏈優化DeFi的地方，OpenLedger優化AI參與：
數據作為流動性。模型作為資產。代理作為自主的、創收的行為者。這不僅僅是基礎設施。這是一個新的金融層次，在這裡，AI本身是一個經濟參與者。

Plume是一個模塊化的Layer-2網絡，旨在使現實世界的資產——貸款、發票、房地產、應收賬款、藝術品等——可以在鏈上進行代幣化、交易和管理，並內置規則、合規性和金融原語。它與EVM兼容，因此ETH工具和智能合約可以工作，但它增加了特定於RWA的構建模塊：法律包裝、保管集成、合規鉤子、結算軌道和市場——所有這些都旨在使機構資產流動在鏈上感覺原生。
這爲什麼重要

Boundless 正在構建零知識 (ZK) 計算的骨幹。與其讓每個區塊鏈或彙總設計自己的證明系統（昂貴、緩慢且冗餘），Boundless 提供共享的證明基礎設施。
把它想象成證明的雲計算：
應用程序、區塊鏈和彙總可以將其繁重的證明任務外包。無限證明節點在鏈外進行計算工作。證明在鏈上得到驗證，保持安全性不變，但降低了成本和延遲。這是由 zkVM（零知識虛擬機）提供支持的——一個通用引擎，可以將任何計算轉換爲證明，從而允許網絡在不重新運行計算的情況下驗證正確性。

Holoworld AI 正在構建一個創造性的宇宙，在這裏 AI 和 Web3 融合在一起。今天，創作者面臨着破碎的系統：
AI 工具分散且未設計爲可擴展。Web3 貨幣化笨拙、不公平且常常無法訪問。AI 代理（聊天機器人、生成器、模型）功能強大，但被困在孤島中，無法參與去中心化協議。Holoworld AI 想要翻轉這個敘述：創建 AI 原生工作室、代幣化經濟和通用連接器，使得 AI 本身成爲 Web3 的一個活躍參與者。

BounceBit 是一個 BTC 重新質押鏈，它將中心化金融 (CeFi) 與去中心化金融 (DeFi) 結合在一起，形成他們所稱的 CeDeFi 框架。理念是：比特幣持有者不應該只是持有。他們應該賺錢。他們可以重新質押，委託，使用流動質押代幣，參與收益策略，同時仍然保留 DeFi 提供的大量透明度和可編程性——同時使用受監管的保管和基礎設施來降低一些風險。
一些核心組件：
雙代幣權益證明 (PoS) 層 1 鏈：驗證者質押 BBTC（一個與 BTC 掛鉤的重新質押代幣）和本地 BB 代幣以確保鏈的安全。流動質押 / 重新質押：您通過受監管的保管鎖定 BTC（或等價的包裝 / 鏡像版本），您會獲得一個代幣（BBTC），然後可以用於質押 & 委託，也可以在 DeFi 活動中進行槓桿。這保持了您的 BTC “賺取” 的同時允許一定的流動性。CeFi 保管 + 受監管的合作伙伴：爲了降低對手風險，BounceBit 使用受監管的保管人（例如 Mainnet Digital，Ceffu）加上保管基礎設施。EVM 兼容性：開發者 / DeFi 應用可以移植或以類似於他們在以太坊上構建的方式進行構建，這有助於組合性和用戶體驗。