ChuanCrypto

Recorded on May 10, 2023 Original author: Crypto Ogawa Please do not misappropriate the results of my labor. If found, we will strictly investigate.

Btc will reach 36000Usd before the end of 2023

Eth will reach 2550Usd before the end of 2023

Btc bull market actual target is 56500usd

Eth bull market actual target is 3735usd

The picture below shows the Btc and Eth disks. "Please do not steal the fruits of your labor."

Btc disk

ETH disk

Logical theory:

Wave theory 5 The principle of rising waves

"Btc needs to meet the requirement not to fall below 2.5w"

"Eth needs to meet the requirement not to fall below 1.5q"

Detailed explanation is mainly based on pictures.

Pay attention to the next round of copycats

When I saw that Pyth has already supported multiple chains and reiterated the strategic goal of 'multi-chain distribution' in the roadmap, I couldn't help but think: this is a war to lay a unified data system across the entire chain world.
Today, I want to break down the technical strategy, challenges, and paths of multi-chain expansion for you to see how it plays this game.

1️⃣ Multi-chain coverage: Why this is a must-do rather than an optional task.
For Pyth, if it can only provide prices on Solana or a few chains, it will always be just a component within the ecosystem. What it wants is 'infrastructure status', allowing DeFi projects on different chains to call the same set of prices, which can truly form a network effect. Therefore, multi-chain coverage is a strategic task for it.

In the past, in the Web2 world, if you trained a model well, deploying it could mean: setting up servers, writing APIs, hanging the frontend, connecting payment systems... both expensive and troublesome.
But in @OpenLedger you only need to complete a few on-chain actions to turn the model into a 'operable, callable, and profitable' blockchain-native asset.
Today, this article will teach you step by step: how exactly does an AI model get 'on-chain'? What happens when it runs? How are the profits distributed?

1️⃣ Step one: Prepare the model resources, you can either build them yourself or reuse them.

Have you ever encountered this situation? You put LP in a certain DeFi protocol, it seems like the returns are quite high, but the actual money you receive is far from what you expected, and no one can clearly tell you the reason.
‘Oh, that's because you are participating in a multi-path strategy, which involves protocol fees, management shares, reward pool unlocking periods, incentive depreciation, and transaction fee slippage, among other things.’ This is exactly where many people feel disappointed with DeFi: too many strategies operate as black boxes, and the sources of returns are a complete mystery.
It wasn't until I used @Mitosis Official that I finally saw clearly for the first time— it turns out, the profit path can be completely 'dissected.'

Many people instinctively label MITO at first glance.
Oh, another platform token for combination strategies.

Is it used to incentivize LPs?

Will it end up like those before, where no one uses it after issuance?
But I suggest you don't rush to conclusions.
I used to have these questions too, until I started using Mitosis to build strategies. After understanding the 'value loop' in the system, I realized:
@Mitosis Official token design is created for the operation of the entire combination execution system.
It is not just a chip in your hand, but an energy unit and governance incentive hub of an entire on-chain strategy logic system.

Sometimes I feel that people view Pyth as just a tool for pricing loans and perpetual contracts, which is actually a misunderstanding.
According to the data and roadmap, the ambition of @Pyth Network goes beyond just 'feeding prices'; it aims to become the hub of on-chain financial infrastructure—allowing all financial logic, asset pricing, and data services to collaborate within its operating system.

1️⃣ From Permissioned to Permissionless: The New Era of Open Data Publishers
In the past, Pyth's data providers were top financial institutions with permission to ensure data quality and credibility. However, in its 2.0 roadmap, there is a very important shift: allowing anyone who can provide high-quality data to become a publisher. In other words, as long as you have a reliable data source, the ability to stake PYTH as credit backing, and can pass backtesting/verification mechanisms, you will have the opportunity to join this network in the future. This step will break the oligopoly control over data and introduce more long-tail data sources, thereby enhancing the network's diversity, resistance to censorship, and robustness.

Many people, when they first see Holoworld, react by saying: "Oh, a video platform that can create AI characters and interact, sounds fun." But if you look closely at its product architecture, you'll find that it aims to be not just an application, but a complete "AI agent marketplace + multi-character economic ecosystem."
This means its role is on-chain assets, its interaction behaviors are traceable, and its incentive mechanisms are continuously computable. HOLO is the "cycler" of this ecosystem.
Not just a functional token, but a hub for governance, incentives, and asset flow, HOLO also plays multiple roles in @Holoworld AI .

In this article, we discuss long-term thinking: how ZKC transformed from a mining coin into the core of ecological operation, and what key stages it must go through.
Why do I say it may become the 'most fundamental computing credit medium between chains'?@Boundless

1: Drive nodes with rewards and control supply with staking.
In the initial stage, ZKC is a type of work reward + collateral certificate.
The node (prover) is here to take orders and generate ZK proofs; complete the tasks to earn rewards.
But you must first stake ZKC to guarantee that you are not here to 'work for free'.
At this point, ZKC is a typical 'mining reward + anti-cheating mechanism'. It won't bring much price imagination but can quickly onboard nodes and promote network operation, belonging to the standard strategy for the network's cold start phase.

You might think that "multi-chain compatibility" is just a nice-sounding technical label, but at Plume, it's not just about saving a few cents on transaction fees; it's about fundamentally solving the issue of RWA being illiquid from the ground up.
Today, let's talk about how deep Plume's "multi-chain design" really is.@Plume - RWA Chain

1️⃣ Multi-chain is not about "being deployable", but about "being schedulable".

Many projects claim to support multi-chain, but in reality, they just deploy code on different chains separately, leading to a fragmented user experience. What Plume does is unify the scheduling of underlying modules. No matter which chain you initiate asset operations on, the system automatically handles yield transfer, structural decoupling, permission configuration, and other processes across chains through SkyLink. What users experience is a smooth experience similar to that of a single chain.

The act of connecting sounds simple, but it is complicated. Especially in Web3, without a central server to back it up, how can we ensure that the communication between the wallet and DApp is both secure and stable? This is where WalletConnect's true 'black technology' lies.
From Relay to Node, and to the underlying $WCT incentive model, it is not just a protocol but a growing communication network.

1️⃣ The wallet and DApp do not communicate directly; they rely on the Relay network in between.
When you scan to connect your wallet using WalletConnect, you are not directly establishing a link between yourself and the DApp, but rather through an encrypted channel via WalletConnect's own Relay network, completing data transmission.

In the past, when we talked about L1 performance, we couldn't avoid 'TPS' and 'finality'. However, I later discovered a problem: even if your TPS goes up, if the chain is 'single-threaded', multi-threaded games will still lag.
One of the biggest innovations of the MultiStream architecture is that it does not make a single chain run faster, but allows multiple processing paths to run simultaneously.
Sounds abstract? Don't worry, I'll clarify it with a real-world example.

1️⃣ A high-speed truck vs a three-dimensional expressway
Imagine a regular L1 as a super-sized truck, capable of carrying thousands of tons of data in one trip, going back and forth.

When I started studying the roadmap of @Pyth Network , I had a feeling of 'this is not about making an oracle'. It not only feeds prices but also paves a bridge connecting traditional finance with the on-chain world.
Next, I will break down the key points in the roadmap that are heavy on information and clear in direction but not easy to observe.

1️⃣ Current Stage: Infrastructure Building and Cross-Chain Expansion
Currently, Pyth has already achieved cross-chain transmission on multiple chains, efficiently distributing price data through a bridging mechanism between the main chain and target chains. The roadmap shows that Pyth is continuously expanding the number of supported chains, no longer limited to Solana or a few others.

@Holoworld AI 's $HOLO gives me a different feeling. It is not like traditional AI projects that only rely on 'blowing up models'; it is one of the very few that is truly working on a closed-loop structure within the 'AI application platform'.
Understanding this structure is key to realizing why $HOLO seems to me like an 'L2 coin in the AI field'.

1️⃣ Most AI project tokens lack a real usage 'hook'.
You may have also noticed that many AI projects are now not primarily about technology or products, but rather about 'I want to create community AI', 'AI decentralized computing power', and 'everyone can train models'.

In the process of following Pyth, I feel that it has too many advantages that are exciting, but the more I research, the more I realize that successfully traversing this path is not without its challenges.

The "next steps" in the roadmap look brilliant, but the hidden technological, governance, market, and regulatory risks behind them are worth our early attention.
Today, let's talk about those often overlooked yet highly explosive potential risks, as well as my ideas on how to respond to them.@Pyth Network
1️⃣ Technical & Architecture Risks: Cross-chain dependencies and single point of failure
The architecture of Pyth inherently carries cross-chain and bridging dependencies. To distribute price messages from the main chain to multiple chains, it is necessary to rely on cross-chain bridges or messaging systems. Once the bridge experiences congestion, attacks, or failures, data delays, packet loss, and errors are all possible.

In the traditional AI model ecosystem, once a model is online, whether developers can profit almost entirely depends on the pricing strategy and traffic input of centralized platforms. Data contributors are worse off; no matter how much the model earns, it's very difficult for them to get a share.
@OpenLedger Want to change all of this. It's not about 'selling models', but about establishing a revenue network for model calls that operates on a chain — allowing the value generated every time a model is called to be proportionally distributed to all the real contributors behind it.
This sounds ideal, but how is it achieved? Let's clarify it today.

'Which chain is most suitable for running AI models on-chain?' This is a question that all teams working on AI projects in Web3 cannot avoid. If you choose too slowly, users won't wait; if you choose too quickly, the community may not have formed yet; if you choose incorrectly, the entire economic system has to be restructured.
@OpenLedger 's choice is —— Berachain. Among many L1, L2, public chains, and modular chains, they chose a newly emerging chain that has just surfaced.
This is not just about 'embracing a new ecosystem', but also a precise prediction of future AI calling demands.

1️⃣ Why not Ethereum, not Solana, but Berachain?