Cryptov

@Cries000

We3 | NFT Collector | Gamefi |

Joined March 2022

306 Following

8 Followers

144 Posts

Cries000 retweeted

Orderly @OrderlyNetwork

over 2 years ago

We just partnered with @SlashWeb3 🟣 🎌 🤝 SlashWeb3's cutting-edge web3 payment solutions are expanding our reach in Japan and globally. To reward our OG users, we are running a campaign to earn yourself a SlashWeb3 and Orderly OAT. Claim it here: https://t.co/8jdXboG117

OrderlyNetwork's tweet photo. We just partnered with @SlashWeb3 🟣 🎌

🤝 SlashWeb3's cutting-edge web3 payment solutions are expanding our reach in Japan and globally.

To reward our OG users, we are running a campaign to earn yourself a SlashWeb3 and Orderly OAT.

Claim it here: https://t.co/8jdXboG117 https://t.co/OZ7Nccae2u

287

26K

24K

254

302K

Cries000 retweeted

Orderly @OrderlyNetwork

over 2 years ago

💥 Boom! The Orderly-LogX Battle Royale is ON! 💥 Yo, degens! Get this: @LogX_trade is now routing through Orderly on @Optimism mainnet, and we're kicking things off with a bang! A trading competition with $25,000 on the line. You in or what? (🟣, 🟢) How to win? Trade $50,000 cumulative volume through LogX via Orderly. Wait there's more... • Exclusive Top 50 Leaderboard NFTs (because being a digital OG matters) • Daily 200 $OP token drops for volume champs (free crypto, anyone?) • Score LogX airdrop points (more goodies!) 🪂 • Bag some bonus Zealy XPs (because every little bit helps) Event time: Feb 7th 0:00 AM UTC to Mar 3rd 23:59 PM UTC Galxe link: https://t.co/H9okPTznA5 LogX trading page : https://t.co/FPd9FN4t76

OrderlyNetwork's tweet photo. 💥 Boom! The Orderly-LogX Battle Royale is ON! 💥

Yo, degens! Get this: @LogX_trade is now routing through Orderly on @Optimism mainnet, and we're kicking things off with a bang!

A trading competition with $25,000 on the line. You in or what? (🟣, 🟢)

How to win? Trade $50,000 cumulative volume through LogX via Orderly.

Wait there's more...

• Exclusive Top 50 Leaderboard NFTs (because being a digital OG matters)

• Daily 200 $OP token drops for volume champs (free crypto, anyone?)

• Score LogX airdrop points (more goodies!) 🪂

• Bag some bonus Zealy XPs (because every little bit helps)

Event time: Feb 7th 0:00 AM UTC to Mar 3rd 23:59 PM UTC

Galxe link: https://t.co/H9okPTznA5

LogX trading page : https://t.co/FPd9FN4t76

284

18K

16K

159

355K

Cries000 retweeted

KIP Protocol

@KIPprotocol

over 2 years ago

Cycle 9 is live! https://t.co/zKASsuNZzk

265

68K

Cries000 retweeted

KIP Protocol

@KIPprotocol

over 2 years ago

KIP Explainer Series: #1 Why is KIP the Web3 Base Layer for AI? First off: KIP is not a single AI app, nor a large language model, nor a database / knowledge base. KIP is a decentralized protocol that AI app owners 📱, model owners🤖 and knowledge base / data owners📚 will find essential to decentralise their work & monetise in Web3. (For brevity, we call these 3 categories 📱, 🤖, 📚 : AI Value Creators) Decentralisation of AI is an extremely large & important topic, and there are multiple groundbreaking projects taking different approaches at the problem. For us at KIP, we are focused on solving the base level problems that AI Value Creators will face when trying to deploy and monetise their work in Web3. AI MODELS 🤖 NEED APPS 📱& DATA 📚 TO CREATE ECONOMIC VALUE While there are easily 20 over categories of companies creating solutions in AI, most of the attention in Generative AI over the past year has been on the AI Models (and there is a huge variety of different categories and approaches here from transformers to GANs to diffusion models to name a few). Indeed, these models represent the true breakthrough of this new era of computing—the true Brains behind it all. But to build a business ecosystem within AI, models need to rely on at least 2 other essential Value Creators. 1) AI Apps📱: 'The Face of AI' It's easy to overlook the importance of apps amidst all the excitement over models. AI Apps are essential for getting users into AI. Apps can take many forms, like chatbots, image generators, search bots, analysis bots or in its most elegant form, simply prompts. They craft the user experience, acquire the users, and perhaps most importantly, collect the fees from the users. Many forget that ChatGPT is OpenAI’s app, powered by OpenAI’s various models (GPT 3.5, GPT 4). The then ground-breaking human-like responses of the OpenAI chatbot were largely coded on the app-end, not the model end. (Connecting to the models directly via API, and comparing the answers, will tell you this.) TLDR: without Apps, models would just be sets of code and weights sitting in a metal box somewhere with no way to utilise them. 2) Data📚: 'The Lifeblood of AI' Data is needed for: a) Training & Fine Tuning Models, & b) Retrieval-Augmented Generation (RAG) All models are trained and fine-tuned on data. Without fine-tuning, models don't get stronger or smarter. But by using data to finetune or train models, it causes the data essentially to be 'assimilated' or 'absorbed' into the models, manifested in the adjustment of the model weights. So in situations where it is impossible, impractical, or illegal to just use data to directly train the models, the innovative technique called Retrieval-Augmented Generation (RAG) steps up to deliver. RAG combines the power of retrieving information from external databases with the capability of generating responses via an AI Model. It's like having a super-smart assistant who understands your questions but also knows where to find the answers even if it doesn't know the answer itself. While RAG is still relatively new, it’s our firm belief that given increased sensitivity and protectionism over data, RAG techniques could emerge as a leading approach, driving significant business value through real-world applications, and making it a dominant framework under which most people access AI in the future. TLDR: Regardless of which approach, continued AI innovation is simply not possible without data. A VIBRANT AI ECOSYSTEM REQUIRES MULTIPLE INDEPENDENT INDUSTRIES OF VALUE CREATORS Individuals and companies skilled at training and finetuning models, may not be the same people who are great at designing and marketing customer-facing apps. Similarly the researchers and domain experts with valuable data sets and knowledge bases, may not have the right skill sets to train AI models or design applications. But in a vibrant and diverse ecosystem, they don’t have to . Seperate industries of companies and individuals can work together to create use cases and economic value for users. An app designer can choose the AI model most suitable for his product plans, and pre-select the external knowledge bases most helpful for his users. But what if all 3 potentially vibrant and independent industries are being slowly absorbed into one closed ecosystem? Because that’s exactly what’s happening right now. We will cover this in detail in future articles, but for now: do a web-search for "openai copyright shield" and consider the implications for data ownership in the AI future. WHY KIP WANTS TO CATALYSE AI DECENTRALISATION Monopolies in AI are uniquely dangerous, and decentralisation of AI is an urgent and necessary response to the subjugation of our collective interests to that of a narrow set of corporate interests. We are 100% for AI accelerationism (e/acc), and we do not deny the significant contributions of Big Tech in advancing AI innovation. But large companies will act only in the best interests of their shareholders, and they will do whatever they can get away with. It’s the nature of capitalism; to expect them to change their nature and ignore their driving motivations is to deny reality. We need a state of adversarial equilibrium in AI, with a multitude of different actors participating and competing in the market, fostering an environment where innovation can thrive. The future of AI must not become subjugated to any megacorp’s corporate interests. And decentralization of AI is, in our opinion, THE ONLY WAY to bring about that desired state. HOW KIP CATALYSES DECENTRALISATION OF AI KIP solves three base level problems that AI apps, models, and data owners will face when trying to decentralize. 1) On-chain / Off-Chain Connectivity 2) Monetisation & Accounting 3) Ownership & Security The “Connectivity” Problem There are > 400,000 models on Hugging Face, which is indicative of how vibrant, but also how nascent the entire AI industry really is. Current blockchain technology cannot deliver the core inference function of models (ie totally decentralised models) at a cost or speed that most ordinary users would find acceptable (although advances in edge computing may get us there soon) Thus most, if not all, of these models are off-chain, and we can expect more innovation and tinkering to be done in off-chain models. In order to unleash all those ideas and innovation in web3, KIP makes it easy to account for off-chain inference, on-chain. KIP facilitates this through a framework that allows the heavy computational tasks associated with machine learning inference to be processed outside the blockchain, while still maintaining the integrity and principles of a decentralized system. The “Money” Problem The best technology in the world would not see adoption if adopters do not enjoy increased economic benefits. The basic revenue model framework for AI can be described as “pay-per-query”, as every single query from a user expends GPU compute, and thus have to be paid for by someone. And to answer a single user query, multiple AI value creators contribute to the answering of that question. We are not advocating decentralization merely for decentralization’s sake, but rather decentralization as an alternative to monopolization. Thus, for AI decentralisation to succeed, we need to ensure that the parties who are decentralizing their AI work are able to earn revenues. This sounds obvious enough, but in the case of AI, this is not as straightforward as it sounds. Let’s give an example of a query run via RAG 1. A user makes a query to an AI Chatbot. 2. The AI Chatbot passes the query over to its brain the AI model. 3. The model retrieves only relevant chunks of data from the Knowledge Base it requires to answer the question, formulates the answer and sends it back to the App. 4. The App packages the answer and delivers it back to the user. In this simplified example, you will see how all 3 actors each contribute towards the answering of the user query. If one platform owns and controls all three (🤖,📱,📚) under a centralized ecosystem (like what OpenAI is trying to do in the 2nd diagram above), then you just have to pay that one centralised platform, as the rest is internal accounting. But if we want decentralization rather than monopolization, then each party needs to be paid, thus requiring solutions for: 1. Recording (on-chain) the contributions of each, 2. Splitting / allocating the revenues from the users 3. Enabling each to withdraw their revenues This is the “money problem” in decentralizing AI that KIP solves. We do this through a low gas, high efficiency Web3 infrastructure that provides for connectivity between AI value creators, a way to collect payments from the user, and a way to withdraw earnings. (We will cover this in an upcoming KIP Explainer) Without solving the money problem first, decentralization of AI will be much more difficult, and will be far more unlikely to gain wide-spread adoption beyond a few true believers. The “Ownership” Problem Monetisation is merely a weak privilege, if it is not tied to true ownership. We have all seen how accounts on centralized platforms can be shutdown, banned, shadowbanned at a moment’s notice. KIP solves this through using blockchain tokens, specifically the ERC-3525 token (SFTs), to “wrap” the work of the AI Value Creator. 1.For data owners: the SFTs wrap vectorised knowledge bases, or a link to an encrypted raw data file to be used for model training. 2.For model makers: the SFTs could wrap an API to an off-chain model, or a set of model weights ready for sale 3.For app devs: the SFTs could wrap the front end APIs, or the prompt itself. These SFTs serve as 'accounting entities' that can interact with each other on-chain, and record the amounts each SFT earned from a particular transaction. By solving these problems, KIP makes it possible and easy for AI Value Creators to decentralize their work, creating the starting conditions for a vibrant and much larger decentralised AI ecosystem. KIP is the necessary Web3 Base Layer for AI.

KIPprotocol's tweet photo. KIP Explainer Series: #1
Why is KIP the Web3 Base Layer for AI?

First off: KIP is not a single AI app, nor a large language model, nor a database / knowledge base.

KIP is a decentralized protocol that AI app owners 📱, model owners🤖 and knowledge base / data owners📚 will find essential to decentralise their work & monetise in Web3.

(For brevity, we call these 3 categories 📱, 🤖, 📚 : AI Value Creators)

Decentralisation of AI is an extremely large & important topic, and there are multiple groundbreaking projects taking different approaches at the problem.

For us at KIP, we are focused on solving the base level problems that AI Value Creators will face when trying to deploy and monetise their work in Web3.

AI MODELS 🤖 NEED APPS 📱& DATA 📚 TO CREATE ECONOMIC VALUE

While there are easily 20 over categories of companies creating solutions in AI, most of the attention in Generative AI over the past year has been on the AI Models (and there is a huge variety of different categories and approaches here from transformers to GANs to diffusion models to name a few).

Indeed, these models represent the true breakthrough of this new era of computing—the true Brains behind it all.

But to build a business ecosystem within AI, models need to rely on at least 2 other essential Value Creators.

1) AI Apps📱: 'The Face of AI'

It's easy to overlook the importance of apps amidst all the excitement over models.

AI Apps are essential for getting users into AI. Apps can take many forms, like chatbots, image generators, search bots, analysis bots or in its most elegant form, simply prompts.

They craft the user experience, acquire the users, and perhaps most importantly, collect the fees from the users.

Many forget that ChatGPT is OpenAI’s app, powered by OpenAI’s various models (GPT 3.5, GPT 4). The then ground-breaking human-like responses of the OpenAI chatbot were largely coded on the app-end, not the model end. (Connecting to the models directly via API, and comparing the answers, will tell you this.)

TLDR: without Apps, models would just be sets of code and weights sitting in a metal box somewhere with no way to utilise them.

2) Data📚: 'The Lifeblood of AI'

Data is needed for:
a) Training & Fine Tuning Models, &
b) Retrieval-Augmented Generation (RAG)

All models are trained and fine-tuned on data. Without fine-tuning, models don't get stronger or smarter.

But by using data to finetune or train models, it causes the data essentially to be 'assimilated' or 'absorbed' into the models, manifested in the adjustment of the model weights.

So in situations where it is impossible, impractical, or illegal to just use data to directly train the models, the innovative technique called Retrieval-Augmented Generation (RAG) steps up to deliver.

RAG combines the power of retrieving information from external databases with the capability of generating responses via an AI Model. It's like having a super-smart assistant who understands your questions but also knows where to find the answers even if it doesn't know the answer itself.

While RAG is still relatively new, it’s our firm belief that given increased sensitivity and protectionism over data, RAG techniques could emerge as a leading approach, driving significant business value through real-world applications, and making it a dominant framework under which most people access AI in the future.

TLDR: Regardless of which approach, continued AI innovation is simply not possible without data.

A VIBRANT AI ECOSYSTEM REQUIRES MULTIPLE INDEPENDENT INDUSTRIES OF VALUE CREATORS

Individuals and companies skilled at training and finetuning models, may not be the same people who are great at designing and marketing customer-facing apps.

Similarly the researchers and domain experts with valuable data sets and knowledge bases, may not have the right skill sets to train AI models or design applications.

But in a vibrant and diverse ecosystem, they don’t have to . Seperate industries of companies and individuals can work together to create use cases and economic value for users.

An app designer can choose the AI model most suitable for his product plans, and pre-select the external knowledge bases most helpful for his users.

But what if all 3 potentially vibrant and independent industries are being slowly absorbed into one closed ecosystem?

Because that’s exactly what’s happening right now. We will cover this in detail in future articles, but for now: do a web-search for "openai copyright shield" and consider the implications for data ownership in the AI future.

WHY KIP WANTS TO CATALYSE AI DECENTRALISATION

Monopolies in AI are uniquely dangerous, and decentralisation of AI is an urgent and necessary response to the subjugation of our collective interests to that of a narrow set of corporate interests.

We are 100% for AI accelerationism (e/acc), and we do not deny the significant contributions of Big Tech in advancing AI innovation.

But large companies will act only in the best interests of their shareholders, and they will do whatever they can get away with. It’s the nature of capitalism; to expect them to change their nature and ignore their driving motivations is to deny reality.

We need a state of adversarial equilibrium in AI, with a multitude of different actors participating and competing in the market, fostering an environment where innovation can thrive. The future of AI must not become subjugated to any megacorp’s corporate interests.

And decentralization of AI is, in our opinion, THE ONLY WAY to bring about that desired state.

HOW KIP CATALYSES DECENTRALISATION OF AI

KIP solves three base level problems that AI apps, models, and data owners will face when trying to decentralize.

1) On-chain / Off-Chain Connectivity
2) Monetisation & Accounting
3) Ownership & Security

The “Connectivity” Problem

There are > 400,000 models on Hugging Face, which is indicative of how vibrant, but also how nascent the entire AI industry really is.

Current blockchain technology cannot deliver the core inference function of models (ie totally decentralised models) at a cost or speed that most ordinary users would find acceptable (although advances in edge computing may get us there soon)

Thus most, if not all, of these models are off-chain, and we can expect more innovation and tinkering to be done in off-chain models.

In order to unleash all those ideas and innovation in web3, KIP makes it easy to account for off-chain inference, on-chain.

KIP facilitates this through a framework that allows the heavy computational tasks associated with machine learning inference to be processed outside the blockchain, while still maintaining the integrity and principles of a decentralized system.

The “Money” Problem

The best technology in the world would not see adoption if adopters do not enjoy increased economic benefits.

The basic revenue model framework for AI can be described as “pay-per-query”, as every single query from a user expends GPU compute, and thus have to be paid for by someone. And to answer a single user query, multiple AI value creators contribute to the answering of that question.

We are not advocating decentralization merely for decentralization’s sake, but rather decentralization as an alternative to monopolization.

Thus, for AI decentralisation to succeed, we need to ensure that the parties who are decentralizing their AI work are able to earn revenues.

This sounds obvious enough, but in the case of AI, this is not as straightforward as it sounds.

Let’s give an example of a query run via RAG

1. A user makes a query to an AI Chatbot.
2. The AI Chatbot passes the query over to its brain the AI model.
3. The model retrieves only relevant chunks of data from the Knowledge Base it requires to answer the question, formulates the answer and sends it back to the App.
4. The App packages the answer and delivers it back to the user.

In this simplified example, you will see how all 3 actors each contribute towards the answering of the user query.

If one platform owns and controls all three (🤖,📱,📚) under a centralized ecosystem (like what OpenAI is trying to do in the 2nd diagram above), then you just have to pay that one centralised platform, as the rest is internal accounting.

But if we want decentralization rather than monopolization, then each party needs to be paid, thus requiring solutions for:

1. Recording (on-chain) the contributions of each,
2. Splitting / allocating the revenues from the users
3. Enabling each to withdraw their revenues

This is the “money problem” in decentralizing AI that KIP solves.

We do this through a low gas, high efficiency Web3 infrastructure that provides for connectivity between AI value creators, a way to collect payments from the user, and a way to withdraw earnings. (We will cover this in an upcoming KIP Explainer)

Without solving the money problem first, decentralization of AI will be much more difficult, and will be far more unlikely to gain wide-spread adoption beyond a few true believers.

The “Ownership” Problem

Monetisation is merely a weak privilege, if it is not tied to true ownership.

We have all seen how accounts on centralized platforms can be shutdown, banned, shadowbanned at a moment’s notice.

KIP solves this through using blockchain tokens, specifically the ERC-3525 token (SFTs), to “wrap” the work of the AI Value Creator.

1.For data owners: the SFTs wrap vectorised knowledge bases, or a link to an encrypted raw data file to be used for model training.

2.For model makers: the SFTs could wrap an API to an off-chain model, or a set of model weights ready for sale

3.For app devs: the SFTs could wrap the front end APIs, or the prompt itself.

These SFTs serve as 'accounting entities' that can interact with each other on-chain, and record the amounts each SFT earned from a particular transaction.

By solving these problems, KIP makes it possible and easy for AI Value Creators to decentralize their work, creating the starting conditions for a vibrant and much larger decentralised AI ecosystem.

KIP is the necessary Web3 Base Layer for AI.

210

320K

Who to follow

Me gusta esperimentar cosas nuevas

Cryptov @Cries000

over 2 years ago

good protocol https://t.co/N9ppApJcSn

Cryptov @Cries000

over 2 years ago

I'm a founding member on @BagsApp 💰 Get your OG Card before the app goes live... https://t.co/MrjHrVBYHQ

Cryptov @Cries000

almost 3 years ago

Verifying my Twitter account for my #GalxeID gid:VKEf5VTbe4LCFbSFuJaSTa @Galxe https://t.co/buKL3BVQoX

Cries000 retweeted

AltLayer

@alt_layer

almost 3 years ago

Unleash the power of restaking by launching rollups. AltLayer teams up with @eigencloud, enabling restakers to launch rollups/flash layers in minutes via https://t.co/U3LZNtAZYu Join ALTITUDE Phase III to try it, available on @Galxe Info & Resources: https://t.co/zhn8kvu5aj

52K

180K

216

Cries000 retweeted

Linea.eth

@LineaBuild

about 3 years ago

🔊Presenting the Linea Voyage: a 9-week odyssey to unravel the Linea ecosystem, build L2 knowledge, and support our mainnet launch! Engage in weekly challenges and gather points to claim your exclusive Linea NFTs. Ready, set, explore! https://t.co/eGZGElZ1Kd

58K

199K

679

Cries000 retweeted

Cyber

@BuildOnCyber

about 3 years ago

Onboarding the 1M daily active users of Oasis to Web3! The 1st Prize Winner at #Connected2023 hackathon, @Oasis_Origin is the Web3 version of the multiplatform metaverse where on average an active user spends 110 mins per day with friends for sports, concerts, and board games👬

BuildOnCyber's tweet photo. Onboarding the 1M daily active users of Oasis to Web3!

The 1st Prize Winner at #Connected2023 hackathon, @Oasis_Origin is the Web3 version of the multiplatform metaverse where on average an active user spends 110 mins per day with friends for sports, concerts, and board games👬 https://t.co/Pozlrzw7wV

527

14K

13K

156K

Cries000 retweeted

ZKX @zkxprotocol

about 3 years ago

Fortune favors the 1 who mints Beacon 🖲️ Thrilled to present ZKX x @DegenScore partnership ✨ Bekanz will get access to our closed Testnet for 3 days along w/ juicy rewards 👉 Shateigashira NFTs, OG1 role & future $ZKX tokens. Read deets here- https://t.co/VRl49fBR42 1/6 🧵

zkxprotocol's tweet photo. Fortune favors the 1 who mints Beacon 🖲️

Thrilled to present ZKX x @DegenScore partnership ✨

Bekanz will get access to our closed Testnet for 3 days along w/ juicy rewards 👉 Shateigashira NFTs, OG1 role & future $ZKX tokens.

Read deets here- https://t.co/VRl49fBR42

1/6 🧵 https://t.co/XrCYNdFNYz

77K

Cries000 retweeted

Games001 @riveruf7

about 3 years ago

如何在80%的撸毛大军前，获得一手 #airdrop 空投信息，让你不再错过任何财富密码🎁 这是我花几天时间，翻了几十个网站搜集到的空投平台，内容包括：海外空投大V、海外空投TG、小而精的空投网站（建议收藏）

526

343

274

65K

Cries000 retweeted

𝟎𝐱𝐫𝐚𝐲.𝐀𝐈

@Ray80230

about 3 years ago

🤏分享一些我们在撸毛中的使用的实用工具（建议收藏）只谈实用好用类型，不做无用的大合集，只分享自己亲自体验过，有话语权的工具。工具会持续在该推文下更新，有好的工具发现了也可以评论一下。

124

910

226K

Cries000 retweeted

olimpio

@OlimpioCrypto

about 3 years ago

▓▓▓▓▓░ 91% $ARB is coming TOMORROW 🔵 🦅 A quick guide on how to be prepared: • how to claim faster • how to LP �� how to buy/sell/snipe faster Don't get front-run 🐵 👇

147

856

775K

Cries000 retweeted

Diary.AI

@Yiyixmb

about 3 years ago

融资2.28亿美刀的 @AleoHQ ，最终测试网的申请表格必填，传送门： https://t.co/B3PEbiwnmb

170

39K

Cries000 retweeted

UNICORN⚡️🦄

@UnicornBitcoin

about 3 years ago

ARB 还有两天3小时领空投还记得 Blur 空投，前8分钟 Blur 价格跌了80% ，充值CEX不到账时间就是金钱现在就可以先在 Uni 上授权 #Arb ，领取空投后少链上交互一次，网络拥堵时节省3-5分钟甚至更多 1/ Arbiscan 区块链浏览器 ARB Token 合约 https://t.co/OinFlqmyaw

UnicornBitcoin's tweet photo. ARB 还有两天3小时领空投
还记得 Blur 空投，前8分钟
Blur 价格跌了80% ，充值CEX不到账

时间就是金钱

现在就可以先在 Uni 上授权 #Arb ，领取空投后少链上交互一次，网络拥堵时节省3-5分钟甚至更多

1/ Arbiscan 区块链浏览器 ARB Token 合约
https://t.co/OinFlqmyaw https://t.co/yvYe0pvGRp

281

140

94K

Cries000 retweeted

Biteye

@BiteyeCN

about 3 years ago

听说zkSync全网最全交互指南已经出炉了？！如果你没跟上我们OP、ARB的车， zkSync的空投千万不能错过了！本期Biteye给大家带来了zkSync全网最全交互指南，优选潜力项目，还有测试币领取攻略，让你实践无忧。详情请见大图，具体网址附在下面的Thread中，需要的小伙伴转发收藏！

BiteyeCN's tweet photo. 听说zkSync全网最全交互指南已经出炉了？！如果你没跟上我们OP、ARB的车， zkSync的空投千万不能错过了！

本期Biteye给大家带来了zkSync全网最全交互指南，优选潜力项目，还有测试币领取攻略，让你实践无忧。详情请见大图，具体网址附在下面的Thread中，需要的小伙伴转发收藏！ https://t.co/YfurtkYeSL

816

558

229K

Cries000 retweeted

ZKsync @zksync

over 3 years ago

Baby Alpha is here! We are proud to announce that today we hit our Baby Alpha milestone and have begun the march to Fair Onboarding Alpha and Full Launch Alpha. https://t.co/8PBC9sRNPS What we've accomplished and what's ahead 🧵 (1/6)

288K

287K

238

Cries000 retweeted

ZKsync @zksync

over 3 years ago

We’re excited to share, we have: 1) Raised $200M and now have a total of $458M in mission-driven funding 2) Decided to go FULL OPEN SOURCE at our next milestone 3) Reserved ⅔ of a potential future token for the ecosystem 4) Formed a partnership with @OpenZeppelin 👇1/8

275K

273K

436

Cries000 retweeted

Slingshot @SlingshotCrypto

over 3 years ago

Our Getting Started Promo is still underway⚡️ Download Slingshot Wallet today and complete the steps to unlock 4 unique wallet skins. And for something extra, Like, Follow, & RT for #GalxeOAT ! https://t.co/w4pHTp3vIY

SlingshotCrypto's tweet photo. Our Getting Started Promo is still underway⚡️

Download Slingshot Wallet today and complete the steps to unlock 4 unique wallet skins.

And for something extra, Like, Follow, & RT for #GalxeOAT !

https://t.co/w4pHTp3vIY https://t.co/e9C30TtFfB

47K

46K

113

563K

Last Seen Users on Sotwe

Trends for you

Most Popular Users

Olivia

Online

✨

⭐

💫