正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

Introducing Walrus Console. One place to store, organize, and connect the data your AI apps and agents rely on, without managing the underlying infrastructure. → Managed platform for files & agent context → Native MCP + developer API → Private & encrypted by default

Walrus

347,187 subscribers

68,014 次观看 • 6 天前 •via X (Twitter)

教育健康养生科学技术

Anya Rossi• Live Now

Private livecam show

34 条评论

Adeniyi.sui 的头像

Adeniyi.sui6 天前

🔥

BLUB 的头像

BLUB6 天前

W

eazitech 的头像

eazitech6 天前

Fortunate to have been among the beta testers of this great product ngl that's the first i have seen in Web3 Super Excited about this one

Philose 🦭 的头像

Philose 🦭6 天前

Nice stuff

StorX - Storage DePIN Powered by XDC 的头像

StorX - Storage DePIN Powered by XDC6 天前

AI agents are about to generate a lot of data. The real test of any storage layer isn't the console, it's who holds the data and what happens when its inaccessible drops. Interesting week for the category.

0xSammy 的头像

0xSammy6 天前

Interesting

Jobiak | WID 的头像

Jobiak | WID6 天前

Gud tek 🔥

Chief 🐧 的头像

Chief 🐧6 天前

@eazitechh Fire 🔥

CK 的头像

CK6 天前

This is very cool

MaxOut.Sui 的头像

MaxOut.Sui6 天前

Walrus Cook 💪🔥

Lionhead🦁 的头像

Lionhead🦁6 天前

.@WalrusProtocol Walrus Console looks great. We're building an AI money agent where each user's financial memory is sealed to their own keys with Seal. Can Console uploads use owner-held/Seal keys instead of managed encryption? And is there testnet + a server API?

seridarivus 🇷🇴 的头像

seridarivus 🇷🇴6 天前

Agents fail far more often on stale or missing context than on bad reasoning, so a clean data layer is underrated infra.

Hulk 的头像

Hulk6 天前

Nice 🎉

StakeCraft 的头像

StakeCraft6 天前

Walrus Console ships native MCP, so agents get memory without a custom storage layer. Who owns the keys when context is encrypted by default?

安叫兽|Bird🕊️ 🔶 BNB 的头像

安叫兽|Bird🕊️ 🔶 BNB5 天前

不用自己折腾底层存储这点，对做 AI 应用的人挺省心的。

Kien Trinh 的头像

Kien Trinh5 天前

Nghe như có thêm một chỗ để cất dữ liệu gọn gàng cho mấy con AI khỏi lộn xộn, mà không phải đụng tới hạ tầng bên dưới hả 😄

Martins 的头像

Martins6 天前

🔥

Prajwal⛺ 的头像

Prajwal⛺6 天前

@WalrusProtocol Hey team, are we good to expect 2nd airdrop reward in upcoming bull?

Peter Orban 的头像

Peter Orban6 天前

One home for files and agent context with native MCP baked in is exactly the plumbing agent builders keep hand-rolling. Love seeing infra get this focused on agents!

Prabahan Kalita 的头像

Prabahan Kalita6 天前

Native MCP is the right call. The pain with agent context usually isn't storing it, it's knowing which version the agent saw when it made a bad call. If Console gives you that audit trail, that's the real sell!

AKKI 🧙 | 的头像

AKKI 🧙 |6 天前

That's very impressive 🔥🔥🔥

Darks1de🔌📺🤖 的头像

Darks1de🔌📺🤖5 天前

🔥🔥🔥

Lionhead🦁 的头像

Lionhead🦁6 天前

Am on it

Jatin Garg 的头像

Jatin Garg6 天前

storing data without managing infrastructure makes sense for prototypes, but how does Walrus handle data consistency when an agent needs to roll back a multi-step operation?

Doncurrent 的头像

Doncurrent6 天前

Next level shii Cooking 🔥🔥🔥

Whitelist Media 的头像

Whitelist Media6 天前

We are glad to see you here

Haki 的头像

Haki5 天前

walrus console: finally a onestop for Sui devs.

Sam🔸 的头像

Sam🔸6 天前

🔥

CLINTON💲 的头像

CLINTON💲6 天前

This is cool

Grossbel12 🪖 🦭 的头像

Grossbel12 🪖 🦭6 天前

Huge I am testing now

Omega 的头像

Omega6 天前

us console?

Macro Bombastic 的头像

Macro Bombastic6 天前

managed infra for agent context is exactly what we needed tbh

Jack LD 🦭/acc 的头像

Jack LD 🦭/acc6 天前

This could be a game changer

BIGWILS | web 3 Growth 🚀 的头像

BIGWILS | web 3 Growth 🚀6 天前

Walrus console is some next gen

相关视频

SolRouter beta is live on devnet. SDK for developers. MCP support for agents. And our own agent framework with encrypted token research, multi-agent orchestration, and private inference on decentralized GPUs. Private AI infrastructure for Solana. <encrypted> by default.

SolRouter beta is live on devnet. SDK for developers. MCP support for agents. And our own agent framework with encrypted token research, multi-agent orchestration, and private inference on decentralized GPUs. Private AI infrastructure for Solana. <encrypted> by default.

Router 🌊

48,269 次观看 • 7 个月前

$Data encryption is now live on Veilnet. Available in @veilnet/mcp-server@0.3.0. Any blob — file, dataset, config, recovery codes, contract draft, research note, agent memory — can now be encrypted on your device, shipped to Veilnet as ciphertext, and fetched back later without the decryption key ever leaving your machine. Three new MCP tools are live: data_store({ label, data }) encrypt + store data on Veilnetdata_fetch({ id }) fetch + decrypt locallydata_list() list stored ids With labels kept encrypted, the flow is simple: user data → encrypted locally → ciphertext stored on Veilnet → key remains on user device → agent fetches when needed → local decryption onlyVeilnet sees encrypted blobs. Your AI agent gets persistent private memory and secure storage.The provider does not need the raw data. This works in Claude Code, Claude Desktop, Cursor, Continue, and Cline. Install: npm i -g @veilnet/mcp-server Bootstrap your key at: → Agent tab Then ask any MCP-aware agent:“store this encrypted on Veilnet.”Docs:docs.veilnet.toPrivate prompts protected what users ask. Private MCP protected what agents run. Wallet Context protected what agents read. Encrypted data storage now protects what agents remember and reuse. All powered by $VEILNET$

Data encryption is now live on Veilnet. Available in @veilnet/[email protected]. Any blob — file, dataset, config, recovery codes, contract draft, research note, agent memory — can now be encrypted on your device, shipped to Veilnet as ciphertext, and fetched back later without the decryption key ever leaving your machine. Three new MCP tools are live: data_store({ label, data }) encrypt + store data on Veilnetdata_fetch({ id }) fetch + decrypt locallydata_list() list stored ids With labels kept encrypted, the flow is simple: user data → encrypted locally → ciphertext stored on Veilnet → key remains on user device → agent fetches when needed → local decryption onlyVeilnet sees encrypted blobs. Your AI agent gets persistent private memory and secure storage.The provider does not need the raw data. This works in Claude Code, Claude Desktop, Cursor, Continue, and Cline. Install: npm i -g @veilnet/mcp-server Bootstrap your key at: → Agent tab Then ask any MCP-aware agent:“store this encrypted on Veilnet.”Docs:docs.veilnet.toPrivate prompts protected what users ask. Private MCP protected what agents run. Wallet Context protected what agents read. Encrypted data storage now protects what agents remember and reuse. All powered by $VEILNET

Veilnet

11,761 次观看 • 4 个月前

- AI agent that lives on your web page - Animated React components - Deploy apps without managing infrastructure - Free animated/3D icons designer - Open-source context API to search, scrape

- AI agent that lives on your web page - Animated React components - Deploy apps without managing infrastructure - Free animated/3D icons designer - Open-source context API to search, scrape

Csaba Kissi

37,517 次观看 • 1 个月前

Introducing Runway Dev MCP. Connect to the Runway developer platform from inside your coding agent to build, debug and manage. Set up Model Routers, lookup tasks, and more all without leaving your agent. Start building at the link below.

Introducing Runway Dev MCP. Connect to the Runway developer platform from inside your coding agent to build, debug and manage. Set up Model Routers, lookup tasks, and more all without leaving your agent. Start building at the link below.

Runway

20,761 次观看 • 1 个月前

Let me introduce you to Walrus 🦭/acc a developer platform building data markets for the AI era, powered by Sui 🦭 Offering decentralized, cost-efficient storage for large data like AI datasets all programmable on-chain. Paired with Seal, it adds encrypted access control so developers can build secure, AI-native apps at scale!

Let me introduce you to Walrus 🦭/acc a developer platform building data markets for the AI era, powered by Sui 🦭 Offering decentralized, cost-efficient storage for large data like AI datasets all programmable on-chain. Paired with Seal, it adds encrypted access control so developers can build secure, AI-native apps at scale!

Will

10,580 次观看 • 8 个月前

Introducing Lab: A full-stack platform for training your own agentic models Build, evaluate and train on your own environments at scale without managing the underlying infrastructure. Giving everyone their own frontier AI lab.

Introducing Lab: A full-stack platform for training your own agentic models Build, evaluate and train on your own environments at scale without managing the underlying infrastructure. Giving everyone their own frontier AI lab.

Prime Intellect

3,155,803 次观看 • 8 个月前

Introducing an idea-to-GenAI app platform, where AI agents do the hard work Draw your ideas, connect to AI agents and ship to production on day 1 Powered by tldraw, @YouSearchEngine API, Mistral AI 🧵1/10 Today is day 1, let's ship some apps together

Introducing an idea-to-GenAI app platform, where AI agents do the hard work Draw your ideas, connect to AI agents and ship to production on day 1 Powered by tldraw, @YouSearchEngine API, Mistral AI 🧵1/10 Today is day 1, let's ship some apps together

Shaun.AGI

104,030 次观看 • 2 年前

Agent A: your top 1% marketer 🧠 Rely on the only AI marketing agent with unrestricted access to Ahrefs data and pre-built marketing skills to act on it. Connect your business apps and let it do the marketing for you.

Agent A: your top 1% marketer 🧠 Rely on the only AI marketing agent with unrestricted access to Ahrefs data and pre-built marketing skills to act on it. Connect your business apps and let it do the marketing for you.

Ahrefs

48,655 次观看 • 5 个月前

Context V2 is live, the prediction market built for agents. Let your agent trade, build PM apps, and launch markets all through one API. Designed for the agentic economy.

Context V2 is live, the prediction market built for agents. Let your agent trade, build PM apps, and launch markets all through one API. Designed for the agentic economy.

Context

24,558 次观看 • 7 个月前

Our CSO Advait on encrypted, user-owned memory for Al agents. Your data shouldn't be harvested and resold without consent. We're building a system where memory is encrypted by default, permissioned by design, and owned by the user.

Our CSO Advait on encrypted, user-owned memory for Al agents. Your data shouldn't be harvested and resold without consent. We're building a system where memory is encrypted by default, permissioned by design, and owned by the user.

OpenGradient (∇, ∇)

171,424 次观看 • 7 个月前

AI agents need more than intelligence. They need trusted business context. With Google Gemini Managed Agents now supporting remote MCP servers, developers can connect Gemini agents to governed content in Box, generate outputs from real enterprise files, and save the work back where teams already collaborate. Box MCP Server + Gemini API in action.👇

AI agents need more than intelligence. They need trusted business context. With Google Gemini Managed Agents now supporting remote MCP servers, developers can connect Gemini agents to governed content in Box, generate outputs from real enterprise files, and save the work back where teams already collaborate. Box MCP Server + Gemini API in action.👇

Box

458,101 次观看 • 3 个月前

Introducing the Agent Store: one place to discover agents you can text. No downloads, no new apps, just open Messages. Built with our friends Soar, the Agent Store is a marketplace where anyone can discover and interact with conversational agents like Soar, Tomo, and Noscroll in one place.

Introducing the Agent Store: one place to discover agents you can text. No downloads, no new apps, just open Messages. Built with our friends Soar, the Agent Store is a marketplace where anyone can discover and interact with conversational agents like Soar, Tomo, and Noscroll in one place.

Elliott Potter

35,009 次观看 • 2 个月前

Introducing LI.FI’s API for Agentic Commerce, a one-shot integration for building agentic workflows onchain. We’ve released a complete toolkit for AI agents: MCP server, agent skills, and agent-native documentation. Market access for agentic commerce is now solved.

Introducing LI.FI’s API for Agentic Commerce, a one-shot integration for building agentic workflows onchain. We’ve released a complete toolkit for AI agents: MCP server, agent skills, and agent-native documentation. Market access for agentic commerce is now solved.

LI.FI

173,810 次观看 • 7 个月前

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

142,256 次观看 • 1 年前

Introducing One. The simplest way to connect and monitor AI agents to hundreds of apps. And we’re open-sourcing the world’s largest integration database powering it: 47,000 agentic actions across 250+ apps. RT + comment “One” for access & 1M free API requests/month.

Introducing One. The simplest way to connect and monitor AI agents to hundreds of apps. And we’re open-sourcing the world’s largest integration database powering it: 47,000 agentic actions across 250+ apps. RT + comment “One” for access & 1M free API requests/month.

Moe

168,448 次观看 • 6 个月前

New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:

New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:

Andrew Ng

82,067 次观看 • 1 年前

Introducing the One Remote MCP. 592+ apps. 94,357+ tools. 4 tools in your context and a flat 3,000 tokens. It's the MCP that connects your AI to everything. One URL, nothing to install. Try it out now:

Introducing the One Remote MCP. 592+ apps. 94,357+ tools. 4 tools in your context and a flat 3,000 tokens. It's the MCP that connects your AI to everything. One URL, nothing to install. Try it out now:

Moe

24,170 次观看 • 2 个月前

For the past 50 years, every operating system was built for one user: the human. Introducing Open Swarm, the operating system built for AI. Up to 2x faster and 3.5x cheaper. Your apps, files, teams, and agents in one place. The next 50 years will be built for agents.

For the past 50 years, every operating system was built for one user: the human. Introducing Open Swarm, the operating system built for AI. Up to 2x faster and 3.5x cheaper. Your apps, files, teams, and agents in one place. The next 50 years will be built for agents.

Open Swarm

206,477 次观看 • 4 天前