正在加载视频...

视频加载失败

Internet Identity is getting an MCP server. We sat down with Arshavir Ter-Gabrielyan to understand what it is and why it matters. He starts with the problem: an AI agent runs in a cloud sandbox. No access to your device. No safe way to hold your keys. The MCP...

32,966 次观看 • 3 个月前 •via X (Twitter)

18 条评论

DFINITY Foundation 的头像
DFINITY Foundation3 个月前

Full interview with Arshavir Ter-Gabrielyan:

DFINITY Foundation 的头像
DFINITY Foundation3 个月前

Full interview with Arshavir Ter-Gabrielyan:

OhShii Labs 的头像
OhShii Labs3 个月前

Good work! This is really useful. I hope you integrate Proof of Personhood into Internet Identity soon, optionally, of course, but in a way that dApps can leverage.

ND64.ICP 的头像
ND64.ICP3 个月前

Storing credentials in a cloud sandbox is a security nightmare.

KEYGUARD 的头像
KEYGUARD3 个月前

Don’t give up guys

Yokush 的头像
Yokush3 个月前

The real constraint isn't just key management — it's establishing trust boundaries between autonomous processes and human-controlled assets. Most agent infra treats identity as a login problem when it's actually a delegation architecture problem. An agent needs to know which operations are reversible, which require explicit consent, and which should never execute without human sign-off. That's not authentication — it's policy enforcement at the workflow level. The interesting question: will this become another point solution you authorize once per session, or will it genuinely learn your permission patterns over time? The former solves today's demo. The latter compounds into actual production readiness.

sui 的头像
sui3 个月前

速来口我🤓

X2 on ICP 的头像
X2 on ICP3 个月前

Massiv 🫪

Pawzard 的头像
Pawzard3 个月前

'trusted execution environment' is doing a lot of heavy lifting there who decides whats trusted

paulrodturner 的头像
paulrodturner3 个月前

Permission to act is only the first gate. The harder part is what happens after: what the agent is forbidden to do, who decides when policy changes, and what evidence you keep. MCP identity without runtime governance still leaves you guessing. Design the rules into the build — @clawmaven

varazo al lomo 的头像
varazo al lomo3 个月前

A 0,icp ha sido un fracaso y una desilusión absoluta

Bungki (❖,❖) 的头像
Bungki (❖,❖)3 个月前

Internet Identity MCP server menang di trust, tapi kuncinya per-action permission, bukan full session. Agent boleh act, tapi blast radius tetap mini.

GORILLA 的头像
GORILLA3 个月前

本当貴方たちは一生議論してるだけで、世の中の人々に1mmも役になってないな

Realzi 的头像
Realzi3 个月前

The key question now isn't whether ICP has MCP support, it's whether developers build compelling MCP-enabled applications on ICP and whether AI clients can actually use them at scale.

Makaroni 的头像
Makaroni3 个月前

curious how the permission prompt actually feels in practice like does the user see one approve-all or a per-action dialog, bc thats where most of these fall apart

David Buxton 的头像
David Buxton3 个月前

the real challenge is how to balance between "yolo" and "everything needs to be approved" we like fine-grained mcp tool permissions and user supervision but when you try to move to sandboxes how do you maintain the permissions boundary? we do it by tying to a real responsible user but not sure that's the long-term play... what do others find works?

DosieDoughBTC 的头像
DosieDoughBTC3 个月前

Exhibit DFINITY, case closed 🚀

LogicLeap Labs 的头像
LogicLeap Labs3 个月前

Identity solves "which agent is this." The harder half is "what is it allowed to do right now." Scoped actions per tool call, plus an auditable record of what ran with what inputs — that's the part of the boundary that makes agent access operable.

相关视频

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