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Introducing ASCII Magic v2 ✨ - A completely restructured interface focused on discoverability and performance - 100+ styles & shaders - Unique animations for each effect - 171 AI generative presets - 4K export - New stackable post processing effects - A new way to create ft. node editor... show more

Kailash

14,088 subscribers

16,689 次观看 • 1 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

32 条评论

Kailash 的头像

Kailash1 个月前

Check it out at

abhidev 的头像

abhidev1 个月前

Wooahh .. lets Gooo 🔥🔥🔥

Kailash 的头像

Kailash1 个月前

🔥🔥

Sweta 的头像

Sweta1 个月前

Woahh!!! Crazyy🔥🫶🏻

Kailash 的头像

Kailash1 个月前

yass thank you!!

The Boring Developer 的头像

The Boring Developer1 个月前

171 presets is a lot of ways to make text look weird in the best way, bookmarking this for when i inevitably need to procrastinate

Kailash 的头像

Kailash1 个月前

Thanks!

Kailash 的头像

Kailash1 个月前

🚀

Pratik 的头像

Pratik1 个月前

banger video bro!

Kailash 的头像

Kailash1 个月前

Thanks so much bro 👊

Arpit 的头像

Arpit1 个月前

Will try all

Kailash 的头像

Kailash1 个月前

letsgo! let me know how your experience was

Neelakshi 的头像

Neelakshi1 个月前

Looks so sick! Also the video is so damn smooth 🔥

Kailash 的头像

Kailash1 个月前

Thanks so muchh🔥😇

Basit A. Khan 的头像

Basit A. Khan1 个月前

that's amazing work bro

Kailash 的头像

Kailash1 个月前

Thanks Basit🫶🔥

Deep 的头像

Deep1 个月前

It is going to go huge miles for suree..... the beast asciiiii.....

Kailash 的头像

Kailash1 个月前

yessir! it should. for the amount of effort put in

Kanha Meher 的头像

Kanha Meher1 个月前

Bro's GOAT for a reason! 🔥

Kailash 的头像

Kailash1 个月前

Hahah thanks so much man!

hashim 的头像

hashim1 个月前

@BenjaminUIX Can’t wait for the mcp!

Kailash 的头像

Kailash1 个月前

@BenjaminUIX dropping in a couple days!

Shubh 的头像

Shubh1 个月前

letsgoo! keep em coming

Kailash 的头像

Kailash1 个月前

Thanks brother🔥🔥🙏 Yes lots more coming soon

Putri Karunia 的头像

Putri Karunia1 个月前

woo this is sick!! these look really good! ❤️‍🔥

Kailash 的头像

Kailash1 个月前

Thank you so much Putri✨

BenjaminUIX 的头像

BenjaminUIX1 个月前

Looks super cool 😎

Kailash 的头像

Kailash1 个月前

Thanks bro🔥🙏

dudu 的头像

dudu1 个月前

Absolute banger of a launch video for a banger product! the MCP server will be game changing

Kailash 的头像

Kailash1 个月前

Yesss! Thanks bro MCP coming soon

Adam 🌿 Unroot.design 的头像

Adam 🌿 Unroot.design1 个月前

congrats, looks great. what got cut going from v1 to v2?

Hammermann 的头像

Hammermann1 个月前

Been waiting for this!

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Tom Krcha

67,506 次观看 • 4 天前

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