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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 views • 1 month ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

32 Comments

Kailash1 month ago

Check it out at

abhidev1 month ago

Wooahh .. lets Gooo 🔥🔥🔥

Kailash1 month ago

🔥🔥

Sweta1 month ago

Woahh!!! Crazyy🔥🫶🏻

Kailash1 month ago

yass thank you!!

The Boring Developer1 month ago

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

Kailash1 month ago

Thanks!

Kailash1 month ago

🚀

Pratik1 month ago

banger video bro!

Kailash1 month ago

Thanks so much bro 👊

Arpit1 month ago

Will try all

Kailash1 month ago

letsgo! let me know how your experience was

Neelakshi1 month ago

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

Kailash1 month ago

Thanks so muchh🔥😇

Basit A. Khan1 month ago

that's amazing work bro

Kailash1 month ago

Thanks Basit🫶🔥

Deep1 month ago

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

Kailash1 month ago

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

Kanha Meher1 month ago

Bro's GOAT for a reason! 🔥

Kailash1 month ago

Hahah thanks so much man!

hashim1 month ago

@BenjaminUIX Can’t wait for the mcp!

Kailash1 month ago

@BenjaminUIX dropping in a couple days!

Shubh1 month ago

letsgoo! keep em coming

Kailash1 month ago

Thanks brother🔥🔥🙏 Yes lots more coming soon

Putri Karunia1 month ago

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

Kailash1 month ago

Thank you so much Putri✨

BenjaminUIX1 month ago

Looks super cool 😎

Kailash1 month ago

Thanks bro🔥🙏

dudu1 month ago

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

Kailash1 month ago

Yesss! Thanks bro MCP coming soon

Adam 🌿 Unroot.design1 month ago

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

Hammermann1 month ago

Been waiting for this!

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66,914 views • 4 days ago

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 views • 1 year ago