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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...

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Kailash

14,088 subscribers

16,689 просмотров • 1 месяц назад •via X (Twitter)

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Комментарии: 32

Фото профиля Kailash
Kailash1 месяц назад

Check it out at

Фото профиля abhidev
abhidev1 месяц назад

Wooahh .. lets Gooo 🔥🔥🔥

Фото профиля Kailash
Kailash1 месяц назад

🔥🔥

Фото профиля Sweta
Sweta1 месяц назад

Woahh!!! Crazyy🔥🫶🏻

Фото профиля Kailash
Kailash1 месяц назад

yass thank you!!

Фото профиля The Boring Developer
The Boring Developer1 месяц назад

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

Фото профиля Kailash
Kailash1 месяц назад

Thanks!

Фото профиля Kailash
Kailash1 месяц назад

🚀

Фото профиля Pratik
Pratik1 месяц назад

banger video bro!

Фото профиля Kailash
Kailash1 месяц назад

Thanks so much bro 👊

Фото профиля Arpit
Arpit1 месяц назад

Will try all

Фото профиля Kailash
Kailash1 месяц назад

letsgo! let me know how your experience was

Фото профиля Neelakshi
Neelakshi1 месяц назад

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

Фото профиля Kailash
Kailash1 месяц назад

Thanks so muchh🔥😇

Фото профиля Basit A. Khan
Basit A. Khan1 месяц назад

that's amazing work bro

Фото профиля Kailash
Kailash1 месяц назад

Thanks Basit🫶🔥

Фото профиля Deep
Deep1 месяц назад

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

Фото профиля Kailash
Kailash1 месяц назад

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

Фото профиля Kanha Meher
Kanha Meher1 месяц назад

Bro's GOAT for a reason! 🔥

Фото профиля Kailash
Kailash1 месяц назад

Hahah thanks so much man!

Фото профиля hashim
hashim1 месяц назад

@BenjaminUIX Can’t wait for the mcp!

Фото профиля Kailash
Kailash1 месяц назад

@BenjaminUIX dropping in a couple days!

Фото профиля Shubh
Shubh1 месяц назад

letsgoo! keep em coming

Фото профиля Kailash
Kailash1 месяц назад

Thanks brother🔥🔥🙏 Yes lots more coming soon

Фото профиля Putri Karunia
Putri Karunia1 месяц назад

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

Фото профиля Kailash
Kailash1 месяц назад

Thank you so much Putri✨

Фото профиля BenjaminUIX
BenjaminUIX1 месяц назад

Looks super cool 😎

Фото профиля Kailash
Kailash1 месяц назад

Thanks bro🔥🙏

Фото профиля dudu
dudu1 месяц назад

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

Фото профиля Kailash
Kailash1 месяц назад

Yesss! Thanks bro MCP coming soon

Фото профиля Adam 🌿 Unroot.design
Adam 🌿 Unroot.design1 месяц назад

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

Фото профиля Hammermann
Hammermann1 месяц назад

Been waiting for this!

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Google AI

13,954,807 просмотров • 5 месяцев назад

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!
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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 просмотров • 1 год назад

Today we are re-launching shaders in We call them Vibe Shaders. There's nothing else like them. They live next to your .pen files as GLSL files and can be easily vibecoded right on the canvas. Built by designers for designers. They deserved a special Shader Mode, a focused mode to finetune the uniforms directly in the context of your design. In addition to the classic Shader Fills, we've also added Shader Effects. This allows for incredible creative flexibility, as it applies the effect on a whole object tree that's part of a particular frame and you can change everything live, no hustling with textures. Additionally we are also launching Background Shaders, they sample the texture from everything below the object. Imagine creating your own custom background blur or a partial dither effect for objects below a frame. You can also create shaders with 3D transformations to vector logos or build custom shader brushes for vectors paths. Forget having just a fixed set of shaders, this is next level and only possible, because we have a full control over our custom renderer. We also automatically compute the SDF (signed distance field) in realtime, so vectors that have applied shaders can be changed directly on canvas without the need to re-export a new SDF file, an incredible time-saver. Look for the samples in Goodies, which has been updated with a ton of new examples. Or simply pair pen.dev with the latest models, drop in a bunch of images, ask agent to build you a shader and it's guaranteed you will feel an explosion of your creative superpowers!
0:42

Today we are re-launching shaders in We call them Vibe Shaders. There's nothing else like them. They live next to your .pen files as GLSL files and can be easily vibecoded right on the canvas. Built by designers for designers. They deserved a special Shader Mode, a focused mode to finetune the uniforms directly in the context of your design. In addition to the classic Shader Fills, we've also added Shader Effects. This allows for incredible creative flexibility, as it applies the effect on a whole object tree that's part of a particular frame and you can change everything live, no hustling with textures. Additionally we are also launching Background Shaders, they sample the texture from everything below the object. Imagine creating your own custom background blur or a partial dither effect for objects below a frame. You can also create shaders with 3D transformations to vector logos or build custom shader brushes for vectors paths. Forget having just a fixed set of shaders, this is next level and only possible, because we have a full control over our custom renderer. We also automatically compute the SDF (signed distance field) in realtime, so vectors that have applied shaders can be changed directly on canvas without the need to re-export a new SDF file, an incredible time-saver. Look for the samples in Goodies, which has been updated with a ton of new examples. Or simply pair pen.dev with the latest models, drop in a bunch of images, ask agent to build you a shader and it's guaranteed you will feel an explosion of your creative superpowers!

Tom Krcha

66,914 просмотров • 4 дней назад