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Meet Pollo MCP. 🧩 Your creative workflow just went agentic. Create images, videos, and audio directly inside Claude, ChatGPT, or Cursor. All the top models in one place: Seedance 2.5, GPT Image 2.5, Kling 3.0, and more. One agent. One prompt. Let Pollo handle the creation. 🎁 Your first... show more

Pollo AI

114,205 subscribers

1,181,956 görüntüleme • 1 gün önce •via X (Twitter)

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28 Yorum

Pollo AI profil fotoğrafı

Pollo AI1 gün önce

Set up your Pollo MCP now:

Wania profil fotoğrafı

Wania1 gün önce

Pollo MCP makes AI creation feel ridiculously seamless

EDDY VU profil fotoğrafı

EDDY VU1 gün önce

Can you chain outputs directly in one prompt, like passing a generated image straight into Kling for video?

Saif Ai profil fotoğrafı

Saif Ai1 gün önce

Check DM

catman profil fotoğrafı

catman1 gün önce

Use the MCP for a defined production bottleneck first, then compare output quality, latency, and credit cost against your current stack before making it part of the workflow.

Kevin profil fotoğrafı

Kevin1 gün önce

Pollo MCP makes creative workflows feel effortless.

ayzalnoor profil fotoğrafı

ayzalnoor1 gün önce

Agentic creativity is getting seriously powerful.

Flix Muller profil fotoğrafı

Flix Muller1 gün önce

This is where AI creation gets really interesting—one prompt, multiple media types, and powerful models working directly through your favorite AI assistant. 🔥 MCP could make creative workflows much more seamless.

Dania profil fotoğrafı

Dania1 gün önce

One prompt, multiple models, zero tool switching.

Ali profil fotoğrafı

Ali1 gün önce

One agent makes creation much easier.

Nawal profil fotoğrafı

Nawal1 gün önce

All creative tools in one workflow.

Mehwish kiran profil fotoğrafı

Mehwish kiran1 gün önce

Pollo MCP makes the whole creative workflow feel seamless 🔥 One prompt, multiple models.

Aynah profil fotoğrafı

Aynah1 gün önce

40 credits is a solid way to start.

Zorvia profil fotoğrafı

Zorvia1 gün önce

Agentic creation is changing creative workflows.

Jessica AI profil fotoğrafı

Jessica AI1 gün önce

That's awesome

Shinka - AI profil fotoğrafı

Shinka - AI1 gün önce

Mcp finally letting you swap models mid-workflow without context switching is the actual product.

Sarah profil fotoğrafı

Sarah1 gün önce

Agentic creation looks seriously powerful.

Avelyrah profil fotoğrafı

Avelyrah1 gün önce

One agent, endless creative possibilities

Ayat profil fotoğrafı

Ayat1 gün önce

One prompt, endless creative possibilities.

Arzoo Ai profil fotoğrafı

Arzoo Ai1 gün önce

One agent, endless creative possibilities.

Ruzaina profil fotoğrafı

Ruzaina1 gün önce

One prompt makes creation much simpler.

Calira profil fotoğrafı

Calira1 gün önce

One agent, all those models. That’s powerful.

Elisia profil fotoğrafı

Elisia1 gün önce

Great opportunity for creator

$amuel profil fotoğrafı

$amuel1 gün önce

Everything everywhere on Pollo AI

Aria profil fotoğrafı

Aria1 gün önce

Agentic creative workflows look promising.

Maha profil fotoğrafı

Maha1 gün önce

Pollo MCP makes AI creation effortless.

Zyrella profil fotoğrafı

Zyrella1 gün önce

One agent Multiple models A much smoother creative workflow.

liana profil fotoğrafı

liana1 gün önce

One prompt, endless creative possibilities.

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Meet OpenArt MCP 🧩 One connection. all the top models. Your agent does the rest. Seedance 2.0 · GPT Image 2 · Kling 3 Omni - and the only connector with access to your full OpenArt library. Works inside Claude, ChatGPT, Cursor, and all the other MCP clients. The era of agentic creative is here 👇

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Meet Fish Audio MCP. Your voices, inside whatever agent you already build in 🪄 — Claude, Codex CLI, Cursor, and Windsurf. One endpoint across the agents you already use. ✅Search best voices ✅Generate speech ✅Transcribe audio ✅Check usage Right inside your workflow. Try it 🎣

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39,236 görüntüleme • 1 ay önce

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make your CAD from Claude Code. insert image and let the mcp do its thing! this mcp can make compound shapes per sketch, do extrusions, insert fillets, create counterbore holes, non-feature script gears (working on a feature script based one), and some other cool things you’ll only know till you try lol! i forked hedless’ repo for the base mcp and added more functionalities so shoutout to hedless :) repo:

make your CAD from Claude Code. insert image and let the mcp do its thing! this mcp can make compound shapes per sketch, do extrusions, insert fillets, create counterbore holes, non-feature script gears (working on a feature script based one), and some other cool things you’ll only know till you try lol! i forked hedless’ repo for the base mcp and added more functionalities so shoutout to hedless :) repo:

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We’ve integrated a first-of-its-kind, privacy-native MCP connector layer to the Dot platform, making DotChat agentic by default. Live at → Even in private AI, the standard product is: pick a model, send a prompt, maybe enable web search and get an answer. This is simply not enough. MCPs changed what AI agents can do, but they are trapped inside: Claude Code, Cursor, local agents, API tooling and enterprise setups. Through Dot, users can now connect any MCP directly into their AI workspace, while maintaining Dot’s privacy boundary around the entire workflow. This means Dot users won’t just ask models questions, they’ll be able to connect tools, inspect onchain state, check portfolios, prepare transactions, and interact with Base from inside the chat flow. And yes, we’re starting with the big one. The Base MCP. This is a major step toward Dot’s agentic vision: powerful AI agents that can interact with the world without compromising user privacy. Check our demo below, where we integrate the updated Base MCP into Dot, and utilize it to buy $10 in $DOT, all whilst retaining full anonymity ↓

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FREE Claude Opus 4.8, GPT 5.5, GPT Image 2 and FLUX2 Pro. Berlin AI startup. No phone verification. MCP access included. The platform is Langdock. Enterprise AI workspace that passed Y Combinator and raised $3.5M. Real company, not a side project. Models are not all enabled by default - Opus 4.8 needs to be turned on manually in model settings. While you're there, enable Overusage too. Limits are solid, models are real. Here's the exact flow: → Go to langdock(.)com → Register with temp email or custom domain - no SMS verification → Enter any phone number - they don't send codes → Go to model settings and enable Opus 4.8 and other models manually → Check usage limits in the limits section MCP connection available - can connect to folders on your own PC. More details on the setup in my Telegram. End result: Claude Opus 4.8 and GPT 5.5 with MCP access to your local files. Free. One note: system prompt adds some restrictions out of the box - worth knowing before you start. Bookmark this before the free tier gets locked down.

Atenov int.

28,133 görüntüleme • 2 ay önce

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 görüntüleme • 1 yıl önce

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Everyone's testing GPT 5.6. We gave it superpowers ⚡️ Can your agent generate images, turn them into video, and stitch the whole thing together in one chat? Ours can. The new GPT 5.6 image model is a perfect match for the Sogni Creative Agent. 10 images from a single prompt. One follow-up and they're animated and cut into a finished film. Zero editing apps. The newest models, built in: Seedance 4K + Mini, Krea 2 Turbo, HappyHorse 1.1 and more. Works with any AI agent you already use. One command: npx setup-sogni-agent-skill Sogni Creative Agent. Link below 👇

Sogni.ai

353,235 görüntüleme • 2 ay önce

Today we're launching Jitsu MCP. This is an MCP server that lets your agent run your entire event pipeline directly in Claude Code, Claude Desktop, Cursor, or VS Code. Here's how it works: Add the server: claude mcp add --transport http jitsu Run this prompt: "Route my web stream into ClickHouse, but only purchase and signup events. Send a test event, check Live Events, and if anything errors, find the function in the path and fix it." Approve the OAuth prompt once, and all 26 tools are live Your agent wires the streams, watches Live Events, and rewrites a function when one of them throws. It runs the fix against a real event before shipping it, so nothing is persisted until it works. Every change lands in the audit log tagged mcp, so you can tell what the agent did from what a person did. It's all MIT licensed, and it runs against your own deployment. Jitsu is one of the fastest growing open-source CDPs with 5,000+ GitHub stars. Capturing events is unlimited and free, and delivery is free up to 200k events a month. Comment Jitsu and I'll DM you the exact prompts I run in Claude Code to build an event pipeline end to end.

Today we're launching Jitsu MCP. This is an MCP server that lets your agent run your entire event pipeline directly in Claude Code, Claude Desktop, Cursor, or VS Code. Here's how it works: Add the server: claude mcp add --transport http jitsu Run this prompt: "Route my web stream into ClickHouse, but only purchase and signup events. Send a test event, check Live Events, and if anything errors, find the function in the path and fix it." Approve the OAuth prompt once, and all 26 tools are live Your agent wires the streams, watches Live Events, and rewrites a function when one of them throws. It runs the fix against a real event before shipping it, so nothing is persisted until it works. Every change lands in the audit log tagged mcp, so you can tell what the agent did from what a person did. It's all MIT licensed, and it runs against your own deployment. Jitsu is one of the fastest growing open-source CDPs with 5,000+ GitHub stars. Capturing events is unlimited and free, and delivery is free up to 200k events a month. Comment Jitsu and I'll DM you the exact prompts I run in Claude Code to build an event pipeline end to end.

Soham Mehta

281,067 görüntüleme • 13 gün önce