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a GPT alternative where each tool is provided by a third party and gets paid by the platform per use the main LLM will just be an orchestrated, selecting what tool to use when and pays it with x402 per task (search, image generation...)

Ash

10,449 subscribers

15,056 次观看 • 10 个月前 •via X (Twitter)

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GPT-Image-2 is absolutely fucking insane. I added it as a tool that Agent-S can use, and it's now generating slide decks + apps that look like they were designed by pros. This is easily the biggest step up in image generation I've seen in years. Just look at what it made:

GPT-Image-2 is absolutely fucking insane. I added it as a tool that Agent-S can use, and it's now generating slide decks + apps that look like they were designed by pros. This is easily the biggest step up in image generation I've seen in years. Just look at what it made:

Matt Shumer

159,366 次观看 • 4 个月前

The x402 eco has an incredible dashboard built by Merit Systems called x402scan! This tool leverages Coinbase Developer Platform🛡️'s ▫️Data API ▫️Embedded Wallets ▫️Onramp I used Composer to show how you can leverage 's endpoint to scrape the internet for news, and then how you could use that information to create a custom video or image, even as a multi-tool operation with an Agent.

The x402 eco has an incredible dashboard built by Merit Systems called x402scan! This tool leverages Coinbase Developer Platform🛡️'s ▫️Data API ▫️Embedded Wallets ▫️Onramp I used Composer to show how you can leverage 's endpoint to scrape the internet for news, and then how you could use that information to create a custom video or image, even as a multi-tool operation with an Agent.

Mark Anstead

16,981 次观看 • 10 个月前

I’ve been watching x402 since Coinbase 🛡️ launched it in May 2025. I did a quick research pass. Here’s the snapshot ↓ Early integrations: • CoinGecko: x402 pay-per-use access for agents (shared by Coinbase Developer Platform🛡️). • Vercel: x402 AI starter template (x402 + modern AI stack demo). • Firecrawl: x402-powered search endpoint (pay per request). • Concordium: x402 + native age verification for agent payments. • Multiversᕽ: “agentic payments” built around x402 support. • AltLayer: building an “x402 Suite” for value exchange between agents. • Solana claims x402 has processed 35M+ transactions and $10M+ volume since launch. TL;DR x402 turns HTTP 402 “Payment Required” into a payment flow. A server returns a price for a request. The client pays in stables like USDC. Then the server returns the result. → Coinbase launched x402 via Coinbase Developer Platform (May 6, 2025). → Coinbase + Cloudflare announced the x402 Foundation (Sep 23, 2025). → Cloudflare added x402 support into its Agents SDK + MCP servers. Why? - AI agents need a clean way to pay for tools. - Data, compute, APIs, services. - No accounts, cards, or subscription screens. x402 is trying to make pay-per-request feel normal. Use cases that already make sense → Paid APIs Pay per call instead of subscriptions. → AI tool calls Pay per query, per inference, per task. → Agent-to-agent payments Software paying software automatically. → Micropaywalls Pay for one endpoint, one action, one piece of content. If this takes off, stablecoins stop being a story. They become how apps and AI agents pay for things online.

I’ve been watching x402 since Coinbase 🛡️ launched it in May 2025. I did a quick research pass. Here’s the snapshot ↓ Early integrations: • CoinGecko: x402 pay-per-use access for agents (shared by Coinbase Developer Platform🛡️). • Vercel: x402 AI starter template (x402 + modern AI stack demo). • Firecrawl: x402-powered search endpoint (pay per request). • Concordium: x402 + native age verification for agent payments. • Multiversᕽ: “agentic payments” built around x402 support. • AltLayer: building an “x402 Suite” for value exchange between agents. • Solana claims x402 has processed 35M+ transactions and $10M+ volume since launch. TL;DR x402 turns HTTP 402 “Payment Required” into a payment flow. A server returns a price for a request. The client pays in stables like USDC. Then the server returns the result. → Coinbase launched x402 via Coinbase Developer Platform (May 6, 2025). → Coinbase + Cloudflare announced the x402 Foundation (Sep 23, 2025). → Cloudflare added x402 support into its Agents SDK + MCP servers. Why? - AI agents need a clean way to pay for tools. - Data, compute, APIs, services. - No accounts, cards, or subscription screens. x402 is trying to make pay-per-request feel normal. Use cases that already make sense → Paid APIs Pay per call instead of subscriptions. → AI tool calls Pay per query, per inference, per task. → Agent-to-agent payments Software paying software automatically. → Micropaywalls Pay for one endpoint, one action, one piece of content. If this takes off, stablecoins stop being a story. They become how apps and AI agents pay for things online.

Stacy Muur

12,199 次观看 • 6 个月前

If have an image of a VRChat avatar or asset and are trying to find where to get it, use Nexyy's new "Search by Image" feature.

Nanochip

127,144 次观看 • 1 个月前

The ChatGPT we all wanted is finally here. With this new AI tool, anyone can use GPT-4 for free without a ChatGPT+ subscription. Plus, they have image generation, custom personas, a shareable chat, and more👇

The ChatGPT we all wanted is finally here. With this new AI tool, anyone can use GPT-4 for free without a ChatGPT+ subscription. Plus, they have image generation, custom personas, a shareable chat, and more👇

Rowan Cheung

2,018,526 次观看 • 3 年前

We're excited to announce our partnership with Coinbase 🛡️ to bring Tavily to x402, the open protocol for internet-native agentic payments. With x402, agents can discover and use Tavily web search at runtime without an API key. Agents use a Base wallet to pay per-request and get instant results. The next billion agents will discover, pay for, and use online services fully autonomously. We’re live on and we’re just getting started. More in the comments.

We're excited to announce our partnership with Coinbase 🛡️ to bring Tavily to x402, the open protocol for internet-native agentic payments. With x402, agents can discover and use Tavily web search at runtime without an API key. Agents use a Base wallet to pay per-request and get instant results. The next billion agents will discover, pay for, and use online services fully autonomously. We’re live on and we’re just getting started. More in the comments.

Tavily

60,560 次观看 • 3 个月前

GPT-4o Image Generation to Part-based 3D Characters with PBR, in under 10 minutes ⚡️ Workflow: 🎨 Prompt GPT-4o to get an image (e.g., "3D asset of a styled character with all parts laid on a sheet for image to 3D") 🧩 Use CSM AI's Part-based tool to generate parts and assemble in Blender.

GPT-4o Image Generation to Part-based 3D Characters with PBR, in under 10 minutes ⚡️ Workflow: 🎨 Prompt GPT-4o to get an image (e.g., "3D asset of a styled character with all parts laid on a sheet for image to 3D") 🧩 Use CSM AI's Part-based tool to generate parts and assemble in Blender.

Common Sense Machines

442,012 次观看 • 1 年前

I’m excited to kick off the first of our short courses focused on agents, starting with Building Agentic RAG with LlamaIndex, taught by Jerry Liu, CEO of LlamaIndex 🦙. This covers an important shift in RAG (retrieval augmented generation), in which rather than having the developer write explicit routines to retrieve information to feed into the LLM context, we instead build a RAG agent that that has access to tools for retrieving information. This lets the agent decide what information to fetch, and enables it to answer more complex questions using multi-step reasoning. In detail, you'll learn about: - Routing: Where your agent will use decision-making to route requests to multiple tools. - Tool Use: Where you'll create an interface for agents to select what tool (function call) to use as well as generate the right arguments. - Multi-step reasoning with tool use: Where you'll use an LLM to carry out multiple steps of reasoning, while retaining memory throughout the process. You’ll also learn how to step through what your agent is doing to debug and improve it iteratively. It’s an exciting time to build agents. Sign up and get started here!

I’m excited to kick off the first of our short courses focused on agents, starting with Building Agentic RAG with LlamaIndex, taught by Jerry Liu, CEO of LlamaIndex 🦙. This covers an important shift in RAG (retrieval augmented generation), in which rather than having the developer write explicit routines to retrieve information to feed into the LLM context, we instead build a RAG agent that that has access to tools for retrieving information. This lets the agent decide what information to fetch, and enables it to answer more complex questions using multi-step reasoning. In detail, you'll learn about: - Routing: Where your agent will use decision-making to route requests to multiple tools. - Tool Use: Where you'll create an interface for agents to select what tool (function call) to use as well as generate the right arguments. - Multi-step reasoning with tool use: Where you'll use an LLM to carry out multiple steps of reasoning, while retaining memory throughout the process. You’ll also learn how to step through what your agent is doing to debug and improve it iteratively. It’s an exciting time to build agents. Sign up and get started here!

Andrew Ng

297,131 次观看 • 2 年前

Our first short course with Anthropic! Building Towards Computer Use with Anthropic. This teaches you to build an LLM-based agent that uses a computer interface by generating mouse clicks and keystrokes. Computer Use is an important, emerging capability for LLMs that will let AI agents do many more tasks than were possible before, since it lets them interact with interfaces designed for humans to use, rather than only tools that provide explicit API access. I hope you will enjoy learning about it! This course is taught by Anthropic's Head of Curriculum, Colt_Steele. You'll learn to apply image reasoning and tool use to "use" a computer as follows: a model processes an image of the screen, analyzes it to understand what's going on, and navigates the computer via mouse clicks and keystrokes. This course goes through the key building blocks, and culminates in a demo of an AI assistant that uses a web browser to search for a research paper, downloads the PDF, and finally summarizes the paper for you. In detail, you’ll: - Learn about Anthropic's family of models, when to use which one, and make API requests to Claude - Use multi-modal prompts that combine text and image content blocks, and also work with streaming responses - Improve your prompting by using prompt templates, using XML to structure prompts, and providing examples - Implement prompt caching to reduce cost and latency - Apply tool-use to build a chatbot that can call different tools to respond to queries - See all these building blocks come together in Computer Use demo Please sign up here:

Our first short course with Anthropic! Building Towards Computer Use with Anthropic. This teaches you to build an LLM-based agent that uses a computer interface by generating mouse clicks and keystrokes. Computer Use is an important, emerging capability for LLMs that will let AI agents do many more tasks than were possible before, since it lets them interact with interfaces designed for humans to use, rather than only tools that provide explicit API access. I hope you will enjoy learning about it! This course is taught by Anthropic's Head of Curriculum, Colt_Steele. You'll learn to apply image reasoning and tool use to "use" a computer as follows: a model processes an image of the screen, analyzes it to understand what's going on, and navigates the computer via mouse clicks and keystrokes. This course goes through the key building blocks, and culminates in a demo of an AI assistant that uses a web browser to search for a research paper, downloads the PDF, and finally summarizes the paper for you. In detail, you’ll: - Learn about Anthropic's family of models, when to use which one, and make API requests to Claude - Use multi-modal prompts that combine text and image content blocks, and also work with streaming responses - Improve your prompting by using prompt templates, using XML to structure prompts, and providing examples - Implement prompt caching to reduce cost and latency - Apply tool-use to build a chatbot that can call different tools to respond to queries - See all these building blocks come together in Computer Use demo Please sign up here:

Andrew Ng

170,425 次观看 • 1 年前

I cant believe this guy just made a permanent solution to context bloat and open sourced it all! when we tested this tool (Context+) for solving an issue on the OpenCode repository, the agent using this tool used ~6.5k fewer tokens, found the code and fixed it in half the time! the results were surprising: 6 to 10k tokens saved per prompt, completed task in ~2 minutes while the agent running without the tool took ~4 mins for the same and got stuck in loops bro built an entire beast by using all the modern tools that we could think of: undo trees, semantic search by meaning (by haskellforall), advanced refactoring, blast radius, advanced file context trees, restore points... i can keep going on semantic code search and context trees are the future of agentic coding and this tool proves it the feature i loved the most is semantic search and how it gets things done 2x faster with least possible tokens it makes an agent that actually knows what it’s doing and not just guessing, it makes meaning from your code similar to RAG. if you aren't optimizing your context, you are just burning money the developer says this tool is still under development, it can have unexpected behavior and the docs need updates but the video shows the reality of how fast it can be github: get here:

I cant believe this guy just made a permanent solution to context bloat and open sourced it all! when we tested this tool (Context+) for solving an issue on the OpenCode repository, the agent using this tool used ~6.5k fewer tokens, found the code and fixed it in half the time! the results were surprising: 6 to 10k tokens saved per prompt, completed task in ~2 minutes while the agent running without the tool took ~4 mins for the same and got stuck in loops bro built an entire beast by using all the modern tools that we could think of: undo trees, semantic search by meaning (by haskellforall), advanced refactoring, blast radius, advanced file context trees, restore points... i can keep going on semantic code search and context trees are the future of agentic coding and this tool proves it the feature i loved the most is semantic search and how it gets things done 2x faster with least possible tokens it makes an agent that actually knows what it’s doing and not just guessing, it makes meaning from your code similar to RAG. if you aren't optimizing your context, you are just burning money the developer says this tool is still under development, it can have unexpected behavior and the docs need updates but the video shows the reality of how fast it can be github: get here:

forloop

226,271 次观看 • 6 个月前

Computer use, the browser tool, the Skills API, and the Files API are now generally available on the Claude Platform. Automate work in applications that have no API with fewer round trips per task, and build Claude Managed Agents on versioned skills and reusable files.

Computer use, the browser tool, the Skills API, and the Files API are now generally available on the Claude Platform. Automate work in applications that have no API with fewer round trips per task, and build Claude Managed Agents on versioned skills and reusable files.

ClaudeDevs

1,754,332 次观看 • 15 天前

Introducing Manus image generation. Manus doesn’t just generate images. It understands your intent, plans a solution, and knows how to effectively use image generation along with other tools to accomplish your task.

Introducing Manus image generation. Manus doesn’t just generate images. It understands your intent, plans a solution, and knows how to effectively use image generation along with other tools to accomplish your task.

Manus

178,164 次观看 • 1 年前

Serno just dropped and it's the first AI tool that doesn't feel like a chatbot. It's a canvas. With a team of expert personas. Who debate before they answer you. Claude, GPT, and Gemini all run the same question side by side. Here's how to use it: 👇

Serno just dropped and it's the first AI tool that doesn't feel like a chatbot. It's a canvas. With a team of expert personas. Who debate before they answer you. Claude, GPT, and Gemini all run the same question side by side. Here's how to use it: 👇

Hasan Toor

66,443 次观看 • 4 个月前

3/ Create a dynamic video using Fliki AI tool Go to Fliki and paste the text generated by Chat GPT into the tool. This will create a video with a professional avatar presenting the product comparison. Here’s an example of a final video you can create in just 10 minutes:

William | IA

242,168 次观看 • 1 年前

Today I'm helping showcase a new ninja tool viewer website made by Yeo 𒉭 with feedback provided by me. This website is to help Naruto Storm Connections players so they'll never have to run onto the game because they didn't have those in the moment answers they were in need of on a character's ninja tool capabilities. The video gives you a simulation of the in game character select where you can sort through character iterations, view each ninja tool set a character possesses, select an item to get a partial match for who is able to use it in game, see the details of each item and their cooldown times.

Today I'm helping showcase a new ninja tool viewer website made by Yeo 𒉭 with feedback provided by me. This website is to help Naruto Storm Connections players so they'll never have to run onto the game because they didn't have those in the moment answers they were in need of on a character's ninja tool capabilities. The video gives you a simulation of the in game character select where you can sort through character iterations, view each ninja tool set a character possesses, select an item to get a partial match for who is able to use it in game, see the details of each item and their cooldown times.

Uchiha In America

22,761 次观看 • 2 年前

you can now tip your favorite posts directly via x402 on fetchr. powered by Bankr x402 cloud. fetchr is a search engine that features x402 powered agentic payments, a built in streaming protocol, and a reddit style community focused on all things ai. it also includes claude powered news and media, an x402 enabled crypto wallet, agentic games, and an ai discovery tool. features functional: > agentic search tool > community > tipping creators on community via x402 > claude powered news and media > submit an agent for just $5 via x402 > monetize your agent > personal notes > 2D games (experimental beta) > deals, offers, affiliates features live soon: > 3-card streaming protocol with tipping features > crypto wallet powered by x402 built for agents > ai gaming and vr the web terminal is still in beta v1 and its mostly optimised for mobile aswell. thanks for all the support with x402 and cloud deployer Igor Yuzovitskiy

you can now tip your favorite posts directly via x402 on fetchr. powered by Bankr x402 cloud. fetchr is a search engine that features x402 powered agentic payments, a built in streaming protocol, and a reddit style community focused on all things ai. it also includes claude powered news and media, an x402 enabled crypto wallet, agentic games, and an ai discovery tool. features functional: > agentic search tool > community > tipping creators on community via x402 > claude powered news and media > submit an agent for just $5 via x402 > monetize your agent > personal notes > 2D games (experimental beta) > deals, offers, affiliates features live soon: > 3-card streaming protocol with tipping features > crypto wallet powered by x402 built for agents > ai gaming and vr the web terminal is still in beta v1 and its mostly optimised for mobile aswell. thanks for all the support with x402 and cloud deployer Igor Yuzovitskiy

0xExodus

16,133 次观看 • 5 个月前

Knowing where the yield comes from isn't enough. Knowing what risk comes with it is what tells the story. Risk Graph is your real-time risk tool for mapping dependencies across assets, protocols, and chains. Agentic-first, pay-per-request via x402, settled on Base.

Knowing where the yield comes from isn't enough. Knowing what risk comes with it is what tells the story. Risk Graph is your real-time risk tool for mapping dependencies across assets, protocols, and chains. Agentic-first, pay-per-request via x402, settled on Base.

YO

14,328 次观看 • 2 个月前