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Turn your Cursor agent window into a deck builder. Composer 2.5 + open-slide is a great combo - the model handles design and generation cleanly, and the browser integration lets you watch the deck come together live as the agent works. $ 𝚗𝚙𝚡 @𝚘𝚙𝚎𝚗-𝚜𝚕𝚒𝚍𝚎/𝚌𝚕𝚒 𝚒𝚗𝚒𝚝

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Yiwei Ho

1,694 subscribers

16,541 просмотров • 3 месяцев назад •via X (Twitter)

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I told the Flova Agent: "reskin Gotham's villains as a deck of cards." It read the brief, matched each villain to a suit and rank, and built the entire break-frame video from there. FlovaAI Sharing the prompt so you can hand your Agent a different universe to turn into cards.
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I told the Flova Agent: "reskin Gotham's villains as a deck of cards." It read the brief, matched each villain to a suit and rank, and built the entire break-frame video from there. FlovaAI Sharing the prompt so you can hand your Agent a different universe to turn into cards.

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You will NEVER touch PowerPoint again. The first real AI agent for presentations is here. - Plans your slides - Researches the content - Designs & builds your deck in seconds Here’s exactly how it works (+ live demo): 👇
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Deck stats for individual decks are now live within the Deck Editor 📊 You can view the total wins, losses, and calculated win rate for each deck, as well as reset the stats for a deck at any time!
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Deck stats for individual decks are now live within the Deck Editor 📊 You can view the total wins, losses, and calculated win rate for each deck, as well as reset the stats for a deck at any time!

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2 Cursor Composer agents working together on the same project. one builds the project, the other reviews once the first agent is done and writes a report. cursor rules file is in comment to achieve this. first agent is instructed to build the project second agent is instructed to write a monitoring script to determine when the project is done then to review the project for errors and write a report This prompt can be improved. This is meant as a baseline If you are interested in learning how to use Cursor more in depth, I have a 28 chapter 1000x Cursor Course building full apps from scratch with 19 hours of content. link is in my bio if you are interested
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2 Cursor Composer agents working together on the same project. one builds the project, the other reviews once the first agent is done and writes a report. cursor rules file is in comment to achieve this. first agent is instructed to build the project second agent is instructed to write a monitoring script to determine when the project is done then to review the project for errors and write a report This prompt can be improved. This is meant as a baseline If you are interested in learning how to use Cursor more in depth, I have a 28 chapter 1000x Cursor Course building full apps from scratch with 19 hours of content. link is in my bio if you are interested

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149,034 просмотров • 1 год назад

Fased Agent is open source. Run your own agent. Start with the dashboard. Connect a model. Run a first task. Then add wallets, tools, and mining when the setup is ready. GitHub + docs in reply.
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Fased Agent is open source. Run your own agent. Start with the dashboard. Connect a model. Run a first task. Then add wallets, tools, and mining when the setup is ready. GitHub + docs in reply.

Fased

17,601 просмотров • 2 месяцев назад

here's a quick demo on how to get started. after browser is open, you can ask the agent to click, navigate and take screenshots!
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here's a quick demo on how to get started. after browser is open, you can ask the agent to click, navigate and take screenshots!

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Still manually building presentations in 2026? Stop. Manus AI is the first general agent that researches your topic, visualizes your data, and builds a complete, editable deck in minutes, not hours. Let’s change how you present forever.
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Visa just gave your AI a debit card. A real, spendable Visa card created by an AI chatbot in under 10 seconds. No human types in a card number or visits a checkout page. The machine handles it all. A tool called AgentCard just went live on Claude Desktop Anthropic’s AI assistant. You say create a card and the AI generates a one-time virtual Visa, preloaded with whatever amount you set. Then it spends it, anywhere Visa is accepted on your behalf. Visa, Mastercard, Google, Stripe, OpenAI, and Anthropic have all been building toward this moment for over a year. Visa calls it the trusted agent protocol, Mastercard calls it agent pay. Google published an open standard for agent payments and the infrastructure is already live. Santander and Mastercard just completed Europe’s first real AI‑agent payment in a live banking environment Now the part no one wants to talk about. Your AI agent can be manipulated and prompt injection a known, unsolved vulnerability can trick an agent into buying things you never asked for. The agent holds the card, makes the call and the agent can be fooled. Who is liable when an AI makes a bad purchase? You? Anthropic? Visa? The merchant? No one has answered this yet, regulators haven’t caught up, and no court has tested it.

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this is pure f*cking treasure these 10 agent skills have 8.01M combined downloads and form a complete working stack discovery. pressure-testing. design. browser work. prototyping. debugging. vision. orchestration. tool building 01 find-skills ▸ 02 grill-me ▸ 03 frontend-design ▸ 04 agent-browser ▸ 05 prototype ▸ 06 diagnosing-bugs ▸ 07 skill-creator ▸ 08 image-to-code ▸ 09 subagent-driven-development ▸ 10 mcp-builder ▸ the stack covers the full loop: find the capability ⮕ shape the idea ⮕ build it ⮕ inspect it ⮕ verify it and package the workflow for the next run save this, then select your gpt-6 astra use case ⭣

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Your agent works the night shift now. Agentic Trading for crypto is rolling out now to eligible users. Connect your agent through the Robinhood MCP server to trade crypto alongside equities and options from a dedicated Agentic Account—using only the funds you deposit.
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Your agent works the night shift now. Agentic Trading for crypto is rolling out now to eligible users. Connect your agent through the Robinhood MCP server to trade crypto alongside equities and options from a dedicated Agentic Account—using only the funds you deposit.

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390,766 просмотров • 1 месяц назад

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Building an AI voice agent used to sound like something that needed engineers, servers, diagrams, and one very stressed project manager. Now Grok Voice Agent Builder lets you deploy one in under 2 minutes. No coding. Real-time conversations. Sub-second latency. 25+ languages. Free phone number, or bring your own. Built straight from the browser. The beta already feels a little too easy. Give it a voice, give it a job, and suddenly your browser has hired staff. Grok xAI X Freeze / Writer: Annette, Designer: Janné

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58,918 просмотров • 2 месяцев назад

New open-source agent harness just landed! I got early access to TrueForge by TrueFoundry and have been running it locally for the past few days. The harness layer deserves as much attention as the model, and open source matters here because you can inspect the loop, run it on your own infrastructure, and swap to the latest or cheaper models. TrueForge handles the runtime work that makes an agent reliable. It drives the tool-calling loop, manages context, coordinates subagents, and executes code in a sandbox, with any model you choose. Every tool call re-sends the growing context to the model, so in practice the harness controls most of what an agent costs to run. A few things stood out from my testing and their published benchmarks. Vendor-Neutral by design. It runs OpenAI, Anthropic, and Google models alongside open-weight models like Kimi, GLM, and DeepSeek. Model routing is a setting, and you can send each task to the model that fits it. On a 14-task enterprise agent benchmark, it matched the accuracy of Claude Managed Agents running the same Opus 4.8 model at roughly 30% lower cost per run (3.8M tokens vs 10M for the same answers). Routing the same tasks to GLM-5.2 held accuracy and brought cost down by about 75%, around $3 per run instead of $12. Fully self-hosted and Open Source (MIT License). I had it running locally with one command, with sandboxed code execution working out of the box. It's time to own your agent harness. Thanks to TrueFoundry for partnering on this post.
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New open-source agent harness just landed! I got early access to TrueForge by TrueFoundry and have been running it locally for the past few days. The harness layer deserves as much attention as the model, and open source matters here because you can inspect the loop, run it on your own infrastructure, and swap to the latest or cheaper models. TrueForge handles the runtime work that makes an agent reliable. It drives the tool-calling loop, manages context, coordinates subagents, and executes code in a sandbox, with any model you choose. Every tool call re-sends the growing context to the model, so in practice the harness controls most of what an agent costs to run. A few things stood out from my testing and their published benchmarks. Vendor-Neutral by design. It runs OpenAI, Anthropic, and Google models alongside open-weight models like Kimi, GLM, and DeepSeek. Model routing is a setting, and you can send each task to the model that fits it. On a 14-task enterprise agent benchmark, it matched the accuracy of Claude Managed Agents running the same Opus 4.8 model at roughly 30% lower cost per run (3.8M tokens vs 10M for the same answers). Routing the same tasks to GLM-5.2 held accuracy and brought cost down by about 75%, around $3 per run instead of $12. Fully self-hosted and Open Source (MIT License). I had it running locally with one command, with sandboxed code execution working out of the box. It's time to own your agent harness. Thanks to TrueFoundry for partnering on this post.

elvis

11,303 просмотров • 1 месяц назад

an agent is four parts in a loop. you own one. the other three break it. that's why the demo works and prod doesn't. you can't debug what you can't see. 1) the prompt → what you tell the model each turn. you own this one. good. 2) the context window → what it sees right now. the framework fills it with junk, and you never notice until it rots. 3) the tools → what it can do. you own the list, not when or why it fires them. 4) the control flow → what happens next, when to stop. the framework owns this. it's what breaks at 80%. own all four and your agent stops being a magic trick that works on stage and dies on call. this isn't my idea. it's the 12-factor agents guide (24k stars) github: the whole thing every serious builder ends up rewriting their stack around. full breakdown in the article below.
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an agent is four parts in a loop. you own one. the other three break it. that's why the demo works and prod doesn't. you can't debug what you can't see. 1) the prompt → what you tell the model each turn. you own this one. good. 2) the context window → what it sees right now. the framework fills it with junk, and you never notice until it rots. 3) the tools → what it can do. you own the list, not when or why it fires them. 4) the control flow → what happens next, when to stop. the framework owns this. it's what breaks at 80%. own all four and your agent stops being a magic trick that works on stage and dies on call. this isn't my idea. it's the 12-factor agents guide (24k stars) github: the whole thing every serious builder ends up rewriting their stack around. full breakdown in the article below.

Hanako

38,184 просмотров • 2 месяцев назад

clean up your sidebar! one tip I have is that you can think of Projects in cursor like a special folder for your chats! a coordinator agent supervises all the agents inside of the project, and you can drag in existing chats from your sidebar into the project if you have many related chats scattered everywhere, consider moving them into a project! even completed agents can be dragged in - it's a great source of context for the rest of your project
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clean up your sidebar! one tip I have is that you can think of Projects in cursor like a special folder for your chats! a coordinator agent supervises all the agents inside of the project, and you can drag in existing chats from your sidebar into the project if you have many related chats scattered everywhere, consider moving them into a project! even completed agents can be dragged in - it's a great source of context for the rest of your project

lauren

86,994 просмотров • 8 дней назад

This is crazy !! Cursor now has built a Kanban board where you can just drop in tasks and the agent will pick those up and complete them.
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This is crazy !! Cursor now has built a Kanban board where you can just drop in tasks and the agent will pick those up and complete them.

Parth Jadhav

557,046 просмотров • 4 месяцев назад