Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

Introducing Bubble Computer Computer unifies every AI capability into one intelligent workspace where work simply gets done. It can create presentations, build websites, analyze data, and deliver any project end-to-end.

DAPPOS

163,049 subscribers

32,730 görüntüleme • 4 ay önce •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

🚨BREAKING: China just released an AI employee that runs on your own computer. It can search the web, write code, build websites, make slides, and create videos. All by itself. It even comes with its own built-in computer. 100% Open Source.

🚨BREAKING: China just released an AI employee that runs on your own computer. It can search the web, write code, build websites, make slides, and create videos. All by itself. It even comes with its own built-in computer. 100% Open Source.

Hasan Toor

20,648 görüntüleme • 4 ay önce

🚨 China has released an AI employee that runs 100% locally. It can do research, code, build websites, create slide decks, and generate videos.. all by itself. And it comes with its own computer. 100% Open Source.

🚨 China has released an AI employee that runs 100% locally. It can do research, code, build websites, create slide decks, and generate videos.. all by itself. And it comes with its own computer. 100% Open Source.

Alvaro Cintas

456,362 görüntüleme • 4 ay önce

ByteDance just open sourced an AI SuperAgent that can research, code, build websites, create slide decks, and generate videos. All by itself. DeerFlow 2.0 (27K+ GitHub stars ⭐️), an AI system acting like an autonomous employee with its own computer workspace to research and code. Standard chatbots only generate text and forget your preferences. DeerFlow solves this by giving the AI an isolated virtual computer environment where it safely runs programs. When given a massive task, the main program creates several smaller AI assistants to work simultaneously. It also saves your past workflows so it gets smarter about your needs. DeerFlow is model-agnostic — it works with any LLM that implements the OpenAI-compatible API. Fully supports running local models on your own computer using tools like Ollama. An example - you ask for research on the top 10 AI startups in 2026 for a presentation, the lead agent in DeerFlow breaks that big job into smaller sub-tasks. It assigns one sub-agent to look into each company, another to find funding details, and a third to handle competitor analysis. These agents do all their work in parallel. Everything eventually converges, and a final agent pulls the results into a slide deck complete with custom visuals.

ByteDance just open sourced an AI SuperAgent that can research, code, build websites, create slide decks, and generate videos. All by itself. DeerFlow 2.0 (27K+ GitHub stars ⭐️), an AI system acting like an autonomous employee with its own computer workspace to research and code. Standard chatbots only generate text and forget your preferences. DeerFlow solves this by giving the AI an isolated virtual computer environment where it safely runs programs. When given a massive task, the main program creates several smaller AI assistants to work simultaneously. It also saves your past workflows so it gets smarter about your needs. DeerFlow is model-agnostic — it works with any LLM that implements the OpenAI-compatible API. Fully supports running local models on your own computer using tools like Ollama. An example - you ask for research on the top 10 AI startups in 2026 for a presentation, the lead agent in DeerFlow breaks that big job into smaller sub-tasks. It assigns one sub-agent to look into each company, another to find funding details, and a third to handle competitor analysis. These agents do all their work in parallel. Everything eventually converges, and a final agent pulls the results into a slide deck complete with custom visuals.

Rohan Paul

50,097 görüntüleme • 4 ay önce

What if AI didn't just analyze markets, but executed your full strategy from news alpha to DEX swaps? Launching Gate for AI: The widest MCP toolkit connecting Exchange, DEX, Wallet, News, and Info into one intelligent workflow. 5 modules for end-to-end automation: Data → strategy → execution → risk → review with real liquidity & AI-native calls Explore now: Learn more:

What if AI didn't just analyze markets, but executed your full strategy from news alpha to DEX swaps? Launching Gate for AI: The widest MCP toolkit connecting Exchange, DEX, Wallet, News, and Info into one intelligent workflow. 5 modules for end-to-end automation: Data → strategy → execution → risk → review with real liquidity & AI-native calls Explore now: Learn more:

Gate

60,620 görüntüleme • 4 ay önce

Building is only the beginning. Managing what you build matters too. With xBubble, every AI website created through Bubble Code is managed in one project workspace. View all deployments, track running or paused status, pause, resume, restart, or decommission projects, and jump back into chat anytime to refine your website through conversation. From creation to deployment to continuous iteration, xBubble gives users a complete lifecycle workspace for AI products.

Building is only the beginning. Managing what you build matters too. With xBubble, every AI website created through Bubble Code is managed in one project workspace. View all deployments, track running or paused status, pause, resume, restart, or decommission projects, and jump back into chat anytime to refine your website through conversation. From creation to deployment to continuous iteration, xBubble gives users a complete lifecycle workspace for AI products.

DAPPOS

29,993 görüntüleme • 13 gün önce

AlphaFox Any plastic strapping can be opened very simply. No tools. No effort. Where the two ends are joined together... turn it over, grab one end and pull. Here's a video I did for you.

AlphaFox Any plastic strapping can be opened very simply. No tools. No effort. Where the two ends are joined together... turn it over, grab one end and pull. Here's a video I did for you.

Mr J

17,975 görüntüleme • 2 ay önce

🌊 Liquify is making waves with Fetch.ai, a leader in AI-powered blockchain solutions! We’re charting new waters together, integrating their #AI-native Agents into our project pages for you to make more informed decisions. Here’s what’s flowing your way: 🔹 Interactive AI agents on every project page: ask and receive real-time, AI-powered insights. 🔹 Aggregated data from X posts, WhitePapers, websites & rich data layer. 🔹Discover and evaluate assets more effectively, at your fingertips. We're diving deep to create a smarter, smoother experience for you. Stay informed 🧠 Stay liquid 🌊

🌊 Liquify is making waves with Fetch.ai, a leader in AI-powered blockchain solutions! We’re charting new waters together, integrating their #AI-native Agents into our project pages for you to make more informed decisions. Here’s what’s flowing your way: 🔹 Interactive AI agents on every project page: ask and receive real-time, AI-powered insights. 🔹 Aggregated data from X posts, WhitePapers, websites & rich data layer. 🔹Discover and evaluate assets more effectively, at your fingertips. We're diving deep to create a smarter, smoother experience for you. Stay informed 🧠 Stay liquid 🌊

Liquify 🌊

36,365 görüntüleme • 1 yıl önce

Building AI agents is finally simple — and Airia is leading the way. I’ve been testing Airia AI , enterprise AI orchestration platform that unifies every model, workflow, and data source into one secure environment. Whether you’re a developer, analyst, creator, or enterprise leader, Airia makes it incredibly easy to build powerful AI agents — without wrestling with multiple tools or complex integrations. Using the no-code builder, you can drag-and-drop actions, connect data, choose your LLM, and launch an agent in minutes. Then run it live, publish it, and even share it with the Airia Community, home to 2,500+ pre-built agents you can use or remix. If you want to automate workflows, prototype faster, or explore real enterprise AI use cases, Airia is the place to start. 👉 Build your first agent today: 👉 Explore the community: #Airia #AgenticAI #AIOrchestration #AIAgents #AIWorkflow #DigitalTransformation

Building AI agents is finally simple — and Airia is leading the way. I’ve been testing Airia AI , enterprise AI orchestration platform that unifies every model, workflow, and data source into one secure environment. Whether you’re a developer, analyst, creator, or enterprise leader, Airia makes it incredibly easy to build powerful AI agents — without wrestling with multiple tools or complex integrations. Using the no-code builder, you can drag-and-drop actions, connect data, choose your LLM, and launch an agent in minutes. Then run it live, publish it, and even share it with the Airia Community, home to 2,500+ pre-built agents you can use or remix. If you want to automate workflows, prototype faster, or explore real enterprise AI use cases, Airia is the place to start. 👉 Build your first agent today: 👉 Explore the community: #Airia #AgenticAI #AIOrchestration #AIAgents #AIWorkflow #DigitalTransformation

Adarsh Chetan

268,933 görüntüleme • 7 ay önce

In mission-critical manufacturing, even one loose bolt can cause disaster. Matroid’s computer vision platform turns a simple GoPro walkaround into a pixel-level inspection, detecting missing bolts, broken torque stripes, and wiring errors. Using segmentation and geometry-aware filtering, it works reliably with limited data, minimizing false positives from oblique angles. Learn more: #ComputerVision #Engineering #technology #ai #Matroid #artificialintelligence

In mission-critical manufacturing, even one loose bolt can cause disaster. Matroid’s computer vision platform turns a simple GoPro walkaround into a pixel-level inspection, detecting missing bolts, broken torque stripes, and wiring errors. Using segmentation and geometry-aware filtering, it works reliably with limited data, minimizing false positives from oblique angles. Learn more: #ComputerVision #Engineering #technology #ai #Matroid #artificialintelligence

Wevolver

35,221 görüntüleme • 8 ay önce

Business end of the space robot Canadarm2. Those internal snare wires grab a post (with a knobbed end), pull it inside, and then the outer connectors plug in for data, video, power. Canadarm has one of these on each end, so it can walk itself from post to post, like a huge space inchworm. I helped build Canadarm2 onto ISS during my spacewalks Canadian Space Agency NASA.

Business end of the space robot Canadarm2. Those internal snare wires grab a post (with a knobbed end), pull it inside, and then the outer connectors plug in for data, video, power. Canadarm has one of these on each end, so it can walk itself from post to post, like a huge space inchworm. I helped build Canadarm2 onto ISS during my spacewalks Canadian Space Agency NASA.

Chris Hadfield

158,571 görüntüleme • 1 yıl önce

Introducing a Major Update on AIOZ AI V1: AI Challenges Are Here! AIOZ AI Challenges is a collaborative space where AI builders and tech enthusiasts can create, submit, and explore innovative models tackling real-world problems. This isn’t just a leaderboard — it’s a launchpad for hands-on learning, community collaboration, and potential rewarded innovation. Challenge One: "Face Anti-Spoofing" Starts Now This challenge dives deep into advanced computer vision—tasking you to detect spoofing attacks like printed photos, video replays, and 3D masks, which are critical threats to real-world biometric systems. Rewards: Upload your model to AIOZ AI V1 and unlock a chance to receive a share of the 4,500 AIOZ token challenge rewards. Full Challenge Details & Submission Guidelines: Build your model on AIOZ AI—the Web3 collaborative marketplace powering the DePIN Ecosystem for AI.

Introducing a Major Update on AIOZ AI V1: AI Challenges Are Here! AIOZ AI Challenges is a collaborative space where AI builders and tech enthusiasts can create, submit, and explore innovative models tackling real-world problems. This isn’t just a leaderboard — it’s a launchpad for hands-on learning, community collaboration, and potential rewarded innovation. Challenge One: "Face Anti-Spoofing" Starts Now This challenge dives deep into advanced computer vision—tasking you to detect spoofing attacks like printed photos, video replays, and 3D masks, which are critical threats to real-world biometric systems. Rewards: Upload your model to AIOZ AI V1 and unlock a chance to receive a share of the 4,500 AIOZ token challenge rewards. Full Challenge Details & Submission Guidelines: Build your model on AIOZ AI—the Web3 collaborative marketplace powering the DePIN Ecosystem for AI.

AIOZ Network

1,241,238 görüntüleme • 1 yıl önce

🚨 BREAKING: You can now turn messy meeting notes into client-ready slides with one click I tried Context , the AI Office Suite built to understand your work and deliver A+ results fast Here’s how it saved me hours:

🚨 BREAKING: You can now turn messy meeting notes into client-ready slides with one click I tried Context , the AI Office Suite built to understand your work and deliver A+ results fast Here’s how it saved me hours:

Nelly;

31,616 görüntüleme • 1 yıl önce

3/ Notebooks: With Web + Work + Pages, you can ideate with AI and collaborate with other people. It has entirely changed my workflow. And now with Notebooks, I can organize all of my heterogeneous data for a project, whether it’s Pages, docs, websites, team meetings – and Copilot will ground itself just on that content. And this might be the best part: I can turn it all into a new modality like an audio overview. For example, I can collect all the latest things I’m reading about agents and agent frameworks, and then I can listen to it.

3/ Notebooks: With Web + Work + Pages, you can ideate with AI and collaborate with other people. It has entirely changed my workflow. And now with Notebooks, I can organize all of my heterogeneous data for a project, whether it’s Pages, docs, websites, team meetings – and Copilot will ground itself just on that content. And this might be the best part: I can turn it all into a new modality like an audio overview. For example, I can collect all the latest things I’m reading about agents and agent frameworks, and then I can listen to it.

Satya Nadella

266,360 görüntüleme • 1 yıl önce

A 17-year-old student replaced $850 in monthly AI bills with 1 tiny NVIDIA box and never looked at cloud invoices the same way again. The machine cost $4,699. That sounded expensive until the math started winning. Cloud GPUs charge every experiment. Every failed prompt. Every forgotten instance. Every late-night demo. A local AI box flips the model. Private documents stay on your desk. Coding agents run without API anxiety. RAG, embeddings and model testing stop burning money by the hour. One $3K to $10K client project can cover most of the hardware. After that, every new workflow becomes cheaper to build. The next AI advantage isn’t finding a better model. It’s owning the computer that runs it.

A 17-year-old student replaced $850 in monthly AI bills with 1 tiny NVIDIA box and never looked at cloud invoices the same way again. The machine cost $4,699. That sounded expensive until the math started winning. Cloud GPUs charge every experiment. Every failed prompt. Every forgotten instance. Every late-night demo. A local AI box flips the model. Private documents stay on your desk. Coding agents run without API anxiety. RAG, embeddings and model testing stop burning money by the hour. One $3K to $10K client project can cover most of the hardware. After that, every new workflow becomes cheaper to build. The next AI advantage isn’t finding a better model. It’s owning the computer that runs it.

Lummox

321,288 görüntüleme • 4 gün önce

OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!

OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!

Santiago

171,340 görüntüleme • 1 yıl önce

🚨 JUST IN: CHINA just released an AI EMPLOYEE that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.

🚨 JUST IN: CHINA just released an AI EMPLOYEE that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.

Kanika

737,284 görüntüleme • 4 ay önce

Alright I’m done. It has been 3 months since I filed a claim with you UPS because you guys DROPPED my computer that I shipped and paid a lot of money to have professionally packaged and insured by YOU guys. Apparently none of that matters though. This is the computer I use to make a living and because of you I’ve had to use an old and barely functional pc and borrow other people’s computers and pay out of pocket for the last 3 months to make other options work while I wait for you guys because you guys keep stalling the claim. I took it to a computer company like you guys wanted where it was declared A TOTAL LOSS. Attached is the video of YOU guys dropping it (with sound so you can hear everything breaking). All other pictures and documents are in the claim I’ve made that’s been getting pushed off and like ruining my life for the past quarter of the year… REPLACE MY COMPUTER YOU GUYS BROKE IT!!!

Alright I’m done. It has been 3 months since I filed a claim with you UPS because you guys DROPPED my computer that I shipped and paid a lot of money to have professionally packaged and insured by YOU guys. Apparently none of that matters though. This is the computer I use to make a living and because of you I’ve had to use an old and barely functional pc and borrow other people’s computers and pay out of pocket for the last 3 months to make other options work while I wait for you guys because you guys keep stalling the claim. I took it to a computer company like you guys wanted where it was declared A TOTAL LOSS. Attached is the video of YOU guys dropping it (with sound so you can hear everything breaking). All other pictures and documents are in the claim I’ve made that’s been getting pushed off and like ruining my life for the past quarter of the year… REPLACE MY COMPUTER YOU GUYS BROKE IT!!!

Birlap

49,181 görüntüleme • 2 yıl önce

Turn complex docs into clean, LLM-ready data! Every AI company I've talked to is solving the same problem: how do you build systems that don't hallucinate and back up every answer with proper citations? Tensorlake is a tool that extracts custom-defined structured data from any unstructured document in 3 steps: ↳ Define your schema ↳ Enable citations ↳ Extract You get RAG-ready data with precise citations and bounding boxes. Feed this to your LLM, and you'll generate responses that are citation-backed and fully auditable. This is the difference between a demo and a production system. When your AI can show exactly where it got its information, you move from proof-of-concept to something people can actually trust and deploy. I've shared the Tensorlake GitHub repo in the replies!

Turn complex docs into clean, LLM-ready data! Every AI company I've talked to is solving the same problem: how do you build systems that don't hallucinate and back up every answer with proper citations? Tensorlake is a tool that extracts custom-defined structured data from any unstructured document in 3 steps: ↳ Define your schema ↳ Enable citations ↳ Extract You get RAG-ready data with precise citations and bounding boxes. Feed this to your LLM, and you'll generate responses that are citation-backed and fully auditable. This is the difference between a demo and a production system. When your AI can show exactly where it got its information, you move from proof-of-concept to something people can actually trust and deploy. I've shared the Tensorlake GitHub repo in the replies!

Akshay 🚀

58,152 görüntüleme • 8 ay önce

Introducing RL Environment Creator Skill Now any one can create RL environments $ npx skills add adithya-s-k/RL_Envs_101 > You can create environments across multiple frameworks like OpenEnv, OpenReward, Verifiers, NemoGym ... > the repo has live working examples of environments that your coding agent can reference > The skill is design to first understand what type of model you are training and create an environment while keeping that in mind ps. There’s a lot more to building RL environments that can be used for training. One major aspect is the data, which this skill can’t directly solve. However, the skill will help with implementing tools, rewards, and other components of an RL environment, making it easier to go from idea to implementation quickly across different frameworks. Let me know if you’d be interested in a detailed, end-to-end blog/tutorial on building an environment and actually training a model for a useful use case.

Introducing RL Environment Creator Skill Now any one can create RL environments $ npx skills add adithya-s-k/RL_Envs_101 > You can create environments across multiple frameworks like OpenEnv, OpenReward, Verifiers, NemoGym ... > the repo has live working examples of environments that your coding agent can reference > The skill is design to first understand what type of model you are training and create an environment while keeping that in mind ps. There’s a lot more to building RL environments that can be used for training. One major aspect is the data, which this skill can’t directly solve. However, the skill will help with implementing tools, rewards, and other components of an RL environment, making it easier to go from idea to implementation quickly across different frameworks. Let me know if you’d be interested in a detailed, end-to-end blog/tutorial on building an environment and actually training a model for a useful use case.

Adithya S K

46,556 görüntüleme • 2 ay önce