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Run promptable segmentation with Ultralytics YOLO26-E! 🧠 Segment objects using natural language prompts with YOLO26-E, ideal for flexible visual search, rapid annotation, and interactive computer vision workflows. #MachineLearning #AI #Research

Muhammad Rizwan Munawar

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23,386 views • 1 month ago •via X (Twitter)

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Run promptable segmentation with Ultralytics YOLO26-E! 🧠 Segment objects using natural language prompts with YOLO26-E, ideal for flexible visual search, rapid annotation, and interactive computer vision workflows. Get started ➡️ #Ultralytics #YOLO26 #AI #Research

Run promptable segmentation with Ultralytics YOLO26-E! 🧠 Segment objects using natural language prompts with YOLO26-E, ideal for flexible visual search, rapid annotation, and interactive computer vision workflows. Get started ➡️ #Ultralytics #YOLO26 #AI #Research

Ultralytics

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Ultralytics YOLO11 vs Ultralytics YOLO26: Which one runs faster? ⚡ Evaluate inference speed and performance across both models in the ONNX format to understand how YOLO26 improves efficiency for real-time computer vision applications. Read more ➡️ #Ultralytics #YOLO26 #AI #Research #ComputerVision

Ultralytics YOLO11 vs Ultralytics YOLO26: Which one runs faster? ⚡ Evaluate inference speed and performance across both models in the ONNX format to understand how YOLO26 improves efficiency for real-time computer vision applications. Read more ➡️ #Ultralytics #YOLO26 #AI #Research #ComputerVision

Ultralytics

22,821 views • 3 months ago

Depth anything v2 vs. Ultralytics YOLO26 depth | Speed comparison 🚀 For this demo, I compared yolo26m-depth with depth-anything-v2-small on an NVIDIA RTX 3050 using the same input settings. Even though I used the medium YOLO26 model, it delivered nearly 2× the throughput: 22 FPS vs. 11 FPS. #Vehicles #MachineLearning #Research

Depth anything v2 vs. Ultralytics YOLO26 depth | Speed comparison 🚀 For this demo, I compared yolo26m-depth with depth-anything-v2-small on an NVIDIA RTX 3050 using the same input settings. Even though I used the medium YOLO26 model, it delivered nearly 2× the throughput: 22 FPS vs. 11 FPS. #Vehicles #MachineLearning #Research

Muhammad Rizwan Munawar

22,019 views • 6 days ago

Vehicle tracking using Ultralytics YOLO26 👀 In this demo, I trained the YOLO26 model on a custom dataset and later used an object tracker (Bytetrack) to track the objects in the video file. Note: I also performed testing with Botsort, which is also a good tracker, but it's slow in comparision to Bytetrack. #Tracking #ComputerVision #MachineLearning

Vehicle tracking using Ultralytics YOLO26 👀 In this demo, I trained the YOLO26 model on a custom dataset and later used an object tracker (Bytetrack) to track the objects in the video file. Note: I also performed testing with Botsort, which is also a good tracker, but it's slow in comparision to Bytetrack. #Tracking #ComputerVision #MachineLearning

Muhammad Rizwan Munawar

16,843 views • 2 months ago

i built an app to search old travel photos on my computer with natural language queries using free local AI models: - smolVLM to describe the scene and colours - roboflow RF-DETR to detect objects - chromaDB to store metadata labels and run semantic search - python + streamlit for the interface fast and free search has been achieved internally

i built an app to search old travel photos on my computer with natural language queries using free local AI models: - smolVLM to describe the scene and colours - roboflow RF-DETR to detect objects - chromaDB to store metadata labels and run semantic search - python + streamlit for the interface fast and free search has been achieved internally

AA

42,038 views • 4 months ago

Pothole detection on the road in real time using Ultralytics YOLO26! 🕳️ Manual road inspections are slow, costly, and hard to scale. With object detection, potholes can be identified directly from street-level images or video feeds, enabling faster and more consistent road condition monitoring. How I built this demo: ✅ Trained a segmentation model on a custom dataset. ✅ Generated mask contours for each pothole. ✅ Leveraged the onnx-exported model for faster processing. #Pothole #RoadDamage #AI

Pothole detection on the road in real time using Ultralytics YOLO26! 🕳️ Manual road inspections are slow, costly, and hard to scale. With object detection, potholes can be identified directly from street-level images or video feeds, enabling faster and more consistent road condition monitoring. How I built this demo: ✅ Trained a segmentation model on a custom dataset. ✅ Generated mask contours for each pothole. ✅ Leveraged the onnx-exported model for faster processing. #Pothole #RoadDamage #AI

Muhammad Rizwan Munawar

30,677 views • 4 months ago

gemini omni flash is here: our high-quality, cost-efficient model for video generation and conversational editing designed to support multimodal workflows, it enables you to refine videos using natural language and simple prompting start building with it today via ai studio and the gemini api

gemini omni flash is here: our high-quality, cost-efficient model for video generation and conversational editing designed to support multimodal workflows, it enables you to refine videos using natural language and simple prompting start building with it today via ai studio and the gemini api

Google AI Studio

965,838 views • 1 month ago

Opal, our no-code visual builder for AI workflows, just got a major upgrade. 🧠💎 We’ve added a new agent step that analyzes your goal, determines the best approach, and automatically calls the right tools — such as Veo for video or web search for research — to complete the task. We’re also adding new tools to make the agent even more capable: 💾 Memory – Remember info, like a user’s name or your style preferences across sessions. 🚀 Dynamic Routing – Let the agent choose the next best step using the “@ Go to” tool. 💬 Interactive Chat – Initiate user interactions to gather missing information or present options before moving on. Try it now →

Opal, our no-code visual builder for AI workflows, just got a major upgrade. 🧠💎 We’ve added a new agent step that analyzes your goal, determines the best approach, and automatically calls the right tools — such as Veo for video or web search for research — to complete the task. We’re also adding new tools to make the agent even more capable: 💾 Memory – Remember info, like a user’s name or your style preferences across sessions. 🚀 Dynamic Routing – Let the agent choose the next best step using the “@ Go to” tool. 💬 Interactive Chat – Initiate user interactions to gather missing information or present options before moving on. Try it now →

Google Labs

1,008,042 views • 5 months ago

Introducing the first demo overview of the Zoof Wallet 🧵 A mobile wallet powered by AI, with passive income capabilities through liquidity pools and staking, integrating Virtuals Protocol projects at it's core Enabling access for everyone with social and e-mail login, gasless transactions, and natural language input to manage your balance. Supporting Ethereum, Base, Arbitrum and Solana from the start.

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Zoof Wallet

69,450 views • 9 months ago

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 views • 1 year ago

🚀 The Segment Anything Model (SAM) has been upgraded to SAM2, featuring an efficient image encoder for segmenting images and videos. But does SAM2 outperform SAM1 in medical image and video segmentation? We're thrilled to present our paper "Segment Anything in Medical Images and Videos: Benchmark and Deployment"! We comprehensively benchmark SAM2 across 11 medical image modalities and videos. 📄 Paper: 💻 Code: **Highlights:** 1. SAM2 doesn’t always outperform SAM1 in 2D medical images, but excels in video segmentation, making it more accurate and efficient for 3D images, such as CT and MR scans. 2. MedSAM still outperforms SAM2 on most 2D modalities, but SAM2 surpasses MedSAM for 3D image segmentation in a slice-by-slice approach. 3. Segmentation performance varies with model size; sometimes the smallest model outperforms larger ones. 4. Fine-tuning SAM2 significantly boosts its performance for medical image segmentation. While SAM2 may struggle with challenging objects that have unclear boundaries or low contrast, it excels in generating good initial segmentation masks for common medical images and videos. However, the official interface doesn’t support medical data formats and has limitations on video length. To address this, we've developed a 3D Slicer Plugin and Gradio API for efficient 3D medical image and video segmentation. We invite you to try them out and provide feedback! 🔧 Deployment: - 3D Slicer Plugin: - Gradio API: (Note: Due to GPU limitations, the online API is available for only 12 hours and may be slow. We highly recommend deploying the Gradio API with your own computing resources: A big shoutout to Jun Ma (JunMa) who recently joined our UHN AI hub (UHN AI Hub) as Machine Learning Lead, and kudos to all co-authors: Sumin Kim, Feifei Li, Mohammed Baharoon (Mohammed Baharoon), Reza Asakereh, and Hongwei Lyu! This is true teamwork! Looking forward to collaborating with the community to advance 3D medical image and video segmentation foundation models! University Health Network U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology Temerty Centre for AI in Medicine (T-CAIREM) Vector Institute #MedTech #AIinHealthcare #DeepLearning #MedicalImaging #SAM2 #MedSAM #AIResearch

🚀 The Segment Anything Model (SAM) has been upgraded to SAM2, featuring an efficient image encoder for segmenting images and videos. But does SAM2 outperform SAM1 in medical image and video segmentation? We're thrilled to present our paper "Segment Anything in Medical Images and Videos: Benchmark and Deployment"! We comprehensively benchmark SAM2 across 11 medical image modalities and videos. 📄 Paper: 💻 Code: Highlights: 1. SAM2 doesn’t always outperform SAM1 in 2D medical images, but excels in video segmentation, making it more accurate and efficient for 3D images, such as CT and MR scans. 2. MedSAM still outperforms SAM2 on most 2D modalities, but SAM2 surpasses MedSAM for 3D image segmentation in a slice-by-slice approach. 3. Segmentation performance varies with model size; sometimes the smallest model outperforms larger ones. 4. Fine-tuning SAM2 significantly boosts its performance for medical image segmentation. While SAM2 may struggle with challenging objects that have unclear boundaries or low contrast, it excels in generating good initial segmentation masks for common medical images and videos. However, the official interface doesn’t support medical data formats and has limitations on video length. To address this, we've developed a 3D Slicer Plugin and Gradio API for efficient 3D medical image and video segmentation. We invite you to try them out and provide feedback! 🔧 Deployment: - 3D Slicer Plugin: - Gradio API: (Note: Due to GPU limitations, the online API is available for only 12 hours and may be slow. We highly recommend deploying the Gradio API with your own computing resources: A big shoutout to Jun Ma (JunMa) who recently joined our UHN AI hub (UHN AI Hub) as Machine Learning Lead, and kudos to all co-authors: Sumin Kim, Feifei Li, Mohammed Baharoon (Mohammed Baharoon), Reza Asakereh, and Hongwei Lyu! This is true teamwork! Looking forward to collaborating with the community to advance 3D medical image and video segmentation foundation models! University Health Network U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology Temerty Centre for AI in Medicine (T-CAIREM) Vector Institute #MedTech #AIinHealthcare #DeepLearning #MedicalImaging #SAM2 #MedSAM #AIResearch

Bo Wang

178,556 views • 2 years ago

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 views • 5 months ago

OpenAI has introduced the ChatGPT Agent, which handles complex multi-step tasks from research to automation. Genspark goes further in some areas: In addition to user-friendly office tools (Slides, Docs, Sheets, AI Secretary, AI Drive), Genspark scores with dynamic tool orchestration and an intelligent feedback loop - a clear added value, especially for individuals and small teams. ChatGPT Agent Offers browser and API access, terminal control and deep search capabilities. Strengths include high security mechanisms, comprehensive user control and integration with productivity tools such as Gmail and Calendar. Ideal for end users and teams who need maximum control and data protection. Genspark Super Agent Enables no-code workflows, creates high-quality visual content (slides, videos) and automates entire workflows. With tool calling, the agent automatically selects the best solution from over 80 integrated tools - e.g. for CRM queries, task management or API access. The feedback loop allows the agent to monitor the use of a tool during execution and dynamically switch to another tool or adapt the workflow if necessary. Thanks to this multi-model architecture, Genspark often works more precisely and efficiently in benchmarks than comparable systems.

OpenAI has introduced the ChatGPT Agent, which handles complex multi-step tasks from research to automation. Genspark goes further in some areas: In addition to user-friendly office tools (Slides, Docs, Sheets, AI Secretary, AI Drive), Genspark scores with dynamic tool orchestration and an intelligent feedback loop - a clear added value, especially for individuals and small teams. ChatGPT Agent Offers browser and API access, terminal control and deep search capabilities. Strengths include high security mechanisms, comprehensive user control and integration with productivity tools such as Gmail and Calendar. Ideal for end users and teams who need maximum control and data protection. Genspark Super Agent Enables no-code workflows, creates high-quality visual content (slides, videos) and automates entire workflows. With tool calling, the agent automatically selects the best solution from over 80 integrated tools - e.g. for CRM queries, task management or API access. The feedback loop allows the agent to monitor the use of a tool during execution and dynamically switch to another tool or adapt the workflow if necessary. Thanks to this multi-model architecture, Genspark often works more precisely and efficiently in benchmarks than comparable systems.

Chubby♨️

176,267 views • 1 year ago

🚨 Alibaba just open sourced a GUI agent that lives inside your webpage and controls it with natural language. It's called Page Agent and it's not a browser extension. It's pure JavaScript no Python, no Puppeteer, no headless browser, no screenshots. Just one script tag and your web app understands natural language. Here's what it actually does: → Embed it with a single tag or npm install → Control any web interface with plain English commands → Text-based DOM manipulation no OCR, no vision models needed → Bring your own LLM (GPT, Claude, Qwen, anything) → Ships a built-in UI with human-in-the-loop support → Turn 20-click ERP/CRM workflows into one sentence → Optional Chrome extension for multi-tab agent tasks → Works on any web app SaaS, admin panels, internal tools Companies are charging $30/month for AI copilots built on this exact idea. This is 3 lines of code. Your users. Your interface. The AI copilot layer for every web app just got open sourced. 1.6K stars. 100% Open Source. (Link in the comments)

🚨 Alibaba just open sourced a GUI agent that lives inside your webpage and controls it with natural language. It's called Page Agent and it's not a browser extension. It's pure JavaScript no Python, no Puppeteer, no headless browser, no screenshots. Just one script tag and your web app understands natural language. Here's what it actually does: → Embed it with a single tag or npm install → Control any web interface with plain English commands → Text-based DOM manipulation no OCR, no vision models needed → Bring your own LLM (GPT, Claude, Qwen, anything) → Ships a built-in UI with human-in-the-loop support → Turn 20-click ERP/CRM workflows into one sentence → Optional Chrome extension for multi-tab agent tasks → Works on any web app SaaS, admin panels, internal tools Companies are charging $30/month for AI copilots built on this exact idea. This is 3 lines of code. Your users. Your interface. The AI copilot layer for every web app just got open sourced. 1.6K stars. 100% Open Source. (Link in the comments)

Ihtesham Ali

135,474 views • 5 months ago

Introducing ASAL: Automating the Search for Artificial Life with Foundation Models Artificial Life (ALife) research holds key insights that can transform and accelerate progress in AI. By speeding up ALife discovery with AI, we accelerate our understanding of emergence, evolution, and intelligence–core principles that can inspire the next generation of AI systems! We proudly collaborated with MIT, OpenAI, Swiss AI Lab IDSIA, and Ken Stanley on this exciting project. Full Paper (Website): Full Paper (arxiv): Code: In this work, we propose a new algorithm called Automated Search for Artificial Life (“ASAL”) to automate the discovery of artificial life using vision-language foundation models. Instead of tediously hand-designing every tiny rule of an Alife simulation, simply describe the space of simulations to search over, and ASAL will automatically discover the most interesting and open-ended artificial lifeforms! Because of the generality of foundation models, ASAL can discover new lifeforms across a diverse range of seminal ALife simulations, including Boids, Particle Life, Game of Life, Lenia, and Neural Cellular Automata. ASAL even discovered novel cellular automata rules that are more open-ended and expressive than the original Conway’s Game of Life. We believe this new paradigm may reignite ALife research by overcoming the bottleneck of manually designed simulations, thus advancing beyond the limits of human ingenuity.

Introducing ASAL: Automating the Search for Artificial Life with Foundation Models Artificial Life (ALife) research holds key insights that can transform and accelerate progress in AI. By speeding up ALife discovery with AI, we accelerate our understanding of emergence, evolution, and intelligence–core principles that can inspire the next generation of AI systems! We proudly collaborated with MIT, OpenAI, Swiss AI Lab IDSIA, and Ken Stanley on this exciting project. Full Paper (Website): Full Paper (arxiv): Code: In this work, we propose a new algorithm called Automated Search for Artificial Life (“ASAL”) to automate the discovery of artificial life using vision-language foundation models. Instead of tediously hand-designing every tiny rule of an Alife simulation, simply describe the space of simulations to search over, and ASAL will automatically discover the most interesting and open-ended artificial lifeforms! Because of the generality of foundation models, ASAL can discover new lifeforms across a diverse range of seminal ALife simulations, including Boids, Particle Life, Game of Life, Lenia, and Neural Cellular Automata. ASAL even discovered novel cellular automata rules that are more open-ended and expressive than the original Conway’s Game of Life. We believe this new paradigm may reignite ALife research by overcoming the bottleneck of manually designed simulations, thus advancing beyond the limits of human ingenuity.

Sakana AI

750,991 views • 1 year ago

Claude + Facebook Ads MCP is absolutely WILD 🤯 This AI system turns Claude into your personal ads analyst using MCP integration. And generates full client reports in seconds. Perfect for agencies & ecomm operators who hate wrestling with Ads Manager. But instead of manually pulling data, building charts, and formatting reports... Just give Claude one natural language prompt and get a complete visual storytelling report with insights. Each report includes account performance, campaign breakdowns, demographics, and actionable recommendations. All with a single prompt. Here's what it does: → Connects directly to your Facebook Ads account → Pulls all performance data automatically → Calculates ROAS, CPA, conversion rates on demand → Creates interactive charts and visualizations → Generates beautiful reports → Builds everything from scratch in real-time Built with Claude MCP. Zero manual work. Want the complete MCP setup? Comment "MCP" + like and I'll send it over (must be following so I can DM)

Claude + Facebook Ads MCP is absolutely WILD 🤯 This AI system turns Claude into your personal ads analyst using MCP integration. And generates full client reports in seconds. Perfect for agencies & ecomm operators who hate wrestling with Ads Manager. But instead of manually pulling data, building charts, and formatting reports... Just give Claude one natural language prompt and get a complete visual storytelling report with insights. Each report includes account performance, campaign breakdowns, demographics, and actionable recommendations. All with a single prompt. Here's what it does: → Connects directly to your Facebook Ads account → Pulls all performance data automatically → Calculates ROAS, CPA, conversion rates on demand → Creates interactive charts and visualizations → Generates beautiful reports → Builds everything from scratch in real-time Built with Claude MCP. Zero manual work. Want the complete MCP setup? Comment "MCP" + like and I'll send it over (must be following so I can DM)

Mike Futia

270,739 views • 11 months ago

Visual Preset #01 Ink-Brush Cinematic 3D: A high-end cinematic 3D style where expressive ink-brush effects become the primary visual language for fast-paced anime action. Lately I've noticed that I've been experimenting with different visual presets across my videos and I'd like to explore that direction even further. Going forward, I'll be sharing some of these style experiments. The video below was generated using only a character sheet, a single-line scene description and the visual preset shown below. Created with Seedance 2.0 on Try ArtCraft Seedance 2.0 Prompt: A mesmerizing display of @[character]'s masterful swordsmanship. High-end cinematic 3D realism fused with expressive ink-brush action. High-sakuga anime choreography, sweeping sumi-e brush strokes, flowing ink splashes, dynamic calligraphic energy and graphic black ink trails define every movement. Extreme perspective, dramatic foreshortening, cinematic tracking shots, volumetric lighting, heavy atmospheric haze and explosive ink bursts replace conventional visual effects, while realistic materials and feature-film rendering preserve depth, weight and scale.

Visual Preset #01 Ink-Brush Cinematic 3D: A high-end cinematic 3D style where expressive ink-brush effects become the primary visual language for fast-paced anime action. Lately I've noticed that I've been experimenting with different visual presets across my videos and I'd like to explore that direction even further. Going forward, I'll be sharing some of these style experiments. The video below was generated using only a character sheet, a single-line scene description and the visual preset shown below. Created with Seedance 2.0 on Try ArtCraft Seedance 2.0 Prompt: A mesmerizing display of @[character]'s masterful swordsmanship. High-end cinematic 3D realism fused with expressive ink-brush action. High-sakuga anime choreography, sweeping sumi-e brush strokes, flowing ink splashes, dynamic calligraphic energy and graphic black ink trails define every movement. Extreme perspective, dramatic foreshortening, cinematic tracking shots, volumetric lighting, heavy atmospheric haze and explosive ink bursts replace conventional visual effects, while realistic materials and feature-film rendering preserve depth, weight and scale.

Kōda

38,527 views • 1 month ago

Claude Cowork is f*cking ridiculous 🤯 One prompt → competitive research, creative briefs, 15 hook variations, and a full performance dashboard. All saved as real files on your computer. All inside Claude Desktop. If you're spending hours every week copy-pasting between tools, pulling competitor ads manually, writing briefs from scratch, and building reports in spreadsheets ... Claude Cowork eliminates the entire loop: → Point it at your project folder with brand voice + context files → It asks YOU clarifying questions instead of guessing → It builds a multi-step plan and executes while you step away → It creates real .docx, .xlsx, .pptx files — not chat responses → It connects to Slack, Google Drive, Airtable, and 50+ tools live No copy-pasting between tools. No babysitting the AI mid-task. No downloading and re-uploading files. What you get: → Competitive research synthesized into actionable creative angles → Ad briefs, hooks, and scripts generated in your brand voice → Interactive HTML dashboards built from your own customer data → Weekly performance reports created while you're getting coffee Built 100% inside Claude Desktop with skills, plugins, and connectors. I put together a full DTC playbook: 10 workflows with copy-paste prompts, the exact setup process, and the weekly operating rhythm I use. Want it for free? > Like this post > Comment "COWORK" And I'll send it over (must be following so I can DM)

Utkarsh Sharma

29,875 views • 4 months ago

Claude Cowork is f*cking ridiculous 🤯 One prompt → competitive research, creative briefs, 15 hook variations, and a full performance dashboard. All saved as real files on your computer. All inside Claude Desktop. If you're spending hours every week copy-pasting between tools, pulling competitor ads manually, writing briefs from scratch, and building reports in spreadsheets ... Claude Cowork eliminates the entire loop: → Point it at your project folder with brand voice + context files → It asks YOU clarifying questions instead of guessing → It builds a multi-step plan and executes while you step away → It creates real .docx, .xlsx, .pptx files — not chat responses → It connects to Slack, Google Drive, Airtable, and 50+ tools live No copy-pasting between tools. No babysitting the AI mid-task. No downloading and re-uploading files. What you get: → Competitive research synthesized into actionable creative angles → Ad briefs, hooks, and scripts generated in your brand voice → Interactive HTML dashboards built from your own customer data → Weekly performance reports created while you're getting coffee Built 100% inside Claude Desktop with skills, plugins, and connectors. I put together a full DTC playbook: 10 workflows with copy-paste prompts, the exact setup process, and the weekly operating rhythm I use. Want it for free? > Like this post > Comment "COWORK" And I'll send it over (must be following so I can DM)

Mike Futia

112,969 views • 5 months ago

Claude Cowork is f*cking ridiculous 🤯 One prompt → competitive research, creative briefs, 15 hook variations, and a full performance dashboard. All saved as real files on your computer. All inside Claude Desktop. If you're spending hours every week copy-pasting between tools, pulling competitor ads manually, writing briefs from scratch, and building reports in spreadsheets ... Claude Cowork eliminates the entire loop: → Point it at your project folder with brand voice + context files → It asks YOU clarifying questions instead of guessing → It builds a multi-step plan and executes while you step away → It creates real .docx, .xlsx, .pptx files — not chat responses → It connects to Slack, Google Drive, Airtable, and 50+ tools live No copy-pasting between tools. No babysitting the AI mid-task. No downloading and re-uploading files. What you get: → Competitive research synthesized into actionable creative angles → Ad briefs, hooks, and scripts generated in your brand voice → Interactive HTML dashboards built from your own customer data → Weekly performance reports created while you're getting coffee Built 100% inside Claude Desktop with skills, plugins, and connectors. I put together a full DTC playbook: 10 workflows with copy-paste prompts, the exact setup process, and the weekly operating rhythm I use. Want it for free? > Like this post > Comment "COWORK" And I'll send it over (must be following so I can DM)

Abdul Sarfraj

30,854 views • 5 months ago