Video yükleniyor...

Video Yüklenemedi

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

Ana Sayfaya Dön

Announcing Roboflow Rapid: the first prompt-based model creation engine. Data labeling is dead. Go from idea to deployed model in minutes without labeling data. Upload a video, type a text prompt, and get an API. No data labeling teams. No manual annotation. No infra / dependency hell. If you... show more

Roboflow

15,412 subscribers

23,039 görüntüleme • 10 ay önce •via X (Twitter)

Eğitim Bilim & Teknoloji

Anya Rossi• Live Now

Private livecam show

27 Yorum

Roboflow profil fotoğrafı

Roboflow10 ay önce

The barrier to building with visual AI just hit zero. And we are giving all users 2,000 free credits through the end of 2025 to try Rapid.

Roboflow profil fotoğrafı

Roboflow10 ay önce

step by step tutorial on how to use rapid

Tristan Rhodes profil fotoğrafı

Tristan Rhodes10 ay önce

So it uses SAM to automatically train a smaller/faster model (like Yolo or RF-Detr)?

Roboflow profil fotoğrafı

Roboflow10 ay önce

yes! rf-detr for the detector key thing is we can always use the best foundation model and detector for the task. adding in more models over time (ie Gemini Pro 3, etc) will ensure this continues to be the best possible path for people to make vision models fast

Tristan Rhodes profil fotoğrafı

Tristan Rhodes10 ay önce

Thanks! This is such a neat project and idea. This lowers the barrier to entry for people to dip their feet in computer vision and create something that could be used in a factory, for instance. I work at a university, and I'm going to share this with both the manufacturing engineering department and the data science department.

Roboflow profil fotoğrafı

Roboflow10 ay önce

yeah that's exactly right if there is an HMI attached to a camera on a line, you could make a model quickly at the edge for a new issue/product very easily

Nathan Handyman profil fotoğrafı

Nathan Handyman10 ay önce

No more labeling hell? Bold move. Edge cases are the real test though.

Polymatics profil fotoğrafı

Polymatics10 ay önce

if it actually works, removing labeling would be a huge productivity boost for builders

Roboflow profil fotoğrafı

Roboflow10 ay önce

try it out and let us know

Luc profil fotoğrafı

Luc10 ay önce

@vikhyatk 👀

Sarthak Bhardwaj profil fotoğrafı

Sarthak Bhardwaj10 ay önce

Congrats team! 🙌

Javier Berger profil fotoğrafı

Javier Berger10 ay önce

Impresionante!!! Gracias por este tremendo regalo de navidad 😊

Roboflow profil fotoğrafı

Roboflow10 ay önce

any projects in mind to try it out with?

Percival Galios profil fotoğrafı

Percival Galios10 ay önce

Love this and thank you. However, I find Roboflow Rapid fails on complex annotation tasks with subtle differentiation. It’s best for clearly visible objects rather than, for example, different classes of road damage

Roboflow profil fotoğrafı

Roboflow10 ay önce

great example of an application that would require fine-tuning a custom detector, totally agree

Percival Galios profil fotoğrafı

Percival Galios10 ay önce

Your reply is validating - thank you!

Roboflow profil fotoğrafı

Roboflow10 ay önce

rapid can probably handle 80%+ of objects people want to detect for vision applications, specific ontology like this is in that 20% :) Looks like a cool project you're building. Tons of valuable use cases in understanding local infrastructure

Finance Cyprus profil fotoğrafı

Finance Cyprus10 ay önce

Just wondering if it can be used in cell type detection and counting for blood test labs?

Roboflow profil fotoğrafı

Roboflow10 ay önce

that will likely work. give it a try, it's totally free, takes just a few minutes

Finance Cyprus profil fotoğrafı

Finance Cyprus10 ay önce

Thnx, will do

Roboflow profil fotoğrafı

Roboflow10 ay önce

lmk how it goes

pradeep kumar profil fotoğrafı

pradeep kumar10 ay önce

How to subscribe to growth plan for dedicated online support? Please reply to my email id: [email protected]

Karan Jagtiani profil fotoğrafı

Karan Jagtiani10 ay önce

Prompt-based model creation is intriguing. Curious how well it handles edge cases and complex tasks. Anyone testing those limits?

Roboflow profil fotoğrafı

Roboflow10 ay önce

very data dependent best way to find out is to try it and share results, it's free what use cases do you have in mind?

pradeep kumar profil fotoğrafı

pradeep kumar10 ay önce

How to subscribe to growth plan for dedicated online support ? My email id: [email protected]

Roboflow profil fotoğrafı

Roboflow10 ay önce

Hello! Please send any support questions to Email support is only available for Enterprise plans

pradeep kumar profil fotoğrafı

pradeep kumar10 ay önce

Then how to subscribe to enterprise plan ?

Benzer Videolar

insane level of manipulation from Google If you search for Pacific Palisades, they are using old map data and labeling it as 2026. If you go to the historical data, you can see the receipts

insane level of manipulation from Google If you search for Pacific Palisades, they are using old map data and labeling it as 2026. If you go to the historical data, you can see the receipts

Hunter Weiss

400,072 görüntüleme • 5 ay önce

Most mapmaker hours are spent labeling; a good map has "no collisions, no elisions" The computer can roughly place your text, but following rivers and stretching type out from the coastline is all manual; hope you like dragging bezier handles in Illustrator

Most mapmaker hours are spent labeling; a good map has "no collisions, no elisions" The computer can roughly place your text, but following rivers and stretching type out from the coastline is all manual; hope you like dragging bezier handles in Illustrator

Evan Applegate

198,346 görüntüleme • 1 yıl önce

The founder of a $4B inference company says that if you're building agents, foundational models could become your IP. According to Lin Qiao, 90% of the world's data is still private and locked inside applications and enterprises. Foundation models are trained on public internet + labeling company data, which is barely 10% of all the data. And this is why your application and foundation models are misaligned by definition. Companies building agents don't treat models as APIs. They opt for a product-model co-design. - Models continuously learn from your private data - Pick up domain-specific intelligence - Run faster - Cost less - Scale to millions of users

The founder of a $4B inference company says that if you're building agents, foundational models could become your IP. According to Lin Qiao, 90% of the world's data is still private and locked inside applications and enterprises. Foundation models are trained on public internet + labeling company data, which is barely 10% of all the data. And this is why your application and foundation models are misaligned by definition. Companies building agents don't treat models as APIs. They opt for a product-model co-design. - Models continuously learn from your private data - Pick up domain-specific intelligence - Run faster - Cost less - Scale to millions of users

Ivan Burazin

55,344 görüntüleme • 6 ay önce

CPAI Demo CPAI’s advanced AI labeling technology delivers unmatched accuracy and saves you money. What’s the game-changer? Automated data labeling - a process that traditionally requires costly manual labor. CPAI is: • Reducing human overhead • Improving efficiency • Eliminating errors • Lowering costs • Increasing data processing speeds Watch @DerekMoonTax demonstrate a head-to-head comparison, highlighting the key differences that sets CPAI tech against one of the leading products in the industry.

CPAI Demo CPAI’s advanced AI labeling technology delivers unmatched accuracy and saves you money. What’s the game-changer? Automated data labeling - a process that traditionally requires costly manual labor. CPAI is: • Reducing human overhead • Improving efficiency • Eliminating errors • Lowering costs • Increasing data processing speeds Watch @DerekMoonTax demonstrate a head-to-head comparison, highlighting the key differences that sets CPAI tech against one of the leading products in the industry.

CPAI

66,764 görüntüleme • 1 yıl önce

No data, no problem introducing agentic synthetic data generation with Cosmos 3 share a few examples, generate more data, automate model training, automatically deploy the latest version with no downtime in a benchmark run with Corning Incorporated's optical fiber manufacturing engineering team, a model trained on 8 real defect images plus synthetic examples generated by Cosmos reached 0.95 mean average precision and perfect recall on the toughest defect class, beating a baseline trained on real data alone. "The Roboflow Agent powered by NVIDIA allows us to generate the training data we need, fine-tune our models, and strengthen model performance and inspection quality while increasing the speed, scalability, and adoption of next-generation technologies,” - Jeremy Knopf, chief information officer, Corning Optical Communications

No data, no problem introducing agentic synthetic data generation with Cosmos 3 share a few examples, generate more data, automate model training, automatically deploy the latest version with no downtime in a benchmark run with Corning Incorporated's optical fiber manufacturing engineering team, a model trained on 8 real defect images plus synthetic examples generated by Cosmos reached 0.95 mean average precision and perfect recall on the toughest defect class, beating a baseline trained on real data alone. "The Roboflow Agent powered by NVIDIA allows us to generate the training data we need, fine-tune our models, and strengthen model performance and inspection quality while increasing the speed, scalability, and adoption of next-generation technologies,” - Jeremy Knopf, chief information officer, Corning Optical Communications

Roboflow

39,416 görüntüleme • 4 ay önce

Introducing Kled Labeling, the next major update of the Kled app. For the first time, everyday people can earn real income from AI’s fastest growing sector: data labeling. Users now have the ability to label content in the most intuitive way possible from the hottest Silicon Valley startups, with millions of dollars allocated in rewards. So whether you're on your couch watching TV, at the dinner table, in bed, anywhere you can earn just with the tap of a button. We’re not hiring VAs. We’re not outsourcing overseas. We’re building a collective of users to outpace the billion dollar giants like Mercor, Scale AI, Surge, and more. This is the first public application that lets anyone earn by labeling data. Every American and global citizen will become the network.

Introducing Kled Labeling, the next major update of the Kled app. For the first time, everyday people can earn real income from AI’s fastest growing sector: data labeling. Users now have the ability to label content in the most intuitive way possible from the hottest Silicon Valley startups, with millions of dollars allocated in rewards. So whether you're on your couch watching TV, at the dinner table, in bed, anywhere you can earn just with the tap of a button. We’re not hiring VAs. We’re not outsourcing overseas. We’re building a collective of users to outpace the billion dollar giants like Mercor, Scale AI, Surge, and more. This is the first public application that lets anyone earn by labeling data. Every American and global citizen will become the network.

Kled AI

58,972 görüntüleme • 11 ay önce

Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. For example, generate graphs from data. 5. AnswerThis will generate variety of graphs 6. It also generates an insightful data analysis report. You can download the graphs and the report. Try AnswerThis today. It’s FREE.

Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. For example, generate graphs from data. 5. AnswerThis will generate variety of graphs 6. It also generates an insightful data analysis report. You can download the graphs and the report. Try AnswerThis today. It’s FREE.

Faheem Ullah

26,496 görüntüleme • 9 ay önce

We introduce Tagger Phase 2, bringing manual #data labeling and #data review into the #DeCorp ecosystem. With this iteration, we have completed the final components necessary for a fully integrated and decentralized cycle: 1) Data processing from scratch 2) Peer-to-peer data validation 3) Automated pre-training of AI copilot models 4) Quasi-autonomous active learning It is not long now before you may directly engage with this unprecedented and comprehensive system. See you soon.

We introduce Tagger Phase 2, bringing manual #data labeling and #data review into the #DeCorp ecosystem. With this iteration, we have completed the final components necessary for a fully integrated and decentralized cycle: 1) Data processing from scratch 2) Peer-to-peer data validation 3) Automated pre-training of AI copilot models 4) Quasi-autonomous active learning It is not long now before you may directly engage with this unprecedented and comprehensive system. See you soon.

Tagger

104,265 görüntüleme • 1 yıl önce

It is very easy to make mistakes when creating evals for your AI product. Shreya Shankar and I run through the most common mistakes in this talk (with memes 🌶️!) . Chapter summaries below: 00:51 Foundation model benchmarks are not the same as your application evals 03:00 Generic Evals Are Useless 04:00 Do not outsource labeling & prompting to non domain experts 09:28 You should make your own data annotation app 12:40 Your LLM prompts should be specific and grounded in error analysis 15:25 Use binary labels 18:57 Look at your data 23:41 Be careful of overfitting to test data 25:40 Do online tests Links more resources in the reply

It is very easy to make mistakes when creating evals for your AI product. Shreya Shankar and I run through the most common mistakes in this talk (with memes 🌶️!) . Chapter summaries below: 00:51 Foundation model benchmarks are not the same as your application evals 03:00 Generic Evals Are Useless 04:00 Do not outsource labeling & prompting to non domain experts 09:28 You should make your own data annotation app 12:40 Your LLM prompts should be specific and grounded in error analysis 15:25 Use binary labels 18:57 Look at your data 23:41 Be careful of overfitting to test data 25:40 Do online tests Links more resources in the reply

Hamel Husain

46,184 görüntüleme • 1 yıl önce

Q: "What do you make of the president labeling affordability as a Democratic hoax? Is that claim backed up by data?" Me: "No, it's a lie. It's patronizing and it's disgusting. This isn't how we should have economic discussions."

Q: "What do you make of the president labeling affordability as a Democratic hoax? Is that claim backed up by data?" Me: "No, it's a lie. It's patronizing and it's disgusting. This isn't how we should have economic discussions."

Justin Wolfers

23,292 görüntüleme • 10 ay önce

PhD Students – Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. For example, generate graphs for data analysis. 5. AnswerThis will generate variety of graphs for you. 6. It also generates an insightful data analysis report. You can download the graphs and the report. Try AnswerThis today. It’s FREE. AnswerThis link:

PhD Students – Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. For example, generate graphs for data analysis. 5. AnswerThis will generate variety of graphs for you. 6. It also generates an insightful data analysis report. You can download the graphs and the report. Try AnswerThis today. It’s FREE. AnswerThis link:

Faheem Ullah

207,403 görüntüleme • 7 ay önce

Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to and log in 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. E.g., generate graphs for exploratory data analysis. 5. Liner will generate a variety of graphs for you. 6. It also generates an insightful data analysis report. ✦ You can edit the report. For example, you can: ✦ Insert citations in the report ✦ Make changes to the text ✦ Add/remove any graphs After making changes, you can download the report. Try Liner today. It’s FREE. Liner link:

Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to and log in 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. E.g., generate graphs for exploratory data analysis. 5. Liner will generate a variety of graphs for you. 6. It also generates an insightful data analysis report. ✦ You can edit the report. For example, you can: ✦ Insert citations in the report ✦ Make changes to the text ✦ Add/remove any graphs After making changes, you can download the report. Try Liner today. It’s FREE. Liner link:

Faheem Ullah

11,984 görüntüleme • 6 ay önce

Announcing Agent Bricks: auto-optimize agents for your domain tasks. Provide a high-level description of the agent’s task, and connect your enterprise data — Agent Bricks handles the rest. Agent Bricks builds out an agent system that automatically optimizes against your goals and generates domain-specific synthetic datasets, accelerating agent development without relying on manual labeling or external sources.

Announcing Agent Bricks: auto-optimize agents for your domain tasks. Provide a high-level description of the agent’s task, and connect your enterprise data — Agent Bricks handles the rest. Agent Bricks builds out an agent system that automatically optimizes against your goals and generates domain-specific synthetic datasets, accelerating agent development without relying on manual labeling or external sources.

Databricks

47,044 görüntüleme • 1 yıl önce

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 görüntüleme • 11 ay önce

The world around us is changing so fast. And to stay relevant, you need to constantly upskill yourself as well as accumulate capabilities. Data Analytics, Automation, and AI skill will continue to grow and evolve. On this channel, I will help you grow your knowledge of in these areas. From building your first machine learning model to deployment, building your first dashboard to becoming a pro, understanding business process automation using low code tools, data management, as well as learning from industry professionals through podcast. If you would like to learn about Microsoft Fabric, Power Platform, Data Management and Governance, Data Analytics, you are in the right place.

The world around us is changing so fast. And to stay relevant, you need to constantly upskill yourself as well as accumulate capabilities. Data Analytics, Automation, and AI skill will continue to grow and evolve. On this channel, I will help you grow your knowledge of in these areas. From building your first machine learning model to deployment, building your first dashboard to becoming a pro, understanding business process automation using low code tools, data management, as well as learning from industry professionals through podcast. If you would like to learn about Microsoft Fabric, Power Platform, Data Management and Governance, Data Analytics, you are in the right place.

TheOyinbooke

15,539 görüntüleme • 2 yıl önce

Model progress is no longer constrained by architecture, but by access to high-quality, human-generated data. Scraped internet data is finite, low-signal and increasingly synthetic. Kled AI pays real people to upload authentic, real world content task by task in their mobile app. We are super excited to back Avi Patel and Kled AI as they build a massive data marketplace. If you are in need of a great data partner, reach out! More from us here (we were not investors when we filmed):

Model progress is no longer constrained by architecture, but by access to high-quality, human-generated data. Scraped internet data is finite, low-signal and increasingly synthetic. Kled AI pays real people to upload authentic, real world content task by task in their mobile app. We are super excited to back Avi Patel and Kled AI as they build a massive data marketplace. If you are in need of a great data partner, reach out! More from us here (we were not investors when we filmed):

Nichole Wischoff

37,246 görüntüleme • 6 ay önce

Data Models - Better AI Code in 5 Minutes Your AI isn't consistent because you're missing one file. I had the same problem until I started using a data model file. In this video, I'm showing you the exact two prompts I use: one generates the data model, the second uses it to generate features without breaking my app. Testing this with AnimeLeak (my actual production app) adding an admin approval feature. Using Droid with Sonnet 4.5 in spec mode, but this works in Cursor, Claude, or whatever you're using. KEY TIMESTAMPS 00:00 - The One File Your AI is Missing 00:44 - Prompt 1: Generate a Data Model 01:12 - The Result: Data Model Documentation 02:12 - Prompt 2: Implement a New Feature 03:00 - Reviewing the AI-Generated Spec 04:17 - Why This Workflow is So Powerful 04:32 - Your 3-Step Action Plan 05:12 - Next Video: Live Code Generation

Data Models - Better AI Code in 5 Minutes Your AI isn't consistent because you're missing one file. I had the same problem until I started using a data model file. In this video, I'm showing you the exact two prompts I use: one generates the data model, the second uses it to generate features without breaking my app. Testing this with AnimeLeak (my actual production app) adding an admin approval feature. Using Droid with Sonnet 4.5 in spec mode, but this works in Cursor, Claude, or whatever you're using. KEY TIMESTAMPS 00:00 - The One File Your AI is Missing 00:44 - Prompt 1: Generate a Data Model 01:12 - The Result: Data Model Documentation 02:12 - Prompt 2: Implement a New Feature 03:00 - Reviewing the AI-Generated Spec 04:17 - Why This Workflow is So Powerful 04:32 - Your 3-Step Action Plan 05:12 - Next Video: Live Code Generation

Ray Fernando

15,479 görüntüleme • 11 ay önce

OMG… you can now fine-tune your own AI model and run it entirely on your device. No servers. No API keys. No data leaks. Gemma 3 270M just made local LLMs real and it only takes about an hour to build your own. Here’s how:

OMG… you can now fine-tune your own AI model and run it entirely on your device. No servers. No API keys. No data leaks. Gemma 3 270M just made local LLMs real and it only takes about an hour to build your own. Here’s how:

Louis Gleeson

79,508 görüntüleme • 1 yıl önce

To register as a member of APC, your name, your data, and your primary data are sourced from NIMC. We are the only political party that sources primary data from NIMC. NIMC verifies data beyond what you think. - Nentawe Yilwatda, APC national chairman, speaking on the membership registration process in the party #PoliticsToday

To register as a member of APC, your name, your data, and your primary data are sourced from NIMC. We are the only political party that sources primary data from NIMC. NIMC verifies data beyond what you think. - Nentawe Yilwatda, APC national chairman, speaking on the membership registration process in the party #PoliticsToday

Channels Television

56,156 görüntüleme • 4 ay önce