正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

Antigravity analyzes massive datasets across multiple cloud environments to isolate data anomalies and build automated production pipelines. In this demo, Gemini 3.7 Flash showcases its ability to reason across BigQuery, PostgreSQL, and Cloud Storage, execute self-healing build and debug loops, and apply multimodal understanding through native PDF parsing.

Google Antigravity

182,135 subscribers

53,856 次观看 • 15 天前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Antigravity leverages Gemini 3.7 Flash’s reasoning to build complete native auth screens across SwiftUI, React Native, Jetpack Compose, and Flutter.

Antigravity leverages Gemini 3.7 Flash’s reasoning to build complete native auth screens across SwiftUI, React Native, Jetpack Compose, and Flutter.

Google Antigravity

83,474 次观看 • 14 天前

Watch Gemini 3.7 Flash handle autonomous website optimization in Google Antigravity. Leveraging MCP servers, multimodal understanding, and long-horizon execution, the agent sets out to achieve a 100 across all Lighthouse metrics and modernize the site to the latest web standards.

Watch Gemini 3.7 Flash handle autonomous website optimization in Google Antigravity. Leveraging MCP servers, multimodal understanding, and long-horizon execution, the agent sets out to achieve a 100 across all Lighthouse metrics and modernize the site to the latest web standards.

Google Antigravity

97,597 次观看 • 16 天前

Computer Use is now a built-in tool supported in Gemini 3.5 Flash. 💻 Developers can now use 3.5 Flash to build custom agents that see and take action across browser, mobile and desktop environments.

Computer Use is now a built-in tool supported in Gemini 3.5 Flash. 💻 Developers can now use 3.5 Flash to build custom agents that see and take action across browser, mobile and desktop environments.

Google

243,071 次观看 • 2 个月前

New course: Building Coding Agents with Tool Execution, taught by Tereza Tizkova and Fra from E2B. Most AI agents are limited to predefined function calls. This short course teaches you to build agents that write and execute code to accomplish tasks, accessing entire programming language ecosystems instead of being restricted to a fixed set of tools. You'll learn to run agent-generated code safely in sandboxed cloud environments that protect your systems from harmful operations. Skills you'll gain: - Build agents that write and execute code, manage files, and handle errors autonomously through feedback loops - Run agent code safely in E2B cloud sandboxes and understand tradeoffs between local, containerized, and cloud execution - Create a data analyst agent that explores visualizes data with Pandas - Create a full-stack agent that builds complete Next.js web applications Join and build agents that code their way through complex tasks:

New course: Building Coding Agents with Tool Execution, taught by Tereza Tizkova and Fra from E2B. Most AI agents are limited to predefined function calls. This short course teaches you to build agents that write and execute code to accomplish tasks, accessing entire programming language ecosystems instead of being restricted to a fixed set of tools. You'll learn to run agent-generated code safely in sandboxed cloud environments that protect your systems from harmful operations. Skills you'll gain: - Build agents that write and execute code, manage files, and handle errors autonomously through feedback loops - Run agent code safely in E2B cloud sandboxes and understand tradeoffs between local, containerized, and cloud execution - Create a data analyst agent that explores visualizes data with Pandas - Create a full-stack agent that builds complete Next.js web applications Join and build agents that code their way through complex tasks:

Andrew Ng

204,109 次观看 • 8 个月前

Static PDF ➡️ interactive web app, all in one prompt! We used Gemini 3.7 Flash in Google AI Studio to transform a dense PDF annual report into a beautiful website. Using its native PDF understanding, the model easily extracted data, crunched the numbers, and generated delightful interactive UI in one go. Learn how from genevieveh@:

Static PDF ➡️ interactive web app, all in one prompt! We used Gemini 3.7 Flash in Google AI Studio to transform a dense PDF annual report into a beautiful website. Using its native PDF understanding, the model easily extracted data, crunched the numbers, and generated delightful interactive UI in one go. Learn how from genevieveh@:

Google for Developers

66,105 次观看 • 11 天前

Gemini 3 Pro is the best model in the world for multimodal understanding. One of its most exciting capabilities is document understanding and reasoning. This means you can convert information in any format and into the medium that works best for you. Gemini 3 also has leading multilingual capabilities, enabling it to process, reason and even capture cultural relevance across a variety of languages. For example, here Gemini 3 is translating handwritten recipes in Korean and English to build a digital family cookbook in different languages.

Gemini 3 Pro is the best model in the world for multimodal understanding. One of its most exciting capabilities is document understanding and reasoning. This means you can convert information in any format and into the medium that works best for you. Gemini 3 also has leading multilingual capabilities, enabling it to process, reason and even capture cultural relevance across a variety of languages. For example, here Gemini 3 is translating handwritten recipes in Korean and English to build a digital family cookbook in different languages.

Google AI

36,696 次观看 • 9 个月前

Hippius offers storage at a lower price per terabyte than Google Drive, iCloud, and Dropbox. On the latest Novelty Search, Hippius breaks down how SN75 turns Bittensor miners into S3-compatible cloud storage, with no egress fees for moving large models and datasets. Its Arion storage layer shards data across miners and self-heals, allowing the network to keep operating even if up to two-thirds of its machines go offline. Hosted by const Full episode in the first comment.

Hippius offers storage at a lower price per terabyte than Google Drive, iCloud, and Dropbox. On the latest Novelty Search, Hippius breaks down how SN75 turns Bittensor miners into S3-compatible cloud storage, with no egress fees for moving large models and datasets. Its Arion storage layer shards data across miners and self-heals, allowing the network to keep operating even if up to two-thirds of its machines go offline. Hosted by const Full episode in the first comment.

Openτensor Foundaτion

20,751 次观看 • 8 天前

Today we're introducing Gemini 3.7 Flash, our most intelligent workhorse model yet for coding and agents. This model brings substantial gains across software engineering, web development, and complex knowledge work. Now through the end of the year, Gemini 3.7 Flash is available at an introductory price* of $0.75/1M input and $3.75/1M output tokens — making it easier and more cost-effective for developers and customers to scale production-ready agents.

Today we're introducing Gemini 3.7 Flash, our most intelligent workhorse model yet for coding and agents. This model brings substantial gains across software engineering, web development, and complex knowledge work. Now through the end of the year, Gemini 3.7 Flash is available at an introductory price* of $0.75/1M input and $3.75/1M output tokens — making it easier and more cost-effective for developers and customers to scale production-ready agents.

Google

1,170,997 次观看 • 16 天前

Build agents that can see, reason, and take action across browser, desktop and mobile environments. Gemini 3.5 Flash now features built-in computer use capabilities: 📱 Custom client-side functions for human-in-the-loop takeover 🔒 Configurable action-level safety policies 🛡️ Intent arguments to maximize transparency Learn more in the blog →

Build agents that can see, reason, and take action across browser, desktop and mobile environments. Gemini 3.5 Flash now features built-in computer use capabilities: 📱 Custom client-side functions for human-in-the-loop takeover 🔒 Configurable action-level safety policies 🛡️ Intent arguments to maximize transparency Learn more in the blog →

Google for Developers

51,563 次观看 • 2 个月前

🚀 🚀OSS Intelligence，Global Debut! ▶️ We cordially invite you to join「OSS Powers AI: Cloud-Scale Data Intelligence」, where Alibaba Cloud will unveil two groundbreaking capabilities—OSS Vector Bucket and OSS Metaquery. These innovations deeply integrate vector search and intelligent semantic querying into object storage service, fulfilling the demands of massive-scale data storage, sub-second retrieval, and intelligent usage—enabling you to build smarter RAG pipelines, AI agents, and AI applications faster and more easily than ever before. ⏰Date & Time: February 4, 2026, 14:00 (UTC+8) 🔗Registration: #AlibabaCloud #CloudComputing #AI #AInnovation #LLM

🚀 🚀OSS Intelligence，Global Debut! ▶️ We cordially invite you to join「OSS Powers AI: Cloud-Scale Data Intelligence」, where Alibaba Cloud will unveil two groundbreaking capabilities—OSS Vector Bucket and OSS Metaquery. These innovations deeply integrate vector search and intelligent semantic querying into object storage service, fulfilling the demands of massive-scale data storage, sub-second retrieval, and intelligent usage—enabling you to build smarter RAG pipelines, AI agents, and AI applications faster and more easily than ever before. ⏰Date & Time: February 4, 2026, 14:00 (UTC+8) 🔗Registration: #AlibabaCloud #CloudComputing #AI #AInnovation #LLM

Alibaba Cloud

17,146 次观看 • 7 个月前

Our most intelligent workhorse model yet for coding and agents has arrived ⚡ Meet Gemini 3.7 Flash. — Crush that seemingly endless to-do list. Gemini Spark in the Google Gemini now uses 3.7 Flash. The new model can equip your personal AI agent to work even smarter for you by seamlessly handling complex, multi-step tasks across your Google Workspace apps like Gmail, Google Calendar and Google Docs — Enjoy a smoother build experience. The model thinks more diligently, putting more effort into multi-step planning and tool calls. A more disciplined execution means less manual oversight and fewer retries across engineering workflows — Build more, spend less. 3.7 Flash is available through the end of the year with an introductory price of half the original 3.6 Flash cost per million tokens ($0.75/1M input tokens and $3.75/1M output tokens)

Our most intelligent workhorse model yet for coding and agents has arrived ⚡ Meet Gemini 3.7 Flash. — Crush that seemingly endless to-do list. Gemini Spark in the Google Gemini now uses 3.7 Flash. The new model can equip your personal AI agent to work even smarter for you by seamlessly handling complex, multi-step tasks across your Google Workspace apps like Gmail, Google Calendar and Google Docs — Enjoy a smoother build experience. The model thinks more diligently, putting more effort into multi-step planning and tool calls. A more disciplined execution means less manual oversight and fewer retries across engineering workflows — Build more, spend less. 3.7 Flash is available through the end of the year with an introductory price of half the original 3.6 Flash cost per million tokens ($0.75/1M input tokens and $3.75/1M output tokens)

Google AI

126,843 次观看 • 16 天前

Here are a couple examples of how Gemini 3.1 Flash-Lite can solve real-world problems: First, this high-volume image sorter showcases the model’s ability to quickly analyze and sort large amounts of content, like pictures (something that could have been too expensive or slow in the past). This demo is just a snapshot 📸😉 of what can be built using 3.1 Flash-Lite's multimodal analysis capabilities. Think: real-time data visualization agents, CRM management tools, automated content moderation software, and beyond.

Here are a couple examples of how Gemini 3.1 Flash-Lite can solve real-world problems: First, this high-volume image sorter showcases the model’s ability to quickly analyze and sort large amounts of content, like pictures (something that could have been too expensive or slow in the past). This demo is just a snapshot 📸😉 of what can be built using 3.1 Flash-Lite's multimodal analysis capabilities. Think: real-time data visualization agents, CRM management tools, automated content moderation software, and beyond.

Google AI

91,385 次观看 • 5 个月前

At #HuaweiCloud COMPASS, Tech Influencer Aziel Medina spoke with Mark Chen, President of Huawei Cloud Latin America Region, about the future of AI and cloud in the region. Mark shared Huawei Cloud's priorities: deploying Pangu Models, advancing data infrastructure, and helping enterprises modernize legacy systems. He also emphasized how China's innovation mindset can inspire digital transformation across Latin America. Huawei Cloud is embracing openness and collaboration to build the future—together. #COMPASS2025 #AHEADINTIME

At #HuaweiCloud COMPASS, Tech Influencer Aziel Medina spoke with Mark Chen, President of Huawei Cloud Latin America Region, about the future of AI and cloud in the region. Mark shared Huawei Cloud's priorities: deploying Pangu Models, advancing data infrastructure, and helping enterprises modernize legacy systems. He also emphasized how China's innovation mindset can inspire digital transformation across Latin America. Huawei Cloud is embracing openness and collaboration to build the future—together. #COMPASS2025 #AHEADINTIME

Huawei Cloud

221,054 次观看 • 1 年前

We wanted to see how Gemini 3.5 Flash-Lite handles massive, repetitive visual tasks. This demo runs the model across 1M+ catalog images, extracting raw features into clean, structured data with the low latency and token efficiency required for large-scale workflows.

We wanted to see how Gemini 3.5 Flash-Lite handles massive, repetitive visual tasks. This demo runs the model across 1M+ catalog images, extracting raw features into clean, structured data with the low latency and token efficiency required for large-scale workflows.

Google AI Developers

23,445 次观看 • 1 个月前

New course: Gemini CLI: Code & Create with an Open-Source Agent, built with Google Cloud Tech/Gemini CLI and taught by Jack Wotherspoon. Agentic coding assistants like Gemini CLI are transforming how developers work. This short course teaches you to use Google's open-source agent to coordinate local tools and cloud services for coding and non-coding workflows. Gemini CLI works from your terminal, so it works with your local files and development tools. You can also connect it to services through MCP. Then provide high-level instructions, and it autonomously plans and executes complex workflows. Skills you'll gain: - Build website features and automate code reviews with GitHub ActionsCreate data dashboards that combine local files with cloud data sources - Use MCP servers and extensions to orchestrate workflows across GitHub, Canva, and Google Workspace - Generate social media content from multimedia files like conference recordings I particularly appreciate that Gemini CLI is open-source. You can see exactly how it works, read the prompts it uses, and understand its architecture. The community has contributed thousands of pull requests. Since Gemini 3’s release I've found Gemini CLI highly capable - this is a tool worth having in your toolbox! Whether you're prototyping applications, automating workflows, or working with multimedia content, join to learn to delegate complex tasks and build faster:

New course: Gemini CLI: Code & Create with an Open-Source Agent, built with Google Cloud Tech/Gemini CLI and taught by Jack Wotherspoon. Agentic coding assistants like Gemini CLI are transforming how developers work. This short course teaches you to use Google's open-source agent to coordinate local tools and cloud services for coding and non-coding workflows. Gemini CLI works from your terminal, so it works with your local files and development tools. You can also connect it to services through MCP. Then provide high-level instructions, and it autonomously plans and executes complex workflows. Skills you'll gain: - Build website features and automate code reviews with GitHub ActionsCreate data dashboards that combine local files with cloud data sources - Use MCP servers and extensions to orchestrate workflows across GitHub, Canva, and Google Workspace - Generate social media content from multimedia files like conference recordings I particularly appreciate that Gemini CLI is open-source. You can see exactly how it works, read the prompts it uses, and understand its architecture. The community has contributed thousands of pull requests. Since Gemini 3’s release I've found Gemini CLI highly capable - this is a tool worth having in your toolbox! Whether you're prototyping applications, automating workflows, or working with multimedia content, join to learn to delegate complex tasks and build faster:

Andrew Ng

125,385 次观看 • 7 个月前

🆕Google DeepMind's Gemini 3.7 Flash dropped this morning, and our evals showed it scored higher and ran about 37% faster than Gemini 3.6 Flash, with the largest gains on the most challenging analytical tasks. Here's how it performs on one of those tasks: private credit work. In this Box Agent demo, Gemini 3.7 Flash reviews 15 documents in a borrower package, catches an outdated leverage limit, applies the amended 5.50x threshold, corrects for $4M in restricted cash and a $1.5M unsupported add-back, flags a potential covenant exception at 5.58x, and saves a cited review back to Box for the portfolio team. Customers can use Gemini 3.7 Flash in Box AI Studio to build custom agents grounded in their enterprise content.

🆕Google DeepMind's Gemini 3.7 Flash dropped this morning, and our evals showed it scored higher and ran about 37% faster than Gemini 3.6 Flash, with the largest gains on the most challenging analytical tasks. Here's how it performs on one of those tasks: private credit work. In this Box Agent demo, Gemini 3.7 Flash reviews 15 documents in a borrower package, catches an outdated leverage limit, applies the amended 5.50x threshold, corrects for $4M in restricted cash and a $1.5M unsupported add-back, flags a potential covenant exception at 5.58x, and saves a cited review back to Box for the portfolio team. Customers can use Gemini 3.7 Flash in Box AI Studio to build custom agents grounded in their enterprise content.

Box

3,817,973 次观看 • 16 天前

How does an AI model actually learn to see? 🤖 Learn about the tech behind native multimodality, how models reason over visual data like documents and video, and the future of proactive AI assistants with Logan Kilpatrick and Gemini Model Behavior Product Lead, Ani Baddepudi. ↓ Timestamps: 01:12 Why Gemini is natively multimodal 02:23 The technology behind multimodal models 05:15 Video understanding with Gemini 2.5 09:25 Deciding what to build next 13:23 Building new product experiences with multimodal AI 17:15 The vision for proactive assistants 24:13 Improving video usability with variable FPS and frame tokenization 27:35 What’s next for Gemini’s multimodal development 31:47 Deep dive on Gemini’s document understanding capabilities 37:56 The teamwork and collaboration behind Gemini 40:56 What’s next with model behavior

How does an AI model actually learn to see? 🤖 Learn about the tech behind native multimodality, how models reason over visual data like documents and video, and the future of proactive AI assistants with Logan Kilpatrick and Gemini Model Behavior Product Lead, Ani Baddepudi. ↓ Timestamps: 01:12 Why Gemini is natively multimodal 02:23 The technology behind multimodal models 05:15 Video understanding with Gemini 2.5 09:25 Deciding what to build next 13:23 Building new product experiences with multimodal AI 17:15 The vision for proactive assistants 24:13 Improving video usability with variable FPS and frame tokenization 27:35 What’s next for Gemini’s multimodal development 31:47 Deep dive on Gemini’s document understanding capabilities 37:56 The teamwork and collaboration behind Gemini 40:56 What’s next with model behavior

Google AI

58,703 次观看 • 1 年前

We’re expanding the Gemini API File Search tool 🔍 with 3 new updates that enable developers to more easily build multimodal RAG systems with enhanced precision: + Multimodal Support: By leveraging our Gemini Embedding 2 model, File Search can now reason across image and text simultaneously. + Custom Metadata Filtering: Bring structure to unstructured data by tagging files with custom key-value labels. This pre-filters your data and boosts search speed. + Exact citations: File Search can now capture and return the exact source (down to the page number) for every piece of information indexed. See multimodal File Search in action with our example app in Google AI Studio. Chat with your entire image and doc library, ask questions, and trace answers back to the source:

We’re expanding the Gemini API File Search tool 🔍 with 3 new updates that enable developers to more easily build multimodal RAG systems with enhanced precision: + Multimodal Support: By leveraging our Gemini Embedding 2 model, File Search can now reason across image and text simultaneously. + Custom Metadata Filtering: Bring structure to unstructured data by tagging files with custom key-value labels. This pre-filters your data and boosts search speed. + Exact citations: File Search can now capture and return the exact source (down to the page number) for every piece of information indexed. See multimodal File Search in action with our example app in Google AI Studio. Chat with your entire image and doc library, ask questions, and trace answers back to the source:

Google AI Developers

108,801 次观看 • 3 个月前

Here's how I would learn data engineering basics in 2025: - Find a data source you care about (examples: gaming APIs, stock market, web scraping, etc) - Use Python to interact and ingest your source. Initially just write the data to a CSV. - Setup an account with Snowflake or Google BigQuery. - update your Python script to load a table in Snowflake/BigQuery - schedule your script with CRON in the cloud with a service like Heroku. - build aggregations and visualizations on top of your ingested data Only thing this misses is data quality and complex job orchestration which you can learn later! How would you learn data engineering nowadays?

Here's how I would learn data engineering basics in 2025: - Find a data source you care about (examples: gaming APIs, stock market, web scraping, etc) - Use Python to interact and ingest your source. Initially just write the data to a CSV. - Setup an account with Snowflake or Google BigQuery. - update your Python script to load a table in Snowflake/BigQuery - schedule your script with CRON in the cloud with a service like Heroku. - build aggregations and visualizations on top of your ingested data Only thing this misses is data quality and complex job orchestration which you can learn later! How would you learn data engineering nowadays?

Zach Wilson

20,368 次观看 • 1 年前

Explore state-of-the-art multimodal prompting in our new short course Large Multimodal Model Prompting with Gemini, taught by Erwin Huizenga in collaboration with Google Cloud. One interesting insight from this course: with multimodal models, prompt structure matters significantly. Placing text inputs, such as a patient's medical history, before image inputs, like an X-ray, can enhance the model's ability to contextualize and interpret visual data effectively. In other contexts, such as image captioning, you may get better results by putting the image first. Multimodal models behave differently than text-only LLMs, and effective prompting for models varies depending on the model you’re using. In this course you’ll learn how to effectively prompt Gemini models. Gemini's multimodal capabilities also enable new approaches in AI application development, for example: - The Gemini library handles various video formats (MP4, MOV, MPEG), streamlining applications using these formats. - Large context window (up to 1 million tokens) enables processing of extensive content, like analyzing multiple 50-minute videos simultaneously. - Function calling feature integrates real-time data (e.g., current exchange rates) into model responses. The course demonstrates building multimodal applications with real-world examples including document analyzers that reason across text and graphs simultaneously, video content extractors that find and timestamp specific information from multiple hours of footage, and automated expense report systems processing receipt images while cross-referencing company policies. Sign up here:

Explore state-of-the-art multimodal prompting in our new short course Large Multimodal Model Prompting with Gemini, taught by Erwin Huizenga in collaboration with Google Cloud. One interesting insight from this course: with multimodal models, prompt structure matters significantly. Placing text inputs, such as a patient's medical history, before image inputs, like an X-ray, can enhance the model's ability to contextualize and interpret visual data effectively. In other contexts, such as image captioning, you may get better results by putting the image first. Multimodal models behave differently than text-only LLMs, and effective prompting for models varies depending on the model you’re using. In this course you’ll learn how to effectively prompt Gemini models. Gemini's multimodal capabilities also enable new approaches in AI application development, for example: - The Gemini library handles various video formats (MP4, MOV, MPEG), streamlining applications using these formats. - Large context window (up to 1 million tokens) enables processing of extensive content, like analyzing multiple 50-minute videos simultaneously. - Function calling feature integrates real-time data (e.g., current exchange rates) into model responses. The course demonstrates building multimodal applications with real-world examples including document analyzers that reason across text and graphs simultaneously, video content extractors that find and timestamp specific information from multiple hours of footage, and automated expense report systems processing receipt images while cross-referencing company policies. Sign up here:

Andrew Ng

74,060 次观看 • 2 年前