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。

Less context-switching, more coding. 🛠️ The latest Android Studio updates bring natively integrated memory profiling, parallel chats, and context-aware crash remediation directly into the IDE - reducing manual friction across the board. Download Android Studio Quail 2 and start exploring these new features today!

Google for Developers

2,575,750 subscribers

15,849 次观看 • 12 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

Let's dive into the Android Studio Jellyfish stable release 🏊🏽🤩🪼 → From Gemini in Android Studio to Android Device Streaming, download the new version and incorporate these new features into your workflow!

Let's dive into the Android Studio Jellyfish stable release 🏊🏽🤩🪼 → From Gemini in Android Studio to Android Device Streaming, download the new version and incorporate these new features into your workflow!

Android Developers

16,651 次观看 • 2 年前

Say goodbye to sequential bottlenecks. In the stable release of Android Studio Quail 2, you can run multiple agent tasks in parallel with Gemini! 💬🔄 Watch how you can initiate and manage multiple agent tasks simultaneously across your chats. Read more in the launch blog post:

Say goodbye to sequential bottlenecks. In the stable release of Android Studio Quail 2, you can run multiple agent tasks in parallel with Gemini! 💬🔄 Watch how you can initiate and manage multiple agent tasks simultaneously across your chats. Read more in the launch blog post:

Google for Developers

14,716 次观看 • 13 天前

Test smarter and build efficiently with the Android Studio Otter 3 Feature Drop. Bring your own models to power AI assistance, run resilient UI tests that adapt to changes, turn design mocks directly into starting code, and connect tools like Figma and Notion for context. Start building 🌟 →

Test smarter and build efficiently with the Android Studio Otter 3 Feature Drop. Bring your own models to power AI assistance, run resilient UI tests that adapt to changes, turn design mocks directly into starting code, and connect tools like Figma and Notion for context. Start building 🌟 →

Google for Developers

12,646 次观看 • 6 个月前

Chad IDE (cladlabs) is the brainrot code editor. AI coding takes 1–5 min between prompts—too long to ignore, too short to start something new. Chad integrates your brainrot (X, IG, Stake, Tinder, etc) into your agentic coding workflow and helps to manage your context-switching. Less doom-scrolling. More shipping.

Chad IDE (cladlabs) is the brainrot code editor. AI coding takes 1–5 min between prompts—too long to ignore, too short to start something new. Chad integrates your brainrot (X, IG, Stake, Tinder, etc) into your agentic coding workflow and helps to manage your context-switching. Less doom-scrolling. More shipping.

Y Combinator

3,396,607 次观看 • 8 个月前

🔍New in LangGraph Studio: Trace mode View your LangSmith traces in real time right inside Studio. Annotate runs, and add them to datasets or annotation queues, bringing the power of LangSmith tracing directly into your workflow. Debug faster and dig deeper, without any of the context switching. Try it today!

🔍New in LangGraph Studio: Trace mode View your LangSmith traces in real time right inside Studio. Annotate runs, and add them to datasets or annotation queues, bringing the power of LangSmith tracing directly into your workflow. Debug faster and dig deeper, without any of the context switching. Try it today!

LangChain

25,866 次观看 • 11 个月前

Excited today to share with you the next step in the evolution of GM World Our new website features exceptional up-and-coming artists and offers more context on our curation, including the first episode of our new video series: From The Studio

Excited today to share with you the next step in the evolution of GM World Our new website features exceptional up-and-coming artists and offers more context on our curation, including the first episode of our new video series: From The Studio

BΞN

10,112 次观看 • 2 年前

Embedder is the world's first hardware-aware coding agent. By understanding and interacting directly with your hardware, it achieves state of the art performance in an embedded systems (C++/Rust) context. Our latest update (v0.3.0) features a stunning new terminal UI, and our fastest, most capable firmware agent yet: Embedder

Embedder is the world's first hardware-aware coding agent. By understanding and interacting directly with your hardware, it achieves state of the art performance in an embedded systems (C++/Rust) context. Our latest update (v0.3.0) features a stunning new terminal UI, and our fastest, most capable firmware agent yet: Embedder

Ethan Gibbs

435,293 次观看 • 6 个月前

Developer Week Day 2. 🛠️ We just gave your Coding Agent a Design Degree🎓🎨 Introducing the Stitch MCP Server. 🔌 You can now pipe Stitch designs directly into your favorite tools like Antigravity. * Generate new screens without leaving your IDE * Fetch the code from any design * Inject context: Give your agent full visual awareness Docs and more information 👇

Developer Week Day 2. 🛠️ We just gave your Coding Agent a Design Degree🎓🎨 Introducing the Stitch MCP Server. 🔌 You can now pipe Stitch designs directly into your favorite tools like Antigravity. * Generate new screens without leaving your IDE * Fetch the code from any design * Inject context: Give your agent full visual awareness Docs and more information 👇

Stitch by Google

359,644 次观看 • 6 个月前

Introducing the new AI first vibe coding experience in Google AI Studio! Built to take you from prompt to production with Gemini, and optimized for AI app creation. Start building AI apps for free : ) More updates and features to come!

Introducing the new AI first vibe coding experience in Google AI Studio! Built to take you from prompt to production with Gemini, and optimized for AI app creation. Start building AI apps for free : ) More updates and features to come!

Logan Kilpatrick

1,581,328 次观看 • 9 个月前

Introducing Gemini CLI extensions, a new framework that allows you to customize Gemini CLI and connect it to the tools you use most—all from the command line. No more context-switching. Start using your favorite tools directly in your workflow today →

Introducing Gemini CLI extensions, a new framework that allows you to customize Gemini CLI and connect it to the tools you use most—all from the command line. No more context-switching. Start using your favorite tools directly in your workflow today →

Google Cloud Tech

80,461 次观看 • 9 个月前

Turn Claude into an autonomous worker that never stops. No more babysitting every prompt. No more manual debugging cycles. Here’s the new play 👇 → Claude loops until task completion. → Set goals then walk away. → Autonomous error and bug fixing. → Ship entire features overnight. → Context-aware code refactoring. Scale your development output today. Developers are already shipping features faster. Want the full guide? DM me.

Turn Claude into an autonomous worker that never stops. No more babysitting every prompt. No more manual debugging cycles. Here’s the new play 👇 → Claude loops until task completion. → Set goals then walk away. → Autonomous error and bug fixing. → Ship entire features overnight. → Context-aware code refactoring. Scale your development output today. Developers are already shipping features faster. Want the full guide? DM me.

Julian Goldie SEO

35,243 次观看 • 6 个月前

For developers, Gemini 3 Pro offers unparalleled results across every major AI benchmark compared to Gemini 2.5 Pro. Here three ways Gemini 3 Pro can improve your workflow today: 1. Gemini 3 Pro fits right into existing production agent and coding workflows. It is also integrated into development environment experiences across the ecosystem of developer products like Google AI Studio, Gemini CLI, and Android Studio. 2. We introduced Google Google Antigravity, a new agentic development platform that reimagines how the model and IDE work together. It enables developers to manage agents to work at a higher, task-oriented level collaborating with agents while they build. 3. We’ve built Google AI Studio to be your fastest path from a prompt to an AI-native app. Build mode lets you add AI capabilities faster than ever, automatically wiring up the right models and APIs, while features like annotations enable fast and intuitive iteration. Whether you’re building a one-shot prompt game or an interactive landing page from unstructured voice notes, you can bring any idea to life with Gemini 3.

For developers, Gemini 3 Pro offers unparalleled results across every major AI benchmark compared to Gemini 2.5 Pro. Here three ways Gemini 3 Pro can improve your workflow today: 1. Gemini 3 Pro fits right into existing production agent and coding workflows. It is also integrated into development environment experiences across the ecosystem of developer products like Google AI Studio, Gemini CLI, and Android Studio. 2. We introduced Google Google Antigravity, a new agentic development platform that reimagines how the model and IDE work together. It enables developers to manage agents to work at a higher, task-oriented level collaborating with agents while they build. 3. We’ve built Google AI Studio to be your fastest path from a prompt to an AI-native app. Build mode lets you add AI capabilities faster than ever, automatically wiring up the right models and APIs, while features like annotations enable fast and intuitive iteration. Whether you’re building a one-shot prompt game or an interactive landing page from unstructured voice notes, you can bring any idea to life with Gemini 3.

Google AI

136,589 次观看 • 8 个月前

AI coding just arrived in Jupyter notebooks - and bhupesh ganger (Jupyter co-founder) and I will show you how to use it. Coding by hand is becoming obsolete. The latest Jupyter AI - built by the Jupyter team and showcased at JupyterCon this week - brings AI assistance directly into notebooks. Most AI coding assistants struggle with Jupyter notebooks. Jupyter AI was designed specifically for them. This is the first course to teach it. In this course, Brian and I teach you to: - Generate and debug code directly in notebook cells through an integrated chat interface - Provide the right context (like API docs) to help AI write accurate code - Use Jupyter AI's unique notebook features: drag cells to chat, generate cells from chat, attach context for the LLM We've integrated Jupyter AI directly into the DeepLearningAI platform, so you can start using it immediately. Since Jupyter AI is open source, you can also install and run it locally afterward. Whether you're experienced with notebooks or learning them for the first time, this course will prepare you for AI-assisted notebook development. Start using Jupyter AI (free):

AI coding just arrived in Jupyter notebooks - and bhupesh ganger (Jupyter co-founder) and I will show you how to use it. Coding by hand is becoming obsolete. The latest Jupyter AI - built by the Jupyter team and showcased at JupyterCon this week - brings AI assistance directly into notebooks. Most AI coding assistants struggle with Jupyter notebooks. Jupyter AI was designed specifically for them. This is the first course to teach it. In this course, Brian and I teach you to: - Generate and debug code directly in notebook cells through an integrated chat interface - Provide the right context (like API docs) to help AI write accurate code - Use Jupyter AI's unique notebook features: drag cells to chat, generate cells from chat, attach context for the LLM We've integrated Jupyter AI directly into the DeepLearningAI platform, so you can start using it immediately. Since Jupyter AI is open source, you can also install and run it locally afterward. Whether you're experienced with notebooks or learning them for the first time, this course will prepare you for AI-assisted notebook development. Start using Jupyter AI (free):

Andrew Ng

118,319 次观看 • 8 个月前

🚀 TokenSight Latest Updates Are Live! We’re making trading smoother and more user-friendly with our newest features: ✨ What’s New? - A revamped 3-column Trade Page: Latest Orders now on the top right for better visibility. - Real-time PNL updates: See your assets directly on the Trade Page. - Redesigned Rewards & Referral pages: Clearer, simpler, better. - New Footer Row: View Latest or Recent Tokens based on your preferences. These updates are just the beginning! Expect more improvements to enhance your trading experience. Why wait? Start trading today and get rewarded for every trade! 👉 Trade Now:

🚀 TokenSight Latest Updates Are Live! We’re making trading smoother and more user-friendly with our newest features: ✨ What’s New? - A revamped 3-column Trade Page: Latest Orders now on the top right for better visibility. - Real-time PNL updates: See your assets directly on the Trade Page. - Redesigned Rewards & Referral pages: Clearer, simpler, better. - New Footer Row: View Latest or Recent Tokens based on your preferences. These updates are just the beginning! Expect more improvements to enhance your trading experience. Why wait? Start trading today and get rewarded for every trade! 👉 Trade Now:

TokenSight

15,825 次观看 • 1 年前

Introducing Gemini 3 ✨ It’s the best model in the world for multimodal understanding, and our most powerful agentic + vibe coding model yet. Gemini 3 can bring any idea to life, quickly grasping context and intent so you can get what you need with less prompting. Find Gemini 3 Pro rolling out today in the Google Gemini and AI Mode in Search. For developers, build with it now in Google AI Studio and Vertex AI. Excited for you to try it!

Introducing Gemini 3 ✨ It’s the best model in the world for multimodal understanding, and our most powerful agentic + vibe coding model yet. Gemini 3 can bring any idea to life, quickly grasping context and intent so you can get what you need with less prompting. Find Gemini 3 Pro rolling out today in the Google Gemini and AI Mode in Search. For developers, build with it now in Google AI Studio and Vertex AI. Excited for you to try it!

Sundar Pichai

2,950,946 次观看 • 8 个月前

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Jurij Skornik

11,070 次观看 • 2 个月前

Excited to announce the Codex App: run multiple projects and threads in one focused app! 🔥 The app natively packs a lot of features making it easier to maximise your productivity: > Worktree mode keeps changes isolated - parallel tasks without touching your checkout > Automations run in background worktrees and drop findings into your inbox > Built‑in Git review: diff, stage/revert hunks, inline comments > Integrated terminal for test, lint, git - no need to switch apps > Voice dictation: hold Ctrl+M and speak your prompt > Skills + slash commands for faster workflows. > IDE sync with auto context - ask about files you’re viewing > Local / Worktree / Cloud modes - choose where tasks run > Shared MCP config across app/CLI/IDE Bonus: For a limited time, we've doubled the rate limits across the tiers from Free all the way to Enterprise! Enjoy! 🤗

Excited to announce the Codex App: run multiple projects and threads in one focused app! 🔥 The app natively packs a lot of features making it easier to maximise your productivity: > Worktree mode keeps changes isolated - parallel tasks without touching your checkout > Automations run in background worktrees and drop findings into your inbox > Built‑in Git review: diff, stage/revert hunks, inline comments > Integrated terminal for test, lint, git - no need to switch apps > Voice dictation: hold Ctrl+M and speak your prompt > Skills + slash commands for faster workflows. > IDE sync with auto context - ask about files you’re viewing > Local / Worktree / Cloud modes - choose where tasks run > Shared MCP config across app/CLI/IDE Bonus: For a limited time, we've doubled the rate limits across the tiers from Free all the way to Enterprise! Enjoy! 🤗

Vaibhav (VB) Srivastav

72,678 次观看 • 5 个月前

Introducing NexaSDK for Android (Beta) — run the latest AI models locally, 9× more energy-efficient and 2× faster, on Android devices, powered by the Qualcomm Hexagon NPU. This is the first SDK to support NPU, GPU and CPU, unlocking the full power of every Android device — for example, LFM2-1.2B achieves 85 t/s on NPU vs 37 t/s on CPU. With just 3 lines of code, you can run the latest state-of-the-art models across every task, for example: Multimodal (Vision, Audio, Text): OmniNeural-4B Embedding: EmbeddingGemma from Google ASR: Parakeet-v3 from NVIDIA OCR: PaddleOCR from Baidu Inc. Rerank: Jina-reranker from Jina AI LLM: LFM2-1.2B from And we continue to deliver Day-0 model support across our framework. Build on-device AI into your Android app today and enjoy no cloud API cost, full privacy, and offline availability. Check out our Quickstart guide and example app below to get started in minutes.

Introducing NexaSDK for Android (Beta) — run the latest AI models locally, 9× more energy-efficient and 2× faster, on Android devices, powered by the Qualcomm Hexagon NPU. This is the first SDK to support NPU, GPU and CPU, unlocking the full power of every Android device — for example, LFM2-1.2B achieves 85 t/s on NPU vs 37 t/s on CPU. With just 3 lines of code, you can run the latest state-of-the-art models across every task, for example: Multimodal (Vision, Audio, Text): OmniNeural-4B Embedding: EmbeddingGemma from Google ASR: Parakeet-v3 from NVIDIA OCR: PaddleOCR from Baidu Inc. Rerank: Jina-reranker from Jina AI LLM: LFM2-1.2B from And we continue to deliver Day-0 model support across our framework. Build on-device AI into your Android app today and enjoy no cloud API cost, full privacy, and offline availability. Check out our Quickstart guide and example app below to get started in minutes.

NEXA AI

26,091 次观看 • 8 个月前

The new OpenCreator is here. Meet OpenCreator Agent —built to help you create complete videos through conversation. Start with an idea. OpenCreator Agent helps you shape the story, build the storyboard, create every shot, refine the details, and bring everything together into the final cut. Just describe what you want to create. More creating. Less managing the process. OpenCreator, your everyday video studio.

The new OpenCreator is here. Meet OpenCreator Agent —built to help you create complete videos through conversation. Start with an idea. OpenCreator Agent helps you shape the story, build the storyboard, create every shot, refine the details, and bring everything together into the final cut. Just describe what you want to create. More creating. Less managing the process. OpenCreator, your everyday video studio.

OpenCreator

435,708 次观看 • 1 个月前

Gemini-1.5 Pro has its spotlight stolen today, and people are poking fun at Sora vs Google memes. Well, I think it's the biggest boost in LLM capability so far in 2024. v1.5's 10M token context (1) excels at retrieval; (2) generalizes zero-shot to extremely long instructions like full tutorials and codebases; and (3) works across modalities such as text, audio, and video. Here's a stunning example: v1.5 learns to translate from English to Kalamang purely in context, following a full linguistic manual at inference time. Kalamang is a language spoken by fewer than 200 speakers in western New Guinea. Gemini has never seen this language during training and is only provided with 500 pages of linguistic documentation, a dictionary, and ~400 parallel sentences in context. It basically acquires a sophisticated new skill in the neural activations, instead of gradient finetuning. I talked about the Myth of Context Length many times before: don't get too excited by claims of 1M or even 1B context tokens. LSTMs already achieved literally infinite context length 25 yrs ago! What truly matters is how well the model actually uses the context to solve real-world problems, and Gemini-1.5 has surpassed the SOTA with flying colors. The paper is also well-written with lots of solid quantitative analysis on in-context memorization and generalization. Paper: “Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context” Congrats to Jeff Dean Oriol Vinyals Sundar Pichai and team!

Gemini-1.5 Pro has its spotlight stolen today, and people are poking fun at Sora vs Google memes. Well, I think it's the biggest boost in LLM capability so far in 2024. v1.5's 10M token context (1) excels at retrieval; (2) generalizes zero-shot to extremely long instructions like full tutorials and codebases; and (3) works across modalities such as text, audio, and video. Here's a stunning example: v1.5 learns to translate from English to Kalamang purely in context, following a full linguistic manual at inference time. Kalamang is a language spoken by fewer than 200 speakers in western New Guinea. Gemini has never seen this language during training and is only provided with 500 pages of linguistic documentation, a dictionary, and ~400 parallel sentences in context. It basically acquires a sophisticated new skill in the neural activations, instead of gradient finetuning. I talked about the Myth of Context Length many times before: don't get too excited by claims of 1M or even 1B context tokens. LSTMs already achieved literally infinite context length 25 yrs ago! What truly matters is how well the model actually uses the context to solve real-world problems, and Gemini-1.5 has surpassed the SOTA with flying colors. The paper is also well-written with lots of solid quantitative analysis on in-context memorization and generalization. Paper: “Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context” Congrats to Jeff Dean Oriol Vinyals Sundar Pichai and team!

Jim Fan

278,483 次观看 • 2 年前