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this is a [deep breath] M5 Stick S3 acting as a frontend for Gemini Flash 3.1 Live, running via a CloudFlare worker, with tools to set device settings (brightness, volume, power), web fetch / search, and vector search (of tldraw docs) via cf's Vectorize. crazy low latency, push to... show more

Steve Ruiz

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38,675 просмотров • 3 месяцев назад •via X (Twitter)

Образование Наука и технологии

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🚨 Google Search just got a massive upgrade. Gemini 2.5 Pro and Deep Search are now part of AI Mode, along with a new agentic feature. (US only for now) Damn so finally Google has indirectly brought Gemini to Google Search Page, This is massive. More details below 👇

🚨 Google Search just got a massive upgrade. Gemini 2.5 Pro and Deep Search are now part of AI Mode, along with a new agentic feature. (US only for now) Damn so finally Google has indirectly brought Gemini to Google Search Page, This is massive. More details below 👇

AshutoshShrivastava

61,862 просмотров • 1 год назад

GOOGLE JUST SHIPPED ITS ENTIRE 2026 ROADMAP IN ONE KEYNOTE Gemini 3.5 Flash → new flagship. frontier brain, agentic, beats 3.1 pro, 4x faster Gemini 3.5 Pro → the bigger one, drops next month Gemini Omni → any input in, editable VIDEO out Gemini Spark → a personal agent that actually DOES things across your apps Daily Brief → your morning, pre-read from gmail, calendar and tasks Neural Expressive → the gemini app got a full redesign Universal Cart → one agentic cart across gemini, youtube and gmail Information Agents → search that monitors the web 24/7 FOR you Intelligent Search Box → expands as you type for real conversations Search Mini Apps → build your own dashboards inside search AI Mode → now fully running on gemini 3.5 flash Gmail Live → talk to your inbox Docs Live → write and edit docs by voice AI Inbox → gmail, organized by ai Google Keep → speak freely, it cleans it into notes Google Pics → a brand new ai image and design app Ask YouTube → search the ENTIRE youtube catalogue with answers Android XR Glasses → "intelligent eyewear," audio glasses this fall Android Halo → a live strip showing what your agent is doing Antigravity 2.0 → the agent-first dev platform, upgraded Flow + Flow Music → now standalone mobile apps

shirish

198,233 просмотров • 1 месяц назад

Search Live is going global! 🌎 Starting today, people around the world can have conversations with Search using their voice and camera. And thanks to Gemini 3.1 Flash Live, it’s faster, more natural, and fully multilingual. It’s my favorite way to use AI Mode - and a total cheat code for exploring & learning about the world with my kids. ⬇️

Search Live is going global! 🌎 Starting today, people around the world can have conversations with Search using their voice and camera. And thanks to Gemini 3.1 Flash Live, it’s faster, more natural, and fully multilingual. It’s my favorite way to use AI Mode - and a total cheat code for exploring & learning about the world with my kids. ⬇️

Rajan Patel

83,568 просмотров • 3 месяцев назад

New Example! We built a fullstack open “Deep Research” quickstart for Google DeepMind Gemini 2.5. It dynamically searches the web, reflects on results, and delivers comprehensive answers with citations in a nice UI with streaming! Built using React and LangChain Langgraph! 🚀 TL;DR: 🔄 Agent iteratively loops through research and reflection until it gathers sufficient information. 🔍 Dynamic query generation, web research via Gemini native Google Search tool, and reflective reasoning. 🧠 Supports different search effors (low, medium, high) for width and depth of search 🛠️ React frontend, LangGraph backend, Tailwind CSS + Shadcn UI components. 🐳 Easily run locally or deploy with Docker. 📄 Answers inlcude citations from gathered web sources.

New Example! We built a fullstack open “Deep Research” quickstart for Google DeepMind Gemini 2.5. It dynamically searches the web, reflects on results, and delivers comprehensive answers with citations in a nice UI with streaming! Built using React and LangChain Langgraph! 🚀 TL;DR: 🔄 Agent iteratively loops through research and reflection until it gathers sufficient information. 🔍 Dynamic query generation, web research via Gemini native Google Search tool, and reflective reasoning. 🧠 Supports different search effors (low, medium, high) for width and depth of search 🛠️ React frontend, LangGraph backend, Tailwind CSS + Shadcn UI components. 🐳 Easily run locally or deploy with Docker. 📄 Answers inlcude citations from gathered web sources.

Philipp Schmid

55,535 просмотров • 1 год назад

From a single voice prompt to a deployed, real-time AI app in minutes. Watch Thor demonstrate the power of Google AI Studio: 1️⃣ Builds a multilingual interview simulator using Gemini 3.1 Flash Live. 2️⃣ Deploy it instantly to the web via Google Cloud Run. 3️⃣ Remixes it into a customer support agent with PDF knowledge docs. Ready to start your vibe coding journey? Build, remix, and check out the gallery at

From a single voice prompt to a deployed, real-time AI app in minutes. Watch Thor demonstrate the power of Google AI Studio: 1️⃣ Builds a multilingual interview simulator using Gemini 3.1 Flash Live. 2️⃣ Deploy it instantly to the web via Google Cloud Run. 3️⃣ Remixes it into a customer support agent with PDF knowledge docs. Ready to start your vibe coding journey? Build, remix, and check out the gallery at

Google for Developers

10,642 просмотров • 1 месяц назад

Today we announced the new Einstein Copilot Search and Salesforce Vector Database in Data Cloud to power semantic search and retrieval augmented generation. This will enhance Einstein Copilot's ability to understand, generate outputs, and automate actions across a wide variety of use cases, contexts, and data/content types. LLMs can't be relied on to recall specific data they were trained on, so the way to make them work in the enterprise, where accuracy is paramount, is to feed them with the right data using hybrid keyword and vector search-powered RAG. By bringing all this seamlessly into our apps and Einstein Copilot UX as well as Hyperforce cloud infrastructure, we can offer high performance, low latency, addressing data privacy, security, and residency requirements🌟

Today we announced the new Einstein Copilot Search and Salesforce Vector Database in Data Cloud to power semantic search and retrieval augmented generation. This will enhance Einstein Copilot's ability to understand, generate outputs, and automate actions across a wide variety of use cases, contexts, and data/content types. LLMs can't be relied on to recall specific data they were trained on, so the way to make them work in the enterprise, where accuracy is paramount, is to feed them with the right data using hybrid keyword and vector search-powered RAG. By bringing all this seamlessly into our apps and Einstein Copilot UX as well as Hyperforce cloud infrastructure, we can offer high performance, low latency, addressing data privacy, security, and residency requirements🌟

Clara Shih

49,620 просмотров • 2 лет назад

We're excited to introduce the Retrieval Harness in LlamaParse - which is the 2026 version of RAG over documents Generalized agents need the right set of tools to scalably search and read through an arbitrary corpus of data (from 10 docs to 1m+ docs). They can already demonstrate great retrieval performance over a local filesystem, need a proper backend for a large collection of managed data. The Retrieval Harness exposes a diverse set of tools for various needs: 1. Hybrid Retrieval: Combine vector search with keyword search, let the agent set the alpha value to toggle between the two 2. List Files: a scalable version of `ls` to list files within an index 3. File Grep: enable regex search within a given file 4. File Read: Allow agents to read a subsection from an existing document. The agent can choose to interleave any sequence of these tools in order to complete a variety of tasks, from simple to hard. Come check it out! Blog: Sign up to LlamaParse:

We're excited to introduce the Retrieval Harness in LlamaParse - which is the 2026 version of RAG over documents Generalized agents need the right set of tools to scalably search and read through an arbitrary corpus of data (from 10 docs to 1m+ docs). They can already demonstrate great retrieval performance over a local filesystem, need a proper backend for a large collection of managed data. The Retrieval Harness exposes a diverse set of tools for various needs: 1. Hybrid Retrieval: Combine vector search with keyword search, let the agent set the alpha value to toggle between the two 2. List Files: a scalable version of `ls` to list files within an index 3. File Grep: enable regex search within a given file 4. File Read: Allow agents to read a subsection from an existing document. The agent can choose to interleave any sequence of these tools in order to complete a variety of tasks, from simple to hard. Come check it out! Blog: Sign up to LlamaParse:

Jerry Liu

18,615 просмотров • 16 дней назад

This song is one of the best gospel songs. It calls for sober reflection, a deep search of the conscience, and a cry to God for the grace to overcome the power of flesh. This is revival 🔥 God bless Chinyere Udoma

This song is one of the best gospel songs. It calls for sober reflection, a deep search of the conscience, and a cry to God for the grace to overcome the power of flesh. This is revival 🔥 God bless Chinyere Udoma

NWACHINEMERE

33,028 просмотров • 5 месяцев назад

Still buzzing from our live #COMPUTEX demos! Watching Chang Liu speak Mandarin while an AMD AI PC translated her words into English — live, on-device, with zero cloud assistance — was a true milestone. This demo proved real-time, voice-to-voice translation can run privately and with low latency on a single Ryzen AI system. Huge thanks to Nick Ni for orchestrating this seamless demo, Chang for her perfect delivery, and the entire AMD AI Group for turning months of hard work into that unforgettable moment. Why it matters: • Latency so low it feels natural — conversations flow smoothly • Data stays on-device — privacy uncompromised This is what next-gen Ryzen AI processors unlock — and it’s only the beginning. We’re committed to improving accuracy and reducing latency as we push the boundaries of what’s possible.

Still buzzing from our live #COMPUTEX demos! Watching Chang Liu speak Mandarin while an AMD AI PC translated her words into English — live, on-device, with zero cloud assistance — was a true milestone. This demo proved real-time, voice-to-voice translation can run privately and with low latency on a single Ryzen AI system. Huge thanks to Nick Ni for orchestrating this seamless demo, Chang for her perfect delivery, and the entire AMD AI Group for turning months of hard work into that unforgettable moment. Why it matters: • Latency so low it feels natural — conversations flow smoothly • Data stays on-device — privacy uncompromised This is what next-gen Ryzen AI processors unlock — and it’s only the beginning. We’re committed to improving accuracy and reducing latency as we push the boundaries of what’s possible.

Jack Huynh

14,634 просмотров • 1 год назад

1/2 Gemini 3 Flash is here ⚡⚡⚡ Starting today, we’re rolling it out globally in Search as the default model in AI Mode ( 3 Flash brings the sophisticated reasoning capabilities of Gemini 3 to Search, without compromising speed. This means AI Mode better interprets your toughest, multi-layered questions – considering each of your constraints or requirements – and provides a visually digestible response with helpful links to explore further.

1/2 Gemini 3 Flash is here ⚡⚡⚡ Starting today, we’re rolling it out globally in Search as the default model in AI Mode ( 3 Flash brings the sophisticated reasoning capabilities of Gemini 3 to Search, without compromising speed. This means AI Mode better interprets your toughest, multi-layered questions – considering each of your constraints or requirements – and provides a visually digestible response with helpful links to explore further.

Robby Stein

71,239 просмотров • 7 месяцев назад

Introducing Jan-v1: 4B model for web search, an open-source alternative to Perplexity Pro. In our evals, Jan v1 delivers 91% SimpleQA accuracy, slightly outperforming Perplexity Pro while running fully locally. Use cases: - Web search - Deep Research Built on the new version of Qwen's Qwen3-4B-Thinking (up to 256k context length), fine-tuned for reasoning and tool use in Jan. You can run the model in Jan, llama.cpp, or vLLM. To enable search in Jan, go to Settings → Experimental Features → On, then Settings → MCP Servers → enable a search-related MCP such as Serper. Use the model: - Jan-v1-4B: - Jan-v1-4B-GGUF: Credit to the Qwen team for Qwen3 4B Thinking & Georgi Gerganov for llama.cpp.

Introducing Jan-v1: 4B model for web search, an open-source alternative to Perplexity Pro. In our evals, Jan v1 delivers 91% SimpleQA accuracy, slightly outperforming Perplexity Pro while running fully locally. Use cases: - Web search - Deep Research Built on the new version of Qwen's Qwen3-4B-Thinking (up to 256k context length), fine-tuned for reasoning and tool use in Jan. You can run the model in Jan, llama.cpp, or vLLM. To enable search in Jan, go to Settings → Experimental Features → On, then Settings → MCP Servers → enable a search-related MCP such as Serper. Use the model: - Jan-v1-4B: - Jan-v1-4B-GGUF: Credit to the Qwen team for Qwen3 4B Thinking & Georgi Gerganov for llama.cpp.

👋 Jan

690,311 просмотров • 11 месяцев назад

This is the GREAT AWAKENING, ladies and gentlemen. It is up to ALL of us to expose and eradicate this evil, acting like a cancer to our society, from all positions of power. To hold the guilty accountable and to set humanity in a new direction of peace and prosperity for all.

This is the GREAT AWAKENING, ladies and gentlemen. It is up to ALL of us to expose and eradicate this evil, acting like a cancer to our society, from all positions of power. To hold the guilty accountable and to set humanity in a new direction of peace and prosperity for all.

The SCIF

57,953 просмотров • 5 месяцев назад

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

91,297 просмотров • 4 месяцев назад

Vector databases are ridiculous. How can you search millions of vectors at light speed? What's the magic making this possible? I wanted to understand how they work, and here is a full video with everything I learned. This is for nerds.

Vector databases are ridiculous. How can you search millions of vectors at light speed? What's the magic making this possible? I wanted to understand how they work, and here is a full video with everything I learned. This is for nerds.

Santiago

231,296 просмотров • 2 лет назад

I can search a database with 32 million records in less than 50 milliseconds. This speed is insane! There's no reason your search is slow. There's no reason you aren't using this library when building applications with a lot of data. Take a look at typesense |. It's an open-source search engine. It's crazy fast and easy to use. They are sponsoring this post. Here is their GitHub repository: You can use the library in Python, JavaScript, PHP, Ruby, Swift, and pretty much any other programming language out there. Here are some of the things you can do with Typesense: • Vector search over embeddings • Conversational search for RAG applications • Image similarity search • Voice search And it does all of this extremely fast. A few interesting notes about the library: • Optimized for developer happiness • Works out of the box for most use cases • Very intuitive API • Easy to deploy and scale Probably the best way to compare them is against ElasticSearch. The latter is much more complex to configure and run with a steep learning curve. Check their GitHub repository. They have examples, tutorials, and everything you need.

I can search a database with 32 million records in less than 50 milliseconds. This speed is insane! There's no reason your search is slow. There's no reason you aren't using this library when building applications with a lot of data. Take a look at typesense |. It's an open-source search engine. It's crazy fast and easy to use. They are sponsoring this post. Here is their GitHub repository: You can use the library in Python, JavaScript, PHP, Ruby, Swift, and pretty much any other programming language out there. Here are some of the things you can do with Typesense: • Vector search over embeddings • Conversational search for RAG applications • Image similarity search • Voice search And it does all of this extremely fast. A few interesting notes about the library: • Optimized for developer happiness • Works out of the box for most use cases • Very intuitive API • Easy to deploy and scale Probably the best way to compare them is against ElasticSearch. The latter is much more complex to configure and run with a steep learning curve. Check their GitHub repository. They have examples, tutorials, and everything you need.

Santiago

160,154 просмотров • 1 год назад

over the weekend i built and shipped Sentra for our docs. we use Mintlify for documentation. the built-in assistant kept running into context issues. wrong answers, missing context, no citations. so we extended Sentra's capability to the docs site directly. now when you're reading Dodo Payments docs, you can ask Sentra questions right there. it pulls context from our entire documentation via RAG (the same contextmcp (completely opensource) + Pinecone vector search with reranking), generates grounded answers, and cites the exact doc pages. built as a single Cloudflare Worker serving both the widget and the chat endpoint. React widget bundled into one self-contained IIFE, mounted in Shadow DOM so it doesn't conflict with Mintlify's styles. OpenAI models routed through Cloudflare AI Gateway. Vercel's AI SDK for streaming and shadcn for beautiful chat components. live now at

over the weekend i built and shipped Sentra for our docs. we use Mintlify for documentation. the built-in assistant kept running into context issues. wrong answers, missing context, no citations. so we extended Sentra's capability to the docs site directly. now when you're reading Dodo Payments docs, you can ask Sentra questions right there. it pulls context from our entire documentation via RAG (the same contextmcp (completely opensource) + Pinecone vector search with reranking), generates grounded answers, and cites the exact doc pages. built as a single Cloudflare Worker serving both the widget and the chat endpoint. React widget bundled into one self-contained IIFE, mounted in Shadow DOM so it doesn't conflict with Mintlify's styles. OpenAI models routed through Cloudflare AI Gateway. Vercel's AI SDK for streaming and shadcn for beautiful chat components. live now at

Ayush Agarwal

18,001 просмотров • 17 дней назад

As promised, here's a thread of what I found that is wrong with the Perfect Dark release on Nintendo Switch Online: First thing I noticed as usual is the sound, it's way too low compared to N64 and this is not a settings issue, as explosions were equally deafening.

As promised, here's a thread of what I found that is wrong with the Perfect Dark release on Nintendo Switch Online: First thing I noticed as usual is the sound, it's way too low compared to N64 and this is not a settings issue, as explosions were equally deafening.

Graslu00

597,188 просмотров • 2 лет назад

now doing gemma 4 moe. it'a crazy how usable this is. just a little web search tool (wiggles its head on tool calls), and it can serve as a mostly capable, local voice assistant. tomorrow i shall replace google auto crap with this in my car. just need to give it access to spotify.

now doing gemma 4 moe. it'a crazy how usable this is. just a little web search tool (wiggles its head on tool calls), and it can serve as a mostly capable, local voice assistant. tomorrow i shall replace google auto crap with this in my car. just need to give it access to spotify.

Mario Zechner

27,265 просмотров • 1 месяц назад

Searching 32 million records in 30 milliseconds is insane! I don't know how large your database is, but there's no reason your search results are slower than this. typesense | is a crazy fast open-source search engine. They are like ElasticSearch but way easier to set up, configure, and use. They are sponsoring this post. You can do all of this with Typesense: • Fuzzy search: Typo-tolerant fuzzy search-as-you-type • Vector search: Return records that are similar to each other • Semantic search: Return records that are conceptually related • Geo Search: Return records that are close to a given lat/long They have client libraries in Python, JavaScript, PHP, Ruby, Go, Rust, Swift, .Net, Java, and pretty much any other programming language out there. You can start searching in less than a minute: 1. Run their Docker image (or one of their native binaries) 2. Import your data 3. Start searching! GitHub repository: I recorded a quick video to show you a demo. I promise you haven't seen something this fast! This is the best upgrade you'll make to your application this year. Guaranteed.

Searching 32 million records in 30 milliseconds is insane! I don't know how large your database is, but there's no reason your search results are slower than this. typesense | is a crazy fast open-source search engine. They are like ElasticSearch but way easier to set up, configure, and use. They are sponsoring this post. You can do all of this with Typesense: • Fuzzy search: Typo-tolerant fuzzy search-as-you-type • Vector search: Return records that are similar to each other • Semantic search: Return records that are conceptually related • Geo Search: Return records that are close to a given lat/long They have client libraries in Python, JavaScript, PHP, Ruby, Go, Rust, Swift, .Net, Java, and pretty much any other programming language out there. You can start searching in less than a minute: 1. Run their Docker image (or one of their native binaries) 2. Import your data 3. Start searching! GitHub repository: I recorded a quick video to show you a demo. I promise you haven't seen something this fast! This is the best upgrade you'll make to your application this year. Guaranteed.

Santiago

172,755 просмотров • 1 год назад