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Search is now updated across Box. This foundational upgrade changes what enterprise search can actually do. 4 to 10x faster results. File indexing reduced from 15 minutes to seconds. And a stronger foundation for AI agents to perform deep-document search across your enterprise content. All at any scale, including... show more

Box

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116,897 views • 1 month ago •via X (Twitter)

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Today, we launched a brand-new intelligent Search box. Here's what that means: An upgrade to the Search experience with our most advanced Gemini 3.5 models, bringing with them our latest agentic capabilities You can ask across modalities (text, images, files, and videos) and Search can reason across them all We're combining AI Overviews and AI Mode into one, seamless AI Search experience. So you can ask follow-up questions, build context, and received even more tailored and personalized responses This new AI Search experience is live today across desktop and mobile, worldwide.

Google AI

44,591 views • 2 months ago

Your enterprise content should power every AI tool and agent you use. With the Box MCP server, Box acts as a secure, governed bridge, so teams can search, retrieve, analyze, and act on Box content directly inside the tools they already use. No one-off integrations. Use it to: 🔹Ask questions over files in Anthropic Claude + Mistral AI Le Chat 🔹Ground designs in Figma or @ mention Box agents in Atlassian Jira 🔹Pull content into GitHub Copilot, Cursor + Claude Code 🔹Build agents with LangChain LangSmith Agent Builder + OpenAI Agent Builder 🔹Automate work in Claude Cowork + Amazon Web Services Quick Suite 🔹Enforce access + audit trails with Runlayer Secure. Standardized. Built for real work →

Your enterprise content should power every AI tool and agent you use. With the Box MCP server, Box acts as a secure, governed bridge, so teams can search, retrieve, analyze, and act on Box content directly inside the tools they already use. No one-off integrations. Use it to: 🔹Ask questions over files in Anthropic Claude + Mistral AI Le Chat 🔹Ground designs in Figma or @ mention Box agents in Atlassian Jira 🔹Pull content into GitHub Copilot, Cursor + Claude Code 🔹Build agents with LangChain LangSmith Agent Builder + OpenAI Agent Builder 🔹Automate work in Claude Cowork + Amazon Web Services Quick Suite 🔹Enforce access + audit trails with Runlayer Secure. Standardized. Built for real work →

Box

481,535 views • 5 months ago

Box Hubs turns content chaos into a searchable, AI-powered knowledge base - built in minutes, no code required. The problem isn't lack of information. It's content sprawl making the right information impossible to find. Box Hubs changes that: 🔷Single source of truth that auto-updates when files change 🔷Ask natural language questions, get instant answers with citations via Box AI 🔷Drag-and-drop builder with multi-page portals, change notifications, and link previews 🔷Enterprise-grade security and governance from the start Stop duplicating files. Stop answering the same questions. Start putting your content to work. Find out how:

Box Hubs turns content chaos into a searchable, AI-powered knowledge base - built in minutes, no code required. The problem isn't lack of information. It's content sprawl making the right information impossible to find. Box Hubs changes that: 🔷Single source of truth that auto-updates when files change 🔷Ask natural language questions, get instant answers with citations via Box AI 🔷Drag-and-drop builder with multi-page portals, change notifications, and link previews 🔷Enterprise-grade security and governance from the start Stop duplicating files. Stop answering the same questions. Start putting your content to work. Find out how:

Box

251,013 views • 5 months ago

my team didn't want me to give this away for free. But I'm going to do it anyway it's the SEO & AI search dashboard I built in Claude Code it connects to your Google Analytics (GA4) and Google Search Console and Claude Code builds it in 5 minutes and I made a Notion document and a skill file so you can build this in Claude Code yourself in literally minutes the dashboard has three tabs: 1. AI Search - How much traffic is coming from ChatGPT, Perplexity, and Gemini ETC. It aggregates the GA4 data and gives single number 2. Paid ads - which keywords rank top 3 for but still pay for ads on, you should cut these to save budget 3. Organic overview - sessions, conversions, top landing pages, demographics. The single view for what is working I built this because this is how I drive our SEO and AEO forward it gives me the insights I need to allocate budget and prioritize what content to work on next I decided to give it away because most companies have no idea AI search is already sending them traffic like this post and comment "AEOdashboard" and I'll send it over

my team didn't want me to give this away for free. But I'm going to do it anyway it's the SEO & AI search dashboard I built in Claude Code it connects to your Google Analytics (GA4) and Google Search Console and Claude Code builds it in 5 minutes and I made a Notion document and a skill file so you can build this in Claude Code yourself in literally minutes the dashboard has three tabs: 1. AI Search - How much traffic is coming from ChatGPT, Perplexity, and Gemini ETC. It aggregates the GA4 data and gives single number 2. Paid ads - which keywords rank top 3 for but still pay for ads on, you should cut these to save budget 3. Organic overview - sessions, conversions, top landing pages, demographics. The single view for what is working I built this because this is how I drive our SEO and AEO forward it gives me the insights I need to allocate budget and prioritize what content to work on next I decided to give it away because most companies have no idea AI search is already sending them traffic like this post and comment "AEOdashboard" and I'll send it over

Cody Schneider

78,430 views • 2 months ago

OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!

OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!

Santiago

171,340 views • 1 year ago

just vibe-coded an internal search tool that finds any file on my computer and answers questions related to them. it's a custom Raycast extension powered by Anthropic's MCP (thanks Alex Albert & Marc Klingen for showing me!)—but it works with any LLM for Q&A across my local files. given Raycast’s slick UX + deep integrations, I’ve always thought it’s perfectly positioned to solve internal search like Glean does with enterprises, so I built one. code is in the comments—curious to hear your thoughts

just vibe-coded an internal search tool that finds any file on my computer and answers questions related to them. it's a custom Raycast extension powered by Anthropic's MCP (thanks Alex Albert & Marc Klingen for showing me!)—but it works with any LLM for Q&A across my local files. given Raycast’s slick UX + deep integrations, I’ve always thought it’s perfectly positioned to solve internal search like Glean does with enterprises, so I built one. code is in the comments—curious to hear your thoughts

viet

107,926 views • 1 year ago

Big moment for Postgres! Search has always been Postgres' weak spot, and everyone just accepted it. If you needed a real relevance-ranked keyword search, the default answer was to spin up Elasticsearch or add Algolia and deal with the data sync headaches forever. The problem isn't that Postgres can't do text search. It can. But the built-in `ts_rank` function uses a basic term frequency algorithm that doesn't come close to what modern search engines deliver. So teams end up: - Running a separate Elasticsearch cluster just for search - Building sync pipelines that inevitably drift out of consistency - Paying for managed search services that charge per query - Accepting mediocre search relevance because "good enough" ships faster But this is actually a solvable problem. You can realistically bring industry-standard search ranking directly into Postgres, which eliminates the need for external infra entirely. This exact solution is now available with the newly open-sourced pg_textsearch by Tiger Data - Creators of TimescaleDB, a Postgres extension that brings true BM25 relevance ranking into the database. BM25 is the algorithm behind Elasticsearch, Lucene, and most modern search engines. Now it runs natively in Postgres. Here's what pg_textsearch enables: - True BM25 ranking with configurable parameters (the same algorithm powering production search systems) - Simple SQL syntax: `ORDER BY content 'search terms'` - Works with Postgres text search configurations for multiple languages - Pairs naturally with pgvector for hybrid keyword + semantic search That last point matters a lot for RAG apps. The video below shows this in action, and I worked with the team to put this together. You can now do hybrid retrieval (combining keyword matching with vector similarity) in a single database, without stitching together multiple systems. The syntax is clean enough that you can add relevance-ranked search to existing queries in minutes. pg_textsearch is fully open-source under the PostgreSQL license. You can find a link to their GitHub repo in the next tweet.

Big moment for Postgres! Search has always been Postgres' weak spot, and everyone just accepted it. If you needed a real relevance-ranked keyword search, the default answer was to spin up Elasticsearch or add Algolia and deal with the data sync headaches forever. The problem isn't that Postgres can't do text search. It can. But the built-in `ts_rank` function uses a basic term frequency algorithm that doesn't come close to what modern search engines deliver. So teams end up: - Running a separate Elasticsearch cluster just for search - Building sync pipelines that inevitably drift out of consistency - Paying for managed search services that charge per query - Accepting mediocre search relevance because "good enough" ships faster But this is actually a solvable problem. You can realistically bring industry-standard search ranking directly into Postgres, which eliminates the need for external infra entirely. This exact solution is now available with the newly open-sourced pg_textsearch by Tiger Data - Creators of TimescaleDB, a Postgres extension that brings true BM25 relevance ranking into the database. BM25 is the algorithm behind Elasticsearch, Lucene, and most modern search engines. Now it runs natively in Postgres. Here's what pg_textsearch enables: - True BM25 ranking with configurable parameters (the same algorithm powering production search systems) - Simple SQL syntax: `ORDER BY content 'search terms'` - Works with Postgres text search configurations for multiple languages - Pairs naturally with pgvector for hybrid keyword + semantic search That last point matters a lot for RAG apps. The video below shows this in action, and I worked with the team to put this together. You can now do hybrid retrieval (combining keyword matching with vector similarity) in a single database, without stitching together multiple systems. The syntax is clean enough that you can add relevance-ranked search to existing queries in minutes. pg_textsearch is fully open-source under the PostgreSQL license. You can find a link to their GitHub repo in the next tweet.

Akshay 🚀

215,344 views • 6 months ago

Woow Google has just rolled out the AI model Flash Thinking 2.0 This is the first reasoning model capable of accessing YouTube and it changes everything: - Search for a video on your topic - Ask Gemini to think about the video - You'll have a tailor-made result in 10 sec. And it's even connected to Google Search and Maps!

Woow Google has just rolled out the AI model Flash Thinking 2.0 This is the first reasoning model capable of accessing YouTube and it changes everything: - Search for a video on your topic - Ask Gemini to think about the video - You'll have a tailor-made result in 10 sec. And it's even connected to Google Search and Maps!

Paul Couvert

79,488 views • 1 year ago

AI agents can already read the web. Now we're giving them eyes. Introducing Semantic Video Search, powered by the UpRock Network. Not transcript search. Not keyword matching. Real multimodal intelligence across: → video → audio → frames → timestamps → context Ask questions. Get answers from video. Powered by a global swarm of 3M+ real devices across 190+ countries, UpRock gives AI agents access to the internet as real users experience it. No datacenter blocks. No proxy games. No synthetic traffic. Just ground-truth intelligence from the real web. Over the next few posts, we'll show what this unlocks. 👇

AI agents can already read the web. Now we're giving them eyes. Introducing Semantic Video Search, powered by the UpRock Network. Not transcript search. Not keyword matching. Real multimodal intelligence across: → video → audio → frames → timestamps → context Ask questions. Get answers from video. Powered by a global swarm of 3M+ real devices across 190+ countries, UpRock gives AI agents access to the internet as real users experience it. No datacenter blocks. No proxy games. No synthetic traffic. Just ground-truth intelligence from the real web. Over the next few posts, we'll show what this unlocks. 👇

UpRock

137,938 views • 1 month ago

Every enterprise deploying AI is making a bet on where their data infrastructure lives. Regulators, customers, and compliance teams now want clear answers about where data is stored and processed before they sign. Announced today at BoxWorks London: Box Zones expands to 10 regions worldwide: • 3 new Zones: Switzerland, Singapore, Israel • In-region compute for France & Canada • Roadmap: in-region AI processing later in 2026 According to Gartner, 35% of countries will be locked into region-specific AI platforms by 2027. Organizations that build their content infrastructure with residency at the center now, will be better positioned to win regulated customers and deploy AI responsibly. Read the full announcement:

Every enterprise deploying AI is making a bet on where their data infrastructure lives. Regulators, customers, and compliance teams now want clear answers about where data is stored and processed before they sign. Announced today at BoxWorks London: Box Zones expands to 10 regions worldwide: • 3 new Zones: Switzerland, Singapore, Israel • In-region compute for France & Canada • Roadmap: in-region AI processing later in 2026 According to Gartner, 35% of countries will be locked into region-specific AI platforms by 2027. Organizations that build their content infrastructure with residency at the center now, will be better positioned to win regulated customers and deploy AI responsibly. Read the full announcement:

Box

2,949,694 views • 1 month ago

most AI chatbots break when you ask a question that requires info from multiple sources for example try asking: “which client contracts are finishing up this month?” you’ll get a half-answer — or none at all why? because traditional chatbots only look at small snippets of your docs - they don’t understand how things connect across clients, services, timelines that’s where knowledge graphs come in they let you turn messy contracts into a web of relationships — like: "Client → Contract Type → Service Provided → End Date" so instead of guessing from a few chunks of text, your chatbot can search across all your clients and contracts to give accurate answers I made a full walkthrough on how I built this: – how to organize your contracts so an AI can actually use them – how to define what matters (like who signed what, and when) – how to get the AI to figure out what info it needs and where to find it – and how to feed that back into your chatbot so it gives accurate answers reply “graph” and I’ll DM it to you (must be following)

most AI chatbots break when you ask a question that requires info from multiple sources for example try asking: “which client contracts are finishing up this month?” you’ll get a half-answer — or none at all why? because traditional chatbots only look at small snippets of your docs - they don’t understand how things connect across clients, services, timelines that’s where knowledge graphs come in they let you turn messy contracts into a web of relationships — like: "Client → Contract Type → Service Provided → End Date" so instead of guessing from a few chunks of text, your chatbot can search across all your clients and contracts to give accurate answers I made a full walkthrough on how I built this: – how to organize your contracts so an AI can actually use them – how to define what matters (like who signed what, and when) – how to get the AI to figure out what info it needs and where to find it – and how to feed that back into your chatbot so it gives accurate answers reply “graph” and I’ll DM it to you (must be following)

Tyler

24,994 views • 1 year ago

And do it begins…. Netflix has begun restricting content for plans that have ads They are now making you upgrade to a higher plan just to watch shows already available on Netflix Locked titles show a padlock icon in search and browse with a message that tells users to “Change plan to watch” Apparently Studios sometimes prohibit showing their content alongside commercials or require higher licensing fees for ad-supported distribution Keep in mind this show came out in 2010, and it’s been available many times without having to upgrade your service We all know this is really increase revenue, and this is actually true. According to Grok: Locked content can encourage upgrades, though Netflix frames it as a licensing issue rather than a deliberate upsell tactic It’s a scam

And do it begins…. Netflix has begun restricting content for plans that have ads They are now making you upgrade to a higher plan just to watch shows already available on Netflix Locked titles show a padlock icon in search and browse with a message that tells users to “Change plan to watch” Apparently Studios sometimes prohibit showing their content alongside commercials or require higher licensing fees for ad-supported distribution Keep in mind this show came out in 2010, and it’s been available many times without having to upgrade your service We all know this is really increase revenue, and this is actually true. According to Grok: Locked content can encourage upgrades, though Netflix frames it as a licensing issue rather than a deliberate upsell tactic It’s a scam

Wall Street Apes

556,408 views • 1 month ago

LayerAI Launches an AI Wallet Assistant That Automates All Transactions 🧬 ChatGPT transformed browsing by using natural text inputs to produce results. Now, LayerAI introduces a similar product - for Web3. Like ChatGPT, you have a text box where you can enter a prompt. For example 'Trade my ETH to LAI' or 'Send 1 ETH from Wallet A to Wallet B', and so on. LayerAI simplifies crypto interactions for everyone - like ChatGPT simplified search and became the 5th most used product worldwide. Forget having to manually use front-ends. Use the LayerAI GPT Assistant to run all of your interactions: from trading, to (soon) farming and bridging. 👉 Live now:

LayerAI Launches an AI Wallet Assistant That Automates All Transactions 🧬 ChatGPT transformed browsing by using natural text inputs to produce results. Now, LayerAI introduces a similar product - for Web3. Like ChatGPT, you have a text box where you can enter a prompt. For example 'Trade my ETH to LAI' or 'Send 1 ETH from Wallet A to Wallet B', and so on. LayerAI simplifies crypto interactions for everyone - like ChatGPT simplified search and became the 5th most used product worldwide. Forget having to manually use front-ends. Use the LayerAI GPT Assistant to run all of your interactions: from trading, to (soon) farming and bridging. 👉 Live now:

LayerAI | AI2Earn

21,662 views • 1 year ago

I'm proud to share that Glean has surpassed $300M ARR, just five months after crossing $200M and growing ~3x over the past 15 months. This is an exciting milestone for Glean, and it's a signal about where the enterprise AI market is heading. We’ve long believed the real challenge in enterprise AI is not access to models. It is grounding AI in how a company actually works: its people, knowledge, workflows, permissions, and systems. That’s even clearer now. The companies creating real value with AI are not just adopting better models. They are building systems that understand their business well enough to deliver reliable outcomes at scale. That is the real moat, and it is what we’ve been building at Glean: an unrivaled context layer for enterprise AI. That context has to work across the business, not just inside a single team or use case. We see that in how customers adopt Glean: more than 85% use it across five or more job functions. It also has to meet the security and governance demands of complex enterprises. We see that in who is choosing Glean: our Fortune 500 customer count nearly doubled year over year. And it has to make economic sense as usage grows. In our recent benchmark with Claude Cowork, Glean was preferred roughly 2.5x as often as off-the-shelf MCP tools and used 30% fewer tokens on average. Better context improves both quality and efficiency. I enjoyed talking with CNBC's Deirdre Bosa about this broader shift. In enterprise AI, the winners will not be defined by better models alone. They will be defined by who builds the strongest foundation for enterprise context. Thank you to our customers, partners, and team for helping us build the future of enterprise AI.

I'm proud to share that Glean has surpassed $300M ARR, just five months after crossing $200M and growing ~3x over the past 15 months. This is an exciting milestone for Glean, and it's a signal about where the enterprise AI market is heading. We’ve long believed the real challenge in enterprise AI is not access to models. It is grounding AI in how a company actually works: its people, knowledge, workflows, permissions, and systems. That’s even clearer now. The companies creating real value with AI are not just adopting better models. They are building systems that understand their business well enough to deliver reliable outcomes at scale. That is the real moat, and it is what we’ve been building at Glean: an unrivaled context layer for enterprise AI. That context has to work across the business, not just inside a single team or use case. We see that in how customers adopt Glean: more than 85% use it across five or more job functions. It also has to meet the security and governance demands of complex enterprises. We see that in who is choosing Glean: our Fortune 500 customer count nearly doubled year over year. And it has to make economic sense as usage grows. In our recent benchmark with Claude Cowork, Glean was preferred roughly 2.5x as often as off-the-shelf MCP tools and used 30% fewer tokens on average. Better context improves both quality and efficiency. I enjoyed talking with CNBC's Deirdre Bosa about this broader shift. In enterprise AI, the winners will not be defined by better models alone. They will be defined by who builds the strongest foundation for enterprise context. Thank you to our customers, partners, and team for helping us build the future of enterprise AI.

Arvind Jain

280,572 views • 2 months ago

This is the fastest way I’ve found to send huge files without compression across the world. It’s called Blip. A file transfer app that lets you send massive files directly from your device to someone else’s. No cloud upload first. No waiting for a download link. No file size panic. No video quality getting destroyed. No “upgrade your storage” nonsense. Just pick the file and send it. What it can do: • Send files of any size • Send entire folders • Resume interrupted transfers • Keep original quality • Work across long distances • Transfer between desktop and mobile • Use end-to-end encrypted transfers • Run on Mac, Windows, Linux, iPhone, iPad, and Android The wild part: Blip says it can handle files up to 99TB. Because it is not trying to be Dropbox. It does not make you upload the file to a cloud drive first, then force the other person to download it later. The receiver starts getting the file while you are still sending it. WeTransfer makes you upload. AirDrop only works nearby. Cloud drives turn sharing into storage management. Blip just sends the file. This is the file transfer app creators should have had years ago. Website:

This is the fastest way I’ve found to send huge files without compression across the world. It’s called Blip. A file transfer app that lets you send massive files directly from your device to someone else’s. No cloud upload first. No waiting for a download link. No file size panic. No video quality getting destroyed. No “upgrade your storage” nonsense. Just pick the file and send it. What it can do: • Send files of any size • Send entire folders • Resume interrupted transfers • Keep original quality • Work across long distances • Transfer between desktop and mobile • Use end-to-end encrypted transfers • Run on Mac, Windows, Linux, iPhone, iPad, and Android The wild part: Blip says it can handle files up to 99TB. Because it is not trying to be Dropbox. It does not make you upload the file to a cloud drive first, then force the other person to download it later. The receiver starts getting the file while you are still sending it. WeTransfer makes you upload. AirDrop only works nearby. Cloud drives turn sharing into storage management. Blip just sends the file. This is the file transfer app creators should have had years ago. Website:

Hasan Toor

13,615 views • 2 months ago

Stop...Your AI shouldn’t sit in a sidebar. It should actually work inside your notes. AnySlate just made that real. Your AI can: • read your markdown files • edit sections • create docs • search your workspace • organize everything No copy paste No switching tabs No proprietary lock-in Every file is a real .md file Your notes stay portable forever And the wild part — it supports MCP natively So Claude, Cursor, or Windsurf can directly work inside your workspace This is the shift from AI that helps you write to AI that manages your knowledge Real-time collaboration Built-in AI assistant PDF + web publishing Version history Works on Mac Windows Linux + browser This is what a modern markdown editor should look like

Stop...Your AI shouldn’t sit in a sidebar. It should actually work inside your notes. AnySlate just made that real. Your AI can: • read your markdown files • edit sections • create docs • search your workspace • organize everything No copy paste No switching tabs No proprietary lock-in Every file is a real .md file Your notes stay portable forever And the wild part — it supports MCP natively So Claude, Cursor, or Windsurf can directly work inside your workspace This is the shift from AI that helps you write to AI that manages your knowledge Real-time collaboration Built-in AI assistant PDF + web publishing Version history Works on Mac Windows Linux + browser This is what a modern markdown editor should look like

Nainsi Dwivedi

13,617 views • 4 months ago

The Next 100 Days: Our Campaign Forward 🌐 LayerAI is the most user-rich web3 AI ecosystem. 12 products with over 2 million combined users. We are now approaching the verge of releasing mainnet versions of products used by 6+ billion people globally: 1. laiOS The world’s first AI-enabled operating system. Pluggable by hardware manufacturers across the world. The equivalent of Google’s Android, but for the AI economy. An OS for trillions of devices. 2. aiSEARCH The world’s AI-native web3 browser system, a competitor of Open AI's Search. The vision: the only browser with a native wallet, payment system, and AI Search, powering the future of AI Agents in the workforce. Built for AI markets: AI2AI transactions are the future of global commerce. Our Campaign Forward starts tomorrow. 100 Day Journey to 1 Billion AI Agents on LayerAI.

The Next 100 Days: Our Campaign Forward 🌐 LayerAI is the most user-rich web3 AI ecosystem. 12 products with over 2 million combined users. We are now approaching the verge of releasing mainnet versions of products used by 6+ billion people globally: 1. laiOS The world’s first AI-enabled operating system. Pluggable by hardware manufacturers across the world. The equivalent of Google’s Android, but for the AI economy. An OS for trillions of devices. 2. aiSEARCH The world’s AI-native web3 browser system, a competitor of Open AI's Search. The vision: the only browser with a native wallet, payment system, and AI Search, powering the future of AI Agents in the workforce. Built for AI markets: AI2AI transactions are the future of global commerce. Our Campaign Forward starts tomorrow. 100 Day Journey to 1 Billion AI Agents on LayerAI.

LayerAI | AI2Earn

273,201 views • 1 year ago

Announcing Personal Intelligence, a more personalized Google Gemini designed just for you. How it works: — Customized: With your permission, it reasons across your Gmail, YouTube, Google Photos, and Search apps to share hyper-relevant and context-aware responses — Secure: If enabled, you control which Google apps to connect to. This setting is off by default — Useful: From travel plans based on your Google Photos to gym recommendations based on goals you’ve shared with Gemini, you get help tailored to your world Personal Intelligence in beta is rolling out to Google AI Pro and AI Ultra subscribers in the U.S., with expansions to the free tier, more countries, and AI Mode in Search to come. Take a look at the Gemini app's personalized assistance in the clip below, then let us know what you would use it for!

Announcing Personal Intelligence, a more personalized Google Gemini designed just for you. How it works: — Customized: With your permission, it reasons across your Gmail, YouTube, Google Photos, and Search apps to share hyper-relevant and context-aware responses — Secure: If enabled, you control which Google apps to connect to. This setting is off by default — Useful: From travel plans based on your Google Photos to gym recommendations based on goals you’ve shared with Gemini, you get help tailored to your world Personal Intelligence in beta is rolling out to Google AI Pro and AI Ultra subscribers in the U.S., with expansions to the free tier, more countries, and AI Mode in Search to come. Take a look at the Gemini app's personalized assistance in the clip below, then let us know what you would use it for!

Google AI

320,451 views • 6 months ago