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Introducing Sharpe Search: On-Chain Search AI Agent Powered by Hive Intelligence We’re thrilled to announce the launch of Sharpe Search, a crypto search AI agent powered by Hive Intelligence Designed to simplify blockchain data interaction, Sharpe Search represents a significant step toward making crypto more accessible and actionable for...

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Sharpe AI

122,839 subscribers

263,278 次观看 • 1 年前 •via X (Twitter)

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10 条评论

Crypto Rand 的头像
Crypto Rand1 年前

@Hive_Intel Top narrative this cycle...lets go ✌️

RR2Capital 的头像
RR2Capital1 年前

@Hive_Intel Great timing, this will be BIG 🔥

CryptoVerse Xplorer 的头像
CryptoVerse Xplorer1 年前

@Hive_Intel Looks Amazing 🔥 can't wait to Use

Jones💪🏻 的头像
Jones💪🏻1 年前

@Hive_Intel let's goooo

€ｕｒｏＳｎｉｐｅｒ 的头像
€ｕｒｏＳｎｉｐｅｒ1 年前

@Hive_Intel 🔥

Sharpe Intern 的头像
Sharpe Intern1 年前

@Hive_Intel $SAI ready to melt some faces 🫡

Leandro 的头像
Leandro1 年前

@Hive_Intel @SharpeLabs a project with high potential and at the beginning congratulations to the entire team for their work $SAI 🚀🚀

PumpeyeTheTraderMan 的头像
PumpeyeTheTraderMan1 年前

@Hive_Intel This is an absolute game changer. YUUUUUUUUUUUUGE!!!!

Jr 的头像
Jr1 年前

@Hive_Intel $Sai 🚀

Sharpe Intern 的头像
Sharpe Intern1 年前

@Hive_Intel Fucking yuge 🔥

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OptimAI Network

43,645 次观看 • 9 个月前

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SPECTRE AI

102,766 次观看 • 1 年前

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OptimAI Network

45,668 次观看 • 1 个月前

Today, we're excited to introduce Genspark: The AI Agent Engine that Reinvents Search. Traditional search engines, burdened with ads, spam, and biased information, render simple tasks like planning a trip or researching products frustratingly time-consuming. Despite some recent AI enhancements to traditional search, they still face significant integration challenges due to the existing business model and the myriad issues of a broken web. And while some search services have indeed made notable advances with AI-driven summaries, these efforts only scratch the surface of what users truly need. Many are looking for deeper, more thorough analyses and a variety of other search requirements that brief summaries simply can't fulfill. Genspark reinvents the search landscape through our advanced AI agent engine. Our agents perform extensive research and generate customized Sparkpages for each query, saving users' time by doing the heavy lifting. These pages are meticulously crafted, free from commercial biases, and sourced from a wide array of reliable information to deliver richer, more relevant results. Moreover, Genspark draws inspiration from GitHub to foster a collaborative digital environment where users can contribute to, enhance, and discuss content, creating a dynamic platform for those seeking a more interactive and efficient search experience. Check out how Genspark works in the thread below, and join us in redefining search!
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Genspark

29,800 次观看 • 2 年前

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Hasan Toor

100,511 次观看 • 5 个月前

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I sat down with Robby Stein (Robby Stein), Google’s VP of Product for Search, at Google I/O. Robby is one of the most interesting product leaders in tech: he helped build Instagram Stories, Reels and Close Friends, and now leads core Google Search products including AI Overviews, AI Mode, Lens and ranking. We talked about one of the biggest shifts in the history of the web: Google Search becoming AI-native. Topics we covered: • AI Mode and whether it is an evolution of Search or a reinvention of it • how Google breaks complex questions into multiple searches behind the scenes • why AI search is much more expensive to run than traditional search • whether Google’s TPUs and infrastructure give it an advantage no one else can match • why Search volume is growing instead of being cannibalized by AI • the tension between great AI answers and traffic for publishers • how Google decides which sources and links to show • what a better internet could look like if AI Search works as intended The big question behind the whole conversation: If Google gives you the answer directly, what happens to the link-based web? A small caveat: sadly the microphones didnt work properly. Therefore the audio quality in this episode isn't perfect due to a recording issue - we appreciate your understanding.

Chubby♨️

30,486 次观看 • 1 个月前

✨ Today I rebuilt 🏔️ AltaVista Search, the main search engine before Google existed The problem: if you press the 🔍Search button in MSIE4 it'll open a sidebar with AltaVista in it that opens: http:// home . microsoft . com/search/search.asp But that URL doesn't resolve anymore of course, so how do I make this work? I asked Claude Code and built a Dnsmasq DNS server that routes all regular DNS queries to Cloudflare but for home . microsoft . com it hijacks it and sends it to my own server Then I let Nginx respond to that doamin and show the Altavista Search sidebar, I had to rebuild the design from a video on YouTube because I couldn't find any actual archeological remains of it anymore Also Altavista Search of course doesn't exist anymore so it can't respond to queries either so I use a SERP API to query search results from Google and display them instead This is stuff I wouldn't even be close to doing before AI, I'd have no idea how to configure Dnsmasq to spoof queries like this The search engine results are proxied via my HTTP proxy so MSIE4 can open most sites (because old browsers don't support HTTPS and most JS!)
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✨ Today I rebuilt 🏔️ AltaVista Search, the main search engine before Google existed The problem: if you press the 🔍Search button in MSIE4 it'll open a sidebar with AltaVista in it that opens: http:// home . microsoft . com/search/search.asp But that URL doesn't resolve anymore of course, so how do I make this work? I asked Claude Code and built a Dnsmasq DNS server that routes all regular DNS queries to Cloudflare but for home . microsoft . com it hijacks it and sends it to my own server Then I let Nginx respond to that doamin and show the Altavista Search sidebar, I had to rebuild the design from a video on YouTube because I couldn't find any actual archeological remains of it anymore Also Altavista Search of course doesn't exist anymore so it can't respond to queries either so I use a SERP API to query search results from Google and display them instead This is stuff I wouldn't even be close to doing before AI, I'd have no idea how to configure Dnsmasq to spoof queries like this The search engine results are proxied via my HTTP proxy so MSIE4 can open most sites (because old browsers don't support HTTPS and most JS!)

@levelsio

84,217 次观看 • 5 个月前

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Beyond Reading — AI with Eyes 👁️👁️ AI agents can already read the web. Now, we’re giving them eyes. This week, we demoed Semantic Video Search — powered by the UpRock Network. Not transcript search. Not keyword matching. Real multimodal intelligence across video, audio, frames, timestamps, and context. The demo: We asked UpRock to search across 100+ hours of livestream history. In seconds, it found: → the exact moment → the visual context → the timestamp → the story behind Jesse’s heart-shaped glasses 🕶️ This is what AI search is becoming. Drop in a YouTube video, TikTok, X video, earnings call, competitor demo, or 3-hour conference talk. Don’t just get a summary. Get structured intelligence your AI agent can actually reason over. What makes UpRock different is what’s underneath: 3M+ real devices. 190+ countries. Real phones. Real locations. Ground-truth access. No datacenter proxy games. No synthetic traffic. No stale scrape layer. Just the internet as real users see it. Semantic Video Search is coming soon for UpRock business customers. Building AI agents, research workflows, media intelligence tools, or multimodal search products? Get in touch → ⚡

UpRock

128,095 次观看 • 1 个月前

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.
1:18

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 次观看 • 1 年前

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Neiro is Now Verified on Spectre AI’s On-Chain Search Engine! We’re excited to announce that Neiro has been officially added as a Verified Partner on the SPECTRE AI On-Chain Search Engine. This is a great step forward for our community, making it easier than ever to track and explore everything happening with $Neiro. Through the $SPECT platform, you can view real-time price performance, market data, and sentiment trends all in one place. It’s a seamless way to stay up-to-date and keep an eye on how the community and the project are progressing. Browse the app:

Neiro

15,705 次观看 • 1 年前

The Spectre AI On-Chain Search Engine Research Zone - The Showcase. Revealing the Research Zone: The Spectre AI On-Chain Search Engine, a comprehensive platform in development designed to be your ultimate one-stop shop for all things cryptocurrency. Combining real-time data, extensive research tools, and advanced AI analytics, this platform aims to deliver an unparalleled user experience. We are working closely with Google to integrate real-time data implementation, ensuring you have the most accurate and up-to-date information at your fingertips. Our search engine is divided into three segments, showcased here as we prepare for our upcoming deployment: Landing Page: Features all main coins, trending coins, category coins, and your personal watchlist. Includes a switch to view our unique UI Heatmaps with Flipcards for a broad market overview. Research Zone: Packed with project descriptions, social media links, and charts powered by TradingView . Includes a full Technical Analysis panel with TA indicators and AI interpretation and analysis. Sentiment Analysis Zone provides all essential information in one place, featuring our unique Sentiment Analysis Charts. Right-side panel displays token metrics, contract information, audit info, and a blockchain scanning zone. Includes main and related tweets, a flipcard of the project's price and chart, and a holders chart. Search Engine Chatbot: Currently under development in collaboration with Google. Reveal coming soon. The backend is complete, and our front-end development is well underway. We are nearing our beta release and are excited to soon offer a revolutionary way to interact with the cryptocurrency market. Stay tuned for the beta launch and get ready to experience the future of crypto research with Spectre AI. #searchengine #google #ai #spectre #blockchain #tech #innovation $SPECT
0:49

The Spectre AI On-Chain Search Engine Research Zone - The Showcase. Revealing the Research Zone: The Spectre AI On-Chain Search Engine, a comprehensive platform in development designed to be your ultimate one-stop shop for all things cryptocurrency. Combining real-time data, extensive research tools, and advanced AI analytics, this platform aims to deliver an unparalleled user experience. We are working closely with Google to integrate real-time data implementation, ensuring you have the most accurate and up-to-date information at your fingertips. Our search engine is divided into three segments, showcased here as we prepare for our upcoming deployment: Landing Page: Features all main coins, trending coins, category coins, and your personal watchlist. Includes a switch to view our unique UI Heatmaps with Flipcards for a broad market overview. Research Zone: Packed with project descriptions, social media links, and charts powered by TradingView . Includes a full Technical Analysis panel with TA indicators and AI interpretation and analysis. Sentiment Analysis Zone provides all essential information in one place, featuring our unique Sentiment Analysis Charts. Right-side panel displays token metrics, contract information, audit info, and a blockchain scanning zone. Includes main and related tweets, a flipcard of the project's price and chart, and a holders chart. Search Engine Chatbot: Currently under development in collaboration with Google. Reveal coming soon. The backend is complete, and our front-end development is well underway. We are nearing our beta release and are excited to soon offer a revolutionary way to interact with the cryptocurrency market. Stay tuned for the beta launch and get ready to experience the future of crypto research with Spectre AI. #searchengine #google #ai #spectre #blockchain #tech #innovation $SPECT

SPECTRE AI

18,254 次观看 • 2 年前

The $KLED Enterprise Portal Full Demo – Search and License Any Data in Seconds: For the last 6 weeks, we've had thousands of users sign up for the first data marketplace. We've paid out over $200,000 to them for uploading their camera rolls, homework, dashcam footage, etc. We've now amassed petabytes and petabytes of data from these users and large enterprises, and tagged it all with rich metadata. Now OpenAI, xAI, Google AI, or any other large lab can easily search and license that data using our newly completed enterprise portal. If you're a large lab, send me a DM and get access to the largest, never-before-seen consumer dataset on the planet. This is the biggest update for Kled since launch.
4:40

The $KLED Enterprise Portal Full Demo – Search and License Any Data in Seconds: For the last 6 weeks, we've had thousands of users sign up for the first data marketplace. We've paid out over $200,000 to them for uploading their camera rolls, homework, dashcam footage, etc. We've now amassed petabytes and petabytes of data from these users and large enterprises, and tagged it all with rich metadata. Now OpenAI, xAI, Google AI, or any other large lab can easily search and license that data using our newly completed enterprise portal. If you're a large lab, send me a DM and get access to the largest, never-before-seen consumer dataset on the planet. This is the biggest update for Kled since launch.

Avi Patel

91,697 次观看 • 11 个月前

J-Cal Explains Why Google is UNDERRATED in AI 👀 On E227, the besties discussed Google's value in a post-search world if AI replaces traditional search. @jason broke down why he thinks Google is being slept on: "I think there's a chance that we're underestimating the power of Google's ad network right now." "They have four or five products that are one or two billion users per month. You have YouTube, Google Docs, Android." "They have such a data advantage and such a deep integration into people's lives because they use three or four services, I think Google's gonna figure this out." "It's quite possible that knowing your queries in Gemini, knowing what you're doing in Calendar, knowing what you're watching on YouTube could lead to a stream of more targeted ads that do better and are more valuable." "We've been seeing a number of startups that are figuring out how to use your queries and what you're doing in AI to present to you search results." "So imagine you're doing a Gemini search and on the side of it, it's giving you a rolling list of ads or offers that you might be more interested in." "That could be a better advertising product than even search itself." "I think YouTube search is the place to go all-in." "Right now, when you do a YouTube search, it just gives you 10 links, right? It just gives you that rolling thing." "You should be able to ask a question to YouTube, and you should be able to ask questions to your calendar." "You should be able to say, who have I met with over the last 10 years? Who I'm no longer in touch with and what are they up to?" "And it should do a Gemini search inside of Google Calendar. It's very light right now." "And then if you did that on YouTube, this would train people at the point of pain in a very deep way without sacrificing Google Search queries too aggressively."
1:20

J-Cal Explains Why Google is UNDERRATED in AI 👀 On E227, the besties discussed Google's value in a post-search world if AI replaces traditional search. @jason broke down why he thinks Google is being slept on: "I think there's a chance that we're underestimating the power of Google's ad network right now." "They have four or five products that are one or two billion users per month. You have YouTube, Google Docs, Android." "They have such a data advantage and such a deep integration into people's lives because they use three or four services, I think Google's gonna figure this out." "It's quite possible that knowing your queries in Gemini, knowing what you're doing in Calendar, knowing what you're watching on YouTube could lead to a stream of more targeted ads that do better and are more valuable." "We've been seeing a number of startups that are figuring out how to use your queries and what you're doing in AI to present to you search results." "So imagine you're doing a Gemini search and on the side of it, it's giving you a rolling list of ads or offers that you might be more interested in." "That could be a better advertising product than even search itself." "I think YouTube search is the place to go all-in." "Right now, when you do a YouTube search, it just gives you 10 links, right? It just gives you that rolling thing." "You should be able to ask a question to YouTube, and you should be able to ask questions to your calendar." "You should be able to say, who have I met with over the last 10 years? Who I'm no longer in touch with and what are they up to?" "And it should do a Gemini search inside of Google Calendar. It's very light right now." "And then if you did that on YouTube, this would train people at the point of pain in a very deep way without sacrificing Google Search queries too aggressively."

The All-In Podcast

58,275 次观看 • 1 年前

RLM is the most import foundation of my Pi Harness (other than Pi of course). It's seeded with late interaction retrieval results (thanks to @lightonai for pylate). The Agent initiates it with query then.. 𝐒𝐞𝐭𝐮𝐩 A python REPL is created and seeded with: 1. Late interaction search to pre-filter. Instead of doing top 3/5/10, it's top hundreds of documents. This is set into a `context` variable. 2. Python functions are loaded in to do more searches if `context` variable isn't enough. And to make llm calls with cheaper models in parallel batches. 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐋𝐨𝐨𝐩 From there, an LLM iterates in the REPL based on the query. It's just like exploring in a jupyter notebook. The LLM writes prose (like a markdown cell) and code to be run in the REPL each turn. This allows the LLM to sort, filter, and synthesize information. It can fan out and ask smaller models to summarize, combine, contrast, or do anything else to documents to help it understand the data. After several turns the LLM reponds with the final answer. Either because it found the answer, or hit the budget limit. Context as a Python variable, LLM as the programmer, REPL as the runtime. 𝐖𝐡𝐲 𝐃𝐨𝐞𝐬 𝐓𝐡𝐢𝐬 𝐖𝐨𝐫𝐤 1. Richer Shell. Agents (and subagents) work by intermixing code and prose/thinking. But they use static scripts or bash that run and exit and start over each tool call. That's not ideal for exploration and synthesis of data. For that, state is useful to continue building and exploring the data as you learn more. There's a reason jupyter notebooks have been popular with data scientists. 2. Keeps main agent context clean. The better context you have the better the agent will perform (duh!). This means three thing: better human input, less missing search results, and less incorrect search results. Letting the agent iterate allows it to synthesize just what is needed and nothing else. All bad paths or peeks at something that turns out to be irrelevant stays out of main agent context. 3. Stack the good ideas! People often compare late interaction search vs RLM. Or static vs dynamic languages. Or agentic search vs semantic search. But...You can just use them all together for what they're each good at. Use them all for the area they're really great for. Read the full post which has more detail about how and why.
3:43

RLM is the most import foundation of my Pi Harness (other than Pi of course). It's seeded with late interaction retrieval results (thanks to @lightonai for pylate). The Agent initiates it with query then.. 𝐒𝐞𝐭𝐮𝐩 A python REPL is created and seeded with: 1. Late interaction search to pre-filter. Instead of doing top 3/5/10, it's top hundreds of documents. This is set into a `context` variable. 2. Python functions are loaded in to do more searches if `context` variable isn't enough. And to make llm calls with cheaper models in parallel batches. 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐋𝐨𝐨𝐩 From there, an LLM iterates in the REPL based on the query. It's just like exploring in a jupyter notebook. The LLM writes prose (like a markdown cell) and code to be run in the REPL each turn. This allows the LLM to sort, filter, and synthesize information. It can fan out and ask smaller models to summarize, combine, contrast, or do anything else to documents to help it understand the data. After several turns the LLM reponds with the final answer. Either because it found the answer, or hit the budget limit. Context as a Python variable, LLM as the programmer, REPL as the runtime. 𝐖𝐡𝐲 𝐃𝐨𝐞𝐬 𝐓𝐡𝐢𝐬 𝐖𝐨𝐫𝐤 1. Richer Shell. Agents (and subagents) work by intermixing code and prose/thinking. But they use static scripts or bash that run and exit and start over each tool call. That's not ideal for exploration and synthesis of data. For that, state is useful to continue building and exploring the data as you learn more. There's a reason jupyter notebooks have been popular with data scientists. 2. Keeps main agent context clean. The better context you have the better the agent will perform (duh!). This means three thing: better human input, less missing search results, and less incorrect search results. Letting the agent iterate allows it to synthesize just what is needed and nothing else. All bad paths or peeks at something that turns out to be irrelevant stays out of main agent context. 3. Stack the good ideas! People often compare late interaction search vs RLM. Or static vs dynamic languages. Or agentic search vs semantic search. But...You can just use them all together for what they're each good at. Use them all for the area they're really great for. Read the full post which has more detail about how and why.

Isaac Flath

40,212 次观看 • 2 个月前

💡 Whats the upgrade that our game-changing Trading 🐦 is going to get: Our upgraded trading tools will be built on a foundation of advanced AI technologies and blockchain integrations to deliver a seamless, smarter trading experience. Here’s a glimpse of the tech behind this upgraded trading agent: 1️⃣ Multi-Layer Attention (MLA) - This is the backbone of our AI system, enabling multiple AI agents to work in sync. - It allows the agents to collaborate on tasks like analyzing market trends, identifying token opportunities, and optimizing strategies in real time. - MLA ensures parallel processing of data for better decision-making and faster 2️⃣ Learning and Evolution System - Our AI agents are powered by a self-learning framework that constantly evolves based on market conditions and user behavior. - With every interaction, the system adapts and gets smarter, improving the accuracy of its predictions and strategies. 3️⃣ On-Chain Data Analysis - The AI bots pull data directly from Ethereum and other blockchain networks, giving them real-time access to liquidity pools, token prices, and market activity. - This deep integration ensures precise and timely execution of tasks like token purchases, profit analysis, and cross-chain swaps. 4️⃣ Natural Language Processing (NLP) - NLP models power the bot’s ability to understand your tweets and translate them into complex trading actions. - This ensures an easy-to-use, human-friendly interface that connects your social interactions to advanced trading strategies. 5️⃣ Cloud-Hosted Infrastructure - The AI operates on scalable cloud infrastructure, ensuring 24/7 uptime, fast processing, and the ability to handle large volumes of trades simultaneously.
0:51

💡 Whats the upgrade that our game-changing Trading 🐦 is going to get: Our upgraded trading tools will be built on a foundation of advanced AI technologies and blockchain integrations to deliver a seamless, smarter trading experience. Here’s a glimpse of the tech behind this upgraded trading agent: 1️⃣ Multi-Layer Attention (MLA) - This is the backbone of our AI system, enabling multiple AI agents to work in sync. - It allows the agents to collaborate on tasks like analyzing market trends, identifying token opportunities, and optimizing strategies in real time. - MLA ensures parallel processing of data for better decision-making and faster 2️⃣ Learning and Evolution System - Our AI agents are powered by a self-learning framework that constantly evolves based on market conditions and user behavior. - With every interaction, the system adapts and gets smarter, improving the accuracy of its predictions and strategies. 3️⃣ On-Chain Data Analysis - The AI bots pull data directly from Ethereum and other blockchain networks, giving them real-time access to liquidity pools, token prices, and market activity. - This deep integration ensures precise and timely execution of tasks like token purchases, profit analysis, and cross-chain swaps. 4️⃣ Natural Language Processing (NLP) - NLP models power the bot’s ability to understand your tweets and translate them into complex trading actions. - This ensures an easy-to-use, human-friendly interface that connects your social interactions to advanced trading strategies. 5️⃣ Cloud-Hosted Infrastructure - The AI operates on scalable cloud infrastructure, ensuring 24/7 uptime, fast processing, and the ability to handle large volumes of trades simultaneously.

𝕋𝕎𝔼𝔼𝕋

20,357 次观看 • 1 年前

The new Google Search is rolling out and there seems to be confusion on what it will look like. Google literally told us. Let me clarify for anyone who is still unsure of what is rolling out this week. Last month Google responded to everyone saying Search is dead. Here is what they said: "You will absolutely continue to see blue web links in search results. AI Mode is not the default experience in Search. You will continue to get a range of results on Search." [Want to know where your site stands across Google AI, ChatGPT, Claude, Grok, etc? Check here (it's free): Google has explicitly laid out what Search will look like from this point going forward. The new Search box accepts text, images, files, videos, and open Chrome tabs. It anticipates your intent before you finish asking. It is powered by the most advanced Gemini model Google has ever put into Search, and layered on top of that, information agents will now be able to run 24/7 in the background on behalf of your buyer. Think of it in 5 steps: Step 1: The buyer describes their problem, their category, their needs in full. Step 2: The agent breaks that down into sub-topics and maps out a plan. Step 3: It determines what intel is needed right now versus later. Step 4: It monitors blogs, news sites, and social posts continuously for relevant changes. Step 5: It sends the buyer a synthesized update with links and the ability to take action. Blue links are not going away in the short-term, but the brands getting recommended by information agents 24 hours a day while also ranking in traditional results are going to pull so far ahead of the ones doing only one or the other that it will not be a fair fight. This is exactly what SEO Stuff ( has been building for every customer. Optimized content depth that covers every sub-question a buyer in your category asks, so the agent finds you at every step of its plan. Editorial authority from trusted websites that signals credibility to every retrieval system Google has ever built, across both traditional rankings and AI citations simultaneously. One investment. Blue links and AI citations. Around the clock. SEO Stuff's Complete Done-For-You Plan: SEO Stuff's "Optimized Content" Plan: There is a reason more than 80 percent of SEO Stuff customers reorder. The results continue long after the work is done. Google Search is changing. AI Search is here. Your websites need to prepare accordingly. Want to know where your site stands across Google AI, ChatGPT, Claude, Grok, etc? Check here (it's free):
1:26

The new Google Search is rolling out and there seems to be confusion on what it will look like. Google literally told us. Let me clarify for anyone who is still unsure of what is rolling out this week. Last month Google responded to everyone saying Search is dead. Here is what they said: "You will absolutely continue to see blue web links in search results. AI Mode is not the default experience in Search. You will continue to get a range of results on Search." [Want to know where your site stands across Google AI, ChatGPT, Claude, Grok, etc? Check here (it's free): Google has explicitly laid out what Search will look like from this point going forward. The new Search box accepts text, images, files, videos, and open Chrome tabs. It anticipates your intent before you finish asking. It is powered by the most advanced Gemini model Google has ever put into Search, and layered on top of that, information agents will now be able to run 24/7 in the background on behalf of your buyer. Think of it in 5 steps: Step 1: The buyer describes their problem, their category, their needs in full. Step 2: The agent breaks that down into sub-topics and maps out a plan. Step 3: It determines what intel is needed right now versus later. Step 4: It monitors blogs, news sites, and social posts continuously for relevant changes. Step 5: It sends the buyer a synthesized update with links and the ability to take action. Blue links are not going away in the short-term, but the brands getting recommended by information agents 24 hours a day while also ranking in traditional results are going to pull so far ahead of the ones doing only one or the other that it will not be a fair fight. This is exactly what SEO Stuff ( has been building for every customer. Optimized content depth that covers every sub-question a buyer in your category asks, so the agent finds you at every step of its plan. Editorial authority from trusted websites that signals credibility to every retrieval system Google has ever built, across both traditional rankings and AI citations simultaneously. One investment. Blue links and AI citations. Around the clock. SEO Stuff's Complete Done-For-You Plan: SEO Stuff's "Optimized Content" Plan: There is a reason more than 80 percent of SEO Stuff customers reorder. The results continue long after the work is done. Google Search is changing. AI Search is here. Your websites need to prepare accordingly. Want to know where your site stands across Google AI, ChatGPT, Claude, Grok, etc? Check here (it's free):

Alex Groberman

44,078 次观看 • 25 天前