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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 просмотров • 1 год назад •via X (Twitter)

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Комментарии: 10

Фото профиля Crypto Rand
Crypto Rand1 год назад

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

Фото профиля RR2Capital
RR2Capital1 год назад

@Hive_Intel Great timing, this will be BIG 🔥

Фото профиля CryptoVerse Xplorer
CryptoVerse Xplorer1 год назад

@Hive_Intel Looks Amazing 🔥 can't wait to Use

Фото профиля Jones💪🏻
Jones💪🏻1 год назад

@Hive_Intel let's goooo

Фото профиля €ｕｒｏＳｎｉｐｅｒ
€ｕｒｏＳｎｉｐｅｒ1 год назад

@Hive_Intel 🔥

Фото профиля Sharpe Intern
Sharpe Intern1 год назад

@Hive_Intel $SAI ready to melt some faces 🫡

Фото профиля Leandro
Leandro1 год назад

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

Фото профиля PumpeyeTheTraderMan
PumpeyeTheTraderMan1 год назад

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

Фото профиля Jr
Jr1 год назад

@Hive_Intel $Sai 🚀

Фото профиля Sharpe Intern
Sharpe Intern1 год назад

@Hive_Intel Fucking yuge 🔥

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45,668 просмотров • 1 месяц назад

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29,800 просмотров • 2 лет назад

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84,217 просмотров • 5 месяцев назад

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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 просмотров • 1 месяц 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.
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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.

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160,154 просмотров • 1 год назад

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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:

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15,705 просмотров • 1 год назад

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SPECTRE AI

18,254 просмотров • 2 лет назад

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 просмотров • 11 месяцев назад

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 просмотров • 1 год назад

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 просмотров • 2 месяцев назад

💡 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 просмотров • 1 год назад

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 просмотров • 25 дней назад