Загрузка видео...

Не удалось загрузить видео

На главную

First upgrade of PhoenixONE is here - featuring agent thought log. As the research agent is thinking, get a glimpse into the data, insight extraction, and underlying processes. On an average query, Phoenix Crypto Research & Deep Research performs rapid AI contextual search & analysis on 40-50 data sources....

Phoenix_Chain's profile picture

Phoenix AI Labs

75,306 subscribers

21,898 просмотров • 1 год назад •via X (Twitter)

Наука и технологииНовости и политикаОбразование
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 9

Фото профиля James
James1 год назад

$PHB $50 soon

Фото профиля Virtue of Selfish Investing (VoSI)
Virtue of Selfish Investing (VoSI)1 год назад

Supercharge your profits using our time-tested, optimized, low risk buying strategies including Pocket pivots, Undercut & Rally, VooDoo low volume signatures, and Buyable Gap-Ups. Two market wizards combine fundamentals with technical timing to stack the odds in your favor.

Фото профиля Serkan Batur💱🪽
Serkan Batur💱🪽1 год назад

$phb best a.i project of crypto world 🚀🫶🔥

Фото профиля tesla369
tesla3691 год назад

Wow 🤩🤩🤩

Фото профиля Patelz
Patelz1 год назад

Hidden gem about to go parabolic $phb

Фото профиля Süleyman
Süleyman1 год назад

@Phoenix_Chain Why is it working so slowly? Thanks

Фото профиля doxuanthao🦴
doxuanthao🦴1 год назад

New ATH soon

Фото профиля Crypto Gamer
Crypto Gamer1 год назад

what about those people who invested in project and your developer dump the coin to the bottom? 2.6 to 0.3

Фото профиля Muhammad Ahsan
Muhammad Ahsan1 год назад

Beautiful

Похожие видео

With every new week PhoenixONE gets smarter, more enhanced data, and new features. Buzz, social, and crypto category-specific data is now integrated with AlphaNet QuickQuery Research Agent. Conduct quick research on the newest trending tokens and the chatter associated with them – combine with Phoenix Crypto Research to dig deep into the details. Try it together with the new UIUX theme customization feature! (starting with Light, Dark, and Phoenix Modes) PhoenixONE is transforming AI-driven intelligence and knowledge acquisition in the crypto space – social sharing & content creation leaderboards coming soon! #DeepResearch #DeFAI $PHB
1:22

With every new week PhoenixONE gets smarter, more enhanced data, and new features. Buzz, social, and crypto category-specific data is now integrated with AlphaNet QuickQuery Research Agent. Conduct quick research on the newest trending tokens and the chatter associated with them – combine with Phoenix Crypto Research to dig deep into the details. Try it together with the new UIUX theme customization feature! (starting with Light, Dark, and Phoenix Modes) PhoenixONE is transforming AI-driven intelligence and knowledge acquisition in the crypto space – social sharing & content creation leaderboards coming soon! #DeepResearch #DeFAI $PHB

Phoenix AI Labs

58,056 просмотров • 1 год назад

PhoenixONE, the next-gen AI agents platform for Web 3 intelligence, is now here in Beta. Today’s launch includes: 1) Phoenix Crypto Research – proprietary AI deep research agent optimized for the crypto market. Obtain in-depth research of projects, investment analysis, technology, specific events, and macro environment in real-time. 2) Phoenix Deep Research - agent for in-depth, multi-step, knowledge-intensive research across various topics, including trading & markets, academic research, journalism, coding, travel & shopping planning, technology, and more. 3) AlphaNet QuickQuery - crypto research assistant that can quickly retrieve information regarding tokens, including prices, supply, market caps, orderbook, holders, on-chain data, DEXs/liquidity pools, and more. 4) Newest foundational base models Deepseek R1 (671B) and Alibaba Qwen 2.5 Access and test PhoenixONE here: In the following week, we have invited various ecosystem partners, KOLs, and platform partners to test drive PhoenixONE – expect new features to be added and performance optimizations to be conducted on a weekly basis. #DeepResearch #AIAgents #PhoenixAI $PHB
1:51

PhoenixONE, the next-gen AI agents platform for Web 3 intelligence, is now here in Beta. Today’s launch includes: 1) Phoenix Crypto Research – proprietary AI deep research agent optimized for the crypto market. Obtain in-depth research of projects, investment analysis, technology, specific events, and macro environment in real-time. 2) Phoenix Deep Research - agent for in-depth, multi-step, knowledge-intensive research across various topics, including trading & markets, academic research, journalism, coding, travel & shopping planning, technology, and more. 3) AlphaNet QuickQuery - crypto research assistant that can quickly retrieve information regarding tokens, including prices, supply, market caps, orderbook, holders, on-chain data, DEXs/liquidity pools, and more. 4) Newest foundational base models Deepseek R1 (671B) and Alibaba Qwen 2.5 Access and test PhoenixONE here: In the following week, we have invited various ecosystem partners, KOLs, and platform partners to test drive PhoenixONE – expect new features to be added and performance optimizations to be conducted on a weekly basis. #DeepResearch #AIAgents #PhoenixAI $PHB

Phoenix AI Labs

33,284 просмотров • 1 год назад

PhoenixONE just got better – have further conversations about content covered in Phoenix Deep Research and Phoenix Crypto Research chats to dive into the topic and research even deeper. Try the “Continue Asking” feature! We are less than 2 weeks in and now over 2500+ users strong. PhoenixONE is reshaping knowledge acquisition and AI-driven intelligence. #DeepResearch #AIAgent $PHB
0:43

PhoenixONE just got better – have further conversations about content covered in Phoenix Deep Research and Phoenix Crypto Research chats to dive into the topic and research even deeper. Try the “Continue Asking” feature! We are less than 2 weeks in and now over 2500+ users strong. PhoenixONE is reshaping knowledge acquisition and AI-driven intelligence. #DeepResearch #AIAgent $PHB

Phoenix AI Labs

21,140 просмотров • 1 год назад

1. The first AI Research Agent, deep research on any topic in one click. visit and sign up, input any topic you want to research for. Then you can get: → Write high-quality articles instantly → Back them with credible sources
0:29

1. The first AI Research Agent, deep research on any topic in one click. visit and sign up, input any topic you want to research for. Then you can get: → Write high-quality articles instantly → Back them with credible sources

Allen Lattimer

45,046 просмотров • 1 год назад

Introducing Phoenix Deep Research, a vertical AI agent for in-depth, multi-step research and real-time knowledge. Phoenix Deep Research is blazingly fast with average response output time of 30 to 90 seconds, much faster than industry averages and is built on top of an accelerated Deepseek R1 utilizing SkyNet High Performance Cluster consisting of the newest Huawei Ascend and Nvidia A100, and A10 GPUs. Phoenix DR combines the most cutting-edge open source LLM, industry-specific agentic workflows, real-time AI search, and novel decentralized compute infrastructure into a differentiated and versatile tool previously unseen in the decentralized AI space. Phoenix DR will be open for Beta whitelist and testing early next week for community users and partners. Stay tuned. #Phoenix $PHB #DeepResearch
1:42

Introducing Phoenix Deep Research, a vertical AI agent for in-depth, multi-step research and real-time knowledge. Phoenix Deep Research is blazingly fast with average response output time of 30 to 90 seconds, much faster than industry averages and is built on top of an accelerated Deepseek R1 utilizing SkyNet High Performance Cluster consisting of the newest Huawei Ascend and Nvidia A100, and A10 GPUs. Phoenix DR combines the most cutting-edge open source LLM, industry-specific agentic workflows, real-time AI search, and novel decentralized compute infrastructure into a differentiated and versatile tool previously unseen in the decentralized AI space. Phoenix DR will be open for Beta whitelist and testing early next week for community users and partners. Stay tuned. #Phoenix $PHB #DeepResearch

Phoenix AI Labs

21,295 просмотров • 1 год назад

Operator is an AI agent by ChatGPT that browses the web and performs tasks. Currently in research preview with restrictions, it evolves with feedback. Check out the use cases below: 1. Crypto investment research based on tokens:
0:38

Operator is an AI agent by ChatGPT that browses the web and performs tasks. Currently in research preview with restrictions, it evolves with feedback. Check out the use cases below: 1. Crypto investment research based on tokens:

Shruti

205,754 просмотров • 1 год назад

Introducing the Generative UI Research Canvas ✨ Every agent needs a powerful UI Combine LangChain, Tavily, Tako and CopilotKit🪁 and you get a LangChain-based research agent with a Generative UI-driven frontend. When agents can explore the underlying data, research becomes more trustworthy and useful. How does this work? - LangChain manages the agent workflow and tool orchestration. - Tako converts retrieved data into structured, visual components - Tavily provides agentic search - CopilotKit renders those components in the UI and keeps the user in the loop while the agent is working So instead of a boring and static report, the output is an interactive research canvas that evolves as the agent runs. 2026 is the year where users want to see the output, not read it... Generative UI makes this possible for agentic applications. Tutorial: Open-Source Repo: Docs:
0:36

Introducing the Generative UI Research Canvas ✨ Every agent needs a powerful UI Combine LangChain, Tavily, Tako and CopilotKit🪁 and you get a LangChain-based research agent with a Generative UI-driven frontend. When agents can explore the underlying data, research becomes more trustworthy and useful. How does this work? - LangChain manages the agent workflow and tool orchestration. - Tako converts retrieved data into structured, visual components - Tavily provides agentic search - CopilotKit renders those components in the UI and keeps the user in the loop while the agent is working So instead of a boring and static report, the output is an interactive research canvas that evolves as the agent runs. 2026 is the year where users want to see the output, not read it... Generative UI makes this possible for agentic applications. Tutorial: Open-Source Repo: Docs:

Atai Barkai

11,997 просмотров • 5 месяцев назад

AI is leaving the data center. NVIDIA is betting on local, personal compute. Edge-agent research is showing why that matters: the closer the agent is to the user, the more usable it becomes. That is the OptimAI Claw thesis. Search brings the signal. Claw turns it into action on your device. The network keeps intelligence distributed. This is what agent-native infrastructure looks like when it is built for the edge.
1:15

AI is leaving the data center. NVIDIA is betting on local, personal compute. Edge-agent research is showing why that matters: the closer the agent is to the user, the more usable it becomes. That is the OptimAI Claw thesis. Search brings the signal. Claw turns it into action on your device. The network keeps intelligence distributed. This is what agent-native infrastructure looks like when it is built for the edge.

OptimAI Network

25,463 просмотров • 1 месяц назад

I’m testing AI agents on real on-chain investigations. Started with Verasity to settle the “10B supply, no dilution” debate once and for all... Agent pulled raw Etherscan data. This is how the future of crypto research will get done 👇
0:36

I’m testing AI agents on real on-chain investigations. Started with Verasity to settle the “10B supply, no dilution” debate once and for all... Agent pulled raw Etherscan data. This is how the future of crypto research will get done 👇

Bob

53,983 просмотров • 6 месяцев назад

$PRIVIX x $SPECT – Verified Partner Announcement SPECTRE AI is an advanced on‑chain research platform built to simplify how traders and investors navigate crypto. At its core is the Spectre AI On‑Chain Search Engine, which combines live blockchain data with integrated sentiment tracking and technical analysis - giving users deep insight into market movements, project health, and emerging trends. As a Verified Partner, Privix will enjoy full visibility across Spectre AI, including real‑time data, on‑chain metrics, and integrated tracking - cementing its role as a leader in the privacy narrative.
0:45

$PRIVIX x $SPECT – Verified Partner Announcement SPECTRE AI is an advanced on‑chain research platform built to simplify how traders and investors navigate crypto. At its core is the Spectre AI On‑Chain Search Engine, which combines live blockchain data with integrated sentiment tracking and technical analysis - giving users deep insight into market movements, project health, and emerging trends. As a Verified Partner, Privix will enjoy full visibility across Spectre AI, including real‑time data, on‑chain metrics, and integrated tracking - cementing its role as a leader in the privacy narrative.

Privix

14,883 просмотров • 1 год назад

As Phoenix AI Growth Points is expected to go live next week – PhoenixONE has leveled up once again in the meantime. Now, with the click of a button (new “Synthesize” feature) create full articles from entire conversations that go beyond in depth, quality, and data-driven rigor than much of the content on major media outlets. Acquire knowledge and insights, share content, and get rewarded in this new system. The more engagement and exposure your content gets, the bigger the reward. Create in-depth content in as little as a few minutes about anything – especially crypto! Phoenix is adding a social element to data-driven generative AI to drive user value and acceleration of growth. $PHB #Phoenix
0:58

As Phoenix AI Growth Points is expected to go live next week – PhoenixONE has leveled up once again in the meantime. Now, with the click of a button (new “Synthesize” feature) create full articles from entire conversations that go beyond in depth, quality, and data-driven rigor than much of the content on major media outlets. Acquire knowledge and insights, share content, and get rewarded in this new system. The more engagement and exposure your content gets, the bigger the reward. Create in-depth content in as little as a few minutes about anything – especially crypto! Phoenix is adding a social element to data-driven generative AI to drive user value and acceleration of growth. $PHB #Phoenix

Phoenix AI Labs

14,390 просмотров • 1 год назад

Trying OpenAI's Operator as my research assistant. Watch me ask the agent to search for AI papers on arXiv and summarize them. The future is here! Agents are no joke!
4:58

Trying OpenAI's Operator as my research assistant. Watch me ask the agent to search for AI papers on arXiv and summarize them. The future is here! Agents are no joke!

elvis

99,420 просмотров • 1 год назад

Just when I thought AI couldn't get any more impressive, AutoGPT and AI Agents blows my mind 🤯 Here, AI agent performs product research and creates a summary on the top headphones. This is insane! And the craziest part is that it's powered by GPT-4.
0:41

Just when I thought AI couldn't get any more impressive, AutoGPT and AI Agents blows my mind 🤯 Here, AI agent performs product research and creates a summary on the top headphones. This is insane! And the craziest part is that it's powered by GPT-4.

Rahul

34,150 просмотров • 3 лет назад

Today on Training Data, the OpenAI team behind ChatGPT agent explain how Agent Mode works, combining: 1) Deep Research (text based research agent) 2) Operator (GUI/action based computer agent) 3) Other new tools (terminal, computer apps) 4) Tied together with shared state to create an agent that's highly capable at most tasks that humans do on a computer: data science analysis, analyzing spreadsheets, making slides, etc. Thanks for joining us Isa Fulford Casey Chu Zhiqing Sun Lauren Reeder!
1:31

Today on Training Data, the OpenAI team behind ChatGPT agent explain how Agent Mode works, combining: 1) Deep Research (text based research agent) 2) Operator (GUI/action based computer agent) 3) Other new tools (terminal, computer apps) 4) Tied together with shared state to create an agent that's highly capable at most tasks that humans do on a computer: data science analysis, analyzing spreadsheets, making slides, etc. Thanks for joining us Isa Fulford Casey Chu Zhiqing Sun Lauren Reeder!

Sonya Huang 🐥

44,149 просмотров • 1 год назад

TARS Research is now live, the first AI research lab built entirely for Solana. Research is where real innovation begins. If TARS is to lead AI infrastructure onchain, it must define the frameworks others will follow. TARS Research is built for that: > A dedicated lab advancing agent architecture, tokenization, and onchain intelligence > Powering everything from the AI Marketplace to Sona > Focused on setting standards, not following them AI on Solana starts here.
0:41

TARS Research is now live, the first AI research lab built entirely for Solana. Research is where real innovation begins. If TARS is to lead AI infrastructure onchain, it must define the frameworks others will follow. TARS Research is built for that: > A dedicated lab advancing agent architecture, tokenization, and onchain intelligence > Powering everything from the AI Marketplace to Sona > Focused on setting standards, not following them AI on Solana starts here.

TARS AI 🤖

169,834 просмотров • 1 год назад

New update for our AI Copilot on Kaito Pro! We've now integrated DeepResearch alongside Scope Search, with visible chain-of-thought reasoning to cover broader topics across hundreds, or thousands, of sources. In combination with our data-set across the top platforms in crypto, it becomes one of the most powerful research tools in the industry. More on the update and the new changes below!
0:56

New update for our AI Copilot on Kaito Pro! We've now integrated DeepResearch alongside Scope Search, with visible chain-of-thought reasoning to cover broader topics across hundreds, or thousands, of sources. In combination with our data-set across the top platforms in crypto, it becomes one of the most powerful research tools in the industry. More on the update and the new changes below!

Kaito AI 🌊

21,497 просмотров • 1 год назад

📚 Build Deep Research Agent: From Question to Insight Complex research rarely fits in one search box. The Deep Research workflow built with Dify Iterates, reasons, and sources until the answer is complete. 💡 Key workflow nodes include: 1) Start: capture the research topic and the loop budget 2) Exa Answer: gather background terminology 3) Intent LLM: clarify the real research goal 4) Loop: drive the iterative cycles 5) Reasoning LLM: frame better follow-up questions 6) Agent: execute searches, extract content, and reflect 7) Variable Assigner: update the state after each pass 8) Final Summary: synthesize a Markdown report with citations Teams using this template cut research time dramatically and surface insights competitors miss. Learn to implement this workflow:
0:24

📚 Build Deep Research Agent: From Question to Insight Complex research rarely fits in one search box. The Deep Research workflow built with Dify Iterates, reasons, and sources until the answer is complete. 💡 Key workflow nodes include: 1) Start: capture the research topic and the loop budget 2) Exa Answer: gather background terminology 3) Intent LLM: clarify the real research goal 4) Loop: drive the iterative cycles 5) Reasoning LLM: frame better follow-up questions 6) Agent: execute searches, extract content, and reflect 7) Variable Assigner: update the state after each pass 8) Final Summary: synthesize a Markdown report with citations Teams using this template cut research time dramatically and surface insights competitors miss. Learn to implement this workflow:

Dify

22,470 просмотров • 1 год назад

I'm excited to introduce my AI Exploratory Data Analysis (EDA) Agent that built EDA reports, performs correlation analysis, and missing data analysis in 20 seconds. Today, I'll share with you how to automate creating EDA reports with the AI EDA Agent, which is available on GitHub. We'll create an EDA Agent focusing on a Customer Churn Problem. I'll guide you through setting up the EDA Agent, creating EDA reports, and analyzing the findings. This AI agent is a huge time-saver! Table of Contents: 00:00 Introduction to EDA Tools Agent 01:20 Get the AI Data Science Team 04:49 Create the EDA Tools Agent 06:35 Using the EDA Tools Agent for Missing Data and Correlation Funnels 10:00 Get SweetViz EDA Reports with AI 12:00 8-Week AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, February 12th, I'm sharing one of my best AI Projects: Business Intelligence with AI Register here (1570+ registered):
13:04

I'm excited to introduce my AI Exploratory Data Analysis (EDA) Agent that built EDA reports, performs correlation analysis, and missing data analysis in 20 seconds. Today, I'll share with you how to automate creating EDA reports with the AI EDA Agent, which is available on GitHub. We'll create an EDA Agent focusing on a Customer Churn Problem. I'll guide you through setting up the EDA Agent, creating EDA reports, and analyzing the findings. This AI agent is a huge time-saver! Table of Contents: 00:00 Introduction to EDA Tools Agent 01:20 Get the AI Data Science Team 04:49 Create the EDA Tools Agent 06:35 Using the EDA Tools Agent for Missing Data and Correlation Funnels 10:00 Get SweetViz EDA Reports with AI 12:00 8-Week AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, February 12th, I'm sharing one of my best AI Projects: Business Intelligence with AI Register here (1570+ registered):

Matt Dancho (Business Science)

31,079 просмотров • 1 год назад

auto-research is starting to gain traction as a very viable paradigm for creating useful research discovery. now, that paradigm is still in its infancy and the infrastructure to hold all that trail of context as the agents blaze through experiments isn't well defined (to say the least). on that topic, I had the chance to chat with my boys francesco and giulio from paradigma about what underlying infra is needed to make this paradigm work. the paradigma's paradigm, which involves copious amount of DAGs, make this auto-research paradigm a paradigmatic case of essential infrastructure. here's the full video in full: - 0:00 - what is missing from auto-research? - 2:02 - giulio and francesco ai journey - 8:10 - research infra is the bottleneck? - 10:18 - paradigma vision of autonomous research - 13:17 - “important discovery per joules” - 17:15 - why is DAG the unit of research for auto-research? - 20:40 - is paradigma trying to replace the research publication? - 24:50 - how does knowledge is shared between experiments in the DAG? - 27:34 - what is even auto-research lol? - 33:53 - the value of the human mind in this auto-research future. - 37:00 - how do you reconcile hallucination in this auto-research paradigm? - 41:33 - the adoption of auto-research across varied fields? - 47:30 - ✨ introduction to the auto-research infrastructure. ✨ - 56:55 - where is the code? - 59:10 - full IDE next? - 1:03:20 - the place of the human in this DAG / code quality? manual node? token spent? - 1:16:02 - who’s the user for auto-research? - 1:18:13 - how to validate bad DAG? - 1:20:18 - ✨ auto-research agent results ✨ - 1:22:53 - ✨ how a big research DAG looks like? ✨ - 1:25:10 - how to get the canonical DAG for the final result? - 1:27:50 - the auto-research DAG being the new pre-print? - 1:30:05 - what’s next for paradigma and the auto-research infra? - 1:35:00 - what are they excited about research wise? enjoyyyyy my guys 🌹
1:37:23

auto-research is starting to gain traction as a very viable paradigm for creating useful research discovery. now, that paradigm is still in its infancy and the infrastructure to hold all that trail of context as the agents blaze through experiments isn't well defined (to say the least). on that topic, I had the chance to chat with my boys francesco and giulio from paradigma about what underlying infra is needed to make this paradigm work. the paradigma's paradigm, which involves copious amount of DAGs, make this auto-research paradigm a paradigmatic case of essential infrastructure. here's the full video in full: - 0:00 - what is missing from auto-research? - 2:02 - giulio and francesco ai journey - 8:10 - research infra is the bottleneck? - 10:18 - paradigma vision of autonomous research - 13:17 - “important discovery per joules” - 17:15 - why is DAG the unit of research for auto-research? - 20:40 - is paradigma trying to replace the research publication? - 24:50 - how does knowledge is shared between experiments in the DAG? - 27:34 - what is even auto-research lol? - 33:53 - the value of the human mind in this auto-research future. - 37:00 - how do you reconcile hallucination in this auto-research paradigm? - 41:33 - the adoption of auto-research across varied fields? - 47:30 - ✨ introduction to the auto-research infrastructure. ✨ - 56:55 - where is the code? - 59:10 - full IDE next? - 1:03:20 - the place of the human in this DAG / code quality? manual node? token spent? - 1:16:02 - who’s the user for auto-research? - 1:18:13 - how to validate bad DAG? - 1:20:18 - ✨ auto-research agent results ✨ - 1:22:53 - ✨ how a big research DAG looks like? ✨ - 1:25:10 - how to get the canonical DAG for the final result? - 1:27:50 - the auto-research DAG being the new pre-print? - 1:30:05 - what’s next for paradigma and the auto-research infra? - 1:35:00 - what are they excited about research wise? enjoyyyyy my guys 🌹

Yacine Mahdid

12,091 просмотров • 1 месяц назад

A lot of people are calling Hermes Agent the end of OpenClaw. BRUH! It's not... Nous Research trains actual models and they built an agent around that expertise. The local model routing is solid, but the part that matters for your business is that your conversations become fine-tuning data. You can train a model on how you actually work. 00:00 The Problem with Local AI Models 00:25 Introduction to Nous Research 01:04 Cross-Platform Agent Capabilities 01:44 Deep Local Model Integration 02:30 Routing Tasks to Different Models 03:06 Conversations as Training Data 03:50 Hermes Agent vs. OpenClaw 04:15 Future Plans and Series Overview
4:45

A lot of people are calling Hermes Agent the end of OpenClaw. BRUH! It's not... Nous Research trains actual models and they built an agent around that expertise. The local model routing is solid, but the part that matters for your business is that your conversations become fine-tuning data. You can train a model on how you actually work. 00:00 The Problem with Local AI Models 00:25 Introduction to Nous Research 01:04 Cross-Platform Agent Capabilities 01:44 Deep Local Model Integration 02:30 Routing Tasks to Different Models 03:06 Conversations as Training Data 03:50 Hermes Agent vs. OpenClaw 04:15 Future Plans and Series Overview

Ray Fernando

42,398 просмотров • 3 месяцев назад