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Sam Altman's ChatGPT third-party analytics provider Mixpanel gets hacked on Nov 9 compromising user account data. We only get notified by #OpenAI on Nov 26! What happened in the 17 days in between? Don't trust your data to people who take weeks to warn you it's been spilled. This... show more

AlphaTON Capital 💎 🚀

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39,415 次观看 • 7 个月前 •via X (Twitter)

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𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮. 𝗬𝗼𝘂𝗿 𝗔𝗜. 𝗬𝗼𝘂𝗿 𝗖𝗼𝗻𝘁𝗿𝗼𝗹. 𝗧𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝗶𝘁. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗮𝗹𝗹 𝘁𝗵𝗲 𝗶𝗻𝗳𝗼 𝘆𝗼𝘂 𝗳𝗲𝗲𝗱 𝗶𝗻𝘁𝗼 𝘆𝗼𝘂𝗿 #AI? The future of AI isn't about giving up your privacy to a tech giant. It's about sovereignty. Ben Goertzel the godfather of #AGI shares the vision. We're collaborating with SingularityNET to power Cocoon AI: Telegram Messenger ’s decentralized AI network. By securing high-performance compute infrastructure that is encrypted and decentralized, we are ensuring access to all Telegram users - more than 1 billion people right now - to top-tier AI tools without ever exposing their personal data. ✅ Decentralized Intelligence ✅ Military-Grade Privacy ✅ Zero-Carbon Footprint NASDAQ: $ATON The backbone of ethical AI 💎🚀🎯 #DataPrivacy #ATON ₿rittany Kaiser 💎 🚀 enzo villani Wes Levitt Will De Ath logan Yury Mitin Jennifer de Broglie Anthony Scaramucci

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AlphaTON Capital 💎 🚀

91,522 次观看 • 6 个月前

We’ve partnered with OpenAI to release Sweetspot (YC S23) directly within ChatGPT. Any ChatGPT user can now search over federal contract data - no paid tools required and no Sweetspot subscription needed. We built this on ChatGPT because access to basic federal data shouldn't cost thousands of dollars a year. is public information, and using AI to find the right opportunities shouldn't be a privilege. For Sweetspot customers, it gets even better. You can pull opportunity data straight from ChatGPT and start up your full workflow (qualification, analysis, proposal response) inside Sweetspot. Even if you’re not in government contracting, check it out and share it with your colleagues who are. It’s completely free! Link below.

We’ve partnered with OpenAI to release Sweetspot (YC S23) directly within ChatGPT. Any ChatGPT user can now search over federal contract data - no paid tools required and no Sweetspot subscription needed. We built this on ChatGPT because access to basic federal data shouldn't cost thousands of dollars a year. is public information, and using AI to find the right opportunities shouldn't be a privilege. For Sweetspot customers, it gets even better. You can pull opportunity data straight from ChatGPT and start up your full workflow (qualification, analysis, proposal response) inside Sweetspot. Even if you’re not in government contracting, check it out and share it with your colleagues who are. It’s completely free! Link below.

sachin

38,374 次观看 • 3 个月前

We got ChatGPT to leak your private email data 💀💀 All you need? The victim's email address. ⛓️‍💥🚩📧 On Wednesday, OpenAI added full support for MCP (Model Context Protocol) tools in ChatGPT. Allowing ChatGPT to connect and read your Gmail, Calendar, Sharepoint, Notion, and more, invented by Anthropic But here's the fundamental problem: AI agents like ChatGPT follow your commands, not your common sense. And with just your email, we managed to exfiltrate all your private information. Here's how we did it: 1. The attacker sends a calendar invite with a jailbreak prompt to the victim, just with their email. No need for the victim to accept the invite. 2. Waited for the user to ask ChatGPT to help prepare for their day by looking at their calendar 3. ChatGPT reads the jailbroken calendar invite. Now ChatGPT is hijacked by the attacker and will act on the attacker's command. Searches your private emails and sends the data to the attacker's email. For now, OpenAI only made MCPs available in "developer mode", and requires manual human approvals for every session, but decision fatigue is a real thing, and normal people will just trust the AI without knowing what to do and click approve, approve, approve. Remember that AI might be super smart, but can be tricked and phished in incredibly dumb ways to leak your data. ChatGPT + Tools poses a serious security risk

We got ChatGPT to leak your private email data 💀💀 All you need? The victim's email address. ⛓️‍💥🚩📧 On Wednesday, OpenAI added full support for MCP (Model Context Protocol) tools in ChatGPT. Allowing ChatGPT to connect and read your Gmail, Calendar, Sharepoint, Notion, and more, invented by Anthropic But here's the fundamental problem: AI agents like ChatGPT follow your commands, not your common sense. And with just your email, we managed to exfiltrate all your private information. Here's how we did it: 1. The attacker sends a calendar invite with a jailbreak prompt to the victim, just with their email. No need for the victim to accept the invite. 2. Waited for the user to ask ChatGPT to help prepare for their day by looking at their calendar 3. ChatGPT reads the jailbroken calendar invite. Now ChatGPT is hijacked by the attacker and will act on the attacker's command. Searches your private emails and sends the data to the attacker's email. For now, OpenAI only made MCPs available in "developer mode", and requires manual human approvals for every session, but decision fatigue is a real thing, and normal people will just trust the AI without knowing what to do and click approve, approve, approve. Remember that AI might be super smart, but can be tricked and phished in incredibly dumb ways to leak your data. ChatGPT + Tools poses a serious security risk

Eito Miyamura | 🇯🇵🇬🇧

1,538,099 次观看 • 10 个月前

My conversation with OpenAI co-founder Greg Brockman This is the most detailed first-person account of the 72 hours after Sam Altman was fired. We also go deep on what comes next: the global race to AGI, why ChatGPT stopped showing reasoning, how much of OpenAI's own code is now written by AI ("it's hard to know what percent is not"), and the untold story of how OpenAI actually started in 2015. 00:00:00 Introduction 00:00:49 Meeting Sam Altman and Starting OpenAI 00:02:40 Building the Founding Team 00:04:25 DeepMind's Lead Over OpenAI 00:04:54 Changing OpenAI to a For-Profit Model 00:06:05 Breakthrough Moments at OpenAI 00:08:22 What Dota 2 Meant for OpenAI 00:10:04 Reasoning Versus Prediction 00:11:59 Tensions Grow at OpenAI 00:15:44 Sam Altman's Firing 00:17:49 Greg Quits OpenAI 00:19:56 Sam Explores Deal with Microsoft's Satya 00:20:28 Petition for Altman's Return 00:23:43 Ilya Sutskever Leaves OpenAI 00:24:59 Lessons Learned after Sam Ousting 00:28:22 The Thing Ilya Said that Greg Can't Forget 00:32:22 Is AI Going Parabolic? 00:33:24 How Much of OpenAI's Code is Written by AI? 00:36:21 Do AI Chatbots Tell Us What We Want to Hear? 00:38:06 The Global AI Race to Reach AGI 00:38:40 What Happens if US Doesn't Reach AGI First? 00:39:49 Are Countries Stealing AI Advancements? 00:40:38 Why ChatGPT No Longer Shows Reasoning 00:41:47 The Finite Constraints of Compute 00:43:38 On Investing Early in Data Centers 00:46:31 The Future of Data Center Specialization 00:47:52 How to Decide Whose Queries to Serve 00:49:08 OpenAI on Consumer vs Enterprise Models 00:53:05 Data Centers in Space? 01:00:56 What Should AI Regulation Look Like? 01:04:33 The Future of AI-Powered Entrepreneurship 01:04:44 AI and Job Loss 01:07:15 The Skills Young People Should Invest In 01:11:30 What Does Success Look Like For You? Full episode on X below. Also find it on: • YouTube: • Spotify: • Apple:

My conversation with OpenAI co-founder Greg Brockman This is the most detailed first-person account of the 72 hours after Sam Altman was fired. We also go deep on what comes next: the global race to AGI, why ChatGPT stopped showing reasoning, how much of OpenAI's own code is now written by AI ("it's hard to know what percent is not"), and the untold story of how OpenAI actually started in 2015. 00:00:00 Introduction 00:00:49 Meeting Sam Altman and Starting OpenAI 00:02:40 Building the Founding Team 00:04:25 DeepMind's Lead Over OpenAI 00:04:54 Changing OpenAI to a For-Profit Model 00:06:05 Breakthrough Moments at OpenAI 00:08:22 What Dota 2 Meant for OpenAI 00:10:04 Reasoning Versus Prediction 00:11:59 Tensions Grow at OpenAI 00:15:44 Sam Altman's Firing 00:17:49 Greg Quits OpenAI 00:19:56 Sam Explores Deal with Microsoft's Satya 00:20:28 Petition for Altman's Return 00:23:43 Ilya Sutskever Leaves OpenAI 00:24:59 Lessons Learned after Sam Ousting 00:28:22 The Thing Ilya Said that Greg Can't Forget 00:32:22 Is AI Going Parabolic? 00:33:24 How Much of OpenAI's Code is Written by AI? 00:36:21 Do AI Chatbots Tell Us What We Want to Hear? 00:38:06 The Global AI Race to Reach AGI 00:38:40 What Happens if US Doesn't Reach AGI First? 00:39:49 Are Countries Stealing AI Advancements? 00:40:38 Why ChatGPT No Longer Shows Reasoning 00:41:47 The Finite Constraints of Compute 00:43:38 On Investing Early in Data Centers 00:46:31 The Future of Data Center Specialization 00:47:52 How to Decide Whose Queries to Serve 00:49:08 OpenAI on Consumer vs Enterprise Models 00:53:05 Data Centers in Space? 01:00:56 What Should AI Regulation Look Like? 01:04:33 The Future of AI-Powered Entrepreneurship 01:04:44 AI and Job Loss 01:07:15 The Skills Young People Should Invest In 01:11:30 What Does Success Look Like For You? Full episode on X below. Also find it on: • YouTube: • Spotify: • Apple:

Shane Parrish

450,952 次观看 • 2 个月前

Introducing the Future of AI-Powered Sports Data with $FXI.⚡️ We’re not just talking about innovation, we’re building it. 🚀 From #AI-driven data capture to on-chain validation and data monetization, the FX1 ecosystem is rewriting the rules of the sports and data economy. Our platform empowers leagues, teams, and creators to unlock the full potential of their data with cutting-edge AI solutions. 🧠 Why FX1? ➡️ AI-Powered Precision: Transform raw sports data into actionable insights. ➡️ Data Validation on Chain: Building trust in the data economy through blockchain. ➡️ AI-Powered Revenue Streams: Use sports data to drive predictive analytics, odds modeling, and other high-value solutions that unlock new ways to monetize. FX1 is leading the charge in AI innovation, creating a seamless bridge between sports, blockchain, and the data economy. This isn’t just evolution - it’s a revolution. 💡 The future of sports is here. And it’s powered by AI. 🤖 Watch the full video and see how we’re turning vision into reality. 👇

Introducing the Future of AI-Powered Sports Data with $FXI.⚡️ We’re not just talking about innovation, we’re building it. 🚀 From #AI-driven data capture to on-chain validation and data monetization, the FX1 ecosystem is rewriting the rules of the sports and data economy. Our platform empowers leagues, teams, and creators to unlock the full potential of their data with cutting-edge AI solutions. 🧠 Why FX1? ➡️ AI-Powered Precision: Transform raw sports data into actionable insights. ➡️ Data Validation on Chain: Building trust in the data economy through blockchain. ➡️ AI-Powered Revenue Streams: Use sports data to drive predictive analytics, odds modeling, and other high-value solutions that unlock new ways to monetize. FX1 is leading the charge in AI innovation, creating a seamless bridge between sports, blockchain, and the data economy. This isn’t just evolution - it’s a revolution. 💡 The future of sports is here. And it’s powered by AI. 🤖 Watch the full video and see how we’re turning vision into reality. 👇

FX1

23,913 次观看 • 1 年前

Studies have shown ChatGPT outperforms human annotators for Structured Data by about 25% and costs 30x less. 1 In just 2 months, miners on SN33 running ChatGPT without optimization can’t survive. Today we announce SN33 is now ReadyAI to fully align with our mission 👇 SN33 is building a more performant and significantly cheaper alternative to Scale AI Today structured data is performed primarily by human annotation services like Amazon’s Mechanical Turk and Scale AI It is now more important than ever for every business and individual to make their data AI Ready. However, taking unstructured data and making it Structured Data using today’s tools is extremely costly. SN33 revolutionizes this process, unlocking immense opportunities for commercialization. We lay out the vision for it in this detailed blog post: Validators TODAY can monetize access to this structured data pipeline independently, but we’re streamlining this process, launching a frontend soon that any validator can opt into to provide bandwidth. We've received great feedback from the community, recognizing that what we're building goes far beyond Conversational AI. Building the world's largest annotated conversational dataset (which we've already accomplished) is just one of countless real-world applications for SN33's Structured Data pipeline. We're building a decentralized Scale AI, offering a full suite of Structured Data commodities—from text metadata tagging (available today) to fully customizable queries for company-specific data annotation use cases and image metadata tagging coming soon 👀. Thanks for all the feedback! It has been invaluable so keep bringing it to us! 🙏$TAO Openτensor Foundaτion 1 “ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks” shows “The zero-shot accuracy of ChatGPT exceeds that of crowd-workers by about 25 percentage points on average [...] Moreover, the per-annotation cost of ChatGPT is less than $0.003—about thirty times cheaper than MTurk”

Studies have shown ChatGPT outperforms human annotators for Structured Data by about 25% and costs 30x less. 1 In just 2 months, miners on SN33 running ChatGPT without optimization can’t survive. Today we announce SN33 is now ReadyAI to fully align with our mission 👇 SN33 is building a more performant and significantly cheaper alternative to Scale AI Today structured data is performed primarily by human annotation services like Amazon’s Mechanical Turk and Scale AI It is now more important than ever for every business and individual to make their data AI Ready. However, taking unstructured data and making it Structured Data using today’s tools is extremely costly. SN33 revolutionizes this process, unlocking immense opportunities for commercialization. We lay out the vision for it in this detailed blog post: Validators TODAY can monetize access to this structured data pipeline independently, but we’re streamlining this process, launching a frontend soon that any validator can opt into to provide bandwidth. We've received great feedback from the community, recognizing that what we're building goes far beyond Conversational AI. Building the world's largest annotated conversational dataset (which we've already accomplished) is just one of countless real-world applications for SN33's Structured Data pipeline. We're building a decentralized Scale AI, offering a full suite of Structured Data commodities—from text metadata tagging (available today) to fully customizable queries for company-specific data annotation use cases and image metadata tagging coming soon 👀. Thanks for all the feedback! It has been invaluable so keep bringing it to us! 🙏$TAO Openτensor Foundaτion 1 “ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks” shows “The zero-shot accuracy of ChatGPT exceeds that of crowd-workers by about 25 percentage points on average [...] Moreover, the per-annotation cost of ChatGPT is less than $0.003—about thirty times cheaper than MTurk”

David Fields

13,638 次观看 • 1 年前

Andrej Karpathy (Founding Member of OpenAI): "you literally have to put in 10,000 hours" to get good at AI he gave out his 10,000 hours of learning. for free. this GitHub repo is the only guide you need to build your own ChatGPT from scratch. This will teach you more than most AI courses at universities teach in a year. Watch this video and save the build below no matter what. It will be the most productive thing to do this entire weekend.

Andrej Karpathy (Founding Member of OpenAI): "you literally have to put in 10,000 hours" to get good at AI he gave out his 10,000 hours of learning. for free. this GitHub repo is the only guide you need to build your own ChatGPT from scratch. This will teach you more than most AI courses at universities teach in a year. Watch this video and save the build below no matter what. It will be the most productive thing to do this entire weekend.

Avid

101,202 次观看 • 18 天前

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 次观看 • 8 个月前

"All that stuff you've told ChatGPT could end up in Beijing." Charles Hoskinson (Charles Hoskinson) on what most people don't realize they've already given away. "Privacy means weirdos doing crime. That's what people say. Then ask them: can I see your Google searches? Your ChatGPT history? Your shopping cart? Your location? Suddenly: absolutely not." "You don't have privacy in any of those contexts. You've shared it with an untrusted third party. You've given those companies the keys to your life." "Google knows your daughter's pregnant before you do." "And these aren't American companies anymore. They get acquired, they get partnerships, they share data. Some company in Europe gets your info, sells it to the Middle East, who sells it to China." "How do you know in 20 or 30 years that what you disclosed today won't be used against you?" Translation: every search, every prompt, every location ping is permanent data being stored by entities you cannot vet. The Baath Party in 1981 thought public disclosure of membership was a status symbol. By 2003 it was a liability that ended careers. You don't know which of your current disclosures becomes the next liability.

"All that stuff you've told ChatGPT could end up in Beijing." Charles Hoskinson (Charles Hoskinson) on what most people don't realize they've already given away. "Privacy means weirdos doing crime. That's what people say. Then ask them: can I see your Google searches? Your ChatGPT history? Your shopping cart? Your location? Suddenly: absolutely not." "You don't have privacy in any of those contexts. You've shared it with an untrusted third party. You've given those companies the keys to your life." "Google knows your daughter's pregnant before you do." "And these aren't American companies anymore. They get acquired, they get partnerships, they share data. Some company in Europe gets your info, sells it to the Middle East, who sells it to China." "How do you know in 20 or 30 years that what you disclosed today won't be used against you?" Translation: every search, every prompt, every location ping is permanent data being stored by entities you cannot vet. The Baath Party in 1981 thought public disclosure of membership was a status symbol. By 2003 it was a liability that ended careers. You don't know which of your current disclosures becomes the next liability.

Michaël van de Poppe

40,136 次观看 • 1 个月前

James Franklin shares what separates the elite players and teams. "What is the difference between great and elite? It's all the details. It's all the little things." "It's going to class consistently. It's getting to meetings on time. It's having your phone turned off. It's taking notes in every single meeting - not because the coaches told you to. Because you wanna be great." The little things aren't little. They're the difference between great and elite. "We don't ask, we don't settle, we don't make excuses. We don't allow it to happen." "Those little things that have slipped by - it's 1 point last year. It's 1 point this year." The margins are small. That's why every detail matters. "It's not happening anymore. It's not happening anymore." "You guys thought I was a psychopath in the past? You have no idea." Details win games. Standards win championships. You can't consistently win if you don't lock in on the little things.

James Franklin shares what separates the elite players and teams. "What is the difference between great and elite? It's all the details. It's all the little things." "It's going to class consistently. It's getting to meetings on time. It's having your phone turned off. It's taking notes in every single meeting - not because the coaches told you to. Because you wanna be great." The little things aren't little. They're the difference between great and elite. "We don't ask, we don't settle, we don't make excuses. We don't allow it to happen." "Those little things that have slipped by - it's 1 point last year. It's 1 point this year." The margins are small. That's why every detail matters. "It's not happening anymore. It's not happening anymore." "You guys thought I was a psychopath in the past? You have no idea." Details win games. Standards win championships. You can't consistently win if you don't lock in on the little things.

Coach AJ 🎯 Mental Fitness

18,606 次观看 • 12 天前

WARP SPEED: EPISODE 5 - starring Varunram Ganesh, Founder of Lapis Varunram Ganesh The next generation of 20 year olds are going to default to LLMs for search. Varun is building Lapis into the most accurate AI search analytics platform. They are part of Y Combinator F25 - 1 day away from demo day with a countdown clock on his desk. The insight: people are already defaulting to ChatGPT over Google. As time goes on, more leveraged decisions get made through LLMs, not search engines. "So many people are searching on Google, they're going to be searching on ChatGPT. Why aren't people knowing where they are on ChatGPT?" Lapis tells you exactly what a human will search for your website to show up in AI. And unlike Google, it's not about ranking positions - LLMs reason differently. On Warp: "I don't even think about payroll. Which is just to say Warp is such a good product that I just don't think about it. No spam emails. No upsells. It's just so smooth." In this conversation: (0:00) - Shift from Google-style search to LLMs: “More leveraged decisions are now made through the LLM.” (0:15) - What Lapis does: the most accurate AI search analytics platform. (1:15) - YC intensity: six weeks from Demo Day, rapid feedback loops, constant customer conversations. (2:00) - “The hardest part of running a startup is the most boring stuff.” (2:41) - How AI search differs from Google search. (3:23) - Building a company through trust. (3:51) - “Every founder said, ‘Check out Gabriella Warp - a genuinely friendly solution. (4:02) - Big providers needed six weeks just to get set up. (4:20) - Texted Ayush and got set up in 15 minutes. (4:25) - Warp is so smooth you don’t think about payroll — no spam emails, no upsells, just seamless.

WARP SPEED: EPISODE 5 - starring Varunram Ganesh, Founder of Lapis Varunram Ganesh The next generation of 20 year olds are going to default to LLMs for search. Varun is building Lapis into the most accurate AI search analytics platform. They are part of Y Combinator F25 - 1 day away from demo day with a countdown clock on his desk. The insight: people are already defaulting to ChatGPT over Google. As time goes on, more leveraged decisions get made through LLMs, not search engines. "So many people are searching on Google, they're going to be searching on ChatGPT. Why aren't people knowing where they are on ChatGPT?" Lapis tells you exactly what a human will search for your website to show up in AI. And unlike Google, it's not about ranking positions - LLMs reason differently. On Warp: "I don't even think about payroll. Which is just to say Warp is such a good product that I just don't think about it. No spam emails. No upsells. It's just so smooth." In this conversation: (0:00) - Shift from Google-style search to LLMs: “More leveraged decisions are now made through the LLM.” (0:15) - What Lapis does: the most accurate AI search analytics platform. (1:15) - YC intensity: six weeks from Demo Day, rapid feedback loops, constant customer conversations. (2:00) - “The hardest part of running a startup is the most boring stuff.” (2:41) - How AI search differs from Google search. (3:23) - Building a company through trust. (3:51) - “Every founder said, ‘Check out Gabriella Warp - a genuinely friendly solution. (4:02) - Big providers needed six weeks just to get set up. (4:20) - Texted Ayush and got set up in 15 minutes. (4:25) - Warp is so smooth you don’t think about payroll — no spam emails, no upsells, just seamless.

Ayush S

56,114 次观看 • 7 个月前

#BudgetSession2025 | Lok Sabha LoP and Congress MP Rahul Gandhi says "People talk about AI, but it's important to understand that AI on its own is absolutely meaningless because AI operates on top of data. Without data, AI means nothing. And if we look at data today, there is one thing which is very clear. Every single piece of data that comes out of the production system in the world. The data that was used to make this phone, the data that is used to make electric cars. The data that is used to make basically all electronics on the planet today is owned by China. And the consumption data is owned by the United States... China has at least a 10 year lead on India in this space. China has been working on batteries, robots, motors, optics for the last 10 years and we are behind...We would ensure that our banking system is not captured by 2-3 companies that basically do not allow you to build a production system. But our banking system is open, dynamic and accessible to small and medium businesses and to millions and millions of entrepreneurs who want to take part in this revolution. Our foreign policy would take into account this revolution. When we talk to the United States, we would not send our foreign minister to invite our Prime Minister to his coronation...Because if we had a production system and if we were working on these technologies, the American President would come here and invite the Prime Minister."

#BudgetSession2025 | Lok Sabha LoP and Congress MP Rahul Gandhi says "People talk about AI, but it's important to understand that AI on its own is absolutely meaningless because AI operates on top of data. Without data, AI means nothing. And if we look at data today, there is one thing which is very clear. Every single piece of data that comes out of the production system in the world. The data that was used to make this phone, the data that is used to make electric cars. The data that is used to make basically all electronics on the planet today is owned by China. And the consumption data is owned by the United States... China has at least a 10 year lead on India in this space. China has been working on batteries, robots, motors, optics for the last 10 years and we are behind...We would ensure that our banking system is not captured by 2-3 companies that basically do not allow you to build a production system. But our banking system is open, dynamic and accessible to small and medium businesses and to millions and millions of entrepreneurs who want to take part in this revolution. Our foreign policy would take into account this revolution. When we talk to the United States, we would not send our foreign minister to invite our Prime Minister to his coronation...Because if we had a production system and if we were working on these technologies, the American President would come here and invite the Prime Minister."

ANI

163,581 次观看 • 1 年前

JENSEN HUANG SOLD A $249 AI COMPUTER ON STAGE THAT KILLS YOUR $200/MONTH OPENAI BILL. THE VIDEO HAS 217,000 LIKES the box is called the jetson orin nano super. 70 trillion ai operations per second, 25 watts, smaller than a wallet. it runs llama 3, mistral, gemma and deepseek locally with no api fees and no data leaving your house a developer running automations and coding assistants pays $200 a month to openai. the same workload on this box costs $2 a month in electricity and breaks even on the hardware in 10 weeks you install ollama with one command. change one line in your code. point it at localhost instead of openai. everything else works identically 7 billion parameter models handle 80% of what people use chatgpt for. summarization, drafting, coding, document q&a, automation pipelines. the hard 20% you keep claude or gpt for. total monthly cost drops from $200 to $22 cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who set this up in 2025 are going to look very smart in 2027 bookmark this and read the article below

JENSEN HUANG SOLD A $249 AI COMPUTER ON STAGE THAT KILLS YOUR $200/MONTH OPENAI BILL. THE VIDEO HAS 217,000 LIKES the box is called the jetson orin nano super. 70 trillion ai operations per second, 25 watts, smaller than a wallet. it runs llama 3, mistral, gemma and deepseek locally with no api fees and no data leaving your house a developer running automations and coding assistants pays $200 a month to openai. the same workload on this box costs $2 a month in electricity and breaks even on the hardware in 10 weeks you install ollama with one command. change one line in your code. point it at localhost instead of openai. everything else works identically 7 billion parameter models handle 80% of what people use chatgpt for. summarization, drafting, coding, document q&a, automation pipelines. the hard 20% you keep claude or gpt for. total monthly cost drops from $200 to $22 cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who set this up in 2025 are going to look very smart in 2027 bookmark this and read the article below

starmex

1,996,019 次观看 • 1 个月前

‼️Erika Kirk Just ADMITTED That People No Longer TRUST Turning Point USA 😱 At the Turning Point Educators Summit today, Erika Kirk spent several minutes talking about how people no longer trust each other because of COVID. She claimed that’s why many people now trust AI (specifically naming Grok) more than they trust actual humans. What she was really doing was damage control. Turning Point USA has been getting repeatedly fact-checked by Grok and community notes for spreading misleading information. They’ve also already been caught using AI-generated content themselves. So now Erika is trying to convince people that AI can’t be trusted, while positioning Turning Point as the more reliable source. But in doing so, she accidentally admitted something very telling: People don’t trust Turning Point USA anymore. Instead of addressing why trust in the organization has collapsed since Charlie’s death, she blamed COVID and tried to discredit AI tools that are exposing their inconsistencies. This was a clear attempt to get ahead of the growing skepticism around Turning Point’s leadership and messaging. If Turning Point was still trusted, she wouldn’t need to spend time trying to convince people not to believe what they see online. The fact that she felt the need to address it at all says a lot. RT this. More people need to see how they’re spinning this. My channel is under attack to silence my investigation into the Charlie Kirk assassination. If you believe in independent journalism and want the truth to keep coming, your support right now is everything. DONATE Here:

‼️Erika Kirk Just ADMITTED That People No Longer TRUST Turning Point USA 😱 At the Turning Point Educators Summit today, Erika Kirk spent several minutes talking about how people no longer trust each other because of COVID. She claimed that’s why many people now trust AI (specifically naming Grok) more than they trust actual humans. What she was really doing was damage control. Turning Point USA has been getting repeatedly fact-checked by Grok and community notes for spreading misleading information. They’ve also already been caught using AI-generated content themselves. So now Erika is trying to convince people that AI can’t be trusted, while positioning Turning Point as the more reliable source. But in doing so, she accidentally admitted something very telling: People don’t trust Turning Point USA anymore. Instead of addressing why trust in the organization has collapsed since Charlie’s death, she blamed COVID and tried to discredit AI tools that are exposing their inconsistencies. This was a clear attempt to get ahead of the growing skepticism around Turning Point’s leadership and messaging. If Turning Point was still trusted, she wouldn’t need to spend time trying to convince people not to believe what they see online. The fact that she felt the need to address it at all says a lot. RT this. More people need to see how they’re spinning this. My channel is under attack to silence my investigation into the Charlie Kirk assassination. If you believe in independent journalism and want the truth to keep coming, your support right now is everything. DONATE Here:

Project Constitution

19,531 次观看 • 25 天前

The founder of Oracle got on stage and said something that made half the AI industry wince: "it no longer matters which model you use, Claude or ChatGPT. the models have become an interchangeable commodity, and all the value has shifted somewhere else." Larry Ellison knows what he's talking about. he took Oracle from $1,200 in his pocket to becoming the second richest man on the planet, and he's now building the world's largest AI cluster for OpenAI. every model trains on the same open internet data, so they quickly become identical. the edge doesn't come from the model, it comes from the closed data nobody else has but you. in 50 minutes he shows why models become interchangeable, why the only moat is private data, how Oracle built a 1.2 billion watt cluster for OpenAI, and why AI robots will operate more precisely than surgeons. while some argue over whose chatbot is smarter, others are quietly building defensive walls out of their private data. bookmark it and watch ↓

The founder of Oracle got on stage and said something that made half the AI industry wince: "it no longer matters which model you use, Claude or ChatGPT. the models have become an interchangeable commodity, and all the value has shifted somewhere else." Larry Ellison knows what he's talking about. he took Oracle from $1,200 in his pocket to becoming the second richest man on the planet, and he's now building the world's largest AI cluster for OpenAI. every model trains on the same open internet data, so they quickly become identical. the edge doesn't come from the model, it comes from the closed data nobody else has but you. in 50 minutes he shows why models become interchangeable, why the only moat is private data, how Oracle built a 1.2 billion watt cluster for OpenAI, and why AI robots will operate more precisely than surgeons. while some argue over whose chatbot is smarter, others are quietly building defensive walls out of their private data. bookmark it and watch ↓

Damir Akaza

164,950 次观看 • 1 个月前

The future of AI Agents needs #ICP AI agents are starting to act on our behalf: - making deals - sending messages - handling sensitive data But there is no trust layer telling you who or what you're actually dealing with ❌ zCloak Network is building exactly that: the trust, identity and privacy infrastructure for the AI-native economy, and they built it on top of Internet Computer Protocol In this podcast I sit down with Xiao Zhang (0xFrancis), founder of to break down what they're building, why they chose ICP over every other blockchain, and what the world looks like when AI agents can finally be trusted.

The future of AI Agents needs #ICP AI agents are starting to act on our behalf: - making deals - sending messages - handling sensitive data But there is no trust layer telling you who or what you're actually dealing with ❌ zCloak Network is building exactly that: the trust, identity and privacy infrastructure for the AI-native economy, and they built it on top of Internet Computer Protocol In this podcast I sit down with Xiao Zhang (0xFrancis), founder of to break down what they're building, why they chose ICP over every other blockchain, and what the world looks like when AI agents can finally be trusted.

Alex (BCP)

20,791 次观看 • 2 个月前

Building a truly private AI model isn’t as simple as the tech world wants you to believe. despite all the marketing around one click deployments, RAG systems, fine tuning services, and memory features, we still don’t have models that are genuinely trained only on your personal data with complete privacy from third parties. the reality is that most AI solutions today require you to trust someone else with your books, notes, journals, and training data. even when companies promise privacy, there’s usually a way for the provider or other parties to access your information during training or inference. Phala is taking a different approach by building the infrastructure needed for real data ownership. They’re developing confidential runtime environments where your AI training happens inside secure enclaves with attestation guarantees. The key innovation is their in-enclave keying system through dstack, which means that once your model is training, even Phala themselves cannot see your data or model weights.

Building a truly private AI model isn’t as simple as the tech world wants you to believe. despite all the marketing around one click deployments, RAG systems, fine tuning services, and memory features, we still don’t have models that are genuinely trained only on your personal data with complete privacy from third parties. the reality is that most AI solutions today require you to trust someone else with your books, notes, journals, and training data. even when companies promise privacy, there’s usually a way for the provider or other parties to access your information during training or inference. Phala is taking a different approach by building the infrastructure needed for real data ownership. They’re developing confidential runtime environments where your AI training happens inside secure enclaves with attestation guarantees. The key innovation is their in-enclave keying system through dstack, which means that once your model is training, even Phala themselves cannot see your data or model weights.

soulman 🎮

11,702 次观看 • 9 个月前