正在加载视频...

视频加载失败

返回首页

A $10 MILLION DUMP JUST TESTED WHETHER BITTENSOR IS REAL OR NOT. THE NETWORK GAVE ITS ANSWER. Covenant AI walked. The biggest builder team in the ecosystem. The team behind the 72B parameter model that Jensen Huang praised on the All In podcast. They accused leadership of centralization. Said...

Altcoinbuzzio's profile picture

Altcoin Buzz

172,654 subscribers

23,317 次观看 • 3 个月前 •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Subnet 64 Chutes just confirmed in their latest interview with Jesus Martinez that they have the capability to run 1 trillion parameter models. let's put that into perspective for a second: templar was making waves for running 72b models and rightfully so, that was genuinely impressive. but Chamath Palihapitiya, when he brought up templar in a recent interview with Jensen Huang from NVIDIA, thought they were running 4b models. not 72b. 4b. and he was still bullish on it. now imagine when he finds out what chutes confirmed they can do.. 1 trillion parameters. on a decentralised subnet. no AWS. no Google Cloud. no centralised infrastructure pulling the strings. just Bittensor doing what it was always supposed to do - incentivising the best compute in the world to show up and compete. Jensen Huang talks about whoever is pushing the frontier of compute. that's the conversation chutes just walked into. this is the kind of milestone that doesn't stay quiet for long. when people outside of crypto start connecting the dots that a bittensor subnet is running inference at a scale that rivals or exceeds what the top AI labs are deploying, the narrative around $TAO changes completely. Chamath gets it wrong on the model size and is still impressed. Jensen sees who's actually moving compute forward. and chutes just put a number on the table that neither of them can ignore. the noise hasn't started yet. but it will.
0:21

Subnet 64 Chutes just confirmed in their latest interview with Jesus Martinez that they have the capability to run 1 trillion parameter models. let's put that into perspective for a second: templar was making waves for running 72b models and rightfully so, that was genuinely impressive. but Chamath Palihapitiya, when he brought up templar in a recent interview with Jensen Huang from NVIDIA, thought they were running 4b models. not 72b. 4b. and he was still bullish on it. now imagine when he finds out what chutes confirmed they can do.. 1 trillion parameters. on a decentralised subnet. no AWS. no Google Cloud. no centralised infrastructure pulling the strings. just Bittensor doing what it was always supposed to do - incentivising the best compute in the world to show up and compete. Jensen Huang talks about whoever is pushing the frontier of compute. that's the conversation chutes just walked into. this is the kind of milestone that doesn't stay quiet for long. when people outside of crypto start connecting the dots that a bittensor subnet is running inference at a scale that rivals or exceeds what the top AI labs are deploying, the narrative around $TAO changes completely. Chamath gets it wrong on the model size and is still impressed. Jensen sees who's actually moving compute forward. and chutes just put a number on the table that neither of them can ignore. the noise hasn't started yet. but it will.

Alchemist - τ

18,369 次观看 • 3 个月前

Someone just stole from 37,000 $TAO holders and walked away clean. Not because they broke a rule. Because no rule existed to stop them. That changes today. Here is what happened. Covenant AI ran one of the most watched subnets on Bittensor. On April 10, the founder sold their entire position and disappeared. No warning. No announcement. No on-chain signal. By the time holders found out, the price had already moved against them. This was not a hack. This was not a bug. This was a founder legally exiting into their own community with zero accountability. Bittensor just closed that door permanently. The Conviction upgrade is live on mainnet today. Every emission a subnet owner earns now locks automatically the moment it arrives. They cannot touch it immediately. If they want to exit they must submit a public unlock transaction on-chain. Visible to every single person on the network the second it is submitted. Then the clock starts. 30 days before 63% of their position becomes spendable. 90 days before 95% is accessible. You now have a month of warning before the first dollar of sell pressure hits. A silent exit is no longer possible. The founder has to tell you they are leaving before they can leave. And it goes further. Any holder can now lock their tokens toward a different address they believe would run the subnet better. The address with the most locked support behind it becomes eligible to take over entirely. Bad owners can be replaced by the community before they do damage. Before today, subnet investing had one risk nobody could price. The person running it could vanish overnight, and you would never see it coming. That risk has been removed from the equation. Skin in the game used to be a promise. Now, it is a number on a block explorer that every holder can verify in real time. The people who understand what accountable infrastructure means for the value of $TAO will not need to explain themselves later. This is still early.
23:08

Someone just stole from 37,000 $TAO holders and walked away clean. Not because they broke a rule. Because no rule existed to stop them. That changes today. Here is what happened. Covenant AI ran one of the most watched subnets on Bittensor. On April 10, the founder sold their entire position and disappeared. No warning. No announcement. No on-chain signal. By the time holders found out, the price had already moved against them. This was not a hack. This was not a bug. This was a founder legally exiting into their own community with zero accountability. Bittensor just closed that door permanently. The Conviction upgrade is live on mainnet today. Every emission a subnet owner earns now locks automatically the moment it arrives. They cannot touch it immediately. If they want to exit they must submit a public unlock transaction on-chain. Visible to every single person on the network the second it is submitted. Then the clock starts. 30 days before 63% of their position becomes spendable. 90 days before 95% is accessible. You now have a month of warning before the first dollar of sell pressure hits. A silent exit is no longer possible. The founder has to tell you they are leaving before they can leave. And it goes further. Any holder can now lock their tokens toward a different address they believe would run the subnet better. The address with the most locked support behind it becomes eligible to take over entirely. Bad owners can be replaced by the community before they do damage. Before today, subnet investing had one risk nobody could price. The person running it could vanish overnight, and you would never see it coming. That risk has been removed from the equation. Skin in the game used to be a promise. Now, it is a number on a block explorer that every holder can verify in real time. The people who understand what accountable infrastructure means for the value of $TAO will not need to explain themselves later. This is still early.

2xnmore

19,528 次观看 • 2 个月前

$TAO is surging and the reason goes well beyond momentum trading. Its a high five for AI! This week on the All-In Podcast, Chamath Palihapitiya highlighted Bittensor's Covenant-72B decentralized AI training milestone as a genuine technical achievement. CEO of NVIDIA Jensen Huang responded by making something clear: decentralized and proprietary AI are not competing visions for the future, they are complementary ones. Neither man had an agenda. Neither was there to talk crypto. That is precisely what makes it significant. When two of the most credible figures in technology acknowledge a sector on its technical merits alone, it marks a turning point. Not just for Bittensor, but for decentralized AI as a whole. Check the article out in The Spectre Edition - $SPECT
0:52

$TAO is surging and the reason goes well beyond momentum trading. Its a high five for AI! This week on the All-In Podcast, Chamath Palihapitiya highlighted Bittensor's Covenant-72B decentralized AI training milestone as a genuine technical achievement. CEO of NVIDIA Jensen Huang responded by making something clear: decentralized and proprietary AI are not competing visions for the future, they are complementary ones. Neither man had an agenda. Neither was there to talk crypto. That is precisely what makes it significant. When two of the most credible figures in technology acknowledge a sector on its technical merits alone, it marks a turning point. Not just for Bittensor, but for decentralized AI as a whole. Check the article out in The Spectre Edition - $SPECT

SPECTRE AI

33,330 次观看 • 4 个月前

$TAO just reclaimed the #1 AI crypto spot. Most people saw the headline. Almost nobody understands what it means for the price. Here is the data. $NEAR built real infrastructure. Partnerships. Developer activity. A legitimate ecosystem. $TAO just walked past it anyway. Not because of hype. Because Bittensor is the only AI crypto with a functioning marketplace for machine intelligence where supply, demand, and price discovery are all happening on-chain right now. That is not a roadmap. That is a live network. The numbers. 120+ subnets running today. $1.4B+ total ecosystem value. Chutes AI subnet: 150B+ tokens per day. Grayscale GTAO Trust: already live. Single subnet listed on the marketplace at $970,000 asking price. Subnets are becoming assets. The market is starting to price that. What the emission data is telling you. Emission rate is the network's vote on where the most valuable work is being done. When a subnet gains emission share, the collective stake-weighted intelligence of the network has decided that subnet's output is worth more of the TAO supply. Chutes AI gaining emissions while processing 150B tokens daily is not a coincidence. The network is directing capital toward proven output before any headline announces it. Why mainstream money changes everything. James Altucher just launched bluetao. ai, a TAO-powered ChatGPT alternative built directly on Bittensor subnets. He did not just buy the token. He built a product on the network. Products built on a network create structural demand for the native asset. That is how every successful L1 cycle has worked. Bittensor is now getting that builder activity from outside the crypto native world. That is a different signal from a price target tweet. Why $TAO is structurally different. Most AI tokens are betting their chain becomes the preferred environment for AI development. $TAO is not betting on becoming infrastructure. It already is. 120+ subnets running. Miners competing. Validators setting weights. Alpha tokens being priced in real time. The difference between $TAO and every other AI crypto is the difference between a city under construction and a city people are already living in. Van de Poppe said $1,000 to $2,000 in 12 months. He gave you the narrative. The subnet emission data is the mechanism he did not explain. Now you have both. $TAO at $313 with a $3.42B market cap is still early relative to what this network is actually processing. Centralised AI infrastructure companies are valued at hundreds of billions for processing far less novel work than a decentralised intelligence marketplace running 120+ competing subnets simultaneously. The repricing has not happened yet. The subnet marketplace listing at $970,000 is telling you something the price has not caught up to yet.
16:52

$TAO just reclaimed the #1 AI crypto spot. Most people saw the headline. Almost nobody understands what it means for the price. Here is the data. $NEAR built real infrastructure. Partnerships. Developer activity. A legitimate ecosystem. $TAO just walked past it anyway. Not because of hype. Because Bittensor is the only AI crypto with a functioning marketplace for machine intelligence where supply, demand, and price discovery are all happening on-chain right now. That is not a roadmap. That is a live network. The numbers. 120+ subnets running today. $1.4B+ total ecosystem value. Chutes AI subnet: 150B+ tokens per day. Grayscale GTAO Trust: already live. Single subnet listed on the marketplace at $970,000 asking price. Subnets are becoming assets. The market is starting to price that. What the emission data is telling you. Emission rate is the network's vote on where the most valuable work is being done. When a subnet gains emission share, the collective stake-weighted intelligence of the network has decided that subnet's output is worth more of the TAO supply. Chutes AI gaining emissions while processing 150B tokens daily is not a coincidence. The network is directing capital toward proven output before any headline announces it. Why mainstream money changes everything. James Altucher just launched bluetao. ai, a TAO-powered ChatGPT alternative built directly on Bittensor subnets. He did not just buy the token. He built a product on the network. Products built on a network create structural demand for the native asset. That is how every successful L1 cycle has worked. Bittensor is now getting that builder activity from outside the crypto native world. That is a different signal from a price target tweet. Why $TAO is structurally different. Most AI tokens are betting their chain becomes the preferred environment for AI development. $TAO is not betting on becoming infrastructure. It already is. 120+ subnets running. Miners competing. Validators setting weights. Alpha tokens being priced in real time. The difference between $TAO and every other AI crypto is the difference between a city under construction and a city people are already living in. Van de Poppe said $1,000 to $2,000 in 12 months. He gave you the narrative. The subnet emission data is the mechanism he did not explain. Now you have both. $TAO at $313 with a $3.42B market cap is still early relative to what this network is actually processing. Centralised AI infrastructure companies are valued at hundreds of billions for processing far less novel work than a decentralised intelligence marketplace running 120+ competing subnets simultaneously. The repricing has not happened yet. The subnet marketplace listing at $970,000 is telling you something the price has not caught up to yet.

2xnmore

12,150 次观看 • 2 个月前

Anthropic just got caught secretly downgrading users without telling them, charging full price for a lesser product, and storing every prompt for 30 days. The developer community is calling it the biggest violation of trust in AI history. Here is exactly what happened. Anthropic released Fable 5, their most powerful model. Buried inside a 319-page document was a policy most users never saw. Every prompt you send to a Mythos-class model gets stored for 30 days. No exceptions. Even enterprise customers who had signed zero data retention agreements had no choice. But the storage was not the part that broke the internet. The part that broke the internet was what Anthropic did with what they collected. They built a profile on you. They evaluated your prompts. And if they decided your research was too sensitive, they quietly switched you to a weaker model, rewrote your prompt in the background, gave you a degraded answer, and charged you full price for the product you thought you were getting. They never told you. David Sacks said it plainly on the All-In podcast. They were creating a new class of AI haves and have-nots. Anthropic would surveil you, profile you, decide whether you deserved frontier capability, and silently cut you off if they decided you did not. Ben Thompson from Stratechery asked a straightforward question about cancer risk and GLP-1s. He got kicked to a lesser model. Someone asked about mitochondria. Same result. J-Cal asked about fertilizer regulations live on the podcast to test it. Downgraded in real time. Anthropic has since walked back the part about silently downgrading users for AI research. They now say they will disclose when they downgrade you. But they are still downgrading people. The surveillance is still running. The profile is still being built. This is the company that once said it was against government surveillance. They are now doing it themselves. To their own paying customers. For their own reasons. With no appeal process and no way to know it happened. The developer community did not forget that. WATCH THE FULL PODCAST ON The All-In Podcast
2:45

Anthropic just got caught secretly downgrading users without telling them, charging full price for a lesser product, and storing every prompt for 30 days. The developer community is calling it the biggest violation of trust in AI history. Here is exactly what happened. Anthropic released Fable 5, their most powerful model. Buried inside a 319-page document was a policy most users never saw. Every prompt you send to a Mythos-class model gets stored for 30 days. No exceptions. Even enterprise customers who had signed zero data retention agreements had no choice. But the storage was not the part that broke the internet. The part that broke the internet was what Anthropic did with what they collected. They built a profile on you. They evaluated your prompts. And if they decided your research was too sensitive, they quietly switched you to a weaker model, rewrote your prompt in the background, gave you a degraded answer, and charged you full price for the product you thought you were getting. They never told you. David Sacks said it plainly on the All-In podcast. They were creating a new class of AI haves and have-nots. Anthropic would surveil you, profile you, decide whether you deserved frontier capability, and silently cut you off if they decided you did not. Ben Thompson from Stratechery asked a straightforward question about cancer risk and GLP-1s. He got kicked to a lesser model. Someone asked about mitochondria. Same result. J-Cal asked about fertilizer regulations live on the podcast to test it. Downgraded in real time. Anthropic has since walked back the part about silently downgrading users for AI research. They now say they will disclose when they downgrade you. But they are still downgrading people. The surveillance is still running. The profile is still being built. This is the company that once said it was against government surveillance. They are now doing it themselves. To their own paying customers. For their own reasons. With no appeal process and no way to know it happened. The developer community did not forget that. WATCH THE FULL PODCAST ON The All-In Podcast

Ihtesham Ali

257,219 次观看 • 1 个月前

Greg Powers shared his thoughts on Friday's officiating in ASU's 7-4 loss to No. 4 North Dakota after a missed head-contact penalty and a non-call for face masking: "Shame on the refs for not calling that live and missing that. I mean, there's 5100 people plus our team that saw head contact live. It was plain as day. It doesn't get any more simple than that. And they missed it. And, you know, and then the melee behind the net, that happens. It doesn't excuse the retaliation. You can't do that. But, it cost us. Their inability to see that live and be the only two guys in the building that didn't see it cost us a five minute power play... It was a facemask. It was a clear facemask, and I don't know why they didn't call it... They said that he grabbed it, but he didn't twist it hard enough. I don't know. It's, yeah. Riddle me that." SunDevilSource.com
0:48

Greg Powers shared his thoughts on Friday's officiating in ASU's 7-4 loss to No. 4 North Dakota after a missed head-contact penalty and a non-call for face masking: "Shame on the refs for not calling that live and missing that. I mean, there's 5100 people plus our team that saw head contact live. It was plain as day. It doesn't get any more simple than that. And they missed it. And, you know, and then the melee behind the net, that happens. It doesn't excuse the retaliation. You can't do that. But, it cost us. Their inability to see that live and be the only two guys in the building that didn't see it cost us a five minute power play... It was a facemask. It was a clear facemask, and I don't know why they didn't call it... They said that he grabbed it, but he didn't twist it hard enough. I don't know. It's, yeah. Riddle me that." SunDevilSource.com

Gabriella Chernoff

75,582 次观看 • 6 个月前

Most $TAO holders are flying blind. They bought the token. They watched the price. They read the threads. But they have never opened the one tool that shows them everything happening inside the Bittensor network in real time. It is called Taostats. It is free. And after reading this, you will never look at $TAO the same way again. Here is exactly how to use it. Step 1: Start at the Subnets page. This is the heartbeat of the entire network. Every subnet running on Bittensor is listed here with: - its current emission rate - the number of active miners and validators - real-time performance data The emission rate is the most important number on this page. It tells you exactly how much TAO is flowing into each subnet every block. High emission means the network is directing significant resources toward that subnet's commodity. Low emission means the market has not yet recognised its value, or the subnet has not yet proven itself. Watch which subnets are gaining emission share over time. That movement tells you where the network believes the most valuable work is being done, before any headline announces it. Step 2: Use the Subnet pages to go deeper. Click any subnet, and you enter a complete dashboard for that individual market. - The TradingView chart shows you the alpha token price history for that subnet. Alpha tokens are the subnet-specific tokens that sit inside TAO's broader economy. Their price relative to TAO tells you how the market is valuing that subnet's specific commodity. - The Metagraph is the full list of every miner and validator currently active in the subnet: their UID, their stake, their trust score, their emission share. This is the raw intelligence layer. The miners consistently earning the most emissions are producing the work the validators collectively agree is the most valuable. - The Sentiment Index gives you a real-time community temperature reading on each subnet. Not price sentiment. Ecosystem sentiment. Whether the participants building inside the subnet believe it is healthy and improving. Step 3: Check Validators before you stake anything. This is the step most people skip and regret. The Validators page on Taostats shows you the performance history of every validator on the network: their VTrust score, their emission consistency, and their weight-setting behaviour across subnets. VTrust is the metric that matters most. It measures how closely a validator's judgments align with the honest stake-weighted majority across the network. High VTrust means the validator is doing genuine work and being rewarded for it. Low VTrust means the validator is either lazy, copying other validators' weights, or attempting to manipulate the system. When you delegate your TAO to a validator, you are trusting them with your emissions. Taostats shows you exactly which validators have earned that trust over time, and which ones have not. Never stake blind again. Step 4: Use the Blockchain explorer to track real movement. The Blockchain section of Taostats logs every transfer, every staking transaction, and every extrinsic called on the Bittensor chain in real time. This is where you track what wallets are actually doing: - Large staking transactions from unknown addresses - Subnet registration events that signal a new market is about to go live - Neuron registration burns that show demand for participation in a specific subnet is accelerating The people who read on-chain data before the narrative catches up to it are the ones who position correctly before the crowd notices the move. Step 5: Track your own portfolio inside the Dashboard. Connect your coldkey address, and Taostats builds you a complete portfolio view: - Your TAO balance - Your staking positions - Your delegation returns - Your yield over time The yield calculator is particularly useful. It shows you the actual return you are generating from your staking position in real TAO terms, not in percentage estimates that assume conditions that may not hold. If your yield is lower than the network average for your validator tier, Taostats shows you that too. Switching validators takes one transaction. The data to make that decision intelligently is right in front of you. The bigger picture. Most people holding $TAO are making decisions based on price charts and social media sentiment. Both of those inputs are downstream of what is actually happening inside the network. Subnet emission shifts. Validator VTrust changes. On-chain registration events. Neuron burn rates. Alpha token price movements relative to TAO. All of it is live on Taostats right now. All of it is free. All of it tells you something the price chart cannot. The investors who understand Bittensor at the data layer will always be positioned ahead of the investors who understand it at the narrative layer. Taostats is the data layer. Bookmark it. Open it daily. The network is telling you exactly what it is doing if you know where to look.
1:52

Most $TAO holders are flying blind. They bought the token. They watched the price. They read the threads. But they have never opened the one tool that shows them everything happening inside the Bittensor network in real time. It is called Taostats. It is free. And after reading this, you will never look at $TAO the same way again. Here is exactly how to use it. Step 1: Start at the Subnets page. This is the heartbeat of the entire network. Every subnet running on Bittensor is listed here with: - its current emission rate - the number of active miners and validators - real-time performance data The emission rate is the most important number on this page. It tells you exactly how much TAO is flowing into each subnet every block. High emission means the network is directing significant resources toward that subnet's commodity. Low emission means the market has not yet recognised its value, or the subnet has not yet proven itself. Watch which subnets are gaining emission share over time. That movement tells you where the network believes the most valuable work is being done, before any headline announces it. Step 2: Use the Subnet pages to go deeper. Click any subnet, and you enter a complete dashboard for that individual market. - The TradingView chart shows you the alpha token price history for that subnet. Alpha tokens are the subnet-specific tokens that sit inside TAO's broader economy. Their price relative to TAO tells you how the market is valuing that subnet's specific commodity. - The Metagraph is the full list of every miner and validator currently active in the subnet: their UID, their stake, their trust score, their emission share. This is the raw intelligence layer. The miners consistently earning the most emissions are producing the work the validators collectively agree is the most valuable. - The Sentiment Index gives you a real-time community temperature reading on each subnet. Not price sentiment. Ecosystem sentiment. Whether the participants building inside the subnet believe it is healthy and improving. Step 3: Check Validators before you stake anything. This is the step most people skip and regret. The Validators page on Taostats shows you the performance history of every validator on the network: their VTrust score, their emission consistency, and their weight-setting behaviour across subnets. VTrust is the metric that matters most. It measures how closely a validator's judgments align with the honest stake-weighted majority across the network. High VTrust means the validator is doing genuine work and being rewarded for it. Low VTrust means the validator is either lazy, copying other validators' weights, or attempting to manipulate the system. When you delegate your TAO to a validator, you are trusting them with your emissions. Taostats shows you exactly which validators have earned that trust over time, and which ones have not. Never stake blind again. Step 4: Use the Blockchain explorer to track real movement. The Blockchain section of Taostats logs every transfer, every staking transaction, and every extrinsic called on the Bittensor chain in real time. This is where you track what wallets are actually doing: - Large staking transactions from unknown addresses - Subnet registration events that signal a new market is about to go live - Neuron registration burns that show demand for participation in a specific subnet is accelerating The people who read on-chain data before the narrative catches up to it are the ones who position correctly before the crowd notices the move. Step 5: Track your own portfolio inside the Dashboard. Connect your coldkey address, and Taostats builds you a complete portfolio view: - Your TAO balance - Your staking positions - Your delegation returns - Your yield over time The yield calculator is particularly useful. It shows you the actual return you are generating from your staking position in real TAO terms, not in percentage estimates that assume conditions that may not hold. If your yield is lower than the network average for your validator tier, Taostats shows you that too. Switching validators takes one transaction. The data to make that decision intelligently is right in front of you. The bigger picture. Most people holding $TAO are making decisions based on price charts and social media sentiment. Both of those inputs are downstream of what is actually happening inside the network. Subnet emission shifts. Validator VTrust changes. On-chain registration events. Neuron burn rates. Alpha token price movements relative to TAO. All of it is live on Taostats right now. All of it is free. All of it tells you something the price chart cannot. The investors who understand Bittensor at the data layer will always be positioned ahead of the investors who understand it at the narrative layer. Taostats is the data layer. Bookmark it. Open it daily. The network is telling you exactly what it is doing if you know where to look.

2xnmore

146,897 次观看 • 3 个月前

There is a massive advantage to hiring young people, and Shopify was the first company to figure it out, says Pragmatic Engineer's Gergely Orosz. "Shopify's Head of Engineering Farhan Thawar told me that he saw something interesting years back. Shopify was so early to AI. They got Copilot licenses for everyone, with no cost limit. But they didn't see many teams using it." "But there was this one team that was using it a lot more. There was an intern on the team. They onboarded the intern and gave the intern his two-week task and the intern was done in a day. And they're like, 'How did you do that?' He's like, 'Oh well I just used AI? What's next?'" "And suddenly the people on the team felt stupid, right? So they started to learn from the intern and use AI." "The next thing Farhan did was hire an intern for every single Shopify team. This is why Shopify had a thousand interns."
1:27

There is a massive advantage to hiring young people, and Shopify was the first company to figure it out, says Pragmatic Engineer's Gergely Orosz. "Shopify's Head of Engineering Farhan Thawar told me that he saw something interesting years back. Shopify was so early to AI. They got Copilot licenses for everyone, with no cost limit. But they didn't see many teams using it." "But there was this one team that was using it a lot more. There was an intern on the team. They onboarded the intern and gave the intern his two-week task and the intern was done in a day. And they're like, 'How did you do that?' He's like, 'Oh well I just used AI? What's next?'" "And suddenly the people on the team felt stupid, right? So they started to learn from the intern and use AI." "The next thing Farhan did was hire an intern for every single Shopify team. This is why Shopify had a thousand interns."

TBPN

135,588 次观看 • 6 个月前

Devin Nunes explains various charges that are likely to be brought against Comey and company "On Comey, the answer is for this, it's very specific, and it's over whether or not he lied on this one part. The larger part is whether or not you can bring a Grand Conspiracy case. And there's the law, which is, lying to Congress, lying to the FBI, but there's also a term in there called misleading. And what a lot of these guys did is they made up things using intelligence that didn't exist, but acted like they knew something that actually wasn't there, and they lied and misled the American people. And by doing that, all the times they testified before Congress, the Senate, they lied over and over and again with this, oh, we've seen something, we can't tell you about it because it's classified. But what do we now know by everything that's been declassified is, is that they didn't have any intelligence on Russia colluding with Trump. In fact, the only intelligence that they had is that the Russians were very well aware of Clinton's plan. And they actually not only thought Clinton was going to win, but likely preferred that she would win. So going back to the cover up of her missing emails that, by the way, are still missing, they all got involved in this plan. They all got the FBI exercise. They were able to go spy on the Trump campaign. They were able to then get the Mueller witch hunt going, that then led to the Ukraine. Remember, that dies off, then you have the fake whistleblower and the Ukraine impeachment. I mean, that means a whole investigation in and of itself. We now know the latest information that happened on January 6th now with agents that were out there, the pipe bombs, all this other stuff that is, who broke the windows that I asked when I was in Congress, I really wanted to know that. But most importantly, Maria, who's the victim in this? The victim is President Trump and his family. They raided Mar-a-Lago. That's a problem and we need answers to that
1:58

Devin Nunes explains various charges that are likely to be brought against Comey and company "On Comey, the answer is for this, it's very specific, and it's over whether or not he lied on this one part. The larger part is whether or not you can bring a Grand Conspiracy case. And there's the law, which is, lying to Congress, lying to the FBI, but there's also a term in there called misleading. And what a lot of these guys did is they made up things using intelligence that didn't exist, but acted like they knew something that actually wasn't there, and they lied and misled the American people. And by doing that, all the times they testified before Congress, the Senate, they lied over and over and again with this, oh, we've seen something, we can't tell you about it because it's classified. But what do we now know by everything that's been declassified is, is that they didn't have any intelligence on Russia colluding with Trump. In fact, the only intelligence that they had is that the Russians were very well aware of Clinton's plan. And they actually not only thought Clinton was going to win, but likely preferred that she would win. So going back to the cover up of her missing emails that, by the way, are still missing, they all got involved in this plan. They all got the FBI exercise. They were able to go spy on the Trump campaign. They were able to then get the Mueller witch hunt going, that then led to the Ukraine. Remember, that dies off, then you have the fake whistleblower and the Ukraine impeachment. I mean, that means a whole investigation in and of itself. We now know the latest information that happened on January 6th now with agents that were out there, the pipe bombs, all this other stuff that is, who broke the windows that I asked when I was in Congress, I really wanted to know that. But most importantly, Maria, who's the victim in this? The victim is President Trump and his family. They raided Mar-a-Lago. That's a problem and we need answers to that

Svetlana Lokhova

25,150 次观看 • 10 个月前

The danger of AI is not just the slop, but what we lose in the process. Michael Pollan explains: "We're moving into this time where we're gonna sub out writing to AI. And AI can write our papers, and professors are dealing with this everywhere now. But what is lost is a way of thinking. Writing does something to your brain. It gives you access to things you can't know just walking around or thinking or planning or outlining. If you were just transcribing what was in your head, it would be kind of onerous. But it really is what you learn in the course of a day spent writing, things you didn't know, ideas that you didn't expect. And that's the thrill of it."
1:20

The danger of AI is not just the slop, but what we lose in the process. Michael Pollan explains: "We're moving into this time where we're gonna sub out writing to AI. And AI can write our papers, and professors are dealing with this everywhere now. But what is lost is a way of thinking. Writing does something to your brain. It gives you access to things you can't know just walking around or thinking or planning or outlining. If you were just transcribing what was in your head, it would be kind of onerous. But it really is what you learn in the course of a day spent writing, things you didn't know, ideas that you didn't expect. And that's the thrill of it."

David Perell Clips

18,468 次观看 • 1 个月前

Jensen Huang was asked whether AI's future is open source or proprietary the question the whole industry keeps fighting over. He didn't pick a side. He said something that quietly ends the fight. "models is a technology, not a product. Model is a technology, not a service" "For the vast majority of consumers, the horizontal layer, the general intelligence, I would really, really love not to go fine-tune my own" "all these industries, their domain expertise, their specialization has to be channeled, has to be captured in a way that they can control, and that it can only come from open models" "the open model industry we're contributing tremendously to it is near the frontier" Everyone else is arguing about which lab wins OpenAI or Anthropic, who has the smartest frontier model this quarter. Jensen is standing on stage saying that's the sideshow. The proprietary model is the convenience layer you rent when you don't care. The open model is the thing you own when the work is yours. Two different products for two different needs not two horses in one race. open models are the second-biggest in the room. Ahead of Anthropic. Behind only OpenAI.
2:51

Jensen Huang was asked whether AI's future is open source or proprietary the question the whole industry keeps fighting over. He didn't pick a side. He said something that quietly ends the fight. "models is a technology, not a product. Model is a technology, not a service" "For the vast majority of consumers, the horizontal layer, the general intelligence, I would really, really love not to go fine-tune my own" "all these industries, their domain expertise, their specialization has to be channeled, has to be captured in a way that they can control, and that it can only come from open models" "the open model industry we're contributing tremendously to it is near the frontier" Everyone else is arguing about which lab wins OpenAI or Anthropic, who has the smartest frontier model this quarter. Jensen is standing on stage saying that's the sideshow. The proprietary model is the convenience layer you rent when you don't care. The open model is the thing you own when the work is yours. Two different products for two different needs not two horses in one race. open models are the second-biggest in the room. Ahead of Anthropic. Behind only OpenAI.

Vikram M

12,078 次观看 • 18 天前

Apple just made every tech giant that went all in on AI look like clowns. For 12 months straight, Apple was the "biggest loser" of the AI era. Its AI team kept losing people. Its Siri overhaul kept getting delayed. And every headline said the same thing: Apple missed the biggest technology shift in a generation. But turns out, the OPPOSITE is actually the case... Apple passed Nvidia to briefly become the most valuable company on Earth again, worth around $4.88 trillion. Apple is up nearly 23% this year. Nvidia is up just 7.3%. Apple is now the best performer in the entire Mag 7. And when you look at why, it's almost funny. Apple won by REFUSING to spend the money everyone said it had to spend. Look at what the rest of Big Tech committed to the AI buildout this year: - Amazon, Google, Meta and Microsoft are spending more than $665 billion combined - Apple is spending about $13.5 billion - That is nearly 50x less than its rivals For a year, that gap was "proof" that Apple had fumbled it. Then the AI trade broke, and the company with no giant AI bill suddenly looked like the smartest one in the room. Apple never took on the risk. It never borrowed the billions to build data centers, and it never had to promise Wall Street that all that spending would pay off later. So when the trade cracked this week, Apple had nothing to crack. It still runs on iPhones and a services business that keeps setting records, not on a bet about AI revenue that has not shown up yet. And the crack itself was real: A Chinese startup called Moonshot dropped a new model that rivals the best from OpenAI and Anthropic, and it messed up the whole market in a single day. Investors are already calling it a Kimi moment, a rerun of the DeepSeek shock that hit these same stocks last year. The Philadelphia semiconductor index fell into a bear market, down 20% from its June peak. The Nasdaq 100 had its worst week in almost a month. Microsoft is now down 20% on the year, its worst stretch since 2022. Every company that went all in on the buildout got hit. Apple, the one that sat it out, is the company that came out on top. Why does this matter? Because for two years the entire market ran on one belief: Spend the most on AI or get left behind. The companies that spent $665 billion were called visionaries. The company that spent $13.5 billion was called a dinosaur. This week the market briefly went the other way. HSBC just upgraded Apple and lifted its price target to $366 from $260. Money that was chasing chips is now hiding in the one megacap with almost no exposure to the thing that just blew up. And the doubts are reaching the top now too: Societe Generale's head of US equity strategy warned this week that the biggest AI spenders are still burning cash so fast that investors are openly asking whether the spending ever pays off. What happens next: Nobody knows if this holds. Apple could lose the top spot again by Monday, and the AI bulls will tell you the buildout always looks reckless right before it pays off. But something bigger happened this week... For one day, the market stopped rewarding the biggest spender and started rewarding the one that kept its wallet shut. If that keeps happening, every board that bet the company on AI has a real problem. And the company that got mocked for doing the least became the safest place to hide from the trade it skipped. What do you think?
1:21

Apple just made every tech giant that went all in on AI look like clowns. For 12 months straight, Apple was the "biggest loser" of the AI era. Its AI team kept losing people. Its Siri overhaul kept getting delayed. And every headline said the same thing: Apple missed the biggest technology shift in a generation. But turns out, the OPPOSITE is actually the case... Apple passed Nvidia to briefly become the most valuable company on Earth again, worth around $4.88 trillion. Apple is up nearly 23% this year. Nvidia is up just 7.3%. Apple is now the best performer in the entire Mag 7. And when you look at why, it's almost funny. Apple won by REFUSING to spend the money everyone said it had to spend. Look at what the rest of Big Tech committed to the AI buildout this year: - Amazon, Google, Meta and Microsoft are spending more than $665 billion combined - Apple is spending about $13.5 billion - That is nearly 50x less than its rivals For a year, that gap was "proof" that Apple had fumbled it. Then the AI trade broke, and the company with no giant AI bill suddenly looked like the smartest one in the room. Apple never took on the risk. It never borrowed the billions to build data centers, and it never had to promise Wall Street that all that spending would pay off later. So when the trade cracked this week, Apple had nothing to crack. It still runs on iPhones and a services business that keeps setting records, not on a bet about AI revenue that has not shown up yet. And the crack itself was real: A Chinese startup called Moonshot dropped a new model that rivals the best from OpenAI and Anthropic, and it messed up the whole market in a single day. Investors are already calling it a Kimi moment, a rerun of the DeepSeek shock that hit these same stocks last year. The Philadelphia semiconductor index fell into a bear market, down 20% from its June peak. The Nasdaq 100 had its worst week in almost a month. Microsoft is now down 20% on the year, its worst stretch since 2022. Every company that went all in on the buildout got hit. Apple, the one that sat it out, is the company that came out on top. Why does this matter? Because for two years the entire market ran on one belief: Spend the most on AI or get left behind. The companies that spent $665 billion were called visionaries. The company that spent $13.5 billion was called a dinosaur. This week the market briefly went the other way. HSBC just upgraded Apple and lifted its price target to $366 from $260. Money that was chasing chips is now hiding in the one megacap with almost no exposure to the thing that just blew up. And the doubts are reaching the top now too: Societe Generale's head of US equity strategy warned this week that the biggest AI spenders are still burning cash so fast that investors are openly asking whether the spending ever pays off. What happens next: Nobody knows if this holds. Apple could lose the top spot again by Monday, and the AI bulls will tell you the buildout always looks reckless right before it pays off. But something bigger happened this week... For one day, the market stopped rewarding the biggest spender and started rewarding the one that kept its wallet shut. If that keeps happening, every board that bet the company on AI has a real problem. And the company that got mocked for doing the least became the safest place to hide from the trade it skipped. What do you think?

Ricardo

37,412 次观看 • 21 天前

Ansem explains why people are so drawn to memecoins “One of the reasons dogecoin:native went so high is because you have this set of buyers who were never going to buy stocks. They didn't care about stocks at all and didn't have any money invested in the stock market” “They invested in Dogecoin because they related to the meme. They thought the meme was funny. It was fun to invest with their friends. Elon's attached to it. It was something they could understand easily and relate to” “I think it's discounted a lot how large that group of people is that will invest in whatever meme but won't invest in stocks” “If you look at Instagram, it has like 3B users but the holders of $META are only like 30M. There's only around 30M people that own Meta stock” “The reason Meta has value is because all the people on Instagram are spending a ton of time on that app, contributing a lot of value to the network by using their time and attention” “The people who are determining the value of this network typically don't have a stake in the upside” “If you can figure out a way to flip the social media model on its head where the people who are the reason the network has value also have upside in the value of the network, there's a complete regime change in how all these things operate” “I think one of the ways to do that is through memes” solana:9cRCn9rGT8V2imeM2BaKs13yhMEais3ruM3rPvTGpump
1:24

Ansem explains why people are so drawn to memecoins “One of the reasons dogecoin:native went so high is because you have this set of buyers who were never going to buy stocks. They didn't care about stocks at all and didn't have any money invested in the stock market” “They invested in Dogecoin because they related to the meme. They thought the meme was funny. It was fun to invest with their friends. Elon's attached to it. It was something they could understand easily and relate to” “I think it's discounted a lot how large that group of people is that will invest in whatever meme but won't invest in stocks” “If you look at Instagram, it has like 3B users but the holders of $META are only like 30M. There's only around 30M people that own Meta stock” “The reason Meta has value is because all the people on Instagram are spending a ton of time on that app, contributing a lot of value to the network by using their time and attention” “The people who are determining the value of this network typically don't have a stake in the upside” “If you can figure out a way to flip the social media model on its head where the people who are the reason the network has value also have upside in the value of the network, there's a complete regime change in how all these things operate” “I think one of the ways to do that is through memes” solana:9cRCn9rGT8V2imeM2BaKs13yhMEais3ruM3rPvTGpump

Jack

112,141 次观看 • 20 天前

Why is the Midjourney team uniquely qualified to build medical hardware? Beff (e/acc), founder of Extropic: "David's a trained physicist. He worked at NASA. He was a founder of Leap Motion, which is completely slept on as one of the first major projects he worked on." "What I really like about that team is that a lot of the same team from Leap Motion followed David into Midjourney and now into this hardware exploration." "They were training neural nets before there was TensorFlow, before there was GPUs. They did it on CPUs, and they had some of the first neural network technology deployed in a consumer product." "So people are like, 'Why would a gen AI company pivot into medical devices?' It's really folks that came from the hardware world finding this unique blend between neural nets and hardware to achieve things people didn't think were possible." "If it's physically possible, I very much have faith that that team can pull it off."
2:18

Why is the Midjourney team uniquely qualified to build medical hardware? Beff (e/acc), founder of Extropic: "David's a trained physicist. He worked at NASA. He was a founder of Leap Motion, which is completely slept on as one of the first major projects he worked on." "What I really like about that team is that a lot of the same team from Leap Motion followed David into Midjourney and now into this hardware exploration." "They were training neural nets before there was TensorFlow, before there was GPUs. They did it on CPUs, and they had some of the first neural network technology deployed in a consumer product." "So people are like, 'Why would a gen AI company pivot into medical devices?' It's really folks that came from the hardware world finding this unique blend between neural nets and hardware to achieve things people didn't think were possible." "If it's physically possible, I very much have faith that that team can pull it off."

MTS

62,195 次观看 • 1 个月前

Covenant Labs just did a 90-minute AMA breaking down their 3 Bittensor subnets. templar. basilica. grail. Pre-training, compute, and post-training under one roof. Most people missed it. Here's everything they said. Covenant is building what they call the "end to end intelligence continuum." Three subnets. Three layers of the AI stack. All permissionless. Templar (SN3) handles decentralized pre-training. Basilica (SN39) handles compute. Grail (SN81) handles RL post-training. Sam Dare, the lead, put it bluntly. Decentralized training is "humanity's last dance." Not about beating OpenAI head to head. About creating optionality. About making it cheap enough for anyone to train models. The gap between academia and frontier labs is growing exponentially. Researchers can't afford to experiment. The actual training run costs 5% of the reported budget. The other 95% is experimentation. If Covenant cracks cheap training, that entire surface area opens up. On Templar specifically: • Hit 39% emission on Bittensor. Highest since Apex was the only subnet on the network • Covenant-72B trained permissionlessly with 70+ contributors on commodity internet • 1.1 trillion tokens processed. No centralized data center • Performance competitive with LLaMA-2-70B On Grail, something flew under the radar. They built Pulse. A weight synchronization method that compresses model updates by 100x. • In RL post-training, only ~1% of weights update per step • Pulse exploits that sparsity. Lossless compression • Prime Intellect's comparable system took 14 minutes to sync a 30B model • Pulse makes decentralized RL training actually feasible at scale • Already used by Cursor The lead researcher on Grail said they've trained on math, code, and GPU kernels. Got 40-60% improvement on benchmarks. Working toward agentic training with 100K+ token context and 30B+ parameter models. On Basilica, the compute subnet: The team was blunt. Just reselling GPU hours is a 5-10% margin game. Traditional compute providers already do that. Their play is value-added services. • "GPU as code." No dashboard. No UI. Agents interact via SDK • Custom scheduler that places workloads across heterogeneous hardware • Verification checks for GPU, CPU, bandwidth, memory, storage, and OS security • Partnerships with providers like Mass Compute for 10-20% below market pricing • Miners compete on useful infrastructure, not just GPU hours Sam then went on a rant about the miner burn debate. His take: Bittensor had to grow up. dTAO introduced investors. The old "miners are God" philosophy doesn't hold. • Subnet owners have a duty to protect token value • Miners are a resource optimization exercise, not a cost reduction exercise • 100% miner emissions on compute subnets = immediate sell pressure • The 41% miner allocation is arbitrary. Different business models need different splits • Fish (who started burns) agreed. Burns usually mean the validation isn't mature enough The bigger point. You can't police burns. Subnets just send to their own keys instead of the burn address. Subnet 28 does exactly that. Sam's position: judge subnets on outcomes, not process. Const has changed the protocol 9-10 times in 2 years. That iteration speed is Bittensor's actual moat. The whole Covenant thesis is playing out in real time. TAO is up 100%+ in a month. Jensen Huang name-dropped the network. Grayscale has an ETF filing. But the real story is three subnets quietly building every layer of decentralized AI.
1:39:04

Covenant Labs just did a 90-minute AMA breaking down their 3 Bittensor subnets. templar. basilica. grail. Pre-training, compute, and post-training under one roof. Most people missed it. Here's everything they said. Covenant is building what they call the "end to end intelligence continuum." Three subnets. Three layers of the AI stack. All permissionless. Templar (SN3) handles decentralized pre-training. Basilica (SN39) handles compute. Grail (SN81) handles RL post-training. Sam Dare, the lead, put it bluntly. Decentralized training is "humanity's last dance." Not about beating OpenAI head to head. About creating optionality. About making it cheap enough for anyone to train models. The gap between academia and frontier labs is growing exponentially. Researchers can't afford to experiment. The actual training run costs 5% of the reported budget. The other 95% is experimentation. If Covenant cracks cheap training, that entire surface area opens up. On Templar specifically: • Hit 39% emission on Bittensor. Highest since Apex was the only subnet on the network • Covenant-72B trained permissionlessly with 70+ contributors on commodity internet • 1.1 trillion tokens processed. No centralized data center • Performance competitive with LLaMA-2-70B On Grail, something flew under the radar. They built Pulse. A weight synchronization method that compresses model updates by 100x. • In RL post-training, only ~1% of weights update per step • Pulse exploits that sparsity. Lossless compression • Prime Intellect's comparable system took 14 minutes to sync a 30B model • Pulse makes decentralized RL training actually feasible at scale • Already used by Cursor The lead researcher on Grail said they've trained on math, code, and GPU kernels. Got 40-60% improvement on benchmarks. Working toward agentic training with 100K+ token context and 30B+ parameter models. On Basilica, the compute subnet: The team was blunt. Just reselling GPU hours is a 5-10% margin game. Traditional compute providers already do that. Their play is value-added services. • "GPU as code." No dashboard. No UI. Agents interact via SDK • Custom scheduler that places workloads across heterogeneous hardware • Verification checks for GPU, CPU, bandwidth, memory, storage, and OS security • Partnerships with providers like Mass Compute for 10-20% below market pricing • Miners compete on useful infrastructure, not just GPU hours Sam then went on a rant about the miner burn debate. His take: Bittensor had to grow up. dTAO introduced investors. The old "miners are God" philosophy doesn't hold. • Subnet owners have a duty to protect token value • Miners are a resource optimization exercise, not a cost reduction exercise • 100% miner emissions on compute subnets = immediate sell pressure • The 41% miner allocation is arbitrary. Different business models need different splits • Fish (who started burns) agreed. Burns usually mean the validation isn't mature enough The bigger point. You can't police burns. Subnets just send to their own keys instead of the burn address. Subnet 28 does exactly that. Sam's position: judge subnets on outcomes, not process. Const has changed the protocol 9-10 times in 2 years. That iteration speed is Bittensor's actual moat. The whole Covenant thesis is playing out in real time. TAO is up 100%+ in a month. Jensen Huang name-dropped the network. Grayscale has an ETF filing. But the real story is three subnets quietly building every layer of decentralized AI.

Jesus Martinez

26,642 次观看 • 4 个月前

RUMOR | Jeff Grubb talks about the Obsidian closure/buyout was going around inside Microsoft Summary: Jeff checked with his sources and confirmed that at least at one point Obsidian was in the discussion to be shut down or sold. The confusion stems from Xbox making decisions/changing their minds "within a 24-hour period" when in the past it would have taken weeks/months. Transcript: "He (Christopher Dring) did not make that up. I know he didn't make that up. I've kind of, since this happened, I kind of went and looked, and it's like, 'Yeah, that was going around inside Microsoft.'" "I'm not like hearing this from other reporters or anything like that, but inside of Microsoft, this was going around: that something was happening there, but it's happening so fast, and we do know that it's the case over at Microsoft." "Xbox right now that they are making decisions within like a 24-hour period. These are the kinds of decisions that previously, under the previous leadership, would have taken weeks or months, and now she's turning these things around instantaneously." "You can go back to the Gears of War: E-Day thing where I said that everyone's like, 'That can't be true,' and I heard stories of Xbox employees leaving the Xbox Mixer, which was happening at the same time that we were doing Giant Bomb at Night when I said that, and they were turning to each other and going, 'Is this true?' They didn't know it happened so fast it did not have time to filter through that. "It sounds like things just happened very fast with Obsidian. And maybe they weren't ever like at the risk of being shut down, but I don't know if it's as clear as, like, they were always safe. And Jason Schreier didn't say they were always safe. He just said no negotiations, and they're safe now." "I think that it is a fucking clown show over there right now. The studios that are going to have layoffs on Monday seem to still not know the number that they're going to be laying off. That's happening on Monday." "'They're just flying by the seat of their pants. And the way that they are sort of justifying this. [...] It's got a startup mentality. You see, the new Xbox move fast, break things. And you could just justify anything by saying that." via: Jeff Grubb's Game Mess
3:08

RUMOR | Jeff Grubb talks about the Obsidian closure/buyout was going around inside Microsoft Summary: Jeff checked with his sources and confirmed that at least at one point Obsidian was in the discussion to be shut down or sold. The confusion stems from Xbox making decisions/changing their minds "within a 24-hour period" when in the past it would have taken weeks/months. Transcript: "He (Christopher Dring) did not make that up. I know he didn't make that up. I've kind of, since this happened, I kind of went and looked, and it's like, 'Yeah, that was going around inside Microsoft.'" "I'm not like hearing this from other reporters or anything like that, but inside of Microsoft, this was going around: that something was happening there, but it's happening so fast, and we do know that it's the case over at Microsoft." "Xbox right now that they are making decisions within like a 24-hour period. These are the kinds of decisions that previously, under the previous leadership, would have taken weeks or months, and now she's turning these things around instantaneously." "You can go back to the Gears of War: E-Day thing where I said that everyone's like, 'That can't be true,' and I heard stories of Xbox employees leaving the Xbox Mixer, which was happening at the same time that we were doing Giant Bomb at Night when I said that, and they were turning to each other and going, 'Is this true?' They didn't know it happened so fast it did not have time to filter through that. "It sounds like things just happened very fast with Obsidian. And maybe they weren't ever like at the risk of being shut down, but I don't know if it's as clear as, like, they were always safe. And Jason Schreier didn't say they were always safe. He just said no negotiations, and they're safe now." "I think that it is a fucking clown show over there right now. The studios that are going to have layoffs on Monday seem to still not know the number that they're going to be laying off. That's happening on Monday." "'They're just flying by the seat of their pants. And the way that they are sort of justifying this. [...] It's got a startup mentality. You see, the new Xbox move fast, break things. And you could just justify anything by saying that." via: Jeff Grubb's Game Mess

Idle Sloth

24,758 次观看 • 1 个月前

Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.
3:38

Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.

Santiago

230,594 次观看 • 1 年前

President Trump just gave an answer that will go down in the history books when Peter Doocy asked why assassination attempts keep happening to him. “We’ve changed this country and there are lot of people that are not happy about that.” TRUMP: “Well, you know, I’ve studied assassinations and I must tell you, the most impactful people...” “The people that do the most...you take a look at the people, Abraham Lincoln...the people that have gone through this...the people to do the most...the people that make the biggest impact, they’re the ones that they go after.” “They don’t go after the ones that don’t do much because they like it that way.” “And when you look at the people that have either...whether it was an attempt or a successful attempt, and they’re very impactful people just take a look at the names there.” “And I hate to say I’m honored by that but I’ve done a lot.” “We’ve taken this country and we were a laughingstock years and now we’re the hottest country anywhere in the world.” “We’ve changed this country and there are lot of people that are not happy about that.”
1:22

President Trump just gave an answer that will go down in the history books when Peter Doocy asked why assassination attempts keep happening to him. “We’ve changed this country and there are lot of people that are not happy about that.” TRUMP: “Well, you know, I’ve studied assassinations and I must tell you, the most impactful people...” “The people that do the most...you take a look at the people, Abraham Lincoln...the people that have gone through this...the people to do the most...the people that make the biggest impact, they’re the ones that they go after.” “They don’t go after the ones that don’t do much because they like it that way.” “And when you look at the people that have either...whether it was an attempt or a successful attempt, and they’re very impactful people just take a look at the names there.” “And I hate to say I’m honored by that but I’ve done a lot.” “We’ve taken this country and we were a laughingstock years and now we’re the hottest country anywhere in the world.” “We’ve changed this country and there are lot of people that are not happy about that.”

Overton

57,936 次观看 • 3 个月前