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𝐖𝐡𝐚𝐭 𝐊𝐨𝐯𝐚 Kova 𝐢𝐬 𝐚𝐥𝐥 𝐚𝐛𝐨𝐮𝐭 𝐚𝐧𝐝 𝐢𝐭 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞𝐬 What if every idle CPU thread and fractional GPU core could become auditable performance you can deploy on demand or monetize when you’re not using it? Kova turns fragmented compute into a precision market: ➡️ Builders get exactly the performance... show more

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Gavin Baker on the one variable that now dominates all others in AI compute: "the more memory you put with flop for a given unit of compute, the more tokens you get out -- it's the single most important thing you could do to increase token output per unit of compute." This is him laying out why the whole AI buildout now turns on memory, why breaking a supply agreement can end your entire franchise, and what Nvidia quietly writes into every equity check it signs. "You might blow up your entire business and your franchise by breaking an LTA." "If you break your LTA and then in the next two or three years for any reason leverage shifts back to the memory guys -- you're out of business." "Essentially every time they haven't taken an equity stake in something, it's been a mistake, they've taken equity stakes in everything essentially except the memory companies." Bookmark & watch ↓

Gavin Baker on the one variable that now dominates all others in AI compute: "the more memory you put with flop for a given unit of compute, the more tokens you get out -- it's the single most important thing you could do to increase token output per unit of compute." This is him laying out why the whole AI buildout now turns on memory, why breaking a supply agreement can end your entire franchise, and what Nvidia quietly writes into every equity check it signs. "You might blow up your entire business and your franchise by breaking an LTA." "If you break your LTA and then in the next two or three years for any reason leverage shifts back to the memory guys -- you're out of business." "Essentially every time they haven't taken an equity stake in something, it's been a mistake, they've taken equity stakes in everything essentially except the memory companies." Bookmark & watch ↓

Fireside Alpha

18,521 Aufrufe • vor 2 Monaten

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.

Fraction AI

67,918 Aufrufe • vor 1 Jahr

JUST IN: const shares the vision for gamma tokens, usage credits that subnets could fund from a slice of their emissions and spend on compute, inference, and other infrastructure. What this means ⬇️ → Subnets stop being isolated prize pools. They can buy services from one another instead of only competing for $TAO → Gamma is meant to work as a transferable credit, not just another speculative subnet token → Access is designed to reach beyond Bittensor, so outside compute and inference providers could take the same credits → Emissions become operating budget, not only a reward that gets sold into TAO If a subnet can spend what it earns on real work, Bittensor starts looking less like a scoreboard and more like an internal market for intelligence.

JUST IN: const shares the vision for gamma tokens, usage credits that subnets could fund from a slice of their emissions and spend on compute, inference, and other infrastructure. What this means ⬇️ → Subnets stop being isolated prize pools. They can buy services from one another instead of only competing for $TAO → Gamma is meant to work as a transferable credit, not just another speculative subnet token → Access is designed to reach beyond Bittensor, so outside compute and inference providers could take the same credits → Emissions become operating budget, not only a reward that gets sold into TAO If a subnet can spend what it earns on real work, Bittensor starts looking less like a scoreboard and more like an internal market for intelligence.

Intelligence ττ

75,768 Aufrufe • vor 10 Tagen

You go on vacation and rent an Airbnb, only to show up and find more rules than a jail. Here are just a few: ➡️The owner left their food in the refrigerator, so they left a "rule" not to touch their food. ➡️Remove shoes when you come into the house ➡️Squeege the shower after each use, even though you pay for cleaning. ➡️Do not move the showerhead. ➡️Do not wash the bedding. There are literally rules posted in every room of the house, some with multiple rules. Is this over-the-top or acceptable?

You go on vacation and rent an Airbnb, only to show up and find more rules than a jail. Here are just a few: ➡️The owner left their food in the refrigerator, so they left a "rule" not to touch their food. ➡️Remove shoes when you come into the house ➡️Squeege the shower after each use, even though you pay for cleaning. ➡️Do not move the showerhead. ➡️Do not wash the bedding. There are literally rules posted in every room of the house, some with multiple rules. Is this over-the-top or acceptable?

Sadie

33,240 Aufrufe • vor 5 Monaten

Jonathan Ross just revealed why AI companies aren’t growing faster. Not demand. Not competition. Physics. Ross: “The demand for compute is insatiable.” There isn’t enough compute in the world. Not a temporary shortage. A fundamental gap between what the market wants and what the infrastructure can deliver. Ross: “Right now, one of the biggest complaints of Anthropic is the rate limits. People can’t get enough tokens.” Rate limits aren’t product decisions. They’re rationing. Companies forced to regulate access because infrastructure cannot meet demand. Slower services. Token caps. The only things standing between these companies and a revenue surge they can’t access. Every token cap is a revenue cap. Every slowdown is a sale that didn’t happen. Ross: “If Anthropic was given twice the inference compute, within one month their revenue would almost double.” Read that again. Double the compute. Double the revenue. Within thirty days. That’s not a growth projection. That’s a measurement of how deep the backlog already is. The demand exists right now. It’s sitting in a queue. The only thing between these companies and that revenue is physical hardware they don’t have. This breaks every assumption about how tech companies scale. Usually you scale by finding customers. AI companies have infinite customers. They scale by finding hardware. The constraint isn’t market fit. It isn’t distribution. It isn’t competition. It’s processing power. This is why Jensen Huang is the most important person in the world right now. NVIDIA doesn’t just make chips. It makes the thing every government, every AI lab, and every company racing for this future needs more of and can’t get enough of. The compute bottleneck isn’t a tech industry problem. It’s a civilizational one. The winner of this era isn’t determined by who builds the smartest model. Every major lab has a frontier model. The winner is whoever secures the most compute fastest while everyone else rations what’s left. The race isn’t for intelligence. It’s for infrastructure. And right now there isn’t enough to go around.

Jonathan Ross just revealed why AI companies aren’t growing faster. Not demand. Not competition. Physics. Ross: “The demand for compute is insatiable.” There isn’t enough compute in the world. Not a temporary shortage. A fundamental gap between what the market wants and what the infrastructure can deliver. Ross: “Right now, one of the biggest complaints of Anthropic is the rate limits. People can’t get enough tokens.” Rate limits aren’t product decisions. They’re rationing. Companies forced to regulate access because infrastructure cannot meet demand. Slower services. Token caps. The only things standing between these companies and a revenue surge they can’t access. Every token cap is a revenue cap. Every slowdown is a sale that didn’t happen. Ross: “If Anthropic was given twice the inference compute, within one month their revenue would almost double.” Read that again. Double the compute. Double the revenue. Within thirty days. That’s not a growth projection. That’s a measurement of how deep the backlog already is. The demand exists right now. It’s sitting in a queue. The only thing between these companies and that revenue is physical hardware they don’t have. This breaks every assumption about how tech companies scale. Usually you scale by finding customers. AI companies have infinite customers. They scale by finding hardware. The constraint isn’t market fit. It isn’t distribution. It isn’t competition. It’s processing power. This is why Jensen Huang is the most important person in the world right now. NVIDIA doesn’t just make chips. It makes the thing every government, every AI lab, and every company racing for this future needs more of and can’t get enough of. The compute bottleneck isn’t a tech industry problem. It’s a civilizational one. The winner of this era isn’t determined by who builds the smartest model. Every major lab has a frontier model. The winner is whoever secures the most compute fastest while everyone else rations what’s left. The race isn’t for intelligence. It’s for infrastructure. And right now there isn’t enough to go around.

Dustin

28,395 Aufrufe • vor 7 Monaten

$NBIS is becoming one of the biggest winners in the new AI economy because it can deliver compute efficiently at scale. The number that mattered most to me was Nebius seeing four or more customers competing for every GPU it brings online (huge for AI cloud market). Management also said they raised prices again and are still selling out across both old and new GPU generations which is one of the cleanest pricing power signals you can get. This is a $100B company in the making.

$NBIS is becoming one of the biggest winners in the new AI economy because it can deliver compute efficiently at scale. The number that mattered most to me was Nebius seeing four or more customers competing for every GPU it brings online (huge for AI cloud market). Management also said they raised prices again and are still selling out across both old and new GPU generations which is one of the cleanest pricing power signals you can get. This is a $100B company in the making.

Shay Boloor

589,512 Aufrufe • vor 4 Monaten

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!

Milk Road AI

130,756 Aufrufe • vor 3 Monaten

$MU $SKHY $DRAM Holy Shit $AMD's Lisa Su just spent two full minutes at the 2026 AI keynote raving about HBM4 capacity as the thing that separates Helios from the competition. Not compute. Memory. "When you compare Helios to the competition, we're delivering 15% more compute, 50% more HBM4 memory capacity and memory bandwidth, and 50% more scale-out bandwidth." Notice which number is bigger. "What that means is that every Helios can deliver more performance for the largest models, more capacity for longer context, and the bandwidth to scale across thousands of racks.... More than 18,000 CDNA five GPU compute units, over 4,600 Zen 6 CPU cores, and 31 terabytes of HBM4 memory, all in a single rack." "That's what it takes to run agentic AI at scale. Today, I'm excited to announce that Helios is in full production. We have shipments on track to start at the end of the third quarter and ramping into the fourth quarter in the second half of the year. I can tell you, customer demand for Helios is extremely strong, we're extremely proud of the work that we have done across the leading AI labs to adopt Helios."

$MU $SKHY $DRAM Holy Shit $AMD's Lisa Su just spent two full minutes at the 2026 AI keynote raving about HBM4 capacity as the thing that separates Helios from the competition. Not compute. Memory. "When you compare Helios to the competition, we're delivering 15% more compute, 50% more HBM4 memory capacity and memory bandwidth, and 50% more scale-out bandwidth." Notice which number is bigger. "What that means is that every Helios can deliver more performance for the largest models, more capacity for longer context, and the bandwidth to scale across thousands of racks.... More than 18,000 CDNA five GPU compute units, over 4,600 Zen 6 CPU cores, and 31 terabytes of HBM4 memory, all in a single rack." "That's what it takes to run agentic AI at scale. Today, I'm excited to announce that Helios is in full production. We have shipments on track to start at the end of the third quarter and ramping into the fourth quarter in the second half of the year. I can tell you, customer demand for Helios is extremely strong, we're extremely proud of the work that we have done across the leading AI labs to adopt Helios."

Trade Whisperer

180,543 Aufrufe • vor 2 Monaten

AMD CEO Lisa Su just doubled her own CPU forecast in 6 months and the reason is one word, agents (Save this). In November, she told investors the CPU market would grow 18-20% per year through 2030. she just revised that number : "That rate is doubling. The CPU market may be over $120 billion by 2030, growing at over 35%." And here's what changed, six months ago AMD modeled 1 CPU for every 4 to 8 GPUs in an AI data center and now the new ratio is closer to 1 to 1. Because agents aren't passive and they don't wait for a question. They work constantly on behalf of every employee, every customer, every process running inside an enterprise. Each of us could have 2, 3, even 5 or 10 agents running at the same time and that's not just more GPU compute but rather a completely different CPU demand curve that nobody had modeled at this scale. And the earnings backed it up. AMD posted $10.3 billion in revenue for Q1, up 38% year over year. Data center revenue hit $5.8 billion, up 57%, free cash flow tripled to a record $2.6 billion and Q2 guidance came in at $11.2 billion, 46% growth year-over-year. The Milk Road Pro portfolio was built around exactly this thesis, that agentic AI would create a massive, underappreciated CPU demand cycle that the market was sleeping on. That position is now up nearly 100%, if you want the full thesis, check out Milk Road Pro, link in bio.

AMD CEO Lisa Su just doubled her own CPU forecast in 6 months and the reason is one word, agents (Save this). In November, she told investors the CPU market would grow 18-20% per year through 2030. she just revised that number : "That rate is doubling. The CPU market may be over $120 billion by 2030, growing at over 35%." And here's what changed, six months ago AMD modeled 1 CPU for every 4 to 8 GPUs in an AI data center and now the new ratio is closer to 1 to 1. Because agents aren't passive and they don't wait for a question. They work constantly on behalf of every employee, every customer, every process running inside an enterprise. Each of us could have 2, 3, even 5 or 10 agents running at the same time and that's not just more GPU compute but rather a completely different CPU demand curve that nobody had modeled at this scale. And the earnings backed it up. AMD posted $10.3 billion in revenue for Q1, up 38% year over year. Data center revenue hit $5.8 billion, up 57%, free cash flow tripled to a record $2.6 billion and Q2 guidance came in at $11.2 billion, 46% growth year-over-year. The Milk Road Pro portfolio was built around exactly this thesis, that agentic AI would create a massive, underappreciated CPU demand cycle that the market was sleeping on. That position is now up nearly 100%, if you want the full thesis, check out Milk Road Pro, link in bio.

Milk Road AI

13,241 Aufrufe • vor 5 Monaten

We are excited to share a deeper look into the GPU Sharing Platform that will power AI applications on the InfraX V4 DApp. In this demo we showcase a GPU running and completing an image generation task. Our backend successfully leverages this GPU (and CPU) in real time to complete the job, earning the user money, and powering the infraX platform. Here’s how it works 👉 when you connect to the InfraX DApp, our platform automatically detects the GPU on your machine and immediately checks it eligibility and where it can be used. From there, each user dictates exactly how and when they want to contribute their GPU power by setting the days their PC is most available (throughout the night, for example). Additional, each user will be able to pause their GPU's connection to infraX whenever they need to use it locally, resuming with a just a single click. Once activated, your GPU seamlessly plugs into the InfraX network and begins fulfilling compute jobs, earning YOU money. Go on, take the $INFRA pill..

We are excited to share a deeper look into the GPU Sharing Platform that will power AI applications on the InfraX V4 DApp. In this demo we showcase a GPU running and completing an image generation task. Our backend successfully leverages this GPU (and CPU) in real time to complete the job, earning the user money, and powering the infraX platform. Here’s how it works 👉 when you connect to the InfraX DApp, our platform automatically detects the GPU on your machine and immediately checks it eligibility and where it can be used. From there, each user dictates exactly how and when they want to contribute their GPU power by setting the days their PC is most available (throughout the night, for example). Additional, each user will be able to pause their GPU's connection to infraX whenever they need to use it locally, resuming with a just a single click. Once activated, your GPU seamlessly plugs into the InfraX network and begins fulfilling compute jobs, earning YOU money. Go on, take the $INFRA pill..

infraX | $INFRA

30,615 Aufrufe • vor 1 Jahr

Whilst you should never walk away from THE Church, sometimes you SHOULD walk away from A church. Just like before entering into a marriage covenant you must analyse the person, you should analyse a church before getting planted. You must find out where the Church stands on core doctrines. ➡️ The Trinity. If they preach oneness, if they deny the trinity, don’t go - this is heresy. ➡️ Christology. What do they believe about the divinity of Christ? If you don’t believe Jesus is God, like “Jehovah’s Witnesses,” if you believe Jesus was created, that’s not my Jesus and you’re not a true Church of Christ. ➡️ The authority of scripture. Bibliology – do they believe the Bible is the inspired word of God? ➡️ Salvation. Do they believe in salvation through faith alone? Or do they believe in sacraments saving you? ➡️ Heaven & Hell. Do they believe heaven and hell is a real place? Do they believe unless you believe in Jesus - you will be cast into hell? If not, don’t go. This is heresy. They must preach the FULL COUNSEL of God’s Word. That means, not preaching about blessing and God’s love every single week without any mention of sin or repentance. A CHURCH THAT COMPROMISES THE TRUTH IS NOT A CHURCH AT ALL. AVOID CHURCHES THAT AVOID CONTROVERISAL TOPICS OR TOPICS CONSIDERED “POLITICAL” LIKE GAY MARRIAGE OR ABORTION. IF YOU SEE A RAINBOW FLAG, RUN. IF THEY DO NOT UPHOLD THE SANCTITY OF HUMAN LIFE, RUN. As Paul Washer said “Don’t go to Church closest to you, but closest to the Bible.” Drive as far as you need to get to a Church that does not shy away from the truth of God’s word.

Whilst you should never walk away from THE Church, sometimes you SHOULD walk away from A church. Just like before entering into a marriage covenant you must analyse the person, you should analyse a church before getting planted. You must find out where the Church stands on core doctrines. ➡️ The Trinity. If they preach oneness, if they deny the trinity, don’t go - this is heresy. ➡️ Christology. What do they believe about the divinity of Christ? If you don’t believe Jesus is God, like “Jehovah’s Witnesses,” if you believe Jesus was created, that’s not my Jesus and you’re not a true Church of Christ. ➡️ The authority of scripture. Bibliology – do they believe the Bible is the inspired word of God? ➡️ Salvation. Do they believe in salvation through faith alone? Or do they believe in sacraments saving you? ➡️ Heaven & Hell. Do they believe heaven and hell is a real place? Do they believe unless you believe in Jesus - you will be cast into hell? If not, don’t go. This is heresy. They must preach the FULL COUNSEL of God’s Word. That means, not preaching about blessing and God’s love every single week without any mention of sin or repentance. A CHURCH THAT COMPROMISES THE TRUTH IS NOT A CHURCH AT ALL. AVOID CHURCHES THAT AVOID CONTROVERISAL TOPICS OR TOPICS CONSIDERED “POLITICAL” LIKE GAY MARRIAGE OR ABORTION. IF YOU SEE A RAINBOW FLAG, RUN. IF THEY DO NOT UPHOLD THE SANCTITY OF HUMAN LIFE, RUN. As Paul Washer said “Don’t go to Church closest to you, but closest to the Bible.” Drive as far as you need to get to a Church that does not shy away from the truth of God’s word.

Millicent Sedra

30,250 Aufrufe • vor 8 Monaten

Former NFL Linebacker Will Compton was PREACHING in this segment with Notre Dame Football about Creating Evidence. A must watch regardless of title, age, or industry: 👮‍♂️ You start with BELIEF, you stand on EVIDENCE. Facts don't care about feelings. When you get there, nobody cares HOW you got there, they care about how you can HELP. 👨‍⚖️ One Rep ➡️ One Day ➡️ One Week ➡️ One Game ➡️ One Year. At the end of the year, you're graded and judged by the evidence of your WORK. Evidence doesn't just arrive, it's acquired and accumulated everyday. It’s never one moment, it’s the accumulation of all of them. 🏷️ Let Proof Override Labels. Creating Evidence isn't about running from the truth, it's about creating so much proof that past labels become irrelevant. 🧵 Everything matters. Every rep has meaning. Even when you can't see it. Too tired, too emotional, too worn out to see or hear it, but it still means something. So you must TIE meaning to it. The smallest action either adds to your case or subtracts from it, tie purpose to all of it. 🖋️ Facts are permanent, but they do not define who you are, or who you have to be in the future. The truth is the starting point, not the ceiling. Face it, use it, and build forward, the obstacle is the way.

Former NFL Linebacker Will Compton was PREACHING in this segment with Notre Dame Football about Creating Evidence. A must watch regardless of title, age, or industry: 👮‍♂️ You start with BELIEF, you stand on EVIDENCE. Facts don't care about feelings. When you get there, nobody cares HOW you got there, they care about how you can HELP. 👨‍⚖️ One Rep ➡️ One Day ➡️ One Week ➡️ One Game ➡️ One Year. At the end of the year, you're graded and judged by the evidence of your WORK. Evidence doesn't just arrive, it's acquired and accumulated everyday. It’s never one moment, it’s the accumulation of all of them. 🏷️ Let Proof Override Labels. Creating Evidence isn't about running from the truth, it's about creating so much proof that past labels become irrelevant. 🧵 Everything matters. Every rep has meaning. Even when you can't see it. Too tired, too emotional, too worn out to see or hear it, but it still means something. So you must TIE meaning to it. The smallest action either adds to your case or subtracts from it, tie purpose to all of it. 🖋️ Facts are permanent, but they do not define who you are, or who you have to be in the future. The truth is the starting point, not the ceiling. Face it, use it, and build forward, the obstacle is the way.

Josh Chambers

370,380 Aufrufe • vor 5 Monaten

In San Francisco, we are bringing accountability to our homelessness system. We will run a new competitive process to redo every one of the city’s homelessness services contracts. Reforms will include: ➡️ Every contract will be focused on client outcomes. ➡️ We are going to link payment to performance so we know we are paying for results—not expenses or inputs. ➡️ Our sites will be good neighbors in the communities they serve, helping those who are struggling and our residents who deserve safe and clean streets. This is about delivering better services for those struggling with homelessness and addiction and better results for all San Franciscans. Through this process, we will be in close conversation with the community and providers who do this important work.

In San Francisco, we are bringing accountability to our homelessness system. We will run a new competitive process to redo every one of the city’s homelessness services contracts. Reforms will include: ➡️ Every contract will be focused on client outcomes. ➡️ We are going to link payment to performance so we know we are paying for results—not expenses or inputs. ➡️ Our sites will be good neighbors in the communities they serve, helping those who are struggling and our residents who deserve safe and clean streets. This is about delivering better services for those struggling with homelessness and addiction and better results for all San Franciscans. Through this process, we will be in close conversation with the community and providers who do this important work.

Daniel Lurie 丹尼爾·羅偉

14,097 Aufrufe • vor 1 Monat

$QUBIC doesn’t talk about scaling. It executes it. 15.52 MILLION transfers per second Finality: instant No L2. No rollups. No smoke and mirrors. All of this on mainnet, audited and confirmed by CertiK But this isn’t just a headline, it’s the result of a radically new architecture: ⚡️ Tick-based execution: no mempool, full sync ⚡️ Quorum consensus: 676 Computors, 451 quorum threshold ⚡️ Bare-metal infrastructure: no OS overhead, pure performance ⚡️ 0 gas fees: every transfer is free & finalized in the same tick ⚡️ UPoW: Useful Proof of Work powering AGI through Aigarth Qubic doesn’t mimic #ethereum . It redefines what a blockchain can be. ➡️ It’s a network. ➡️ It’s a distributed compute engine. ➡️ It’s a foundation for AGI. And it’s not theoretical. It’s live. #Qubic #AGI #Blockchain #TPS #Web3 #Certik #BareMetal #TickBased #NoGas #NextGenInfra Certik Analysis report :

$QUBIC doesn’t talk about scaling. It executes it. 15.52 MILLION transfers per second Finality: instant No L2. No rollups. No smoke and mirrors. All of this on mainnet, audited and confirmed by CertiK But this isn’t just a headline, it’s the result of a radically new architecture: ⚡️ Tick-based execution: no mempool, full sync ⚡️ Quorum consensus: 676 Computors, 451 quorum threshold ⚡️ Bare-metal infrastructure: no OS overhead, pure performance ⚡️ 0 gas fees: every transfer is free & finalized in the same tick ⚡️ UPoW: Useful Proof of Work powering AGI through Aigarth Qubic doesn’t mimic #ethereum . It redefines what a blockchain can be. ➡️ It’s a network. ➡️ It’s a distributed compute engine. ➡️ It’s a foundation for AGI. And it’s not theoretical. It’s live. #Qubic #AGI #Blockchain #TPS #Web3 #Certik #BareMetal #TickBased #NoGas #NextGenInfra Certik Analysis report :

Rudy Nakamoto ₿ ױ

27,942 Aufrufe • vor 1 Jahr

How could you possibly be bearish on compute right now? (Save this). Every 10 seconds in 2026, the world generates 31.7 billion tokens and by 2030, that number hits 1.27 trillion, every 10 seconds. That's a 40x increase and that's before the full agent economy comes online. The Qualcomm CEO said total token demand by 2030 is in the quintillions. Here's what most people miss because when you use ChatGPT, you generate tokens one conversation at a time but agents don't sleep. ] They run 24/7, spawning sub-agents, carrying context, updating memory, catching mistakes and every single one of those actions burns tokens. The shift from human paced to agent paced activity is the single biggest structural change in compute demand we've ever seen. You don't need a perfect forecast but rather just need to believe agents become persistent and if they do, compute demand goes vertical. The infrastructure has to be built before the demand fully arrives, which means the window to own the picks and shovels is right now. That's where neoclouds like Nebius come in. Nebius isn't trying to be AWS, it is a pure-play AI cloud, GPU clusters, inference infrastructure, and developer tooling built from scratch for AI workloads. Q1 2026 revenue hit $399M, up 684% year over year and they're guiding for $7–$9 billion annualized run rate by end of 2026. Analysts are modeling roughly 2,000% total revenue growth from end of 2025 to end of 2027. They already have contracts with Microsoft and Meta already signed. Capex guidance raised to $20–$25 billion because customer commitments justified it. They are sold out of capacity because the constraint isn't customers, it's how fast they can build. Adjusted EBITDA margin on the core AI business hit 45% in Q1 and Jensen Huang called Nebius a close partner at GTC 2026. And in a world where GPU access is the single biggest competitive moat, that relationship matters more than most people realize. The bear case on compute requires you to believe the agent economy stalls and that's a very lonely bet to make right now. Bullish on Nebius and Milk Pro subscribers are already up massively on this trade, come join us using the link below to get our full AI trades and we have a HUGE 33% off right now!

How could you possibly be bearish on compute right now? (Save this). Every 10 seconds in 2026, the world generates 31.7 billion tokens and by 2030, that number hits 1.27 trillion, every 10 seconds. That's a 40x increase and that's before the full agent economy comes online. The Qualcomm CEO said total token demand by 2030 is in the quintillions. Here's what most people miss because when you use ChatGPT, you generate tokens one conversation at a time but agents don't sleep. ] They run 24/7, spawning sub-agents, carrying context, updating memory, catching mistakes and every single one of those actions burns tokens. The shift from human paced to agent paced activity is the single biggest structural change in compute demand we've ever seen. You don't need a perfect forecast but rather just need to believe agents become persistent and if they do, compute demand goes vertical. The infrastructure has to be built before the demand fully arrives, which means the window to own the picks and shovels is right now. That's where neoclouds like Nebius come in. Nebius isn't trying to be AWS, it is a pure-play AI cloud, GPU clusters, inference infrastructure, and developer tooling built from scratch for AI workloads. Q1 2026 revenue hit $399M, up 684% year over year and they're guiding for $7–$9 billion annualized run rate by end of 2026. Analysts are modeling roughly 2,000% total revenue growth from end of 2025 to end of 2027. They already have contracts with Microsoft and Meta already signed. Capex guidance raised to $20–$25 billion because customer commitments justified it. They are sold out of capacity because the constraint isn't customers, it's how fast they can build. Adjusted EBITDA margin on the core AI business hit 45% in Q1 and Jensen Huang called Nebius a close partner at GTC 2026. And in a world where GPU access is the single biggest competitive moat, that relationship matters more than most people realize. The bear case on compute requires you to believe the agent economy stalls and that's a very lonely bet to make right now. Bullish on Nebius and Milk Pro subscribers are already up massively on this trade, come join us using the link below to get our full AI trades and we have a HUGE 33% off right now!

Milk Road AI

16,310 Aufrufe • vor 3 Monaten

This is some good information to know. The US mail system aggregates stewardship of delivery but not deadlines. The work around this is signing up for USPS digital or online account and it is free service. It sends you an image scan via email of the current postal mail to be delivered that day so the proof of what was delivered or not is given to you. You get a notification everyday whether you have mail or not. I don't know if this service varies by state so consider looking into the availability for your postal zip code covered area. . 's a state-by-state. If you can get this access depending on the state you live in. But I believe every state should have the account. You can set it up, it's easy. And then you can see your mail. That's coming. If you have packages coming, you can see your account and it gives you notifications every day regardless if you get mail or not, whether you have something coming or not. #Inspirenaire

This is some good information to know. The US mail system aggregates stewardship of delivery but not deadlines. The work around this is signing up for USPS digital or online account and it is free service. It sends you an image scan via email of the current postal mail to be delivered that day so the proof of what was delivered or not is given to you. You get a notification everyday whether you have mail or not. I don't know if this service varies by state so consider looking into the availability for your postal zip code covered area. . 's a state-by-state. If you can get this access depending on the state you live in. But I believe every state should have the account. You can set it up, it's easy. And then you can see your mail. That's coming. If you have packages coming, you can see your account and it gives you notifications every day regardless if you get mail or not, whether you have something coming or not. #Inspirenaire

Inspirenaire

18,205 Aufrufe • vor 6 Monaten

$INTC Lip-Bu Tan admits Intel can fill only half its CPU orders and apologizes when CEOs call him "Yeah, I think inferencing is very important. When you want to drive some of this reinforced learning, and also in terms of agentic AI, CPU is the best." "GPU is very good for training, but CPU is really good for orchestration, control plane, and driving some of this even single thread rather than multi-thread has become very useful." "It happened that I invest quite a few companies in the frontier model. They all tell me that, 'Lip-Bu, we need more CPU.'" "So good news is, right now, sometimes in life you need some help. And so the help that come to me is that CPU is so high demand, I only can provide 50% of what the customer want. So many CEO call me up, I had to apologize, I'm not manufacturing enough for them."

$INTC Lip-Bu Tan admits Intel can fill only half its CPU orders and apologizes when CEOs call him "Yeah, I think inferencing is very important. When you want to drive some of this reinforced learning, and also in terms of agentic AI, CPU is the best." "GPU is very good for training, but CPU is really good for orchestration, control plane, and driving some of this even single thread rather than multi-thread has become very useful." "It happened that I invest quite a few companies in the frontier model. They all tell me that, 'Lip-Bu, we need more CPU.'" "So good news is, right now, sometimes in life you need some help. And so the help that come to me is that CPU is so high demand, I only can provide 50% of what the customer want. So many CEO call me up, I had to apologize, I'm not manufacturing enough for them."

Fireside Alpha

142,853 Aufrufe • vor 23 Tagen

Demis Hassabis just explained why the real AI bottleneck has nothing to do with training runs. Most people picture the AI arms race as who can build the biggest model. GPT-4 or Gemini Ultra style training runs, a few hundred million in compute, fired once or twice a year. The constraint sits somewhere else. Every time a researcher has a new algorithmic idea, a new architecture, a new training technique, they can't just test it on a laptop. They have to run it at the scale where it would actually be deployed, because ideas that look promising at small scale fall apart completely when you put them into a real system. Every research hypothesis burns significant compute before a single line of production code gets written. At a lab like DeepMind, hundreds of researchers are running hundreds of ideas simultaneously. The demand for experimental compute is continuous. It never stops. Now layer the hardware reality on top. GPU lead times are currently 36 to 52 weeks for data center hardware. Global AI data centers are already drawing 29.6 gigawatts, equivalent to the peak power demand of the entire state of New York, and they still can't meet demand. Companies willing to pay any price can't just buy more compute. They wait in line. The speed of scientific discovery in AI is now gated by hardware availability. The next breakthrough is sitting in a researcher's head right now. Whether it gets validated fast enough to matter depends entirely on whether the compute is there when they need it. The AI race gets won by whoever can run the most experiments per month.

Demis Hassabis just explained why the real AI bottleneck has nothing to do with training runs. Most people picture the AI arms race as who can build the biggest model. GPT-4 or Gemini Ultra style training runs, a few hundred million in compute, fired once or twice a year. The constraint sits somewhere else. Every time a researcher has a new algorithmic idea, a new architecture, a new training technique, they can't just test it on a laptop. They have to run it at the scale where it would actually be deployed, because ideas that look promising at small scale fall apart completely when you put them into a real system. Every research hypothesis burns significant compute before a single line of production code gets written. At a lab like DeepMind, hundreds of researchers are running hundreds of ideas simultaneously. The demand for experimental compute is continuous. It never stops. Now layer the hardware reality on top. GPU lead times are currently 36 to 52 weeks for data center hardware. Global AI data centers are already drawing 29.6 gigawatts, equivalent to the peak power demand of the entire state of New York, and they still can't meet demand. Companies willing to pay any price can't just buy more compute. They wait in line. The speed of scientific discovery in AI is now gated by hardware availability. The next breakthrough is sitting in a researcher's head right now. Whether it gets validated fast enough to matter depends entirely on whether the compute is there when they need it. The AI race gets won by whoever can run the most experiments per month.

Aakash Gupta

32,150 Aufrufe • vor 5 Monaten