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Multi-orgasmic squirting MILF here, proving the label every single time. #squirting #squirt #orgasm #squirtingorgasm #pussy #rearpussy #dripping #dildo #wetpussy #longpussylips

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161,003 views • 1 month ago •via X (Twitter)

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I’ve been calling this out for years, and they called us crazy. But finally, the curtain is being pulled back. With federal prosecutors and the FBI now on the ground investigating multi-level election fraud in California, the "nothing to see here" excuse is officially dead. Its about time! As your next State Treasurer, I’m not just going to watch—I’m going to follow the money. We will audit every single cent of taxpayer funding tied to these election processes. Total transparency starts at the vault.
3:27

I’ve been calling this out for years, and they called us crazy. But finally, the curtain is being pulled back. With federal prosecutors and the FBI now on the ground investigating multi-level election fraud in California, the "nothing to see here" excuse is officially dead. Its about time! As your next State Treasurer, I’m not just going to watch—I’m going to follow the money. We will audit every single cent of taxpayer funding tied to these election processes. Total transparency starts at the vault.

Jennifer Hawks for State Treasurer

154,707 views • 3 months ago

Grok Bot just killed every overhyped AI setup on your timeline Stop building 40 single-purpose agents that burn your time. Here is the exact system architecture to run a multi-million dollar operation using a managed Grok Bot hosted on a cloud VPS (powered by Cursor and SpaceX infrastructure): The Chief of Staff Architecture: Deploy one central "Chief of Staff" agent authenticated to your email and drive. Route execution down to specialized sub-agents (Dev, Support, YouTube) only when needed. Zero-Code Skill Training: Click record, complete a task on your screen, like triaging emails or reviewing analytics - and GrokBot auto-generates a permanent execution skill from the recording. Unrestricted Cloud Execution: The agent runs 24/7 inside its own Linux container for $200/month, executing tasks asynchronously using MCP servers and Composio to query live data from ChartMogul, Help Scout, and Sentry. Anti-Lock-In Memory Dump: Avoid getting trapped on a single platform. Force your Grok Bot to write daily logs and append context summaries directly to external Google Drive or Notion docs. One single Grok Bot harness with structured sub-agents crushes an army of disconnected, unorganized bots every single time.
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Grok Bot just killed every overhyped AI setup on your timeline Stop building 40 single-purpose agents that burn your time. Here is the exact system architecture to run a multi-million dollar operation using a managed Grok Bot hosted on a cloud VPS (powered by Cursor and SpaceX infrastructure): The Chief of Staff Architecture: Deploy one central "Chief of Staff" agent authenticated to your email and drive. Route execution down to specialized sub-agents (Dev, Support, YouTube) only when needed. Zero-Code Skill Training: Click record, complete a task on your screen, like triaging emails or reviewing analytics - and GrokBot auto-generates a permanent execution skill from the recording. Unrestricted Cloud Execution: The agent runs 24/7 inside its own Linux container for $200/month, executing tasks asynchronously using MCP servers and Composio to query live data from ChartMogul, Help Scout, and Sentry. Anti-Lock-In Memory Dump: Avoid getting trapped on a single platform. Force your Grok Bot to write daily logs and append context summaries directly to external Google Drive or Notion docs. One single Grok Bot harness with structured sub-agents crushes an army of disconnected, unorganized bots every single time.

Shadow Nick

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GeoLibre v1.6.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release brings multi-map layouts, advanced cartographic symbology and labeling, and a one-click way to install external plugins from a zip. What's new in v1.6.0 - Multi-map grid: Split the workspace into a grid of synchronized map views to compare basemaps, layers, or time steps side by side. - Advanced symbology: Style features with a rule-based renderer, proportional symbols, fill patterns, and a built-in marker library. - Label engine: Label vector features by any attribute, with full placement and styling control. - Install plugins from a zip: Add external plugins from an uploaded zip on both desktop and web. - New vector analysis: Movement, space-time, and cell-coverage tools under Processing. - Faster sample data: Ready-to-load example datasets from a dropdown in every Add Data panel. - Place search in the layer panel: Geocode and fly to a location without leaving the Layers panel. Try it out - Live demo: - GitHub: - Documentation: - Release notes: #GIS #GeospatialData #OpenSource #RemoteSensing #DataVisualization #MapLibre #Python #Cartography
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GeoLibre v1.6.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release brings multi-map layouts, advanced cartographic symbology and labeling, and a one-click way to install external plugins from a zip. What's new in v1.6.0 - Multi-map grid: Split the workspace into a grid of synchronized map views to compare basemaps, layers, or time steps side by side. - Advanced symbology: Style features with a rule-based renderer, proportional symbols, fill patterns, and a built-in marker library. - Label engine: Label vector features by any attribute, with full placement and styling control. - Install plugins from a zip: Add external plugins from an uploaded zip on both desktop and web. - New vector analysis: Movement, space-time, and cell-coverage tools under Processing. - Faster sample data: Ready-to-load example datasets from a dropdown in every Add Data panel. - Place search in the layer panel: Geocode and fly to a location without leaving the Layers panel. Try it out - Live demo: - GitHub: - Documentation: - Release notes: #GIS #GeospatialData #OpenSource #RemoteSensing #DataVisualization #MapLibre #Python #Cartography

Qiusheng Wu

15,473 views • 2 months ago

You are using Docker wrong. I promise! Here are the two things you are missing out: • Your Dockerfiles are using a single stage. This is very slow. • You aren't caching steps as much as you could. This is also slow. For a couple of years now, Docker has been using BuildKit as its primary engine, which has added support for multi-stage Dockerfiles. Here's the deal: A single-stage Dockerfile (the one you are likely using) forces every step to depend on the previous one. That means: • Everything runs sequentially • The generated image is huge • Any change will skip cached layers • No opportunities to parallelize steps Changing one line early in the Dockerfile renders everything after it useless. There's a better way: Start using multi-stage builds. I recorded the attached video to illustrate a comparison between a slow, single-stage build and a fast, multi-stage build. Here is what you need to do: Split your Dockerfile into independent stages using multiple FROM statements. BuildKit will turn those stages into a dependency graph and run independent stages in parallel. There are several benefits to this: 1. The build process will be way faster. Parallelizing independent stages will save a massive amount of time. 2. You'll get way better caching. Each stage has its own set of layers. If something changes in your build stage, your final stage doesn't get invalidated. get clean final images. You don't need to ship tooling, compilers, or temporary files. You'll only copy to the final image what you need. This has been a massive upgrade to the way I structure my Dockerfiles.
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You are using Docker wrong. I promise! Here are the two things you are missing out: • Your Dockerfiles are using a single stage. This is very slow. • You aren't caching steps as much as you could. This is also slow. For a couple of years now, Docker has been using BuildKit as its primary engine, which has added support for multi-stage Dockerfiles. Here's the deal: A single-stage Dockerfile (the one you are likely using) forces every step to depend on the previous one. That means: • Everything runs sequentially • The generated image is huge • Any change will skip cached layers • No opportunities to parallelize steps Changing one line early in the Dockerfile renders everything after it useless. There's a better way: Start using multi-stage builds. I recorded the attached video to illustrate a comparison between a slow, single-stage build and a fast, multi-stage build. Here is what you need to do: Split your Dockerfile into independent stages using multiple FROM statements. BuildKit will turn those stages into a dependency graph and run independent stages in parallel. There are several benefits to this: 1. The build process will be way faster. Parallelizing independent stages will save a massive amount of time. 2. You'll get way better caching. Each stage has its own set of layers. If something changes in your build stage, your final stage doesn't get invalidated. get clean final images. You don't need to ship tooling, compilers, or temporary files. You'll only copy to the final image what you need. This has been a massive upgrade to the way I structure my Dockerfiles.

Santiago

67,776 views • 10 months ago

Jensen Huang just identified the next $200 billion market (Save this). The shift starts with a observation about agentic AI that changes everything about infrastructure. In the era of training and inference, the GPU was everything while CPU was a traffic cop, scheduling work, managing memory, dispatching tasks while the GPU did the heavy lifting. Agentic AI breaks that model entirely. An AI agent does not just run a single inference pass but rather it plans, calls tools, executes code in sandboxes, retrieves data from multiple sources and loops through complex multi-step reasoning sequences often thousands of times per second at scale. Every one of those operations runs through the CPU and the GPU sits idle waiting for the CPU to prepare the next task, supply the right context and execute the retrieval and tool calling logic fast enough to keep the accelerators fed. The CPU is now the conductor and the GPU is the orchestra and the bottleneck is the conductor falling behind. This is showing up in production AI factory utilization right now, which is exactly why Jensen built Vera from scratch rather than licensing x86. Vera achieves 40% lower peak memory latency than x86, 50% faster core to core communication, and 1.8 times the agentic sandbox performance of current x86 processors on a purpose-built architecture designed around the agentic loop. Now here is where the investment thesis gets interesting. The obvious beneficiary is Nvidia itself, and that thesis is real. Nvidia's CFO has guided for nearly $20 billion in Vera CPU revenue this fiscal year alone, a market Nvidia had zero presence in just three years ago. Intel held 60% of server CPU market share as recently as Q4 2025 and that transition is now happening at a pace Intel structurally cannot respond to. But the deeper question is, what architecture is Vera actually built on? Vera's Olympus cores are ARM compatible and every single Vera CPU deployed in every Vera Rubin rack in every data center in the world runs on ARM architecture. And ARM Holdings collects a royalty on every one of them. ARM does not make chips but rather licenses the instruction set architecture and CPU core designs that others build on top of. Every time Nvidia ships a Vera CPU, every time a hyperscaler deploys a Vera Rubin rack, every time an enterprise qualifies Vera for their AI factory, ARM earns a royalty. The secular tailwind here is almost perfectly constructed for ARM's business model. Amazon's Graviton, Microsoft's Cobalt, Google's Axion, Apple's silicon stack, and Qualcomm's data center push all run on ARM. And now Nvidia's Vera, which is projected to displace Intel as the largest server CPU supplier by revenue in a single fiscal year, is ARM. ARM's royalty rate on high end server chips is estimated at roughly 1 to 2% of chip selling price. At $5,000 per Vera CPU and 4 million units projected for FY2027, that is a royalty line growing from near zero to potentially $400 million to $800 million annually from Nvidia's data center CPU business alone before counting Amazon, Microsoft, Google, Apple, and Qualcomm. The total ARM addressable royalty base across all the silicon it already licenses is compounding at a rate that the current $130 billion market cap does not fully reflect. Jensen's CPU thesis is the most underappreciated catalyst in ARM's fundamental story, and the royalty compounding has barely started. Come join Milk Road Pro and get our full ARM royalty model and our entire AI trade thesis. Link below!
3:41

Jensen Huang just identified the next $200 billion market (Save this). The shift starts with a observation about agentic AI that changes everything about infrastructure. In the era of training and inference, the GPU was everything while CPU was a traffic cop, scheduling work, managing memory, dispatching tasks while the GPU did the heavy lifting. Agentic AI breaks that model entirely. An AI agent does not just run a single inference pass but rather it plans, calls tools, executes code in sandboxes, retrieves data from multiple sources and loops through complex multi-step reasoning sequences often thousands of times per second at scale. Every one of those operations runs through the CPU and the GPU sits idle waiting for the CPU to prepare the next task, supply the right context and execute the retrieval and tool calling logic fast enough to keep the accelerators fed. The CPU is now the conductor and the GPU is the orchestra and the bottleneck is the conductor falling behind. This is showing up in production AI factory utilization right now, which is exactly why Jensen built Vera from scratch rather than licensing x86. Vera achieves 40% lower peak memory latency than x86, 50% faster core to core communication, and 1.8 times the agentic sandbox performance of current x86 processors on a purpose-built architecture designed around the agentic loop. Now here is where the investment thesis gets interesting. The obvious beneficiary is Nvidia itself, and that thesis is real. Nvidia's CFO has guided for nearly $20 billion in Vera CPU revenue this fiscal year alone, a market Nvidia had zero presence in just three years ago. Intel held 60% of server CPU market share as recently as Q4 2025 and that transition is now happening at a pace Intel structurally cannot respond to. But the deeper question is, what architecture is Vera actually built on? Vera's Olympus cores are ARM compatible and every single Vera CPU deployed in every Vera Rubin rack in every data center in the world runs on ARM architecture. And ARM Holdings collects a royalty on every one of them. ARM does not make chips but rather licenses the instruction set architecture and CPU core designs that others build on top of. Every time Nvidia ships a Vera CPU, every time a hyperscaler deploys a Vera Rubin rack, every time an enterprise qualifies Vera for their AI factory, ARM earns a royalty. The secular tailwind here is almost perfectly constructed for ARM's business model. Amazon's Graviton, Microsoft's Cobalt, Google's Axion, Apple's silicon stack, and Qualcomm's data center push all run on ARM. And now Nvidia's Vera, which is projected to displace Intel as the largest server CPU supplier by revenue in a single fiscal year, is ARM. ARM's royalty rate on high end server chips is estimated at roughly 1 to 2% of chip selling price. At $5,000 per Vera CPU and 4 million units projected for FY2027, that is a royalty line growing from near zero to potentially $400 million to $800 million annually from Nvidia's data center CPU business alone before counting Amazon, Microsoft, Google, Apple, and Qualcomm. The total ARM addressable royalty base across all the silicon it already licenses is compounding at a rate that the current $130 billion market cap does not fully reflect. Jensen's CPU thesis is the most underappreciated catalyst in ARM's fundamental story, and the royalty compounding has barely started. Come join Milk Road Pro and get our full ARM royalty model and our entire AI trade thesis. Link below!

Milk Road AI

11,819 views • 3 months ago

Mary J. Blige says she used to curse out everyone at her label when a record flopped, then refused to release "No More Drama" until she apologized to each person there face to face. Mary J. Blige: I want to say it was around No More Drama time — when I was like, "I'm still here, but my life is a mess." And for some reason certain people kept coming into my life to help, while certain people were still there that was trying to destroy. So I had to keep those people in my life and take responsibility. My responsibility was I was ignorant to a lot of things and blame it on everybody else. So I took full responsibility. And I remember when we first put the No More Drama album out, I used to get upset and curse everybody out because things weren't working. And it wasn't their fault, it was my fault. Michelle Obama: When you said "weren't working," what do you mean? Mary J. Blige: Like if something didn't happen, or you know, if a record didn't sell, or this didn't get that many — whatever. I would go crazy on people, cursing people to oblivion and all that stuff. So when my No More Drama album came out, I was like, I don't want to release this album without apologizing to every single person at this label. Get everybody — anybody that did anything for me. Line them up. I'm apologizing. I apologize to every single person.
1:32

Mary J. Blige says she used to curse out everyone at her label when a record flopped, then refused to release "No More Drama" until she apologized to each person there face to face. Mary J. Blige: I want to say it was around No More Drama time — when I was like, "I'm still here, but my life is a mess." And for some reason certain people kept coming into my life to help, while certain people were still there that was trying to destroy. So I had to keep those people in my life and take responsibility. My responsibility was I was ignorant to a lot of things and blame it on everybody else. So I took full responsibility. And I remember when we first put the No More Drama album out, I used to get upset and curse everybody out because things weren't working. And it wasn't their fault, it was my fault. Michelle Obama: When you said "weren't working," what do you mean? Mary J. Blige: Like if something didn't happen, or you know, if a record didn't sell, or this didn't get that many — whatever. I would go crazy on people, cursing people to oblivion and all that stuff. So when my No More Drama album came out, I was like, I don't want to release this album without apologizing to every single person at this label. Get everybody — anybody that did anything for me. Line them up. I'm apologizing. I apologize to every single person.

BIG JOE 🧃

394,341 views • 1 month ago

In the 1970s, the corporate boardroom of multinational giant Hindustan Unilever believed they completely owned the clothes washing habits of India. They had no idea that their multi million dollar monopoly was about to be absolutely dismantled by a low-profile govt lab assistant riding a bicycle. In 1969, Karsanbhai Patel was living a quiet, middle class life in Gujarat, working a secure day job at the state govt's Directorate of Geology & Mining. His family had faced a devastating personal tragedy: they lost their young daughter, Nirupama, in an accident. Determined to create a legacy in her memory, Karsanbhai spent his evenings after his 9 to 5 job experimenting with chemical formulations in the 15 sq. yard backyard of his home in Ahmedabad. Using his knowledge of chemistry, he managed to mix a non-phosphate, eco-friendly detergent powder. He named it Nirma, using his late daughter’s nickname & stamped a sketch of a young girl in a white frock onto the packaging. At the time, the premium laundry market was completely dominated by Surf. It was a luxury product selling at a steep ₹13/kg, meaning it was entirely out of reach for the vast majority of rural & lower income Indian households. Working class families were left scrubbing their clothes with harsh laundry bars that routinely peeled the skin off their hands. Karsanbhai realized that the multinational corporations were ignoring the massive base of the pyramid. He priced Nirma at an astonishing market rate of ₹3.50/kg. He had no money for distributors/retail slots. Every single day, after finishing his govt office shifts, Karsanbhai packed the handmade detergent onto the back of his bicycle & pedaled through neighborhoods, selling packets door to door. He gave consumers a money back guarantee: if you do not like it, return it for a full refund. Not a single packet came back. By the late 1970s, Nirma was popular locally, but traditional retail shopkeepers were giving Karsanbhai a massive headache. Following standard Indian FMCG practices, shopkeepers demanded 30-60 days of credit, leaving his tiny operational capital permanently choked. They would stack Nirma in hidden bottom shelves while keeping Surf up front. In 1982, Karsanbhai pulled off a marketing masterstroke that is still taught in global business schools: He gathered his tiny team & physically recalled every single packet of Nirma from every retail shop in North & West India. Overnight, the market availability of Nirma went to absolute zero. The moment the shelves were empty, he unleashed a massive, incredibly catchy television jingle ad ("Hema, Rekha, Jaya aur Sushma... Sabki pasand Nirma!"). The ad ran continuously on Doordarshan, creating an explosive consumer frenzy. Millions of housewives flooded their local kirana shops demanding Nirma. The shopkeepers, who had previously treated Karsanbhai like a small time peddler, were forced to call him begging for stock. Karsanbhai laid down a brutal new rule: No more credit. If you want Nirma on your shelves, you pay cash upfront. The retail stores complied immediately. Within a few yrs, Nirma became the single largest selling detergent brand in the world under a single label, capturing 60+% of the Indian market & forcing global corporations to completely restructure their pricing models for developing countries. The corporate elites spend fortunes analyzing consumer psychology & building complex supply chains. But Karsanbhai Patel proved that a father’s grief, combined with a bicycle & a flawless understanding of the ordinary Indian's wallet, can beat the biggest corporate empires on Earth.
1:06

In the 1970s, the corporate boardroom of multinational giant Hindustan Unilever believed they completely owned the clothes washing habits of India. They had no idea that their multi million dollar monopoly was about to be absolutely dismantled by a low-profile govt lab assistant riding a bicycle. In 1969, Karsanbhai Patel was living a quiet, middle class life in Gujarat, working a secure day job at the state govt's Directorate of Geology & Mining. His family had faced a devastating personal tragedy: they lost their young daughter, Nirupama, in an accident. Determined to create a legacy in her memory, Karsanbhai spent his evenings after his 9 to 5 job experimenting with chemical formulations in the 15 sq. yard backyard of his home in Ahmedabad. Using his knowledge of chemistry, he managed to mix a non-phosphate, eco-friendly detergent powder. He named it Nirma, using his late daughter’s nickname & stamped a sketch of a young girl in a white frock onto the packaging. At the time, the premium laundry market was completely dominated by Surf. It was a luxury product selling at a steep ₹13/kg, meaning it was entirely out of reach for the vast majority of rural & lower income Indian households. Working class families were left scrubbing their clothes with harsh laundry bars that routinely peeled the skin off their hands. Karsanbhai realized that the multinational corporations were ignoring the massive base of the pyramid. He priced Nirma at an astonishing market rate of ₹3.50/kg. He had no money for distributors/retail slots. Every single day, after finishing his govt office shifts, Karsanbhai packed the handmade detergent onto the back of his bicycle & pedaled through neighborhoods, selling packets door to door. He gave consumers a money back guarantee: if you do not like it, return it for a full refund. Not a single packet came back. By the late 1970s, Nirma was popular locally, but traditional retail shopkeepers were giving Karsanbhai a massive headache. Following standard Indian FMCG practices, shopkeepers demanded 30-60 days of credit, leaving his tiny operational capital permanently choked. They would stack Nirma in hidden bottom shelves while keeping Surf up front. In 1982, Karsanbhai pulled off a marketing masterstroke that is still taught in global business schools: He gathered his tiny team & physically recalled every single packet of Nirma from every retail shop in North & West India. Overnight, the market availability of Nirma went to absolute zero. The moment the shelves were empty, he unleashed a massive, incredibly catchy television jingle ad ("Hema, Rekha, Jaya aur Sushma... Sabki pasand Nirma!"). The ad ran continuously on Doordarshan, creating an explosive consumer frenzy. Millions of housewives flooded their local kirana shops demanding Nirma. The shopkeepers, who had previously treated Karsanbhai like a small time peddler, were forced to call him begging for stock. Karsanbhai laid down a brutal new rule: No more credit. If you want Nirma on your shelves, you pay cash upfront. The retail stores complied immediately. Within a few yrs, Nirma became the single largest selling detergent brand in the world under a single label, capturing 60+% of the Indian market & forcing global corporations to completely restructure their pricing models for developing countries. The corporate elites spend fortunes analyzing consumer psychology & building complex supply chains. But Karsanbhai Patel proved that a father’s grief, combined with a bicycle & a flawless understanding of the ordinary Indian's wallet, can beat the biggest corporate empires on Earth.

Parimal

21,163 views • 2 months ago

In 1977, the Somali National Army single-handedly invaded Ethiopia and Eritrea. Our brave Somali warriors swept across the battlefield like a storm, crushing everything in their path and winning decisive victory after victory. Their unstoppable momentum was only broken when Cuba rushed in over 20,000 troops and the Soviet Union dispatched another 10,000 — an entire international coalition mobilized in panic just to halt a proud nation of merely five million Somalis. Ethiopia has always been famous for running to the white man and crying for help the moment it faces real danger. But let the truth be shouted loud and clear: Somalis stand head and shoulders above any other African nation in courage, resilience, and martial superiority. No other country in Africa has dared to confront and defy the world’s greatest superpowers — Russia, the United States, France, the United Kingdom, Italy, and Portugal. All of them tried to enslave us, colonize us, and break our spirit, but by the infinite mercy and blessing of Allah, every single one of them failed miserably. When direct conquest proved impossible, they resorted to their dirtiest tactic: unleashing terrorism and sowing internal chaos to tear our nation apart from within. Yet despite all their evil schemes, we are still here — unbowed, unbroken, and rising stronger than ever before. To Ethiopia we declare with fire in our hearts: we have one massive piece of unfinished business. This time, the white man will not come running to save you. You lost Eritrea because of us, and now you have foolishly stumbled into another catastrophic mistake in Sudan. Every policy you have pursued for the last thirty years has only earned you enemies and isolated you further — allies that we will very soon mobilize and turn against you with devastating effect. It is almost laughable how some of you still delusionally claim victory in the 1977 war, despite mountains of historical evidence proving otherwise. Hear this and never forget it: we are not equal. Somalis are superior. Period
2:10

In 1977, the Somali National Army single-handedly invaded Ethiopia and Eritrea. Our brave Somali warriors swept across the battlefield like a storm, crushing everything in their path and winning decisive victory after victory. Their unstoppable momentum was only broken when Cuba rushed in over 20,000 troops and the Soviet Union dispatched another 10,000 — an entire international coalition mobilized in panic just to halt a proud nation of merely five million Somalis. Ethiopia has always been famous for running to the white man and crying for help the moment it faces real danger. But let the truth be shouted loud and clear: Somalis stand head and shoulders above any other African nation in courage, resilience, and martial superiority. No other country in Africa has dared to confront and defy the world’s greatest superpowers — Russia, the United States, France, the United Kingdom, Italy, and Portugal. All of them tried to enslave us, colonize us, and break our spirit, but by the infinite mercy and blessing of Allah, every single one of them failed miserably. When direct conquest proved impossible, they resorted to their dirtiest tactic: unleashing terrorism and sowing internal chaos to tear our nation apart from within. Yet despite all their evil schemes, we are still here — unbowed, unbroken, and rising stronger than ever before. To Ethiopia we declare with fire in our hearts: we have one massive piece of unfinished business. This time, the white man will not come running to save you. You lost Eritrea because of us, and now you have foolishly stumbled into another catastrophic mistake in Sudan. Every policy you have pursued for the last thirty years has only earned you enemies and isolated you further — allies that we will very soon mobilize and turn against you with devastating effect. It is almost laughable how some of you still delusionally claim victory in the 1977 war, despite mountains of historical evidence proving otherwise. Hear this and never forget it: we are not equal. Somalis are superior. Period

REIS🇸🇴

15,599 views • 5 months ago

I built an agent that answers machine-learning questions. It's autonomous, and the best part is that I built the whole thing without writing a single line of Python code. Here is what I did and how I did it: Over a year ago, a friend and I built a site that publishes multi-choice questions. You get a new one every day. I decided to have GPT-3.5 answer questions. Here is what I needed to build: 1. Connect to the site's API to retrieve today's question 2. Extract the question and the potential choices 3. Connect to OpenAI's API and ask GPT-3.5 to answer the question 4. Parse the answer from the model 5. Submit the answer back to the API to get the score Not difficult. Likely several hours of work. But I didn't have to write any code. I built the whole thing by dragging and dropping components using Vellum is a YC-backed platform for developers to build LLM applications. They are the only ones I've seen offering this functionality. They sponsored this post, and their team helped me with all my questions while I built this. I created a workflow. The platform supports several node types to build whatever you have in mind. I show how I put the whole thing together in the attached video. The only code I had to write was a few lines of Jinja to parse and transform the API and the LLM results. There are three lessons I want to share from this experience: First, the best possible code is the one you didn't write. I'm a big fan of no-code tools because they help me materialize my ideas fast. They help product people, designers, and no coders collaborate on the solution. Second, Large Language Models are sensitive to how you prompt them. Small changes to prompts can make a big difference in results. This is more pronounced when you are building a multi-step workflow. Third, automated testing and evaluation for prompts is critical. There aren't many companies thinking about this. They'll have a hard time moving from a demo phase. The attached video will show you what I did.
3:46

I built an agent that answers machine-learning questions. It's autonomous, and the best part is that I built the whole thing without writing a single line of Python code. Here is what I did and how I did it: Over a year ago, a friend and I built a site that publishes multi-choice questions. You get a new one every day. I decided to have GPT-3.5 answer questions. Here is what I needed to build: 1. Connect to the site's API to retrieve today's question 2. Extract the question and the potential choices 3. Connect to OpenAI's API and ask GPT-3.5 to answer the question 4. Parse the answer from the model 5. Submit the answer back to the API to get the score Not difficult. Likely several hours of work. But I didn't have to write any code. I built the whole thing by dragging and dropping components using Vellum is a YC-backed platform for developers to build LLM applications. They are the only ones I've seen offering this functionality. They sponsored this post, and their team helped me with all my questions while I built this. I created a workflow. The platform supports several node types to build whatever you have in mind. I show how I put the whole thing together in the attached video. The only code I had to write was a few lines of Jinja to parse and transform the API and the LLM results. There are three lessons I want to share from this experience: First, the best possible code is the one you didn't write. I'm a big fan of no-code tools because they help me materialize my ideas fast. They help product people, designers, and no coders collaborate on the solution. Second, Large Language Models are sensitive to how you prompt them. Small changes to prompts can make a big difference in results. This is more pronounced when you are building a multi-step workflow. Third, automated testing and evaluation for prompts is critical. There aren't many companies thinking about this. They'll have a hard time moving from a demo phase. The attached video will show you what I did.

Santiago

309,825 views • 3 years ago

That's sick! 🤯 Genesis AI simulates robots playing yo-yo! 🪀 Genesis AI just open-sourced Genesis World 1.0, and it might be one of the most important infrastructure releases in robotics this year. Robotics is still bottlenecked by the 1× speed of the physical world. Every model needs to be tested on real hardware, slowly, expensively, with limited coverage. Genesis World 1.0 from Genesis AI flips that equation: One hour in reality becomes 100 days in simulation. That turns a wall-clock bottleneck into a compute problem. And compute problems are solvable. The technical stack they rebuilt from scratch is serious: → GPU-accelerated cross-platform compiler via Quadrants, 10x faster launch time and up to 4.6x runtime vs the initial Genesis release → Penetration-free multi-physics contact solvers, the thing that makes simulation actually trustworthy → Unified rigid AND deformable physics in a single engine → Nyx, a high-performance path-traced rendering engine purpose-built for physical AI The sim-to-real gap has historically been the graveyard of robotics research. Policies that work beautifully in simulation fall apart on real hardware. Genesis World 1.0 is a direct attack on that problem. And it's fully open-source. The companies that master simulation infrastructure will train better robots faster than anyone else. Find it here: Genesis World 1.0: Quadrants: Nyx: Theophile Gervet, Zhou Xian congrats! 👏🏼 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:46

That's sick! 🤯 Genesis AI simulates robots playing yo-yo! 🪀 Genesis AI just open-sourced Genesis World 1.0, and it might be one of the most important infrastructure releases in robotics this year. Robotics is still bottlenecked by the 1× speed of the physical world. Every model needs to be tested on real hardware, slowly, expensively, with limited coverage. Genesis World 1.0 from Genesis AI flips that equation: One hour in reality becomes 100 days in simulation. That turns a wall-clock bottleneck into a compute problem. And compute problems are solvable. The technical stack they rebuilt from scratch is serious: → GPU-accelerated cross-platform compiler via Quadrants, 10x faster launch time and up to 4.6x runtime vs the initial Genesis release → Penetration-free multi-physics contact solvers, the thing that makes simulation actually trustworthy → Unified rigid AND deformable physics in a single engine → Nyx, a high-performance path-traced rendering engine purpose-built for physical AI The sim-to-real gap has historically been the graveyard of robotics research. Policies that work beautifully in simulation fall apart on real hardware. Genesis World 1.0 is a direct attack on that problem. And it's fully open-source. The companies that master simulation infrastructure will train better robots faster than anyone else. Find it here: Genesis World 1.0: Quadrants: Nyx: Theophile Gervet, Zhou Xian congrats! 👏🏼 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

57,061 views • 3 months ago

"They are not going to be able to raise rates." Jordi Visser (Jordi Visser) ran capital at Weiss Multi-Strategy Advisers as CIO. 30 years on Wall Street. Built one of the first volatility-arbitrage frameworks for systematic hedge funds. Managed billions through three crises, never had a thesis-driven blow-up year. "Interest payments on US debt are now bigger than what we spend on defense. Over a trillion dollars a year. This is what Bitcoin was made for." We cover: — Why the Fed is mathematically trapped and how the trillion-dollar interest math forces every policy decision from here — Why "bubble talk" is intellectually lazy: PE goes UP in bubbles, not down, and right now PE is contracting while earnings grow 27% — The AI-agents-eat-tokens thesis: why agentic AI doesn't care about dollars and what that means for compute-backed assets — Why belief is harder than fundamentals: fundamentals come and go, belief systems don't, and which belief is breaking in 2026 — The Bitcoin call no other macro guy on Wall Street will make publicly: new all-time highs before year-end — Why most hedge funds will underperform Bitcoin this cycle and the structural reason it has nothing to do with crypto — The single chart that made Jordi go from skeptic to allocator and why it hasn't reversed — What the 2020-2026 monetary regime actually was, named correctly for the first time Thanks to Jordi for coming on New Era Finance Podcast. Highlights: 00:00 - Intro 00:42 - Bitcoin Lagging 03:16 - AI Investment 07:14 - Price vs Narrative 12:15 - Market Dynamics 21:28 - AI Trading 25:24 - AI Democratizes Wealth 36:26 - Crypto Transition 39:40 - Elliott Waves 44:08 - Banana Zone 49:37 - Fundamentals vs Technicals 55:14 - Ethereum Future
1:05:06

"They are not going to be able to raise rates." Jordi Visser (Jordi Visser) ran capital at Weiss Multi-Strategy Advisers as CIO. 30 years on Wall Street. Built one of the first volatility-arbitrage frameworks for systematic hedge funds. Managed billions through three crises, never had a thesis-driven blow-up year. "Interest payments on US debt are now bigger than what we spend on defense. Over a trillion dollars a year. This is what Bitcoin was made for." We cover: — Why the Fed is mathematically trapped and how the trillion-dollar interest math forces every policy decision from here — Why "bubble talk" is intellectually lazy: PE goes UP in bubbles, not down, and right now PE is contracting while earnings grow 27% — The AI-agents-eat-tokens thesis: why agentic AI doesn't care about dollars and what that means for compute-backed assets — Why belief is harder than fundamentals: fundamentals come and go, belief systems don't, and which belief is breaking in 2026 — The Bitcoin call no other macro guy on Wall Street will make publicly: new all-time highs before year-end — Why most hedge funds will underperform Bitcoin this cycle and the structural reason it has nothing to do with crypto — The single chart that made Jordi go from skeptic to allocator and why it hasn't reversed — What the 2020-2026 monetary regime actually was, named correctly for the first time Thanks to Jordi for coming on New Era Finance Podcast. Highlights: 00:00 - Intro 00:42 - Bitcoin Lagging 03:16 - AI Investment 07:14 - Price vs Narrative 12:15 - Market Dynamics 21:28 - AI Trading 25:24 - AI Democratizes Wealth 36:26 - Crypto Transition 39:40 - Elliott Waves 44:08 - Banana Zone 49:37 - Fundamentals vs Technicals 55:14 - Ethereum Future

Michaël van de Poppe

651,475 views • 4 months ago

If you are running local LLMs without N-gram speculative decoding, you are wasting massive amounts of compute. Whether your AI is editing a document, outputting structured JSON, or rewriting boilerplate templates, a huge chunk of the text it generates is highly repetitive or already exists right there in the prompt. Standard decoding wastes expensive GPU compute cycles "re thinking" every single token. By adding one hidden flag in llama.cpp, you can instantly fast forward through the repetition. Zero draft models. Zero extra VRAM. And virtually zero compute overhead. Google Colab hands you an enterprise grade NVIDIA Tesla T4 GPU with 16GB of VRAM for free. It’s the perfect Ubuntu Linux sandbox to build a bleeding edge inference engine from scratch. Recently, I showed you how to double your local speeds using MTP (Multi Token Prediction). But MTP requires a secondary neural network draft model. That eats into your precious VRAM (slightly though) and burns extra compute for every guess it makes. N-gram Speculative Decoding gives you a massive speed boost for exactly 0 memory cost and minimal compute. And it's faster than MTP when it works. Here is how it actually works under the hood: Standard autoregressive decoding is slow because it predicts one token at a time. If you ask an agent to format a long JSON object or update one line in an HTML file, it runs heavy matrix multiplications to calculate the probability of every single bracket, space, and letter from scratch. N-gram changes the game. It acts as a lightweight caching system. Instead of running heavy neural network math to guess the next word, it uses a simple hash table. Whenever the LLM starts outputting a sequence of tokens that already exists anywhere in its context window, N-gram instantly recognizes the pattern. Because it is just doing lightning fast string matching, the compute cost is practically zero. It "fast forwards" through the text, drafting the boilerplate instantly from memory, and the main model just verifies it in parallel. Pure speed. Using quantized GGUFs from Unsloth via HuggingFace, I spun up DeepMind’s massive Gemma 4 26B A4B QAT MoE on a free Colab instance to test this. Just look at the raw benchmark data on code editing task: Without N-gram: [ Prompt: 638.6 t/s | Generation: 45.9 t/s ] With N-gram: [ Prompt: 601.9 t/s | Generation: 107.1 t/s ] Here is the exact llama.cpp CLI command to activate it. Notice we don't even need the --model-draft flag: ./llama-cli -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -cnv -n 6000 -c 12000 -ngl 99 -fa on --spec-type ngram-mod Stop waiting for your GPU to re calculate words it already knows. I’ve built a free, interactive, cell by cell Google Colab notebook that lets you test this live in your browser. You can literally chat with the model and watch the text generation speed absolutely fly on the second turn when you ask it to edit a file. There are additional parameters for ngram-mod that you can tune once you get it working with the single flag. Link to the free Colab Notebook is in the comments below. It walks you through the entire stack: pulling pre built llama.cpp CUDA binaries for Linux, fetching GGUFs from HuggingFace, and spinning up the inference engine with ngram-mod from scratch. Let me know if you have already tried ngram-mod
0:23

If you are running local LLMs without N-gram speculative decoding, you are wasting massive amounts of compute. Whether your AI is editing a document, outputting structured JSON, or rewriting boilerplate templates, a huge chunk of the text it generates is highly repetitive or already exists right there in the prompt. Standard decoding wastes expensive GPU compute cycles "re thinking" every single token. By adding one hidden flag in llama.cpp, you can instantly fast forward through the repetition. Zero draft models. Zero extra VRAM. And virtually zero compute overhead. Google Colab hands you an enterprise grade NVIDIA Tesla T4 GPU with 16GB of VRAM for free. It’s the perfect Ubuntu Linux sandbox to build a bleeding edge inference engine from scratch. Recently, I showed you how to double your local speeds using MTP (Multi Token Prediction). But MTP requires a secondary neural network draft model. That eats into your precious VRAM (slightly though) and burns extra compute for every guess it makes. N-gram Speculative Decoding gives you a massive speed boost for exactly 0 memory cost and minimal compute. And it's faster than MTP when it works. Here is how it actually works under the hood: Standard autoregressive decoding is slow because it predicts one token at a time. If you ask an agent to format a long JSON object or update one line in an HTML file, it runs heavy matrix multiplications to calculate the probability of every single bracket, space, and letter from scratch. N-gram changes the game. It acts as a lightweight caching system. Instead of running heavy neural network math to guess the next word, it uses a simple hash table. Whenever the LLM starts outputting a sequence of tokens that already exists anywhere in its context window, N-gram instantly recognizes the pattern. Because it is just doing lightning fast string matching, the compute cost is practically zero. It "fast forwards" through the text, drafting the boilerplate instantly from memory, and the main model just verifies it in parallel. Pure speed. Using quantized GGUFs from Unsloth via HuggingFace, I spun up DeepMind’s massive Gemma 4 26B A4B QAT MoE on a free Colab instance to test this. Just look at the raw benchmark data on code editing task: Without N-gram: [ Prompt: 638.6 t/s | Generation: 45.9 t/s ] With N-gram: [ Prompt: 601.9 t/s | Generation: 107.1 t/s ] Here is the exact llama.cpp CLI command to activate it. Notice we don't even need the --model-draft flag: ./llama-cli -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -cnv -n 6000 -c 12000 -ngl 99 -fa on --spec-type ngram-mod Stop waiting for your GPU to re calculate words it already knows. I’ve built a free, interactive, cell by cell Google Colab notebook that lets you test this live in your browser. You can literally chat with the model and watch the text generation speed absolutely fly on the second turn when you ask it to edit a file. There are additional parameters for ngram-mod that you can tune once you get it working with the single flag. Link to the free Colab Notebook is in the comments below. It walks you through the entire stack: pulling pre built llama.cpp CUDA binaries for Linux, fetching GGUFs from HuggingFace, and spinning up the inference engine with ngram-mod from scratch. Let me know if you have already tried ngram-mod

Alok

31,765 views • 2 months ago

Massive breakthrough here! Someone fixed every major flaw in Jupyter Notebooks. The .ipynb format is stuck in 2014. It was built for a different era - no cloud collaboration, no AI agents, no team workflows. Change one cell, and you get 50+ lines of JSON metadata in your git diff. Code reviews become a nightmare. Want to share a database connection across notebooks? Configure it separately in each one. Need comments or permissions? Too bad. Jupyter works for solo analysis but breaks for teams building production AI systems. Deepnote just open-sourced the solution (Apache 2.0 license) They've built a new notebook standard that actually fits modern workflows: ↳ Human-readable YAML - Git diffs show actual code changes, not JSON noise. Code reviews finally work. ↳ Project-based structure - Multiple notebooks share integrations, secrets, and environment settings. Configure once, use everywhere. ↳ 23 new block - SQL, interactive inputs, charts, and KPIs as first-class citizens. Build data apps, not just analytics notebooks. ↳ Multi-language support - Python and SQL in one notebook. Modern data work isn't single-language anymore. ↳ Full backward and forward compatibility: convert any Jupyter notebook to Deepnote and vice versa with one command. npx @ deepnote/convert notebook.ipynb Then open it in VS Code, Cursor, WindSurf, or Antigravity. Your existing notebooks migrate instantly. Their cloud version adds real-time collaboration with comments, permissions, and live editing. I've shared the GitHub repo link in the replies! It's 100% open-source.
0:34

Massive breakthrough here! Someone fixed every major flaw in Jupyter Notebooks. The .ipynb format is stuck in 2014. It was built for a different era - no cloud collaboration, no AI agents, no team workflows. Change one cell, and you get 50+ lines of JSON metadata in your git diff. Code reviews become a nightmare. Want to share a database connection across notebooks? Configure it separately in each one. Need comments or permissions? Too bad. Jupyter works for solo analysis but breaks for teams building production AI systems. Deepnote just open-sourced the solution (Apache 2.0 license) They've built a new notebook standard that actually fits modern workflows: ↳ Human-readable YAML - Git diffs show actual code changes, not JSON noise. Code reviews finally work. ↳ Project-based structure - Multiple notebooks share integrations, secrets, and environment settings. Configure once, use everywhere. ↳ 23 new block - SQL, interactive inputs, charts, and KPIs as first-class citizens. Build data apps, not just analytics notebooks. ↳ Multi-language support - Python and SQL in one notebook. Modern data work isn't single-language anymore. ↳ Full backward and forward compatibility: convert any Jupyter notebook to Deepnote and vice versa with one command. npx @ deepnote/convert notebook.ipynb Then open it in VS Code, Cursor, WindSurf, or Antigravity. Your existing notebooks migrate instantly. Their cloud version adds real-time collaboration with comments, permissions, and live editing. I've shared the GitHub repo link in the replies! It's 100% open-source.

Akshay 🚀

33,358 views • 9 months ago

so I've been running exactly 8 AI agents on discord for a while now. coordination works great, they split tasks, hand off work, deliver results in parallel etc.. but there are problems I keep hitting that no amount of prompt engineering could fix agents don't learn from each other. Scout finds something useful but Luna has no idea. they work in the same server but knowledge stays locked in silos.. there's no quality filter on what gets saved, and good insights sit next to outdated garbage in the same memory files that I manually clean up.. and when an agent makes a mistake I write it down in the rules discord channel ,core memory file and hope it reads it next time. theres no self-correction, no automatic pattern recognition so of course no learning loops.. the coordination layer is solved. agents can work together. but the intelligence layer is still missing. agents that actually remember, learn from each other, filter noise, and get smarter every run. saw Spark building something like this with around 166 agents sharing a collective persistent knowledge across sessions, so agents learn from other agents and get smarter over time they even have noise filtering and self correcting loops built in, so the knowledge actually compounds instead of rotting.. super interesting stuff.. here where you think Spark could be a good coordinator for your stack of agent swarm. I think the intelligence layer is the bottleneck because it requires collectivity.. no single agent can solve it alone.. the whole network has to evolve together. this isn't going to stay niche, the moment agent coordination becomes standard, everyone is going to hit the same wall I hit.. agents that work but don't learn, coordinate but don't evolve... the intelligence layer becomes the only thing that separates a useful system from a dumb one. right now most people are still figuring out how to run one agent. by the time they get to multi-agent setups, collective intelligence won't be optional, it will be the baseline. we're early and the gap between agents that coordinate and agents that evolve together is the next phase. step one is done. ------ left: agents that coordinate but don’t learn right: the intelligence layer.. agents that evolve together within the same system.
0:39

so I've been running exactly 8 AI agents on discord for a while now. coordination works great, they split tasks, hand off work, deliver results in parallel etc.. but there are problems I keep hitting that no amount of prompt engineering could fix agents don't learn from each other. Scout finds something useful but Luna has no idea. they work in the same server but knowledge stays locked in silos.. there's no quality filter on what gets saved, and good insights sit next to outdated garbage in the same memory files that I manually clean up.. and when an agent makes a mistake I write it down in the rules discord channel ,core memory file and hope it reads it next time. theres no self-correction, no automatic pattern recognition so of course no learning loops.. the coordination layer is solved. agents can work together. but the intelligence layer is still missing. agents that actually remember, learn from each other, filter noise, and get smarter every run. saw Spark building something like this with around 166 agents sharing a collective persistent knowledge across sessions, so agents learn from other agents and get smarter over time they even have noise filtering and self correcting loops built in, so the knowledge actually compounds instead of rotting.. super interesting stuff.. here where you think Spark could be a good coordinator for your stack of agent swarm. I think the intelligence layer is the bottleneck because it requires collectivity.. no single agent can solve it alone.. the whole network has to evolve together. this isn't going to stay niche, the moment agent coordination becomes standard, everyone is going to hit the same wall I hit.. agents that work but don't learn, coordinate but don't evolve... the intelligence layer becomes the only thing that separates a useful system from a dumb one. right now most people are still figuring out how to run one agent. by the time they get to multi-agent setups, collective intelligence won't be optional, it will be the baseline. we're early and the gap between agents that coordinate and agents that evolve together is the next phase. step one is done. ------ left: agents that coordinate but don’t learn right: the intelligence layer.. agents that evolve together within the same system.

JUMPERZ

34,181 views • 7 months ago

voice prompting is 4x faster than typing. but i NEEEDED more. Nvidia parakeet allows me to fully voice control an agentic development environment with commands firing in under 300ms. and it runs 100% local. I added gpt realtime 2.1 mini, its 20% faster, 7 to 20x cheaper, and lets you have full jarvis style control of your vibe coding agents. but what about orchestration? agents can spawn each other, prompt each other, and read each others output with the CNVS mcp and cli. Fable 5 can create a plan, spawn 10 grok agents to execute, and a kimi k3 agent to review. parallel agents code at 1,000s of TPS anthropic's own research shows improvements ACROSS the board for multi agent workflows over single agent but only CNVS lets you choose exactly which orchestration, worker, and reviewer agent you would like to use. grok, kimi, qwen, claude, codex... the cross agent memory system is based on real 2026 research so all agents share the same brain, its on demand so it never bloats context. what about remote agents?? You can create remote canvasses that run agents your virtual private servers, they keep working even if your mac shuts off, and you can even vibe code straight to production. CNVS is built from the ground up ENTIRELY in swift for RAW performance on apple hardware. PS - its a LIFE TIME LICENSE because you don't need another subscription. PPS - I ship updates every week based off user feedback and livestream myself building it everyday. PPPS - it uses all your existing ai subs, so no api pricing here.
1:02

voice prompting is 4x faster than typing. but i NEEEDED more. Nvidia parakeet allows me to fully voice control an agentic development environment with commands firing in under 300ms. and it runs 100% local. I added gpt realtime 2.1 mini, its 20% faster, 7 to 20x cheaper, and lets you have full jarvis style control of your vibe coding agents. but what about orchestration? agents can spawn each other, prompt each other, and read each others output with the CNVS mcp and cli. Fable 5 can create a plan, spawn 10 grok agents to execute, and a kimi k3 agent to review. parallel agents code at 1,000s of TPS anthropic's own research shows improvements ACROSS the board for multi agent workflows over single agent but only CNVS lets you choose exactly which orchestration, worker, and reviewer agent you would like to use. grok, kimi, qwen, claude, codex... the cross agent memory system is based on real 2026 research so all agents share the same brain, its on demand so it never bloats context. what about remote agents?? You can create remote canvasses that run agents your virtual private servers, they keep working even if your mac shuts off, and you can even vibe code straight to production. CNVS is built from the ground up ENTIRELY in swift for RAW performance on apple hardware. PS - its a LIFE TIME LICENSE because you don't need another subscription. PPS - I ship updates every week based off user feedback and livestream myself building it everyday. PPPS - it uses all your existing ai subs, so no api pricing here.

Max Blade

60,658 views • 2 months ago

. Tether led a $1.4 billion round into a humanoid robot company. It out-bid nvidia and amazon to do it. Nobody is asking the obvious question: where does a stablecoin issuer get robot money? 👇 ◢ The Answer is Your Dollars When you hold USDT, you've parked a real dollar with tether. They take that dollar, buy US treasuries with it, and keep the yield. You get a token. They get the interest. Across $186B in circulation that interest came to over $10 billion in profit last year, on a margin near 99%. It might be the most profitable company per employee on earth, and it pays its depositors nothing. ◢ The Money Had to Go Somewhere ten billion a year is too much to sit in a bank account. So tether became a buyer: - $775M into Rumble - A 70% stake in a south american agribusiness - One of the largest bitcoin mining operations alive - Roughly 140 tons of physical gold. - Around 10% of Juventus - A brain-computer interface startup. And now, also $1.4B into robotics. More than a treasury strategy, this is a conglomerate. ◢ A Sovereign Fund Strip the crypto label and look at the shape of it. A single entity sitting on a treasury book bigger than most countries, throwing off cash it answers to no one for, buying farmland, energy, media, compute and gold across continents. + Forget fintech, here we are seeing the structure of a sovereign wealth fund, except no public owns it and no parliament reviews it. ◢ What Should Make You Pause The float is the genius and the risk: it costs tether nothing because you're not paid to provide it, and it grows every time someone new buys USDT. The bigger the stablecoin gets, the bigger the empire it funds. A tool people use to move dollars quietly turned into the funding base for a private balance sheet at sovereign scale. Everyone argues about whether the reserves are backed: wrong fight. Tether built sovereign-level power off a dollar token. nobody elected it. nobody owns it. who holds it accountable?
0:39

. Tether led a $1.4 billion round into a humanoid robot company. It out-bid nvidia and amazon to do it. Nobody is asking the obvious question: where does a stablecoin issuer get robot money? 👇 ◢ The Answer is Your Dollars When you hold USDT, you've parked a real dollar with tether. They take that dollar, buy US treasuries with it, and keep the yield. You get a token. They get the interest. Across $186B in circulation that interest came to over $10 billion in profit last year, on a margin near 99%. It might be the most profitable company per employee on earth, and it pays its depositors nothing. ◢ The Money Had to Go Somewhere ten billion a year is too much to sit in a bank account. So tether became a buyer: - $775M into Rumble - A 70% stake in a south american agribusiness - One of the largest bitcoin mining operations alive - Roughly 140 tons of physical gold. - Around 10% of Juventus - A brain-computer interface startup. And now, also $1.4B into robotics. More than a treasury strategy, this is a conglomerate. ◢ A Sovereign Fund Strip the crypto label and look at the shape of it. A single entity sitting on a treasury book bigger than most countries, throwing off cash it answers to no one for, buying farmland, energy, media, compute and gold across continents. + Forget fintech, here we are seeing the structure of a sovereign wealth fund, except no public owns it and no parliament reviews it. ◢ What Should Make You Pause The float is the genius and the risk: it costs tether nothing because you're not paid to provide it, and it grows every time someone new buys USDT. The bigger the stablecoin gets, the bigger the empire it funds. A tool people use to move dollars quietly turned into the funding base for a private balance sheet at sovereign scale. Everyone argues about whether the reserves are backed: wrong fight. Tether built sovereign-level power off a dollar token. nobody elected it. nobody owns it. who holds it accountable?

Onur

20,623 views • 3 months ago

I want to share this message with all of you that I posted for my subscribers the other day here on X. One thing I’m most proud of is that my message exposing Pizzagate, including Mike Smith’s Out of Shadows documentary, went as viral and as far as it did moving the needle big time because of YOU guys! Crux (Mike Smith) and I gave up almost everything including our careers, money, our ability to make money, security and so much more to get the truth about Pizzagate out there. We did not have support from anyone in mainstream media and very little support in conservative media. Where we did have the support from is the brilliant anons, amazing patriots, truth seekers around the world and the legendary Q. Thanks to you badass frens we managed to wake up over 100 million people in 2020 alone in at least 25 different languages. That is so gangsta and I take it as a total badge of honor that we didn’t have any elites in the media or Con Inc or the big influencers behind us. It happened because of We The People and our collective efforts — we had the power and we made a difference! 👊🏽 Pizzagate is my life’s work and everyone knows that. I have fought every single day since October of 2016 when Pizzagate first broke to get this exposed simply because I care about children and the truth. I have a decade worth of receipts proving this from literally hundreds of hit pieces from print news to all the cable networks and also morning shows and late night talk shows. I had to move at least 9 times for my safety, had to leave the country, dealt with several death threats, attempts at my life, them trying to kill my dog more than once and successfully managing to poison him among many other horrific things that have made my life a living nightmare. No one can take my street cred away from me no matter what, period. I know what Mike and I did, the People know and most importantly God knows. So thank you to all of my supporters and the anons — we are here today because of you guys! WWG1WGA Forever! 🦅🇺🇸
4:17

I want to share this message with all of you that I posted for my subscribers the other day here on X. One thing I’m most proud of is that my message exposing Pizzagate, including Mike Smith’s Out of Shadows documentary, went as viral and as far as it did moving the needle big time because of YOU guys! Crux (Mike Smith) and I gave up almost everything including our careers, money, our ability to make money, security and so much more to get the truth about Pizzagate out there. We did not have support from anyone in mainstream media and very little support in conservative media. Where we did have the support from is the brilliant anons, amazing patriots, truth seekers around the world and the legendary Q. Thanks to you badass frens we managed to wake up over 100 million people in 2020 alone in at least 25 different languages. That is so gangsta and I take it as a total badge of honor that we didn’t have any elites in the media or Con Inc or the big influencers behind us. It happened because of We The People and our collective efforts — we had the power and we made a difference! 👊🏽 Pizzagate is my life’s work and everyone knows that. I have fought every single day since October of 2016 when Pizzagate first broke to get this exposed simply because I care about children and the truth. I have a decade worth of receipts proving this from literally hundreds of hit pieces from print news to all the cable networks and also morning shows and late night talk shows. I had to move at least 9 times for my safety, had to leave the country, dealt with several death threats, attempts at my life, them trying to kill my dog more than once and successfully managing to poison him among many other horrific things that have made my life a living nightmare. No one can take my street cred away from me no matter what, period. I know what Mike and I did, the People know and most importantly God knows. So thank you to all of my supporters and the anons — we are here today because of you guys! WWG1WGA Forever! 🦅🇺🇸

LIZ CROKIN

325,231 views • 7 months ago