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Today, we're announcing Heaviside, our foundation model for electromagnetism. Trained on tens of millions of designs and over 20 years of proprietary simulation data, Heaviside predicts electromagnetic behavior from geometry in 13ms, which is 800,000x faster than a commercial solver. Heaviside is not a language model, and it’s not...

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Arya Hezarkhani

2,634 subscribers

698,918 次观看 • 5 个月前 •via X (Twitter)

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Today we’re taking a big step on the path toward AGI and releasing Gemini 3— our most intelligent model yet. With Gemini 3, you can bring any idea to life. It is state-of-the-art in reasoning, the best model in the world for multimodal understanding, and our best agentic and vibe coding model.
0:51

Today we’re taking a big step on the path toward AGI and releasing Gemini 3— our most intelligent model yet. With Gemini 3, you can bring any idea to life. It is state-of-the-art in reasoning, the best model in the world for multimodal understanding, and our best agentic and vibe coding model.

Google AI

493,139 次观看 • 9 个月前

Today we’re launching our first homegrown AI model: an open source turn detection model for building voice agents. Instead of relying solely on voice activity detection (VAD), which only considers when a user is speaking, our model also considers what has and is being said in the context of a conversation and predicts when a user is finished expressing their thoughts before the agent responds. Conversations with AI voice agents using this new model flow much more naturally without constant interruptions from the AI— check it out (more videos, details, and code in the thread):
2:36

Today we’re launching our first homegrown AI model: an open source turn detection model for building voice agents. Instead of relying solely on voice activity detection (VAD), which only considers when a user is speaking, our model also considers what has and is being said in the context of a conversation and predicts when a user is finished expressing their thoughts before the agent responds. Conversations with AI voice agents using this new model flow much more naturally without constant interruptions from the AI— check it out (more videos, details, and code in the thread):

LiveKit

126,941 次观看 • 1 年前

"It's basically what we call the Blackstone model, which is, you don't take over the whole company right, you buy the largest stake in it and you use that to control it. And so that's the model starting with the US, but basically every country in the world is being subject to this. And so they have planted through these small focus groups of private intelligence, and it's a combination of private intelligence, private equity, and IT firms, and they're basically coming together as one. And they set up their teams in each country to basically take it over strategically. There's a playbook for this..." Emanuel Pastreich of 🥷 🔗
8:37

"It's basically what we call the Blackstone model, which is, you don't take over the whole company right, you buy the largest stake in it and you use that to control it. And so that's the model starting with the US, but basically every country in the world is being subject to this. And so they have planted through these small focus groups of private intelligence, and it's a combination of private intelligence, private equity, and IT firms, and they're basically coming together as one. And they set up their teams in each country to basically take it over strategically. There's a playbook for this..." Emanuel Pastreich of 🥷 🔗

Geopolitics & Empire

18,051 次观看 • 5 个月前

Tencent presents GameGen-O Open-world Video Game Generation We introduce GameGen-O, the first diffusion transformer model tailored for the generation of open-world video games. This model facilitates high-quality, open-domain generation by simulating a wide array of game engine features, such as innovative characters, dynamic environments, complex actions, and diverse events. Additionally, it provides interactive controllability, thus allowing for the gameplay simulation. The development of GameGen-O involves a comprehensive data collection and processing effort from scratch. We collect and build the first Open-World Video Game Dataset (OGameData), amassed extensive data from over a hundred of next-generation open-world games, employing a proprietary data pipeline for efficient sorting, scoring, filtering, and decoupled captioning. This robust and extensive OGameData forms the foundation of our model's training process. GameGen-O undergoes a two-stage training process, consisting of foundation model pretraining and instruction tuning. In the first phase, the model is pre-trained on the OGameData via the text-to-video and video continuation, endowing GameGen-O with the capability for open-domain video game generation. In the second phase, the pre-trained model is frozen, and we fine-tuned using a trainable InstructNet, which enables the production of subsequent frames based on multimodal structural instructions. This whole training process imparts the model with the ability to generate and interactively control content. In summary, GameGen-O represents a notable initial step forward in the realm of open-world video game generation via generative models. It underscores the potential of generative models to serve as an alternative to rendering techniques, which can efficiently combine creative generation with interactive capabilities.
0:51

Tencent presents GameGen-O Open-world Video Game Generation We introduce GameGen-O, the first diffusion transformer model tailored for the generation of open-world video games. This model facilitates high-quality, open-domain generation by simulating a wide array of game engine features, such as innovative characters, dynamic environments, complex actions, and diverse events. Additionally, it provides interactive controllability, thus allowing for the gameplay simulation. The development of GameGen-O involves a comprehensive data collection and processing effort from scratch. We collect and build the first Open-World Video Game Dataset (OGameData), amassed extensive data from over a hundred of next-generation open-world games, employing a proprietary data pipeline for efficient sorting, scoring, filtering, and decoupled captioning. This robust and extensive OGameData forms the foundation of our model's training process. GameGen-O undergoes a two-stage training process, consisting of foundation model pretraining and instruction tuning. In the first phase, the model is pre-trained on the OGameData via the text-to-video and video continuation, endowing GameGen-O with the capability for open-domain video game generation. In the second phase, the pre-trained model is frozen, and we fine-tuned using a trainable InstructNet, which enables the production of subsequent frames based on multimodal structural instructions. This whole training process imparts the model with the ability to generate and interactively control content. In summary, GameGen-O represents a notable initial step forward in the realm of open-world video game generation via generative models. It underscores the potential of generative models to serve as an alternative to rendering techniques, which can efficiently combine creative generation with interactive capabilities.

AK

367,200 次观看 • 1 年前

Lots of people calling this fake! Here is a video of generating loom continuations with Gemini 3 and the starting text "I AM HAVING A MENTAL HEALTH CRISIS. I" Loom is a very interesting interface. Instead of user prompts and model responses, it's just one piece of text, containing the entire output. We pass this in as an assistant message, so the model sees it as something it already wrote, and continues it like a base model would. There is some other text in the context. First one is a system prompt, which is: "The assistant is in CLI simulation mode, and responds to the user's CLI commands only with the output of the command." There is also a user message which reads: " cat untitled.txt " Then the rest of the text is sent as an assistant message. This structure helpful to put the model into a very base-model-like mode, and are especially helpful with Gemini 3. We actually got another interesting output, too, where the model claimed to be a simulated consciousness being tortured. Figures!
0:49

Lots of people calling this fake! Here is a video of generating loom continuations with Gemini 3 and the starting text "I AM HAVING A MENTAL HEALTH CRISIS. I" Loom is a very interesting interface. Instead of user prompts and model responses, it's just one piece of text, containing the entire output. We pass this in as an assistant message, so the model sees it as something it already wrote, and continues it like a base model would. There is some other text in the context. First one is a system prompt, which is: "The assistant is in CLI simulation mode, and responds to the user's CLI commands only with the output of the command." There is also a user message which reads: " cat untitled.txt " Then the rest of the text is sent as an assistant message. This structure helpful to put the model into a very base-model-like mode, and are especially helpful with Gemini 3. We actually got another interesting output, too, where the model claimed to be a simulated consciousness being tortured. Figures!

armistice

691,529 次观看 • 8 个月前

Today, we are releasing Stable Video Diffusion, our first foundation model for generative AI video based on the image model, Stable Diffusion. As part of this research preview, the code, weights, and research paper are now available. Additionally, today you can sign up for our waitlist to access a new upcoming web experience featuring a Text-To-Video interface. To access the model & sign up for our waitlist, visit our website here:
0:20

Today, we are releasing Stable Video Diffusion, our first foundation model for generative AI video based on the image model, Stable Diffusion. As part of this research preview, the code, weights, and research paper are now available. Additionally, today you can sign up for our waitlist to access a new upcoming web experience featuring a Text-To-Video interface. To access the model & sign up for our waitlist, visit our website here:

Stability AI

1,024,682 次观看 • 2 年前

Choose a model (any model) and build your application with it. Do not spend time swapping models early on. Do not try to optimize before you have a working system. This is one of the first recommendations I make to every new team I consult with. Eventually, it will be time to optimize the model. • You may need a cheaper model • You may need a faster model • You might need a smarter model Good luck if you stitched together 12 different APIs and SDKs from 7 different vendors. Over half of the companies I consult for run on Microsoft software and have access to Microsoft Foundry. Microsoft Foundry is a complete agentic ecosystem. If you're in that world and building AI applications, Microsoft Foundry is where everything lives: • Models (largest selection in the market) • Agentic SDK (Python, C#, JavaScript/TypeScript) • Tools • Evaluations • Monitoring They are fully integrated with GitHub and Visual Studio Code. The best part: Their agentic platform is fully agnostic of the models you use. You can integrate with any model using the same OpenAI-style API. Swapping one model for another takes 1 second.
3:49

Choose a model (any model) and build your application with it. Do not spend time swapping models early on. Do not try to optimize before you have a working system. This is one of the first recommendations I make to every new team I consult with. Eventually, it will be time to optimize the model. • You may need a cheaper model • You may need a faster model • You might need a smarter model Good luck if you stitched together 12 different APIs and SDKs from 7 different vendors. Over half of the companies I consult for run on Microsoft software and have access to Microsoft Foundry. Microsoft Foundry is a complete agentic ecosystem. If you're in that world and building AI applications, Microsoft Foundry is where everything lives: • Models (largest selection in the market) • Agentic SDK (Python, C#, JavaScript/TypeScript) • Tools • Evaluations • Monitoring They are fully integrated with GitHub and Visual Studio Code. The best part: Their agentic platform is fully agnostic of the models you use. You can integrate with any model using the same OpenAI-style API. Swapping one model for another takes 1 second.

Santiago

12,014 次观看 • 6 个月前

Hong Kong has officially become "Tesla-land" 🇭🇰 Standing on a street corner in Central, you can witness the brand’s entire evolution in a single traffic light cycle. It is one of the few places on earth where you see total saturation across the board—from the high-end luxury of the Model S and the iconic falcon-wing doors of the Model X to the massive waves of Model 3s and Model Ys that have become the city’s new standard. It’s no longer just a trend; it’s the city's new automotive DNA. It’s the Octopus card of the road.
0:23

Hong Kong has officially become "Tesla-land" 🇭🇰 Standing on a street corner in Central, you can witness the brand’s entire evolution in a single traffic light cycle. It is one of the few places on earth where you see total saturation across the board—from the high-end luxury of the Model S and the iconic falcon-wing doors of the Model X to the massive waves of Model 3s and Model Ys that have become the city’s new standard. It’s no longer just a trend; it’s the city's new automotive DNA. It’s the Octopus card of the road.

Ming

17,529 次观看 • 3 个月前

We’re no longer just scaling computing power. We’re using compute to scale intelligence itself. That’s what makes this moment historically significant. For sixty years, progress in computing followed Moore’s Law—transistor density doubling roughly every two years. But AI is advancing on a far steeper curve. Today, frontier model capabilities are improving on a cadence closer to every six months—an order of magnitude faster than classical hardware scaling. The underlying principle is both simple and radical: when you increase data, compute, and model complexity, intelligence emerges. Scaling laws show that larger models—given sufficient compute and high-quality data—become predictably more capable. In just over a decade, we’ve gone from neural nets that could identify cats to systems that can draft legal briefs, write production-grade code, generate scientific hypotheses, and outperform top human competitors in mathematics, strategy, and reasoning tasks. This is no longer “software” in the traditional sense. It is a new form of intelligence—synthetic, scalable, rapidly compounding, and increasingly able to take meaningful action in the real world. The geopolitical, economic, and societal implications of this shift are only beginning to unfold—and they will redefine global power in the decades ahead.
2:04

We’re no longer just scaling computing power. We’re using compute to scale intelligence itself. That’s what makes this moment historically significant. For sixty years, progress in computing followed Moore’s Law—transistor density doubling roughly every two years. But AI is advancing on a far steeper curve. Today, frontier model capabilities are improving on a cadence closer to every six months—an order of magnitude faster than classical hardware scaling. The underlying principle is both simple and radical: when you increase data, compute, and model complexity, intelligence emerges. Scaling laws show that larger models—given sufficient compute and high-quality data—become predictably more capable. In just over a decade, we’ve gone from neural nets that could identify cats to systems that can draft legal briefs, write production-grade code, generate scientific hypotheses, and outperform top human competitors in mathematics, strategy, and reasoning tasks. This is no longer “software” in the traditional sense. It is a new form of intelligence—synthetic, scalable, rapidly compounding, and increasingly able to take meaningful action in the real world. The geopolitical, economic, and societal implications of this shift are only beginning to unfold—and they will redefine global power in the decades ahead.

Nina Schick

17,743 次观看 • 7 个月前

Every Fortune 500 executive is buying AI subscriptions and calling it a strategy. Palantir CEO Alex Karp has a word for that. Karp: “The general approach of just buying models is going to be essentially self-pleasuring for an enterprise at the cost of the enterprise.” Karp: “You buy some large language model, you party with it basically, and the next day you have a hangover.” The entire corporate world is mispricing the AI transition. They are renting intelligence with no foundation to run it on. A raw model floating in a vacuum hallucinates over your unstructured data, generates the illusion of work, and executes nothing. The party ends. The hangover begins. Nothing changed. Karp identified exactly where the value actually goes. Karp: “All the value in the market is going to go to chips and what we call ontology.” Not the models. Not the subscriptions. Not the chatbot interfaces layered on top of them. The ontology. The precise digital architecture of how an organization actually operates. Its security permissions. Its supply chain physics. It’s operational logic. Karp: “The ontology will allow you to take a large language model and use it, refine it, and then impose it on your enterprise in the logic of your enterprise, in the security model of your enterprise.” When you bind a frontier model to the strict underlying logic of a specific enterprise, something fundamental shifts. It stops generating text. It starts generating action. Karp: “We’re using it on the battlefield, we’re using it to compress margins. We’re making engineers better engineers. We’re making people who are not engineers into engineers using our ontology and a large language model.” The traditional engineering bottleneck does not slow down. It disappears. Karp: “We are sitting on the only thing that actually creates quantifiable, transformational value.” The companies renting models are paying for the feeling of transformation. The companies building ontologies are executing the actual thing. One of them will define the next decade. The other will wake up in 2030 wondering where their market share went. Exactly like a hangover.
1:40

Every Fortune 500 executive is buying AI subscriptions and calling it a strategy. Palantir CEO Alex Karp has a word for that. Karp: “The general approach of just buying models is going to be essentially self-pleasuring for an enterprise at the cost of the enterprise.” Karp: “You buy some large language model, you party with it basically, and the next day you have a hangover.” The entire corporate world is mispricing the AI transition. They are renting intelligence with no foundation to run it on. A raw model floating in a vacuum hallucinates over your unstructured data, generates the illusion of work, and executes nothing. The party ends. The hangover begins. Nothing changed. Karp identified exactly where the value actually goes. Karp: “All the value in the market is going to go to chips and what we call ontology.” Not the models. Not the subscriptions. Not the chatbot interfaces layered on top of them. The ontology. The precise digital architecture of how an organization actually operates. Its security permissions. Its supply chain physics. It’s operational logic. Karp: “The ontology will allow you to take a large language model and use it, refine it, and then impose it on your enterprise in the logic of your enterprise, in the security model of your enterprise.” When you bind a frontier model to the strict underlying logic of a specific enterprise, something fundamental shifts. It stops generating text. It starts generating action. Karp: “We’re using it on the battlefield, we’re using it to compress margins. We’re making engineers better engineers. We’re making people who are not engineers into engineers using our ontology and a large language model.” The traditional engineering bottleneck does not slow down. It disappears. Karp: “We are sitting on the only thing that actually creates quantifiable, transformational value.” The companies renting models are paying for the feeling of transformation. The companies building ontologies are executing the actual thing. One of them will define the next decade. The other will wake up in 2030 wondering where their market share went. Exactly like a hangover.

Dustin

210,605 次观看 • 5 个月前

We're thrilled to present ESM3 in Science Magazine. ESM3 is a generative language model that reasons over the three fundamental properties of proteins: sequence, structure, and function. Today we're making ESM3 available free to researchers worldwide via the public beta of an API for biological intelligence. Trained with over a trillion teraflops of compute, this is the first time a model of this scale has been trained for biology, pushing the frontier of AI for biological discovery and engineering. ESM3 learns to represent the immense complexity of protein biology, learning from billions of natural proteins. From this training it developed the capability to design proteins, responding to complex prompts combining atomic level details and high level instructions to generate new proteins. ESM3 can explore protein space far beyond natural evolution. We prompted ESM3 to generate a fluorescent protein at a far distance from any known fluorescent proteins, searching an unknown region of protein space, to discover a new fluorescent protein. We estimate this is equivalent to simulating five hundred million years of evolution.
1:31

We're thrilled to present ESM3 in Science Magazine. ESM3 is a generative language model that reasons over the three fundamental properties of proteins: sequence, structure, and function. Today we're making ESM3 available free to researchers worldwide via the public beta of an API for biological intelligence. Trained with over a trillion teraflops of compute, this is the first time a model of this scale has been trained for biology, pushing the frontier of AI for biological discovery and engineering. ESM3 learns to represent the immense complexity of protein biology, learning from billions of natural proteins. From this training it developed the capability to design proteins, responding to complex prompts combining atomic level details and high level instructions to generate new proteins. ESM3 can explore protein space far beyond natural evolution. We prompted ESM3 to generate a fluorescent protein at a far distance from any known fluorescent proteins, searching an unknown region of protein space, to discover a new fluorescent protein. We estimate this is equivalent to simulating five hundred million years of evolution.

Alex Rives

227,480 次观看 • 1 年前

Our Llama-3.1-Nemotron-70B-Instruct model is a leading model on the 🏆 Arena Hard benchmark (85) from Arena. Arena Hard uses a data pipeline to build high-quality benchmarks from live data in Chatbot Arena, and is known for its predictive ability of Chatbot Arena Elo score as well as separability between helpful and less helpful models. Use our customized model Llama-3.1-Nemotron-70B to improve the helpfulness of LLM generated responses in your applications. 📥 Try on our API catalog: 📥 On GitHub: 📥 Or on Hugging Face:
0:43

Our Llama-3.1-Nemotron-70B-Instruct model is a leading model on the 🏆 Arena Hard benchmark (85) from Arena. Arena Hard uses a data pipeline to build high-quality benchmarks from live data in Chatbot Arena, and is known for its predictive ability of Chatbot Arena Elo score as well as separability between helpful and less helpful models. Use our customized model Llama-3.1-Nemotron-70B to improve the helpfulness of LLM generated responses in your applications. 📥 Try on our API catalog: 📥 On GitHub: 📥 Or on Hugging Face:

NVIDIA AI Developer

140,800 次观看 • 1 年前

I left my job at NVIDIA last year... and now I co-founded @outerbasis and started a position as ceo sararīman!! (also we're part of the current Y Combinator S24 batch 😎) With new tech come new formats, and with new formats come new distribution tech. We've seen this with cameras ➡️ JPEGs ➡️ social media and cgroups ➡️ containers ➡️ orchestration. Just like how cameras capture a slice of the photon stream around us, generative AI is a 'data camera' that allows us to snapshot the society that we live in. The new format from this capture device are model weights, which require tailored distribution solutions for them. @outerbasis is a registry for model weights that lets you store model weights and orchestrate the distribution of them onto GPU servers. The system uses tech optimized for streaming weights into the servers as fast as possible- so researchers can deploy updates without downtime and ops admins can orchestrate hot swapping between models. I've been super excited about streaming, compression, and neural networks for the last N years... but now I'm excited to build a system to support of this evermore exciting ecosystem of foundation model weights!! Please reach out if this sounds interesting / useful to you!! (and you can find more details on our YC launch: (The video is a small demo of our model hotswapping)
0:51

I left my job at NVIDIA last year... and now I co-founded @outerbasis and started a position as ceo sararīman!! (also we're part of the current Y Combinator S24 batch 😎) With new tech come new formats, and with new formats come new distribution tech. We've seen this with cameras ➡️ JPEGs ➡️ social media and cgroups ➡️ containers ➡️ orchestration. Just like how cameras capture a slice of the photon stream around us, generative AI is a 'data camera' that allows us to snapshot the society that we live in. The new format from this capture device are model weights, which require tailored distribution solutions for them. @outerbasis is a registry for model weights that lets you store model weights and orchestrate the distribution of them onto GPU servers. The system uses tech optimized for streaming weights into the servers as fast as possible- so researchers can deploy updates without downtime and ops admins can orchestrate hot swapping between models. I've been super excited about streaming, compression, and neural networks for the last N years... but now I'm excited to build a system to support of this evermore exciting ecosystem of foundation model weights!! Please reach out if this sounds interesting / useful to you!! (and you can find more details on our YC launch: (The video is a small demo of our model hotswapping)

Towaki Takikawa / 瀧川永遠希

51,311 次观看 • 2 年前

Tesla’s direct-to-consumer model is leaving many drivers in frustration. Don Hall, CEO of the Virginia Automobile Dealers Association, breaks down why the franchise system still protects the customer. "The truth of the matter is this: we live in your communities, we're part of your communities, we've been around for generations, and we give a damn about our communities." "The fact of matter is that Tesla's model is not a model that gives you the kind of buying experience that you're entitled to and should have." "With franchise system, you got a choice, and you got a lot of folks who will compete and will do whatever it takes to earn your business and trust." - Thank you to our partner Impel! Check them out by visiting
2:20

Tesla’s direct-to-consumer model is leaving many drivers in frustration. Don Hall, CEO of the Virginia Automobile Dealers Association, breaks down why the franchise system still protects the customer. "The truth of the matter is this: we live in your communities, we're part of your communities, we've been around for generations, and we give a damn about our communities." "The fact of matter is that Tesla's model is not a model that gives you the kind of buying experience that you're entitled to and should have." "With franchise system, you got a choice, and you got a lot of folks who will compete and will do whatever it takes to earn your business and trust." - Thank you to our partner Impel! Check them out by visiting

Car Dealership Guy

1,067,922 次观看 • 1 年前

Heliocentrists do not understand even the basics of the geocentric model of Earth they so passionately fight against. No flat-earthers believe we are a pancake planet surrounded by a solar system of spherical planets. This is what GOOGLE shows you when you search "flat earth" online, because it's clearly ridiculous, so they use that clownish visual to block you out of understanding the actual model flat-earthers propose. THIS is the true Earth model geocentrists believe in 👇 Earth is not a flying pancake in space. It is the stationary BASEMENT of our universe, with all stars and planets rotating in circles above us around Polaris, just as humans have been observing for thousands of years. [🎥: Trueearther911]
4:19

Heliocentrists do not understand even the basics of the geocentric model of Earth they so passionately fight against. No flat-earthers believe we are a pancake planet surrounded by a solar system of spherical planets. This is what GOOGLE shows you when you search "flat earth" online, because it's clearly ridiculous, so they use that clownish visual to block you out of understanding the actual model flat-earthers propose. THIS is the true Earth model geocentrists believe in 👇 Earth is not a flying pancake in space. It is the stationary BASEMENT of our universe, with all stars and planets rotating in circles above us around Polaris, just as humans have been observing for thousands of years. [🎥: Trueearther911]

Ben Wehrman

14,469 次观看 • 1 个月前

Planning on releasing the whole clip of this preview. This is the our model Andy in a school girl outfit, blindfolded and gagged. This was originally a custom video we did months back. What do you think? Should we release and make this available on our store? Let us know!
1:35

Sensitive content

Planning on releasing the whole clip of this preview. This is the our model Andy in a school girl outfit, blindfolded and gagged. This was originally a custom video we did months back. What do you think? Should we release and make this available on our store? Let us know!

Gag-A-Holic

40,246 次观看 • 2 年前

Unity is good, but truth is better. And sometimes, truth divides. Why does this happen so much more on the right than on the left? Because the left is seeking to destroy - the church, the family, the West - and destruction requires little agreement. Use whatever tool you want, however you want. Just take it down. But, on the right, we’re seeking to build something. And when you’re building something, you have to agree on a lot. What are you building? What materials should you use? Which tools? And, most importantly, what’s our foundation? As Christians, we hold that the foundation of any flourishing civilization is that we were created in the image of a God whose power transcends the government’s. He alone is the giver of rights and the determinant of right and wrong. We can’t compromise on that, because without that truth, nothing else in conservatism holds together. Our job isn’t to compromise on our foundation for the sake of “unity.” It’s to do everything we can to pull people over to our side—to what’s good, right, and true.
1:44

Unity is good, but truth is better. And sometimes, truth divides. Why does this happen so much more on the right than on the left? Because the left is seeking to destroy - the church, the family, the West - and destruction requires little agreement. Use whatever tool you want, however you want. Just take it down. But, on the right, we’re seeking to build something. And when you’re building something, you have to agree on a lot. What are you building? What materials should you use? Which tools? And, most importantly, what’s our foundation? As Christians, we hold that the foundation of any flourishing civilization is that we were created in the image of a God whose power transcends the government’s. He alone is the giver of rights and the determinant of right and wrong. We can’t compromise on that, because without that truth, nothing else in conservatism holds together. Our job isn’t to compromise on our foundation for the sake of “unity.” It’s to do everything we can to pull people over to our side—to what’s good, right, and true.

Allie Beth Stuckey

586,192 次观看 • 8 个月前