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This is what weeks of quiet R&D looks like. Testing Paradigm vs. FLIP side by side, refining foam and viscosity workflows, and laying the groundwork for AI-assisted simulation pipelines built on my own data. Still finessing. But the foundation is solid. #Houdini #FXResearch #FluidSim #Paradigm #VFX #KeylanaStudios #HoudiniVFX #MotionDesign

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Jacktone Okore

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41,568 просмотров • 3 месяцев назад •via X (Twitter)

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The DEPRESSING reality of AI adoption curves Advanced AI just broke into it's third major paradigm since launching. Paradigm One was the simple autocomplete engine. GPT-2 and original GPT-3 were glorified autocomplete tools, just "next token predictors." Paradigm 1.5 was when we added "instruct-aligned" GPT-3, which set the stage for Paradigm Two. Paradigm Two was the chatbot era, with ChatGPT being the front-runner. Paradigm 2.5 was when we started adding reasoning, tool use, and RAG, which set the stage for agentic abilities (Paradigm 3). OpenClaw just blew the lid off Paradigm 3, and we're just at the beginning of this new ramp-up in capabilities. The thing is... each of these paradigm shifts creates fundamentally different UX and technical affordances. While most Fortune 500 companies are still struggling to figure out the cyber security, legal, and financial risks of chatbots, the industry is going whole hog into autonomous agents. And many people will try to graft their understanding of chatbots onto agents. But that's like trying to compare the electric lightbulb to the electric motor.... ...yes they both ran on electricity, but their uses, affordances, limitations, and risks were fundamentally different. Yes, autocomplete, chatbots, and agents all run on "next token prediction" but that's like saying "it all runs on electrons." My goal today is to help give you a better intuition as to why adoption is so slow and to give you a new reference frame to understand that agents are a phase change from chatbots. But also... the state and big companies are going to move depressingly slow on all of this...
30:02

The DEPRESSING reality of AI adoption curves Advanced AI just broke into it's third major paradigm since launching. Paradigm One was the simple autocomplete engine. GPT-2 and original GPT-3 were glorified autocomplete tools, just "next token predictors." Paradigm 1.5 was when we added "instruct-aligned" GPT-3, which set the stage for Paradigm Two. Paradigm Two was the chatbot era, with ChatGPT being the front-runner. Paradigm 2.5 was when we started adding reasoning, tool use, and RAG, which set the stage for agentic abilities (Paradigm 3). OpenClaw just blew the lid off Paradigm 3, and we're just at the beginning of this new ramp-up in capabilities. The thing is... each of these paradigm shifts creates fundamentally different UX and technical affordances. While most Fortune 500 companies are still struggling to figure out the cyber security, legal, and financial risks of chatbots, the industry is going whole hog into autonomous agents. And many people will try to graft their understanding of chatbots onto agents. But that's like trying to compare the electric lightbulb to the electric motor.... ...yes they both ran on electricity, but their uses, affordances, limitations, and risks were fundamentally different. Yes, autocomplete, chatbots, and agents all run on "next token prediction" but that's like saying "it all runs on electrons." My goal today is to help give you a better intuition as to why adoption is so slow and to give you a new reference frame to understand that agents are a phase change from chatbots. But also... the state and big companies are going to move depressingly slow on all of this...

David Shapiro (L/0)

17,266 просмотров • 6 месяцев назад

Insta360 X6 vs DJI Osmo 360 vs X5: 7 rounds, point by point, based on weeks of side-by-side testing. Image quality, dynamic range, low light, 360 photo, weight, overheating, and battery runtime. One camera took a clear win. But do you agree with my scoring? Drop your own score in the comments. #insta360x6 #360camera
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Insta360 X6 vs DJI Osmo 360 vs X5: 7 rounds, point by point, based on weeks of side-by-side testing. Image quality, dynamic range, low light, 360 photo, weight, overheating, and battery runtime. One camera took a clear win. But do you agree with my scoring? Drop your own score in the comments. #insta360x6 #360camera

Hugh Hou

11,360 просмотров • 22 дней назад

Most of my AI films are experiments—tests of tools, models, or workflows—but Anxiety for Tomorrow is something different. This one is about testing emotion, storytelling, and the quiet power of cinematography. It deals with something deeply human: the weight of mental health. (ps this is all text to video)
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Most of my AI films are experiments—tests of tools, models, or workflows—but Anxiety for Tomorrow is something different. This one is about testing emotion, storytelling, and the quiet power of cinematography. It deals with something deeply human: the weight of mental health. (ps this is all text to video)

Jason Zada

16,468 просмотров • 1 год назад

To Save College Sports, we must solve a business problem: Fix a system that is fully professionalized on the cost side but only semi-professionalized on the revenue side. This problem CAN be solved. The solutions are common business sense and proven by decades of solid, clear data. I talked about these problems and the solutions at a CNBC event last week.
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To Save College Sports, we must solve a business problem: Fix a system that is fully professionalized on the cost side but only semi-professionalized on the revenue side. This problem CAN be solved. The solutions are common business sense and proven by decades of solid, clear data. I talked about these problems and the solutions at a CNBC event last week.

Cody Campbell

899,031 просмотров • 11 месяцев назад

That one? We’re still waiting on the results, but it’s looks like Blakey in 4k. But this photo? It is real - and side by side, it doesn’t just look tone-deaf. It looks like a mockery. Blakey is standing in essentially Hunter Kozak’s exact position at the mic, recreating the visual setup from Charlie’s final moments. Yet somehow we’re the villains for asking questions - questions Andrew Kolvet himself suddenly jumped on the bandwagon to ask after three days of preliminary hearings.
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That one? We’re still waiting on the results, but it’s looks like Blakey in 4k. But this photo? It is real - and side by side, it doesn’t just look tone-deaf. It looks like a mockery. Blakey is standing in essentially Hunter Kozak’s exact position at the mic, recreating the visual setup from Charlie’s final moments. Yet somehow we’re the villains for asking questions - questions Andrew Kolvet himself suddenly jumped on the bandwagon to ask after three days of preliminary hearings.

shiloh.mp4_

12,491 просмотров • 1 месяц назад

Say what you want but the emotional playoff this moment had on first season watchers is and still will be unmatched. It's raw, it's electrical. They're sitting side by side, not even reaching for each other, and yet you could cut the tension with a knife.
2:27

Say what you want but the emotional playoff this moment had on first season watchers is and still will be unmatched. It's raw, it's electrical. They're sitting side by side, not even reaching for each other, and yet you could cut the tension with a knife.

mari ☁️

20,180 просмотров • 1 месяц назад

AI is transforming the way businesses operate 🤖. It’s no longer just a tool, it powers decision-making, efficiency, and growth. From automating tasks to predicting trends and uncovering insights, AI is becoming the backbone of modern business. But AI isn’t magic. It needs massive amounts of high-quality data 📊. Companies like Scale AI and other Web2 giants thrive because they control the pipelines of this data, turning it into a huge competitive advantage. The problem is most of this data is locked in centralized systems 🔒, expensive, and often inaccessible for smaller companies or innovators. That’s where OORT | The Data Cloud for Decentralized AI comes in 🌐✨. By building decentralized storage and AI infrastructure, OORTECH makes data open, secure, and accessible to everyone. It’s a foundation for the next generation of AI-powered businesses, giving all innovators the chance to compete and create 🚀
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AI is transforming the way businesses operate 🤖. It’s no longer just a tool, it powers decision-making, efficiency, and growth. From automating tasks to predicting trends and uncovering insights, AI is becoming the backbone of modern business. But AI isn’t magic. It needs massive amounts of high-quality data 📊. Companies like Scale AI and other Web2 giants thrive because they control the pipelines of this data, turning it into a huge competitive advantage. The problem is most of this data is locked in centralized systems 🔒, expensive, and often inaccessible for smaller companies or innovators. That’s where OORT | The Data Cloud for Decentralized AI comes in 🌐✨. By building decentralized storage and AI infrastructure, OORTECH makes data open, secure, and accessible to everyone. It’s a foundation for the next generation of AI-powered businesses, giving all innovators the chance to compete and create 🚀

Sky Wee

67,557 просмотров • 11 месяцев назад

Everyone is talking about AI models, but almost no one is talking about data ownership. For years, centralized systems have dominated data pipelines, opaque processes, hidden biases, and zero contributor visibility. We call it innovation…, but in reality, it’s a black box. And that’s exactly how we ended up facing model degradation risks today. Because here’s the truth: AI is only as good as the data it learns from. That’s why Sovereign AI Data matters. To me, it means control, traceability, and accountability. Perle Labs is approaching this differently: ◈ On-chain provenance → Every data point is verifiable ◈ Expert-driven input → Quality over quantity ◈ Reputation systems → Trust becomes measurable This isn’t just a technical upgrade. It’s a paradigm shift. Instead of: Anonymous contributors ➠Unverified datasets ➠Centralized ownership ➠We move toward: Transparent data flows, Credible expertise and Distributed value creation If AI is the brain, then data is the memory. And right now most systems are learning from memory, which they can’t verify. Perle is solving that. While others focus on scaling models… This is about fixing the foundation they stand on. And foundations decide everything. #PerleAI #ToPerle participating in Perle Labs community campaign
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Everyone is talking about AI models, but almost no one is talking about data ownership. For years, centralized systems have dominated data pipelines, opaque processes, hidden biases, and zero contributor visibility. We call it innovation…, but in reality, it’s a black box. And that’s exactly how we ended up facing model degradation risks today. Because here’s the truth: AI is only as good as the data it learns from. That’s why Sovereign AI Data matters. To me, it means control, traceability, and accountability. Perle Labs is approaching this differently: ◈ On-chain provenance → Every data point is verifiable ◈ Expert-driven input → Quality over quantity ◈ Reputation systems → Trust becomes measurable This isn’t just a technical upgrade. It’s a paradigm shift. Instead of: Anonymous contributors ➠Unverified datasets ➠Centralized ownership ➠We move toward: Transparent data flows, Credible expertise and Distributed value creation If AI is the brain, then data is the memory. And right now most systems are learning from memory, which they can’t verify. Perle is solving that. While others focus on scaling models… This is about fixing the foundation they stand on. And foundations decide everything. #PerleAI #ToPerle participating in Perle Labs community campaign

Big_Wealth❤️‍🔥

11,486 просмотров • 5 месяцев назад

Truth be told, you don't have a Charlie Kirk. But we did. And even after his death, we still do. So ask yourselves what is the foundation of your platform actually built on? Because from where I'm sitting, it looks like WET SAND and your empire of hatred is crumbling.
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Truth be told, you don't have a Charlie Kirk. But we did. And even after his death, we still do. So ask yourselves what is the foundation of your platform actually built on? Because from where I'm sitting, it looks like WET SAND and your empire of hatred is crumbling.

🖤Jenny🖤

20,661 просмотров • 11 месяцев назад

This is what pure Evil looks like! These are ICE officers and they decided to go to the Memorial for Renee Nicole Good and kick over the candles and step on the flowers left by people! This should make everyone angry no matter what side you’re on! This woman was a mother & wife!
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This is what pure Evil looks like! These are ICE officers and they decided to go to the Memorial for Renee Nicole Good and kick over the candles and step on the flowers left by people! This should make everyone angry no matter what side you’re on! This woman was a mother & wife!

Suzie rizzio

41,808 просмотров • 7 месяцев назад

This is what quiet replacement looks like in North Texas. There was no protest and no vote - just broken labor laws. Once-quiet towns in historically deep-red districts are now packed with people from the other side of the world, settling in like they own the place. American workers never got a say, the visa machine just kept running.
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This is what quiet replacement looks like in North Texas. There was no protest and no vote - just broken labor laws. Once-quiet towns in historically deep-red districts are now packed with people from the other side of the world, settling in like they own the place. American workers never got a say, the visa machine just kept running.

Nick Plumb

381,339 просмотров • 22 дней назад

The co-founders of Flapping Airplanes call the current RL paradigm for model training "environment slop." They explain: "The reinforcement paradigms of today are shockingly inefficient. You don't really get much generalization across tasks, you teach a model through one kind of learning and then you teach it the next one. It's kind of like whack-a-mole. We look at this and think it's kind of crazy. The next paradigm of AI will not be environment slop." "Human level intelligence is not the ceiling, it is merely the floor on what is possible. If you can train models with vastly less data and possibly more compute in very different ways, what is going to happen? We actually don't know. But I do think they'll be different and weird and they'll have interesting capabilities that we'll find really valuable ways to use."
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The co-founders of Flapping Airplanes call the current RL paradigm for model training "environment slop." They explain: "The reinforcement paradigms of today are shockingly inefficient. You don't really get much generalization across tasks, you teach a model through one kind of learning and then you teach it the next one. It's kind of like whack-a-mole. We look at this and think it's kind of crazy. The next paradigm of AI will not be environment slop." "Human level intelligence is not the ceiling, it is merely the floor on what is possible. If you can train models with vastly less data and possibly more compute in very different ways, what is going to happen? We actually don't know. But I do think they'll be different and weird and they'll have interesting capabilities that we'll find really valuable ways to use."

TBPN

85,358 просмотров • 7 месяцев назад

What the hell Magneto got going on ??? Looks like he’s set up some kind of Mutant tournament of power shit looks fucking crazy. And he got Cyclops to join his side by using his brother this is boutta get really interesting
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What the hell Magneto got going on ??? Looks like he’s set up some kind of Mutant tournament of power shit looks fucking crazy. And he got Cyclops to join his side by using his brother this is boutta get really interesting

Bam²⁵

33,280 просмотров • 1 месяц назад

auto-research is starting to gain traction as a very viable paradigm for creating useful research discovery. now, that paradigm is still in its infancy and the infrastructure to hold all that trail of context as the agents blaze through experiments isn't well defined (to say the least). on that topic, I had the chance to chat with my boys francesco and giulio from paradigma about what underlying infra is needed to make this paradigm work. the paradigma's paradigm, which involves copious amount of DAGs, make this auto-research paradigm a paradigmatic case of essential infrastructure. here's the full video in full: - 0:00 - what is missing from auto-research? - 2:02 - giulio and francesco ai journey - 8:10 - research infra is the bottleneck? - 10:18 - paradigma vision of autonomous research - 13:17 - “important discovery per joules” - 17:15 - why is DAG the unit of research for auto-research? - 20:40 - is paradigma trying to replace the research publication? - 24:50 - how does knowledge is shared between experiments in the DAG? - 27:34 - what is even auto-research lol? - 33:53 - the value of the human mind in this auto-research future. - 37:00 - how do you reconcile hallucination in this auto-research paradigm? - 41:33 - the adoption of auto-research across varied fields? - 47:30 - ✨ introduction to the auto-research infrastructure. ✨ - 56:55 - where is the code? - 59:10 - full IDE next? - 1:03:20 - the place of the human in this DAG / code quality? manual node? token spent? - 1:16:02 - who’s the user for auto-research? - 1:18:13 - how to validate bad DAG? - 1:20:18 - ✨ auto-research agent results ✨ - 1:22:53 - ✨ how a big research DAG looks like? ✨ - 1:25:10 - how to get the canonical DAG for the final result? - 1:27:50 - the auto-research DAG being the new pre-print? - 1:30:05 - what’s next for paradigma and the auto-research infra? - 1:35:00 - what are they excited about research wise? enjoyyyyy my guys 🌹
1:37:23

auto-research is starting to gain traction as a very viable paradigm for creating useful research discovery. now, that paradigm is still in its infancy and the infrastructure to hold all that trail of context as the agents blaze through experiments isn't well defined (to say the least). on that topic, I had the chance to chat with my boys francesco and giulio from paradigma about what underlying infra is needed to make this paradigm work. the paradigma's paradigm, which involves copious amount of DAGs, make this auto-research paradigm a paradigmatic case of essential infrastructure. here's the full video in full: - 0:00 - what is missing from auto-research? - 2:02 - giulio and francesco ai journey - 8:10 - research infra is the bottleneck? - 10:18 - paradigma vision of autonomous research - 13:17 - “important discovery per joules” - 17:15 - why is DAG the unit of research for auto-research? - 20:40 - is paradigma trying to replace the research publication? - 24:50 - how does knowledge is shared between experiments in the DAG? - 27:34 - what is even auto-research lol? - 33:53 - the value of the human mind in this auto-research future. - 37:00 - how do you reconcile hallucination in this auto-research paradigm? - 41:33 - the adoption of auto-research across varied fields? - 47:30 - ✨ introduction to the auto-research infrastructure. ✨ - 56:55 - where is the code? - 59:10 - full IDE next? - 1:03:20 - the place of the human in this DAG / code quality? manual node? token spent? - 1:16:02 - who’s the user for auto-research? - 1:18:13 - how to validate bad DAG? - 1:20:18 - ✨ auto-research agent results ✨ - 1:22:53 - ✨ how a big research DAG looks like? ✨ - 1:25:10 - how to get the canonical DAG for the final result? - 1:27:50 - the auto-research DAG being the new pre-print? - 1:30:05 - what’s next for paradigma and the auto-research infra? - 1:35:00 - what are they excited about research wise? enjoyyyyy my guys 🌹

Yacine Mahdid

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Almost $1 TRILLION has already gone into building AI infrastructure. Let that sink in for a moment. In just two years, we’ve poured the equivalent of an entire country’s GDP into chips, data centers, and compute power. That’s not hype that’s commitment. And the wild part? This is still early innings. The foundation is being built right now. What gets built on top of it is going to change everything. 🔥🚀
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Almost $1 TRILLION has already gone into building AI infrastructure. Let that sink in for a moment. In just two years, we’ve poured the equivalent of an entire country’s GDP into chips, data centers, and compute power. That’s not hype that’s commitment. And the wild part? This is still early innings. The foundation is being built right now. What gets built on top of it is going to change everything. 🔥🚀

Junaid Dar

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James Cameron says an opportunity with AI is to make more blockbuster films by cutting “in half” the cost of computer-generated graphics. For him, cost-saving isn’t about “laying off VFX staff” but about “doubling their speed to completion on a given shot.” With AI integrated into VFX workflows, Cameron told Meta’s CTO Andrew Bosworth in April 2025 that the “cadence [can be] faster and your throughput cycle is faster and artists get to move on and do other cool things.” Cameron joined the board of Stability AI to learn new generative AI tools and figure out how Hollywood can adopt the technology (he obviosuly previously pioneered VFX with The Abyss, Terminator, Avatar and Titanic).
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James Cameron says an opportunity with AI is to make more blockbuster films by cutting “in half” the cost of computer-generated graphics. For him, cost-saving isn’t about “laying off VFX staff” but about “doubling their speed to completion on a given shot.” With AI integrated into VFX workflows, Cameron told Meta’s CTO Andrew Bosworth in April 2025 that the “cadence [can be] faster and your throughput cycle is faster and artists get to move on and do other cool things.” Cameron joined the board of Stability AI to learn new generative AI tools and figure out how Hollywood can adopt the technology (he obviosuly previously pioneered VFX with The Abyss, Terminator, Avatar and Titanic).

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The race to own AI is on and Big Tech is winning. They want your data. Your value. Your control. FORK THAT. NEAR was built to flip the script: 🔓 Open-source agents 🤖 AI you control ⚡️ Infra that just works No middlemen. No friction. Just AI for everyone. Hold it NEAR. This is what it means to build the User-Owned Future:
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The race to own AI is on and Big Tech is winning. They want your data. Your value. Your control. FORK THAT. NEAR was built to flip the script: 🔓 Open-source agents 🤖 AI you control ⚡️ Infra that just works No middlemen. No friction. Just AI for everyone. Hold it NEAR. This is what it means to build the User-Owned Future:

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