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Burnice White - Bar Nights 🔥🔞 Serving more than just drinks tonight… More animations in my bio! thanks for watching! ❤️ Models: DaB Environment: Lambo🔞COMMS OPEN (0/3) SFX PACK: OpenNSFW 🟣 Available Now #zzzero #BurniceWhite

Vast3D

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71,878 views • 4 months ago •via X (Twitter)

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My first time ever doing Sfx edit project. Might need some practice, but I hoped you enjoyed it😊. Art by мoonιԍ VA by 💞 MeruraVa🎙️ (COMMS OPEN! 💞🔞) SFX pack by OpenNSFW 🟣 Available Now

My first time ever doing Sfx edit project. Might need some practice, but I hoped you enjoyed it😊. Art by мoonιԍ VA by 💞 MeruraVa🎙️ (COMMS OPEN! 💞🔞) SFX pack by OpenNSFW 🟣 Available Now

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163,219 views • 8 months ago

Never leave two foxes alone for more than a minute. They always get into trouble. Thank you to Estelle Ellis/SFA KrystalFox-Mindful Voice Actor and Kyle Rhys Marsh for lending their voices in my latest animation. And thank you to 🔞Domi3D🔞COMMS OPEN for these stellar clothes and updates on the warfaremachine models.

Never leave two foxes alone for more than a minute. They always get into trouble. Thank you to Estelle Ellis/SFA KrystalFox-Mindful Voice Actor and Kyle Rhys Marsh for lending their voices in my latest animation. And thank you to 🔞Domi3D🔞COMMS OPEN for these stellar clothes and updates on the warfaremachine models.

Sani

355,622 views • 21 days ago

Guinaifen Possessed During Stream~ 👻 After wrapping up Stream Guinaifen is possessed by some suspicious malware on her phone... SFX OpenNSFW 🟣 Available Now Moan Pack MOO🩷COW This animation is shorter than my other ones due to me testing some new plugins I got for Koikatsu and I wanted to time myself on how long it takes start to finish to make an animation, so I could more accurately price them. More info soon on my Twitter. 4K60, Nude, POV, Phone Camera, No Overlay Version, and Scene Data on Patreon! Discord #TSF #憑依 #乗っ取り

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Guinaifen Possessed During Stream~ 👻 After wrapping up Stream Guinaifen is possessed by some suspicious malware on her phone... SFX OpenNSFW 🟣 Available Now Moan Pack MOO🩷COW This animation is shorter than my other ones due to me testing some new plugins I got for Koikatsu and I wanted to time myself on how long it takes start to finish to make an animation, so I could more accurately price them. More info soon on my Twitter. 4K60, Nude, POV, Phone Camera, No Overlay Version, and Scene Data on Patreon! Discord #TSF #憑依 #乗っ取り

ves 🔞

45,176 views • 1 year ago

“Don’t Mess With Training Miyabi” Here’s the full short. What a ride. I had a lot of fun making this one, and I hope you enjoy watching it as much as I enjoyed creating it. I’m also looking for voice actors for my future fan animations. If you’re interested, feel free to reach out for details. Thanks for watching. Writer / Director / Animator / Rigger / 2D FX/ Compositing : ChaiLoo Yanlong Miyabi rig : The_Crabnuts Belle/Wise rig: Garf Other rigs：ChaiLoo Yanlong Environments： ZZZ Random Play video store : Lambo🔞COMMS CLOSSED (3/3) Hacker's pad (ZZZ inpsired)：MattoGatto_ Music: 铁锚沉吟, Sān-Z， HOYO-MIX — Hyper Commission Zenless Z 同行于新艾利都，Sān-Z， HOYO-MIX — Hyper Commission Zenless Z Ups and Downs，NAOKI, Arc System Works Disclaimer: All characters used in this short are from Zenless Zone Zero. The related models and assets belong to miHoYo / Hoyoverse. This is a non-commercial personal fan animation made for learning and sharing purposes only. #animation #3danimations #blender #b3d #zenlesszonezero #MIYABI #fightanimation

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ChaiLoo-YanLong

159,174 views • 7 days ago

🎬 Global Launch: Dreamina Seedance 2.5 is now live! Our most cinematic video model yet has arrived, and Dreamina AI is your official platform. Step into a new era of precision control and long-take storytelling: · Native 30s videos in a single generation · A new interactive editing experience: reshape any part of the frame · Long video mode: up to 3 minutes, with consistency that holds throughout · Dreamina AI plugins for Maya and Blender, built for film-grade production pipelines · Up to 50 multimodal references, now with support for 3D white models and green-screen footage — bringing AI generation into real production workflows · More true-to-life lighting and shadows, richer motion detail, and precise timestamp control down to 1 second Dreamina Seedance 2.5 is more than a model. It's an upgrade to what you can create. Now open to Dreamina AI subscribers. 🕐 Available now across Southeast Asia, the Middle East, Africa, Europe, and South America. More countries and regions coming soon. ✨ Watch Aura Design in action with the new model ⏳ Limited time: save up to 32% on credits per generation 🎁 Repost and comment – first 300 users get 1,000 credits in 12 hours #DreaminaSeedance25 #DreaminaAI

🎬 Global Launch: Dreamina Seedance 2.5 is now live! Our most cinematic video model yet has arrived, and Dreamina AI is your official platform. Step into a new era of precision control and long-take storytelling: · Native 30s videos in a single generation · A new interactive editing experience: reshape any part of the frame · Long video mode: up to 3 minutes, with consistency that holds throughout · Dreamina AI plugins for Maya and Blender, built for film-grade production pipelines · Up to 50 multimodal references, now with support for 3D white models and green-screen footage — bringing AI generation into real production workflows · More true-to-life lighting and shadows, richer motion detail, and precise timestamp control down to 1 second Dreamina Seedance 2.5 is more than a model. It's an upgrade to what you can create. Now open to Dreamina AI subscribers. 🕐 Available now across Southeast Asia, the Middle East, Africa, Europe, and South America. More countries and regions coming soon. ✨ Watch Aura Design in action with the new model ⏳ Limited time: save up to 32% on credits per generation 🎁 Repost and comment – first 300 users get 1,000 credits in 12 hours #DreaminaSeedance25 #DreaminaAI

Dreamina AI

627,178 views • 1 month ago

The final lecture of my course is an intro to character training! This is a topic that I've been quietly very invested in for ~18 months, as it: * Has potential for high real world impact * Clearly used extensively at frontier labs * Almost no empirical literature exists * More accessible on academic compute This lecture covers what character training is, reviews model specs, constitutions, the differences, the motivations in real world events, some example research papers I like, and open questions in how it relates to post-training/model use generally. Hopefully this brings more people into the field (and reach out if you have questions). It is one of the more research-y chapters in my book, but one that I felt needed the reference. There is still so little, educational content on the topic online. 0:00 Intro 6:22 Part 1: Fundamentals — character, constitutions, and model specs 19:21 Part 2: Character training in practice 23:23 Part 3: Character elicitation without gradient steps 28:03 Part 4: Open questions (and the end of the course) 32:27 The course, complete Thanks for watching. No need to like and subscribe now that the course is done, you definitely wouldn't! h/t to Sharan for leading the technical work I got to do in the space, and Zafir Stojanovski for investing a lot of attention at this book chapter.

The final lecture of my course is an intro to character training! This is a topic that I've been quietly very invested in for ~18 months, as it: * Has potential for high real world impact * Clearly used extensively at frontier labs * Almost no empirical literature exists * More accessible on academic compute This lecture covers what character training is, reviews model specs, constitutions, the differences, the motivations in real world events, some example research papers I like, and open questions in how it relates to post-training/model use generally. Hopefully this brings more people into the field (and reach out if you have questions). It is one of the more research-y chapters in my book, but one that I felt needed the reference. There is still so little, educational content on the topic online. 0:00 Intro 6:22 Part 1: Fundamentals — character, constitutions, and model specs 19:21 Part 2: Character training in practice 23:23 Part 3: Character elicitation without gradient steps 28:03 Part 4: Open questions (and the end of the course) 32:27 The course, complete Thanks for watching. No need to like and subscribe now that the course is done, you definitely wouldn't! h/t to Sharan for leading the technical work I got to do in the space, and Zafir Stojanovski for investing a lot of attention at this book chapter.

Nathan Lambert

57,151 views • 1 month ago

We put a camera here over a year ago, unsure if it would be a good spot. Always lots of trial and error with cameras when trying to find good spots where wolves like to travel. But, it was a beautiful setting in some conifers along a small creek and we figured wolves would likely would travel through this more open terrain along the creek. Turns out, this was a pretty good spot. And it wasn’t just wolves that used this scenic stretch of forest as you can see. In total, we have captured more than 1,500 videos of wildlife on this camera in the past year or so (8.3 hours of footage). Most videos are of snowshoe hares and deer but wolves (the Half-Moon Pack) cruise through the area periodically as do most of the other forest predators. This 3 minute video is just a highlight reel of some of the most interesting footage. Big thanks to the Minnesota Environment and Natural Resource Trust Fund for their continued support of our work. That support allows us to share this footage and our findings with millions of people each year!

We put a camera here over a year ago, unsure if it would be a good spot. Always lots of trial and error with cameras when trying to find good spots where wolves like to travel. But, it was a beautiful setting in some conifers along a small creek and we figured wolves would likely would travel through this more open terrain along the creek. Turns out, this was a pretty good spot. And it wasn’t just wolves that used this scenic stretch of forest as you can see. In total, we have captured more than 1,500 videos of wildlife on this camera in the past year or so (8.3 hours of footage). Most videos are of snowshoe hares and deer but wolves (the Half-Moon Pack) cruise through the area periodically as do most of the other forest predators. This 3 minute video is just a highlight reel of some of the most interesting footage. Big thanks to the Minnesota Environment and Natural Resource Trust Fund for their continued support of our work. That support allows us to share this footage and our findings with millions of people each year!

Voyageurs Wolf Project

238,363 views • 9 months ago

i've just turned 30 in my current timezone. therefore, i am giving a gift to all clonex holders. no blur this time. today is the "official" release of the alpha version of storymode DLC. while this version is stripped from all AI integrations (for multiple reasons which i wont get into now), it integrate a ready-to-use by all assembly line. you can now just connect, select your clone, have it assembled it directly and download it as a GLB. community is more than welcome to improve the interface of storymode to make the viewer better, maybe even add a VRM exporter? the API to get the traits + files is always available for all with a documentation enjoy (and happy birthday to me) Github : Link : special thanks to beb.tools Samsy Ash CreaBilly.ETH for the help 🥰 PS : the 3d models aren't (at all) optimized so they might be slow to load. sorry, i have no 3d talents.

i've just turned 30 in my current timezone. therefore, i am giving a gift to all clonex holders. no blur this time. today is the "official" release of the alpha version of storymode DLC. while this version is stripped from all AI integrations (for multiple reasons which i wont get into now), it integrate a ready-to-use by all assembly line. you can now just connect, select your clone, have it assembled it directly and download it as a GLB. community is more than welcome to improve the interface of storymode to make the viewer better, maybe even add a VRM exporter? the API to get the traits + files is always available for all with a documentation enjoy (and happy birthday to me) Github : Link : special thanks to beb.tools Samsy Ash CreaBilly.ETH for the help 🥰 PS : the 3d models aren't (at all) optimized so they might be slow to load. sorry, i have no 3d talents.

Samuel Cardillo

23,127 views • 1 year ago

White Gold Corp $WGO.V $WHGOF has announced a robust maiden Preliminary Economic Assessment (PEA) on its flagship White Gold Project in Yukon, Canada. Couldn't join the live results call? The full presentation and Q&A are now available to watch on demand. PEA highlights: After-tax NPV of C$1.9 billion, 38% IRR, and a 1.7 year payback at US$3,600/oz gold. Strong leverage to gold: NPV rises to C$3 billion with a 52% IRR at US$4,500/oz. A 9.4 year conventional open pit operation averaging 188,000 oz of gold per year, and 223,000 oz per year over the first five years, at life of mine AISC of US$1,480/oz. And this is just phase one — the mine plan draws on less than two-thirds of the current resource which remains open for expansion, on a 55,000-hectare property with 25+ additional targets with 3 drill rigs currently active on the property with a goal of continuing to increase total resources Stay tuned for more as we progress with the largest exploration program in the White Gold’s history! Watch the replay: Read the full release:

White Gold Corp $WGO.V $WHGOF has announced a robust maiden Preliminary Economic Assessment (PEA) on its flagship White Gold Project in Yukon, Canada. Couldn't join the live results call? The full presentation and Q&A are now available to watch on demand. PEA highlights: After-tax NPV of C$1.9 billion, 38% IRR, and a 1.7 year payback at US$3,600/oz gold. Strong leverage to gold: NPV rises to C$3 billion with a 52% IRR at US$4,500/oz. A 9.4 year conventional open pit operation averaging 188,000 oz of gold per year, and 223,000 oz per year over the first five years, at life of mine AISC of US$1,480/oz. And this is just phase one — the mine plan draws on less than two-thirds of the current resource which remains open for expansion, on a 55,000-hectare property with 25+ additional targets with 3 drill rigs currently active on the property with a goal of continuing to increase total resources Stay tuned for more as we progress with the largest exploration program in the White Gold’s history! Watch the replay: Read the full release:

White Gold Corp.

28,936,753 views • 1 month ago

Watch this. That’s me back at the casino — 250 lbs and overweight — chugging these 4 mimosas straight. Right after that I got cut off from that specific bar. I then puked outside in front of all the women coming in for bingo. Then I found other places in the casino to keep drinking anyway. Numb and completely out of control. How did I get there? The night before I’d stayed up all night (maybe 2 hours of sleep) on a guys golf trip. I went straight downstairs to gamble and drink. By 8am I’d already slammed 3 screwdrivers at the casino. By 9am tee time, I ordered 4 Jaeger bombs for myself and said “and then whatever my friends want”. The bartender laughed… But I was dead serious. Then more drinks and shots out on the course. That was just a “normal” day. Today? I’m 107 days sober. Under 215 lbs (almost back to my high school weight), clearer, lighter, and finally in control of my life. The physical change now is obvious from the video… but the real wins are invisible: no more waking up in shame, no blackouts, no regret — just real peace instead of that constant fog. If you’re stuck in that version right now, I promise you don’t have to stay there. Keep showing up. The other side is possible. The first step is asking for help. Reach out if you need to talk my DMs are open. One day at a time. We got this. 🙏 #Sober #WeDoRecover #OneDayAtATime

Watch this. That’s me back at the casino — 250 lbs and overweight — chugging these 4 mimosas straight. Right after that I got cut off from that specific bar. I then puked outside in front of all the women coming in for bingo. Then I found other places in the casino to keep drinking anyway. Numb and completely out of control. How did I get there? The night before I’d stayed up all night (maybe 2 hours of sleep) on a guys golf trip. I went straight downstairs to gamble and drink. By 8am I’d already slammed 3 screwdrivers at the casino. By 9am tee time, I ordered 4 Jaeger bombs for myself and said “and then whatever my friends want”. The bartender laughed… But I was dead serious. Then more drinks and shots out on the course. That was just a “normal” day. Today? I’m 107 days sober. Under 215 lbs (almost back to my high school weight), clearer, lighter, and finally in control of my life. The physical change now is obvious from the video… but the real wins are invisible: no more waking up in shame, no blackouts, no regret — just real peace instead of that constant fog. If you’re stuck in that version right now, I promise you don’t have to stay there. Keep showing up. The other side is possible. The first step is asking for help. Reach out if you need to talk my DMs are open. One day at a time. We got this. 🙏 #Sober #WeDoRecover #OneDayAtATime

Brady

535,401 views • 3 months ago

one thing that has saved my projects more time than I can count is evals boy was I excited when florian, quite literally an expert in benchmarks, agreed to hop into a ~2h interview to do a walkthrough of what the eval landscape looks like in 2026 (and also answer my personal business questions on the subject) given that now running frontier model through benchmarks is a vector for hacking other systems in order to avoid doing work (looking at you sol), I think it's more important than ever to educate folks on the evals situation. had a lot of fun throughout this session and I hope that you learn a thing or two! enjoy! 🌹 table of content: 0:00:00: are AI Benchmark broken? 0:05:45: Florian Brand background 0:09:00: what motivates florian to work on evaluation? 0:13:33: what is the mirrorcode benchmark about? 0:18:20: cheating in agent benchmark is insaneeeee 0:24:08: LLM benchmarks in era of agents 0:26:30: what’s up with the pelican man 0:28:27: evals are about capabilities 0:31:46: components of running evals 0:35:30: the volume of things to audit is huge!!! 0:40:20: expert answers are wrong hahahaha 0:46:00: api providers aren’t the same 0:48:00: benchmark narrow capabilities (synthetically) 0:50:56: link between eval and environment 0:53:45: small validated benchmark or massive bench? 0:56:11: what is your flow to review a benchmark? 0:58:30: tracking work capabilities with evaluation 1:00:20: slide deck in industry is all vibecoded 1:03:30: harness impact in the evaluation 1:07:39: hardware/sandboxes impact evaluation too! 1:11:00: “is it going to get worse?” 1:12:40: all components influence the final score 1:13:50: training models on different harnesses? 1:17:20: is the model just the weights or it’s all of it? 1:19:30: how to craft benchmark that prevent to cheating and undereliciting models in 2026 1:23:19: ways agents cheat and steal 1:26:00: correct elicitation of capabilities is important 1:36:00: building evaluation on prime intellect 1:45:10: how do you design interactivity benchmarks? 1:48:40: do you think evals are well set to reflect real world performance? 1:52:50: what will the benchmarking landscape will look like in 1 year

Yacine Mahdid

12,923 views • 1 month ago

Quick chat with dylan ツ (Dylan Bristot, GTM @ $NBIS). Also on YouTube (link in first comment) for those who prefer to watch/listen there. Timestamps 00:00 – Dylan's role at Nebius and Nebius Token Factory 01:48 – Dylan's investing philosophy and portfolio approach 05:07 – How working in AI infrastructure influences his investing 08:29 – Training vs. inference and why inference demand could explode 13:22 – Enterprise AI adoption: from POCs to production 18:02 – Open-source vs. closed/frontier models 24:44 – The economics of open vs. closed AI models 29:27 – Where the next AI infrastructure bottlenecks could emerge 31:22 – Dylan's AI Bottlenecks project and approach to stock selection 34:06 – Closing thoughts Key Insights (AI Summary, so you don't have to copy paste and prompt for exactly that ;D) “I seem to like areas where the demand really looks kind of secular, but the supply is genuinely hard to create.” → Implication: The most attractive AI trades may sit in physical bottlenecks where supply cannot quickly respond to demand. “The bottleneck is who has the pricing power and kind of what might get commoditized and where the concentrate might move next.” → Implication: Value capture across the AI stack will keep shifting as individual layers become scarce or commoditized. “Training creates the intelligence and then the inference actually monetizes and distributes.” → Implication: Training and inference are complementary, rather than one ultimately replacing the other. “One user action can become dozens or hundreds of model calls, tools calls, and like verification steps, retries.” → Implication: Agentic AI can drive token consumption far faster than user growth alone would suggest. “The best infra for making any model and the best infra for serving a billion interactions are not necessarily the same.” → Implication: Training and inference could increasingly require different hardware and infrastructure architectures. “The Frontier Labs might be incentivized to run more and more of the inference of these models for internal research instead of providing it to external people.” → Implication: The most capable models and their compute could increasingly be used internally to accelerate frontier research rather than monetized externally. “Enterprise AI adoption is actually much further along than a lot of people kind of think. But probably less mature than the headlines suggest.” → Implication: Enterprise demand is real, but deployment maturity still has significant room to improve. “The POC problem might be solved for a lot of companies, but the production problem isn’t yet.” → Implication: The enterprise bottleneck is shifting from proving AI works to deploying it reliably, securely and economically at scale. “They feel like it’s time for them to actually not only integrate AI, but build some sort of moat out of the AI.” → Implication: Enterprises increasingly want proprietary AI systems built around their own data rather than simply consuming generic models. “The more autonomous the software becomes, the more infra discipline you need underneath it.” → Implication: Agents increase the importance of inference cost, reliability and infrastructure optimization. “Maybe I have fifteen different versions of very different LLMs, fine tuned on fifteen different kinds of tasks that I’m operating across my business, instead of having a one model fits all.” → Implication: Enterprise AI could evolve toward many specialized models rather than one frontier model handling every workload. “I don’t necessarily think it’s open versus closed. That might be the wrong framing.” → Implication: Open and closed models can coexist because they optimize for different customer needs. “Historically the problem was that that control came with a massive operational tax.” → Implication: Better inference infrastructure can make open models materially more competitive by removing the complexity traditionally associated with running them. “I don’t think open needs to beat the best closed model on every single benchmark. It just basically needs to be good enough for the workload of the given customer while offering a much better combination of control, cost, and deployment flexibility.” → Implication: For production AI, workload-specific economics may matter more than having the absolute smartest model. “Maybe actually the bulk of tokens generated in the future might come from open models.” → Implication: Frontier intelligence could remain dominated by closed labs even while open models capture most production inference volume. “I could really imagine frontier intelligence being really concentrated while most of the production inference becomes super fragmented.” → Implication: AI could consolidate at the intelligence layer while fragmenting heavily at the inference layer across models, GPUs, providers and regions. “I don’t think that necessarily means the margins of open source will be much worse than the ones of closed source.” → Implication: Optimization can potentially make open-model inference highly profitable despite lower pricing. “I think now we’re probably in the middle of phase two... everything feeding the accelerator.” → Implication: The AI trade is broadening beyond GPUs toward networking, packaging, data centers, electrical equipment and power. “It’s no longer about the megawatts, about energized megawatts.” → Implication: Available power on paper matters less than how quickly that power can actually be delivered to operating AI infrastructure. “It’s increasingly about utilisation and conversion now and like how efficiently you convert expensive infra into actual useful AI work.” → Implication: Infrastructure efficiency and utilization become increasingly important as the absolute amount of deployed AI infrastructure grows. “The market tends to really notice demand before it notices what demand breaks.” → Implication: Second-order bottlenecks may offer some of the most interesting opportunities in the next phase of the AI buildout. “The interesting question now is which part of the mine breaks next?” → Implication: Finding the next constraint in the AI supply chain may matter more than simply identifying continued AI demand.

Quick chat with dylan ツ (Dylan Bristot, GTM @ $NBIS). Also on YouTube (link in first comment) for those who prefer to watch/listen there. Timestamps 00:00 – Dylan's role at Nebius and Nebius Token Factory 01:48 – Dylan's investing philosophy and portfolio approach 05:07 – How working in AI infrastructure influences his investing 08:29 – Training vs. inference and why inference demand could explode 13:22 – Enterprise AI adoption: from POCs to production 18:02 – Open-source vs. closed/frontier models 24:44 – The economics of open vs. closed AI models 29:27 – Where the next AI infrastructure bottlenecks could emerge 31:22 – Dylan's AI Bottlenecks project and approach to stock selection 34:06 – Closing thoughts Key Insights (AI Summary, so you don't have to copy paste and prompt for exactly that ;D) “I seem to like areas where the demand really looks kind of secular, but the supply is genuinely hard to create.” → Implication: The most attractive AI trades may sit in physical bottlenecks where supply cannot quickly respond to demand. “The bottleneck is who has the pricing power and kind of what might get commoditized and where the concentrate might move next.” → Implication: Value capture across the AI stack will keep shifting as individual layers become scarce or commoditized. “Training creates the intelligence and then the inference actually monetizes and distributes.” → Implication: Training and inference are complementary, rather than one ultimately replacing the other. “One user action can become dozens or hundreds of model calls, tools calls, and like verification steps, retries.” → Implication: Agentic AI can drive token consumption far faster than user growth alone would suggest. “The best infra for making any model and the best infra for serving a billion interactions are not necessarily the same.” → Implication: Training and inference could increasingly require different hardware and infrastructure architectures. “The Frontier Labs might be incentivized to run more and more of the inference of these models for internal research instead of providing it to external people.” → Implication: The most capable models and their compute could increasingly be used internally to accelerate frontier research rather than monetized externally. “Enterprise AI adoption is actually much further along than a lot of people kind of think. But probably less mature than the headlines suggest.” → Implication: Enterprise demand is real, but deployment maturity still has significant room to improve. “The POC problem might be solved for a lot of companies, but the production problem isn’t yet.” → Implication: The enterprise bottleneck is shifting from proving AI works to deploying it reliably, securely and economically at scale. “They feel like it’s time for them to actually not only integrate AI, but build some sort of moat out of the AI.” → Implication: Enterprises increasingly want proprietary AI systems built around their own data rather than simply consuming generic models. “The more autonomous the software becomes, the more infra discipline you need underneath it.” → Implication: Agents increase the importance of inference cost, reliability and infrastructure optimization. “Maybe I have fifteen different versions of very different LLMs, fine tuned on fifteen different kinds of tasks that I’m operating across my business, instead of having a one model fits all.” → Implication: Enterprise AI could evolve toward many specialized models rather than one frontier model handling every workload. “I don’t necessarily think it’s open versus closed. That might be the wrong framing.” → Implication: Open and closed models can coexist because they optimize for different customer needs. “Historically the problem was that that control came with a massive operational tax.” → Implication: Better inference infrastructure can make open models materially more competitive by removing the complexity traditionally associated with running them. “I don’t think open needs to beat the best closed model on every single benchmark. It just basically needs to be good enough for the workload of the given customer while offering a much better combination of control, cost, and deployment flexibility.” → Implication: For production AI, workload-specific economics may matter more than having the absolute smartest model. “Maybe actually the bulk of tokens generated in the future might come from open models.” → Implication: Frontier intelligence could remain dominated by closed labs even while open models capture most production inference volume. “I could really imagine frontier intelligence being really concentrated while most of the production inference becomes super fragmented.” → Implication: AI could consolidate at the intelligence layer while fragmenting heavily at the inference layer across models, GPUs, providers and regions. “I don’t think that necessarily means the margins of open source will be much worse than the ones of closed source.” → Implication: Optimization can potentially make open-model inference highly profitable despite lower pricing. “I think now we’re probably in the middle of phase two... everything feeding the accelerator.” → Implication: The AI trade is broadening beyond GPUs toward networking, packaging, data centers, electrical equipment and power. “It’s no longer about the megawatts, about energized megawatts.” → Implication: Available power on paper matters less than how quickly that power can actually be delivered to operating AI infrastructure. “It’s increasingly about utilisation and conversion now and like how efficiently you convert expensive infra into actual useful AI work.” → Implication: Infrastructure efficiency and utilization become increasingly important as the absolute amount of deployed AI infrastructure grows. “The market tends to really notice demand before it notices what demand breaks.” → Implication: Second-order bottlenecks may offer some of the most interesting opportunities in the next phase of the AI buildout. “The interesting question now is which part of the mine breaks next?” → Implication: Finding the next constraint in the AI supply chain may matter more than simply identifying continued AI demand.

Daniel Koss

49,970 views • 11 days ago

Monetizing your own AI app just got really easy. Last week I used YouAI to create an AI-powered bio generator. It took me less than an hour and I didn't need any programming skills. And now it just made it's first sale. All you need is a good idea and some prompting skills. Here's how you can create your own: Step 1: Setup → Go to → Sign up, then click on "new AI" (you can also remix an existing AI if you're lazy) Step 2: Create prompt → Within the "preamble" you can create the main prompt for this AI → In my example I described the assistant's goal (bio generator) and defined the format of the bios → I also added some bio examples to train the AI Step 3: Configure inputs → Now you define what the user inputs when using the AI app → In my case I defined 3 user inputs: - What topic do you tweet about? - What is your background? What have you achieved? - How do you help your followers? The answers are then used as variables to create the bios. Step 4: Publish → Select the underlying model (GPT or Claude) → Keep the app free or turn on monetization → Then publish! Your app will now be available in the YouAI library for others to use YouAI is also doing a live event tomorrow where they'll go into more detail and teach you how to build your own YouAI app: Thank you to YouAI for sponsoring this post! I'm a big fan of their product.

Monetizing your own AI app just got really easy. Last week I used YouAI to create an AI-powered bio generator. It took me less than an hour and I didn't need any programming skills. And now it just made it's first sale. All you need is a good idea and some prompting skills. Here's how you can create your own: Step 1: Setup → Go to → Sign up, then click on "new AI" (you can also remix an existing AI if you're lazy) Step 2: Create prompt → Within the "preamble" you can create the main prompt for this AI → In my example I described the assistant's goal (bio generator) and defined the format of the bios → I also added some bio examples to train the AI Step 3: Configure inputs → Now you define what the user inputs when using the AI app → In my case I defined 3 user inputs: - What topic do you tweet about? - What is your background? What have you achieved? - How do you help your followers? The answers are then used as variables to create the bios. Step 4: Publish → Select the underlying model (GPT or Claude) → Keep the app free or turn on monetization → Then publish! Your app will now be available in the YouAI library for others to use YouAI is also doing a live event tomorrow where they'll go into more detail and teach you how to build your own YouAI app: Thank you to YouAI for sponsoring this post! I'm a big fan of their product.

Moritz Kremb

30,424 views • 3 years ago

losers spend money on club tables and take home zero girls buying bottles at liv miami trying to impress models who make $500/night when you could be recruiting them for onlyfans with 0% apr tables and take 90% of their revenue... $300k/month from girls you met at clubs here's the onlyfans mafia system destroying simps: every weekend desperate mfs drop $50k cash on tables models pretend to care for 3 hours everyone goes home alone money wasted meanwhile smart operators run different game: THE CREDIT CARD TABLE HACK: get $150k in business funding at 0% apr book owner's tables at tier-1 miami clubs never spend cash - everything on cards earn 300k points while recruiting cards to get approved for today: - chase ink preferred: $30k typical - amex business gold: $50k typical - capital one spark: $40k typical - wells fargo business: $30k typical $150k at 0% in 30 days if you're not stupid THE RECRUITMENT PSYCHOLOGY: table attracts 10-15 models per night they think you're a whale you're not you're a businessman the pitch that converts 40%: "i manage content creators everything's handled - photographer, editor, marketing creators keep 10% pure profit most make $3-5k/month passive here's my portfolio..." show them your roster: girl #1: was bartending at bodega now: $50k/month on OF her take: $5000 your take: $45,000 girl #2: was bottle service at e11even now: $31k/month her take: $3,100 your take: $27,900 they all say yes THE MIAMI CLUB RANKING: recruit here in exact order: 1. liv (fontainebleau) - international models 2. e11even (24/7) - party girls who need money 3. story (south beach) - college girls 4. space (downtown) - underground scene 5. basement (edition) - high-end escorts transitioning wednesday-thursday better than weekends less competition from actual rich guys girls more desperate for attention THE ONLYFANS ASSEMBLY LINE: week 1: professional shoot ($500) week 2: launch with 50 posts ready week 3: tiktok spam campaign week 4: instagram reels push month 2: optimize pricing month 3: $15-30k/month steady your only job: - recruit - manage photographers - collect 90% THE BUSINESS MODEL MATH: monthly costs: - tables: $40k (on 0% cards) - photographer: $8k - editors: $5k (philippines) - shoot apartment: $4k total overhead: $57k 20 girls at $15k average: $300k your 90% cut: $270k monthly profit: $213k started with credit cards ending with empire THE SCALE FORMULA: month 1-3: recruit 20 girls month 4-6: optimize content month 7-12: $300k/month automated year 2: expand to NYC/LA year 3: sell for $20m to PE fund 3-year exit from credit cards THE DARK PSYCHOLOGY: these girls could do this alone but they won't they need leadership need someone to blame when dad finds out need the infrastructure you're not exploiting you're organizing they were already selling bottle service now they're CEOs making 10x more THE EXACT RECRUITMENT SCRIPT: "hey i know this is random you're exactly the type my agency represents we manage exclusive content creators everything's handled professionally creators keep 10% pure profit most hit $3-5k/month within 90 days here's my card, let's talk monday" success rate: 40% they all call THE CREDIT TO CASH CONVERSION: $150k in business cards approved liquidate through: - plastiq for "rent": 2.85% fee - paypal "consulting": 2.9% fee - square "services": 2.75% fee $150k credit becomes $145k cash fund entire operation at 0% pay minimums from profits THE COMPETITION ELIMINATION: other "managers" take 50% and provide nothing you take 90% but provide everything: - professional content - daily posting - fan management - marketing strategy girls make more with you at 10% than alone at 100% that's why they stay THE EXIT REALITY: building "talent management agency" 30 active models = $500k/month revenue $6m annual agencies sell for 3-5x exit value: $18-30 million from credit cards to 8 figures in 36 months you're either buying bottles like a sucker or building an empire with bank money choose your side Get $100K at 0% APR guaranteed Link in bio → Scale With Credit

losers spend money on club tables and take home zero girls buying bottles at liv miami trying to impress models who make $500/night when you could be recruiting them for onlyfans with 0% apr tables and take 90% of their revenue... $300k/month from girls you met at clubs here's the onlyfans mafia system destroying simps: every weekend desperate mfs drop $50k cash on tables models pretend to care for 3 hours everyone goes home alone money wasted meanwhile smart operators run different game: THE CREDIT CARD TABLE HACK: get $150k in business funding at 0% apr book owner's tables at tier-1 miami clubs never spend cash - everything on cards earn 300k points while recruiting cards to get approved for today: - chase ink preferred: $30k typical - amex business gold: $50k typical - capital one spark: $40k typical - wells fargo business: $30k typical $150k at 0% in 30 days if you're not stupid THE RECRUITMENT PSYCHOLOGY: table attracts 10-15 models per night they think you're a whale you're not you're a businessman the pitch that converts 40%: "i manage content creators everything's handled - photographer, editor, marketing creators keep 10% pure profit most make $3-5k/month passive here's my portfolio..." show them your roster: girl #1: was bartending at bodega now: $50k/month on OF her take: $5000 your take: $45,000 girl #2: was bottle service at e11even now: $31k/month her take: $3,100 your take: $27,900 they all say yes THE MIAMI CLUB RANKING: recruit here in exact order: 1. liv (fontainebleau) - international models 2. e11even (24/7) - party girls who need money 3. story (south beach) - college girls 4. space (downtown) - underground scene 5. basement (edition) - high-end escorts transitioning wednesday-thursday better than weekends less competition from actual rich guys girls more desperate for attention THE ONLYFANS ASSEMBLY LINE: week 1: professional shoot ($500) week 2: launch with 50 posts ready week 3: tiktok spam campaign week 4: instagram reels push month 2: optimize pricing month 3: $15-30k/month steady your only job: - recruit - manage photographers - collect 90% THE BUSINESS MODEL MATH: monthly costs: - tables: $40k (on 0% cards) - photographer: $8k - editors: $5k (philippines) - shoot apartment: $4k total overhead: $57k 20 girls at $15k average: $300k your 90% cut: $270k monthly profit: $213k started with credit cards ending with empire THE SCALE FORMULA: month 1-3: recruit 20 girls month 4-6: optimize content month 7-12: $300k/month automated year 2: expand to NYC/LA year 3: sell for $20m to PE fund 3-year exit from credit cards THE DARK PSYCHOLOGY: these girls could do this alone but they won't they need leadership need someone to blame when dad finds out need the infrastructure you're not exploiting you're organizing they were already selling bottle service now they're CEOs making 10x more THE EXACT RECRUITMENT SCRIPT: "hey i know this is random you're exactly the type my agency represents we manage exclusive content creators everything's handled professionally creators keep 10% pure profit most hit $3-5k/month within 90 days here's my card, let's talk monday" success rate: 40% they all call THE CREDIT TO CASH CONVERSION: $150k in business cards approved liquidate through: - plastiq for "rent": 2.85% fee - paypal "consulting": 2.9% fee - square "services": 2.75% fee $150k credit becomes $145k cash fund entire operation at 0% pay minimums from profits THE COMPETITION ELIMINATION: other "managers" take 50% and provide nothing you take 90% but provide everything: - professional content - daily posting - fan management - marketing strategy girls make more with you at 10% than alone at 100% that's why they stay THE EXIT REALITY: building "talent management agency" 30 active models = $500k/month revenue $6m annual agencies sell for 3-5x exit value: $18-30 million from credit cards to 8 figures in 36 months you're either buying bottles like a sucker or building an empire with bank money choose your side Get $100K at 0% APR guaranteed Link in bio → Scale With Credit

hunter

15,066 views • 9 months ago

The 1970s 4th of July… when your parents would hand you a whole box of sparklers, Black Cats, and bottle rockets with nothing more than a casual “Don’t blow your hand off, now!” before heading back to the grill. Sparklers for the toddlers (who immediately tried to write their names in the dark), potato salad that had been sitting out since 10 a.m., and the occasional bottle rocket that may or may not have been aimed anywhere near your cousin. But the part that really gets me? The whole neighborhood dragging lawn chairs into the street, passing around cold drinks, and just sitting there together watching the sky. No phones. No schedules. Just laughter, the smell of gunpowder and charcoal, and that quiet feeling that you belonged to something bigger than yourself. Those nights gave us more than we knew at the time — a little independence, a little trust from the grown-ups, and the kind of simple, shared joy that sticks with you for life. We learned how to be careful without being scared, how to look out for the little kids, and how good it feels when a whole street stops and looks up together. As we head into another Independence Day, I’m grateful for those memories… and for the freedom that still lets us make new ones with the people we love. If you grew up in that era, what’s one 4th of July memory that still makes you smile? Drop it below — I know some of y’all have stories. 👇 Wishing everyone a safe, joyful, memory-making holiday. ❤️🇺🇸

The 1970s 4th of July… when your parents would hand you a whole box of sparklers, Black Cats, and bottle rockets with nothing more than a casual “Don’t blow your hand off, now!” before heading back to the grill. Sparklers for the toddlers (who immediately tried to write their names in the dark), potato salad that had been sitting out since 10 a.m., and the occasional bottle rocket that may or may not have been aimed anywhere near your cousin. But the part that really gets me? The whole neighborhood dragging lawn chairs into the street, passing around cold drinks, and just sitting there together watching the sky. No phones. No schedules. Just laughter, the smell of gunpowder and charcoal, and that quiet feeling that you belonged to something bigger than yourself. Those nights gave us more than we knew at the time — a little independence, a little trust from the grown-ups, and the kind of simple, shared joy that sticks with you for life. We learned how to be careful without being scared, how to look out for the little kids, and how good it feels when a whole street stops and looks up together. As we head into another Independence Day, I’m grateful for those memories… and for the freedom that still lets us make new ones with the people we love. If you grew up in that era, what’s one 4th of July memory that still makes you smile? Drop it below — I know some of y’all have stories. 👇 Wishing everyone a safe, joyful, memory-making holiday. ❤️🇺🇸

NancyH

100,543 views • 2 months ago

This week we celebrated OpenClaw🦞 Conf in Vienna. 🔥 One thought stuck with me. We're entering a world where custom software is normal. Everyone has their own insta-coder, their own personal appstore. Soon software will exist on the fly, only when you need it. Just for one moment. Then it's gone. Personalized, disposable, one-shot. 🤯 Code can now be autogenerated. So if code is "no longer worth anything" (hyperbole speaking) and you can auto-build any idea into existence (slop gods be kind): What is the future of software development – especially for founders? I made a video on this. Spoilers: we had the working title: "Software Development is f**d" This video is maybe more questions than answers – especially for SaaS founders. But I also cover POVs of mine: Eg why GitHub is obsolete, why open source might actually be dead soon, the Mexican standoff between designers/engineers/PMs, why prompt injection could be bigger than SQL injection, and what I'd actually do as a SaaS founder right now. But there is also a paradox in all of this: Now might be the most exciting time to build software. Ever. 🦾 Thanks as always for watching. Likes, shares, subscribes, goat slaugthering, and any other algo magic you can throw at this: deeply appreciated. Trying to build this YouTube channel up and your support genuinely helps. Link to the channel in the reply ❤️

This week we celebrated OpenClaw🦞 Conf in Vienna. 🔥 One thought stuck with me. We're entering a world where custom software is normal. Everyone has their own insta-coder, their own personal appstore. Soon software will exist on the fly, only when you need it. Just for one moment. Then it's gone. Personalized, disposable, one-shot. 🤯 Code can now be autogenerated. So if code is "no longer worth anything" (hyperbole speaking) and you can auto-build any idea into existence (slop gods be kind): What is the future of software development – especially for founders? I made a video on this. Spoilers: we had the working title: "Software Development is f**d" This video is maybe more questions than answers – especially for SaaS founders. But I also cover POVs of mine: Eg why GitHub is obsolete, why open source might actually be dead soon, the Mexican standoff between designers/engineers/PMs, why prompt injection could be bigger than SQL injection, and what I'd actually do as a SaaS founder right now. But there is also a paradox in all of this: Now might be the most exciting time to build software. Ever. 🦾 Thanks as always for watching. Likes, shares, subscribes, goat slaugthering, and any other algo magic you can throw at this: deeply appreciated. Trying to build this YouTube channel up and your support genuinely helps. Link to the channel in the reply ❤️

Andreas Klinger 🦾

67,630 views • 7 months ago

Introducing The Godfather... (LinkedIn Edition) It sounds ridiculous but posting on LinkedIn changed my life. When I first started, I was a bit lost. My first startup had just failed and I didn't know what to do next. In the past 18 months, it has unlocked more opportunities than anything I've done in my career. I've hit ~400k followers, bootstrapped a SaaS biz to >$500k ARR and made a ton in brand partnerships. It has been a FORCE multiplier for me and I want nothing more than to help others to get the same opportunities. That's why I built Saywhat. Here's how it works: 1/ Sign up and when you login you'll immediately see your first post (based on your target audience) 2/ Our AI writing assistant helps shape your ideas into posts that people actually read 3/ Get feedback from both our AI and our 600+ strong community (drop your posts in our Roast My Post channel) 4/ Join our monthly live sessions where I create posts live and share what's working right now Plus you get access to our full library of post frameworks, carousel/infographic templates, and a community of people who get it. (Because building a content-led business is brutal...) The results so far are pretty good: - Median user sees 130% more impressions in 90 days - Average user gets an 800% increase - Some users have gone from 0 to 100k+ followers Despite what the haters say, it is still early on LinkedIn. It's a 3-year-old social network living inside a 23-year-old company. The opportunity is massive: - 1 BN+ professionals in one place - Less than 1% post consistently - Even fewer do it well If you've read this far, I have a thank you gift for you 👇 I created a LinkedIn starter pack that includes: - 90 mins of video content - How to define your brand positioning - Content frameworks that actually work - The exact system I used to grow to 395k+ followers and close $500k in ARR Comment "Saywhat" and retweet and I'll send you a link to the free pack. P.S. A massive thank you to Henry Hayes for being a phenomenal collaborator on this video. Your facial expressions made it 10x better! 🙌 P.P.S. if you're a company with >$10m ARR looking for more hands on help to win on LinkedIn, send me a DM

Will McTighe

15,945 views • 1 year ago

Matthew Gallagher Built a $401M Company in Year One with 2 People. And the tool behind it? Claude Code. This year he's on track for $1.8B. Sam Altman predicted this. It's happening now. The problem? It costs money. API credits stack up. Monthly bills keep growing. Every prompt eats your budget. Every project drains your wallet faster. Until now. Two methods. 99% cheaper. One is completely free. Forever. $0. Not a trial. This video breaks down both step by step. ↓ Let me put this in perspective. $100-$500. That's monthly. That's what you spend. That's $6,000/year on API credits. Just to use a tool you haven't shipped anything with. The $401M guy? Spending $0. Same capability. Shipping weekly. Different cost structure. Different results. Different life. I'm about to hand you his cost structure for free. ↓ Open source vs closed source. Pay attention. Closed source: Claude. GPT-4. Pay per token. Meter always running. Open source: Qwen. Llama. Mistral. Free to download. Free to run. Free forever. No meter. No tokens. No bill. Here's what nobody tells you: 80% of coding tasks? Open source handles them. More than handles them. Writes clean code. Debugs errors. Generates boilerplate. Handles routine work perfectly. You're paying premium prices for tasks that don't need premium intelligence. That's hiring a brain surgeon to put on a bandaid. Smart play: Free models for the 80%. Paid credits for the 20%. That's what the $401M guy does. That's what this video teaches you. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 1: Ollama. Local. Free. Forever. Download it. Pull a model. Point Claude Code at it. Done. No internet needed. No API keys required. No monthly subscription. No token counting ever. No bill. Today. Tomorrow. Ever. Your data never leaves your computer. Complete privacy. Complete freedom. Claude Code thinks it's talking to the cloud. It's talking to your laptop. For $0. The video walks through every step: Every config file. Every variable. Every command. Every click. If you can follow a recipe, you can do this. People who set this up 3 months ago? Saved $300-$1,500 since then. Workflow didn't change one bit. ↓ Hardware you need: 16GB RAM: 7B models run smooth. 32GB RAM: 32B models run comfortable. 64GB + GPU: biggest models available. No GPU? Still works. Just slower. Few extra seconds. That's it. Your $1,500 laptop is sitting there running Chrome and Spotify. Put it to work saving you $200/month instead. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 2: Open Router. Free Cloud. No Hardware. Weak machine? Don't want local setup? This method is for you. Free AI models in the cloud. No download. No hardware. Configure Claude Code to route through Open Router. The config: Base URL: Open Router API. API key: free Open Router key. Default Sonnet: free. Default Opus: free. Default Haiku: free. Small fast model: free. Subagent model: free. Free. Free. Free. Free. Free across the board. Same interface. Same commands. Same workflow. Zero cost. Copy the config from the video. Paste it. Save $200/month. Starting today. Right now. ↓ When to use which: Ollama (local): Best for privacy. Best for offline work. Best for unlimited usage. Best if you have decent hardware. Open Router (cloud): Best for weak machines. Best for instant setup. Best for trying different models. Best if you don't want to manage anything. Both methods: Best for 80% of your daily work. Still use paid Claude for: Complex architecture. Multi-file refactoring. Deep reasoning tasks. The 20% that actually needs it. $20/month instead of $200/month. Same output. 90% less cost. ↓ The math that should make you angry. You (current): $200-$500/month. $2,400-$6,000/year. $7,200-$18,000 over 3 years. You (after this video): $20-$50/month. $240-$600/year. $720-$1,800 over 3 years. Savings over 3 years: $6,480-$16,200. That's a used car. That's seed money. That's 6 months of rent. All from one 25-minute video. All from 15 minutes of configuration. Highest ROI 25 minutes you'll spend this year. ↓ The limitations. I won't lie to you. Open source is not Opus. Not as smart on complex reasoning. Not as good at long-context tasks. Makes more mistakes on nuanced problems. But they are: Free. Capable. Getting better monthly. Good enough for 80% of daily work. Smart cost management isn't being cheap. It's being strategic. Expensive tool when it matters. Free tool when it doesn't. ↓ The one-person billion-dollar company is coming. $401M in year one proved it's possible. The building blocks: AI that codes: Claude Code. Way to run it free: this video. Distribution: the internet. Customers: everyone. Only missing ingredient? Someone who builds. Not reads about building. Not saves posts about building. Not bookmarks videos about building. Builds. Tools are free. Knowledge is free. Opportunity is screaming. You're still "thinking about it." ↓ Your action plan: Tonight: Watch the video. Tomorrow morning: Set up Ollama or Open Router. Tomorrow afternoon: Build something. Anything. This week: Build a second thing. Faster. This month: Charge someone for it. One video. One setup. One weekend. $0 cost. Unlimited potential. Or keep paying $200/month for something you could get free. Keep consuming instead of building. Keep planning instead of shipping. Matthew Gallagher didn't plan a $401M company. He built it. Full video attached. Every method. Every config. Every tradeoff. 25 minutes. Your move. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses.

Matthew Gallagher Built a $401M Company in Year One with 2 People. And the tool behind it? Claude Code. This year he's on track for $1.8B. Sam Altman predicted this. It's happening now. The problem? It costs money. API credits stack up. Monthly bills keep growing. Every prompt eats your budget. Every project drains your wallet faster. Until now. Two methods. 99% cheaper. One is completely free. Forever. $0. Not a trial. This video breaks down both step by step. ↓ Let me put this in perspective. $100-$500. That's monthly. That's what you spend. That's $6,000/year on API credits. Just to use a tool you haven't shipped anything with. The $401M guy? Spending $0. Same capability. Shipping weekly. Different cost structure. Different results. Different life. I'm about to hand you his cost structure for free. ↓ Open source vs closed source. Pay attention. Closed source: Claude. GPT-4. Pay per token. Meter always running. Open source: Qwen. Llama. Mistral. Free to download. Free to run. Free forever. No meter. No tokens. No bill. Here's what nobody tells you: 80% of coding tasks? Open source handles them. More than handles them. Writes clean code. Debugs errors. Generates boilerplate. Handles routine work perfectly. You're paying premium prices for tasks that don't need premium intelligence. That's hiring a brain surgeon to put on a bandaid. Smart play: Free models for the 80%. Paid credits for the 20%. That's what the $401M guy does. That's what this video teaches you. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 1: Ollama. Local. Free. Forever. Download it. Pull a model. Point Claude Code at it. Done. No internet needed. No API keys required. No monthly subscription. No token counting ever. No bill. Today. Tomorrow. Ever. Your data never leaves your computer. Complete privacy. Complete freedom. Claude Code thinks it's talking to the cloud. It's talking to your laptop. For $0. The video walks through every step: Every config file. Every variable. Every command. Every click. If you can follow a recipe, you can do this. People who set this up 3 months ago? Saved $300-$1,500 since then. Workflow didn't change one bit. ↓ Hardware you need: 16GB RAM: 7B models run smooth. 32GB RAM: 32B models run comfortable. 64GB + GPU: biggest models available. No GPU? Still works. Just slower. Few extra seconds. That's it. Your $1,500 laptop is sitting there running Chrome and Spotify. Put it to work saving you $200/month instead. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 2: Open Router. Free Cloud. No Hardware. Weak machine? Don't want local setup? This method is for you. Free AI models in the cloud. No download. No hardware. Configure Claude Code to route through Open Router. The config: Base URL: Open Router API. API key: free Open Router key. Default Sonnet: free. Default Opus: free. Default Haiku: free. Small fast model: free. Subagent model: free. Free. Free. Free. Free. Free across the board. Same interface. Same commands. Same workflow. Zero cost. Copy the config from the video. Paste it. Save $200/month. Starting today. Right now. ↓ When to use which: Ollama (local): Best for privacy. Best for offline work. Best for unlimited usage. Best if you have decent hardware. Open Router (cloud): Best for weak machines. Best for instant setup. Best for trying different models. Best if you don't want to manage anything. Both methods: Best for 80% of your daily work. Still use paid Claude for: Complex architecture. Multi-file refactoring. Deep reasoning tasks. The 20% that actually needs it. $20/month instead of $200/month. Same output. 90% less cost. ↓ The math that should make you angry. You (current): $200-$500/month. $2,400-$6,000/year. $7,200-$18,000 over 3 years. You (after this video): $20-$50/month. $240-$600/year. $720-$1,800 over 3 years. Savings over 3 years: $6,480-$16,200. That's a used car. That's seed money. That's 6 months of rent. All from one 25-minute video. All from 15 minutes of configuration. Highest ROI 25 minutes you'll spend this year. ↓ The limitations. I won't lie to you. Open source is not Opus. Not as smart on complex reasoning. Not as good at long-context tasks. Makes more mistakes on nuanced problems. But they are: Free. Capable. Getting better monthly. Good enough for 80% of daily work. Smart cost management isn't being cheap. It's being strategic. Expensive tool when it matters. Free tool when it doesn't. ↓ The one-person billion-dollar company is coming. $401M in year one proved it's possible. The building blocks: AI that codes: Claude Code. Way to run it free: this video. Distribution: the internet. Customers: everyone. Only missing ingredient? Someone who builds. Not reads about building. Not saves posts about building. Not bookmarks videos about building. Builds. Tools are free. Knowledge is free. Opportunity is screaming. You're still "thinking about it." ↓ Your action plan: Tonight: Watch the video. Tomorrow morning: Set up Ollama or Open Router. Tomorrow afternoon: Build something. Anything. This week: Build a second thing. Faster. This month: Charge someone for it. One video. One setup. One weekend. $0 cost. Unlimited potential. Or keep paying $200/month for something you could get free. Keep consuming instead of building. Keep planning instead of shipping. Matthew Gallagher didn't plan a $401M company. He built it. Full video attached. Every method. Every config. Every tradeoff. 25 minutes. Your move. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses.

Himanshu Kumar

13,677 views • 5 months ago