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Lazy Bones - Uploading documentation videos now. So close. Here's a slower example than before. Omabuarts Studio - Game Assets models. @higgsas simulation methods. #b3d #blender

Blue Nile

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19,769 Aufrufe • vor 2 Jahren •via X (Twitter)

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AI-GENERATED SNES GAMES? “WE’RE PRETTY CLOSE,” SAYS ELON A full Super Nintendo game fits into just 4MB - the same size as a single Veo 3 AI-generated video. On paper, that’s nothing. Today’s AI models chew through gigabytes without breaking a digital sweat. So why aren’t we getting auto-generated SNES classics? The short answer? A frustrating pile-up of bottlenecks: combining logic, physics, sprite art, music, controls, level design, and actual fun into a compressed, playable ROM is harder than it looks. Elon Musk: “We’re pretty close.” So, in other words, game design may soon join the list of creative industries about to get retro-fitted by AI. Time for next-level simulation in 16-bit clothing. Source: bone, Allen T.

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We built a studio, we made a game, and I'm really proud of both. Before we close the doors at Midsummer Studios I'd like to share a glimpse of Burbank, the game we poured our hearts into. It's like "Life Sims + The Truman Show," but it's more than that. I believe people are storytellers, and I want them to share whatever stories and characters they can dream up. Burbank let's you do that. We have moments playing this game where characters come alive in a way we've never experienced. And for an old game developer like me that's special. What you're about to see is definitely pre-alpha. But this game was a dream of mine, our team made it come true, so watch and dream with us.

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Danny Limanseta

404,057 Aufrufe • vor 22 Tagen

Dario Amodei, the CEO of Anthropic, went on CNN and put a number on the thing most AI CEOs will not say out loud: Half of all entry-level white-collar jobs gone. Unemployment at 10 to 20%. Inside 1 to 5 years. "The most salient feature of the technology, and what is driving all of this, is how fast the technology is getting better." "A couple of years ago, you could say that AI models were maybe as good as a smart high school student. I would say that now they're as good as a smart college student, and sort of reaching past that." "I really worry, particularly at the entry level, that the AI models are very much at the center of what an entry level human worker would do." "What is striking to me about this AI boom is that it's bigger and it's broader and it's moving faster than anything has before." "People will adapt, but they may not adapt fast enough. And so there may be an adjustment period." The tell: this is not a critic outside the industry. It is the man building the models saying the quiet part on camera -- because he thinks the people whose jobs are on the line adapt slower than the models improve. High school to college in two years was the warm-up. The entry level is where it lands first.

Dario Amodei, the CEO of Anthropic, went on CNN and put a number on the thing most AI CEOs will not say out loud: Half of all entry-level white-collar jobs gone. Unemployment at 10 to 20%. Inside 1 to 5 years. "The most salient feature of the technology, and what is driving all of this, is how fast the technology is getting better." "A couple of years ago, you could say that AI models were maybe as good as a smart high school student. I would say that now they're as good as a smart college student, and sort of reaching past that." "I really worry, particularly at the entry level, that the AI models are very much at the center of what an entry level human worker would do." "What is striking to me about this AI boom is that it's bigger and it's broader and it's moving faster than anything has before." "People will adapt, but they may not adapt fast enough. And so there may be an adjustment period." The tell: this is not a critic outside the industry. It is the man building the models saying the quiet part on camera -- because he thinks the people whose jobs are on the line adapt slower than the models improve. High school to college in two years was the warm-up. The entry level is where it lands first.

Karl Mehta

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GUYS IM SO HYPED! This was all theoretical, made it just for fun; I did not know if a merge of two different fine tunes would be usable let alone an improvement! But the final seam-healed 18B merge is genuinely awesome! And a real improvement over either 9B alone (at least I think make your own conclusions and let me know) TLDR: I’ve merged 2 of Jackrongs excellent fine tunes into one really awesome 18B model, sitting nicely between the 9B and 27B using only 10GB of VRAM! It one shotted a bunch of web dashboards AND A SNAKE GAME THAT WORKS and looks nice too! All in the video below you can also check them in the repo! Uploading the healed model now, unfortunately T-Mobile internet is throttling me to 1MB/second upload so it will be 30 minutes or so before it’s done but it will be live at the repo in the comments! And I will post again when it’s live! In the meantime, you can open the html examples in the repo to check them out! I also have included a full documentation of the merge and healing process! WERE GONNA MAKE SO MUCH COOL STUFF WITH THIS METHOD!

Kyle Hessling

15,212 Aufrufe • vor 4 Monaten

watch and let this sit for a second anon, a 35B AI model, building a complete multi-file game by itself on hermes agent. no cloud or api. not one line of code written by a human. running on a DGX spark in 2026. because not long ago this was science fiction. this is Ornith, a new open agentic coding model, running near lossless on a single DGX Spark, a 128GB box that sits on your desk. i hand it one spec and step back. it reasons about the architecture, creates the project structure, writes every module one by one, and here's the part that got me, it checks its own work as it goes. makes a call, reads the result, decides if it's right, corrects itself before moving on. no steering from me. this is what people keep underestimating about local AI. they hear "local" and think weaker, slower, a toy. watch the clip. a 35B at near full precision, planning and self correcting on a single desktop. the open models got genuinely good at agentic work, not benchmark good, actually builds the thing good. sped 5x so you can watch the whole build start to finish. this is where local AI actually is now. not coming soon. here.

watch and let this sit for a second anon, a 35B AI model, building a complete multi-file game by itself on hermes agent. no cloud or api. not one line of code written by a human. running on a DGX spark in 2026. because not long ago this was science fiction. this is Ornith, a new open agentic coding model, running near lossless on a single DGX Spark, a 128GB box that sits on your desk. i hand it one spec and step back. it reasons about the architecture, creates the project structure, writes every module one by one, and here's the part that got me, it checks its own work as it goes. makes a call, reads the result, decides if it's right, corrects itself before moving on. no steering from me. this is what people keep underestimating about local AI. they hear "local" and think weaker, slower, a toy. watch the clip. a 35B at near full precision, planning and self correcting on a single desktop. the open models got genuinely good at agentic work, not benchmark good, actually builds the thing good. sped 5x so you can watch the whole build start to finish. this is where local AI actually is now. not coming soon. here.

Sudo su

30,299 Aufrufe • vor 2 Monaten

pinterest trends + chatgpt workflow is actually f*cking cracked for affiliates people are turning a single Pinterest product photo into $1 UGC videos with photorealistic AI models and stacking affiliate commissions on autopilot. no camera, no actors, no inventory. the products are already proven to sell before you touch them here's the system anyone can run today: - pull Pinterest shopping trends filtered by outbound clicks (real buyer intent, not scrollers. dresses is at 37% MoM growth right now) - read the demographic data: 86% women aged 25 to 34, so the AI model matches the exact buyer - feed the product photo into Vmake Agent and get a 20 second UGC video with script, b-roll, close-ups and 360 spins. a creator charges $150 to $500 for this. the tool does it for roughly $1 - post to Pinterest with the affiliate link in the product tag (never the destination box, that gets you banned) - compile 10 videos into one YouTube upload. one creator hit 19,000 views in a day on a single dress video - recreate proven viral thumbnails in 30 seconds by swapping the face and cleaning the layout 3 pins a day, same niche, different products. commissions while you sleep. reply "SYSTEM" + RT and i'll send you the full video so you can build this too.

pinterest trends + chatgpt workflow is actually f*cking cracked for affiliates people are turning a single Pinterest product photo into $1 UGC videos with photorealistic AI models and stacking affiliate commissions on autopilot. no camera, no actors, no inventory. the products are already proven to sell before you touch them here's the system anyone can run today: - pull Pinterest shopping trends filtered by outbound clicks (real buyer intent, not scrollers. dresses is at 37% MoM growth right now) - read the demographic data: 86% women aged 25 to 34, so the AI model matches the exact buyer - feed the product photo into Vmake Agent and get a 20 second UGC video with script, b-roll, close-ups and 360 spins. a creator charges $150 to $500 for this. the tool does it for roughly $1 - post to Pinterest with the affiliate link in the product tag (never the destination box, that gets you banned) - compile 10 videos into one YouTube upload. one creator hit 19,000 views in a day on a single dress video - recreate proven viral thumbnails in 30 seconds by swapping the face and cleaning the layout 3 pins a day, same niche, different products. commissions while you sleep. reply "SYSTEM" + RT and i'll send you the full video so you can build this too.

Sulfur

13,899 Aufrufe • vor 1 Monat

WHITNEY WEBB on the military now "realizing [its] initial dream for the internet" "LifeLog [has become] Facebook" "Total Information Awareness [has become] Palantir" "we're coming... close to the end game" "A lot of these 'Big Tech'... companies are just masquerading... [they] were created by military intelligence" "I think it's important to keep in mind too that even going beyond Palantir, the Internet itself was created by the military" "And... now we're coming kind of close to the end game in terms of full circle of them kind of realizing their initial dream for the Internet" This clip of Webb (Whitney Webb), author of One Nation Under Blackmail and contributing editor of unlimitedhangout(.)com, is taken from a discussion with Ryan Cristian, Hrvoje Morić, et al. posted to the Corbett Report YouTube channel on May 29, 2026. ---------------Partial transcription of clip---------------- "I mean, I remember stories 10 years ago about massive NSA data centers in like Utah and various states before people were even talking about data centers, about all the information they've been compiling on people. "And I think it's important to keep in mind too that even going beyond Palantir, the Internet itself was created by the military. And I think now we're coming kind of close to the end game in terms of full circle of them kind of realizing their initial dream for the Internet. "And then, you know, in the case of Palantir, you know, I argue, and a lot of my research points to various efforts within the DARPA military sphere aimed at the Internet being privatized. For example, LifeLog becoming Facebook, Total Information Awareness becoming Palantir. You know, these have become reality. "A lot of these big, 'Big Tech' Silicon Valley companies are just masquerading as we just started in our garage. And we're regular guys. Like, no, these are guys that were created by military intelligence and they privatized these programs and we have willingly given them our data and we continue to do so. "And so even if you have Palantir having access to privileged information, think about something in that same family of company, like Clearview AI, for example, which is also Peter Thiel–connected, that has amassed this huge database used for facial recognition by law enforcement and militaries. So, you know, to arrest and also to murder people. "And they just scraped most of that from stuff, pictures people freely and willingly posted. We voluntarily gave a lot of our data away, and they've been able to scrape a lot more of it than you, than you probably think."

WHITNEY WEBB on the military now "realizing [its] initial dream for the internet" "LifeLog [has become] Facebook" "Total Information Awareness [has become] Palantir" "we're coming... close to the end game" "A lot of these 'Big Tech'... companies are just masquerading... [they] were created by military intelligence" "I think it's important to keep in mind too that even going beyond Palantir, the Internet itself was created by the military" "And... now we're coming kind of close to the end game in terms of full circle of them kind of realizing their initial dream for the Internet" This clip of Webb (Whitney Webb), author of One Nation Under Blackmail and contributing editor of unlimitedhangout(.)com, is taken from a discussion with Ryan Cristian, Hrvoje Morić, et al. posted to the Corbett Report YouTube channel on May 29, 2026. ---------------Partial transcription of clip---------------- "I mean, I remember stories 10 years ago about massive NSA data centers in like Utah and various states before people were even talking about data centers, about all the information they've been compiling on people. "And I think it's important to keep in mind too that even going beyond Palantir, the Internet itself was created by the military. And I think now we're coming kind of close to the end game in terms of full circle of them kind of realizing their initial dream for the Internet. "And then, you know, in the case of Palantir, you know, I argue, and a lot of my research points to various efforts within the DARPA military sphere aimed at the Internet being privatized. For example, LifeLog becoming Facebook, Total Information Awareness becoming Palantir. You know, these have become reality. "A lot of these big, 'Big Tech' Silicon Valley companies are just masquerading as we just started in our garage. And we're regular guys. Like, no, these are guys that were created by military intelligence and they privatized these programs and we have willingly given them our data and we continue to do so. "And so even if you have Palantir having access to privileged information, think about something in that same family of company, like Clearview AI, for example, which is also Peter Thiel–connected, that has amassed this huge database used for facial recognition by law enforcement and militaries. So, you know, to arrest and also to murder people. "And they just scraped most of that from stuff, pictures people freely and willingly posted. We voluntarily gave a lot of our data away, and they've been able to scrape a lot more of it than you, than you probably think."

Sense Receptor

85,720 Aufrufe • vor 3 Monaten

Cerebras inference is very fast. So fast that it changes how we think about configuring our LLMs for voice agent use cases. Kimi K2.6 is a 1T parameter reasoning model that Cerebras serves at 650 - 1,000 tokens per second (end-to-end throughput), with time to first token metrics as low as 150ms (latency). These numbers are two to three times faster than other similarly capable models. The biggest lever we get from this kind of speed is that we can use the model in reasoning mode, and still have excellent "time to first non-thinking token." This solves a big pain point we have in 2026 for voice agent use cases. Almost all recent innovation in post-training has focused on making models good at reasoning ("test time compute"). This is great, but it makes the user-facing model latency much, much slower. Which is a problem for conversational voice agents. We can run Kimi K2.6 with reasoning turned on, and get responses faster than other models produce with reasoning disabled. On my 30-turn voice agent benchmark, Kimi K2.6 with reasoning enabled ties GPT 5.1 and Haiku 4.5 with reasoning disabled, and is still about 200ms seconds faster! On my primary task agent benchmark, Kimi K2.6 is now the #2 model. It ranks just behind Gemini 3.5 Flash in "high" reasoning mode, and tied with GLM 5, Sonnet 4.6, and GPT 5.4 with reasoning set to "low." But Kimi K2.6 completes each turn in the agent loop in under 500ms. The other four models are all at least 3x slower. (Models only qualify for this benchmark if they can complete task turns at a P50 <4s.) A couple of other things that this speed buys us, for production voice agents: - Tool calls happen fast enough that we don't have to work around tool call latency in our pipeline design. - We can prompt the model to output structured data at the beginning of a response, followed by plain text for voice generation. This opens up possibilities like asking the model to do complex classification/generation tasks that influence the rest of the pipeline. For example, the model could create a detailed style prompt for a steerable TTS model, for each individual conversation turn. And, of course, you can use Kimi K2.6 with reasoning turned off. Cerebras calls this "instant" mode. Here's a video of a Cerebras Kimi K2.6 voice agent with voice-to-voice response time, measured at the client, under 500ms. This is the true response latency as perceived by the user, including all network and audio codec overhead, transcription and turn detection, Kimi K2.6 token generation, and voice generation. 500ms is, effectively, instant. So the Cerebras naming for this mode is a propos. :-)

Cerebras inference is very fast. So fast that it changes how we think about configuring our LLMs for voice agent use cases. Kimi K2.6 is a 1T parameter reasoning model that Cerebras serves at 650 - 1,000 tokens per second (end-to-end throughput), with time to first token metrics as low as 150ms (latency). These numbers are two to three times faster than other similarly capable models. The biggest lever we get from this kind of speed is that we can use the model in reasoning mode, and still have excellent "time to first non-thinking token." This solves a big pain point we have in 2026 for voice agent use cases. Almost all recent innovation in post-training has focused on making models good at reasoning ("test time compute"). This is great, but it makes the user-facing model latency much, much slower. Which is a problem for conversational voice agents. We can run Kimi K2.6 with reasoning turned on, and get responses faster than other models produce with reasoning disabled. On my 30-turn voice agent benchmark, Kimi K2.6 with reasoning enabled ties GPT 5.1 and Haiku 4.5 with reasoning disabled, and is still about 200ms seconds faster! On my primary task agent benchmark, Kimi K2.6 is now the #2 model. It ranks just behind Gemini 3.5 Flash in "high" reasoning mode, and tied with GLM 5, Sonnet 4.6, and GPT 5.4 with reasoning set to "low." But Kimi K2.6 completes each turn in the agent loop in under 500ms. The other four models are all at least 3x slower. (Models only qualify for this benchmark if they can complete task turns at a P50 <4s.) A couple of other things that this speed buys us, for production voice agents: - Tool calls happen fast enough that we don't have to work around tool call latency in our pipeline design. - We can prompt the model to output structured data at the beginning of a response, followed by plain text for voice generation. This opens up possibilities like asking the model to do complex classification/generation tasks that influence the rest of the pipeline. For example, the model could create a detailed style prompt for a steerable TTS model, for each individual conversation turn. And, of course, you can use Kimi K2.6 with reasoning turned off. Cerebras calls this "instant" mode. Here's a video of a Cerebras Kimi K2.6 voice agent with voice-to-voice response time, measured at the client, under 500ms. This is the true response latency as perceived by the user, including all network and audio codec overhead, transcription and turn detection, Kimi K2.6 token generation, and voice generation. 500ms is, effectively, instant. So the Cerebras naming for this mode is a propos. :-)

kwindla

40,593 Aufrufe • vor 3 Monaten