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working towards action-conditioned video diffusion models for now this is just cs2 gameplay, and i parse the keypresses from the .dem game file next steps will be to work on a scalable data loader then coding the model

Arnie Ramesh

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31,148 görüntüleme • 6 ay önce •via X (Twitter)

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New Stage2 PoC payload loader commit fixes the USB Loader and can now goldhen (or any payload/HEN) from payload.bin from USB root or from /data, the USB Loader has now been set to the default loader, this is the end game Offsets file to port to 11.00

New Stage2 PoC payload loader commit fixes the USB Loader and can now goldhen (or any payload/HEN) from payload.bin from USB root or from /data, the USB Loader has now been set to the default loader, this is the end game Offsets file to port to 11.00

LM

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a16z

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TestingCatalog News 🗞

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After building out a game of my own and using Retro Diffusion for every piece of art, I'm really curious to see what other people can come up with. So I'm going to host a mini game jam. $500 of Retro Diffusion credits in prizes (1st = $300, 2nd & 3rd = $100 each). Build a game over the next week that uses RD for the artwork. Whatever genre, whatever type of gameplay. On Tuesday March 10th I'll gather up all the games (video of gameplay is fine) and decide which I think is the most interesting. Make sure to repost this and/or @ me so I know what you're making! I'd love to see progress along the way as well :) TLDR: Make a game, $500 in prizes, you've got until the 10th, gotta use Retro Diffusion for the art, it has to be a *new* game, not something you've been working on.

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Another video! This weeks goal is to get the outfit in game and working, after that ill push an update to the testing discord and we can iron out bugs that will appear from the quest. then its just spell checking, and I will throw this bad boy up for public use.

Another video! This weeks goal is to get the outfit in game and working, after that ill push an update to the testing discord and we can iron out bugs that will appear from the quest. then its just spell checking, and I will throw this bad boy up for public use.

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I give you, 7 minutes of gameplay of Sol's current progress on FOOTMEN FRENZY 5.4 AR, the warcraft 3 custom game. I did absolutely NOTHING besides prompt and give some suggestions on how to solve some problems. Sol on its own went online, downloaded a w3x file of the game, found an npm package to parse the data out of it, and attempted to recreate the game 1:1. It generated its own custom assets using blender and even animated it. Far from perfect, but extremely impressive what it could do all on its own working about 15 hours.

I give you, 7 minutes of gameplay of Sol's current progress on FOOTMEN FRENZY 5.4 AR, the warcraft 3 custom game. I did absolutely NOTHING besides prompt and give some suggestions on how to solve some problems. Sol on its own went online, downloaded a w3x file of the game, found an npm package to parse the data out of it, and attempted to recreate the game 1:1. It generated its own custom assets using blender and even animated it. Far from perfect, but extremely impressive what it could do all on its own working about 15 hours.

Barbog Da Big Finka

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

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

AK

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“ xAI team is currently working heavily on coding models. Right now, the main focus is training a specialized coding model that will be both fast and smart. I believe we’ll share it with you guys in a few weeks. That’s exciting. Second ,after coding, we all see that the main weakness of Grok 4 is its multimodal capabilities. In fact, it was so bad that Grok was effectively looking at the world while squinting through glass trying to see blurry features and make sense of them. The most immediate improvement we’re stepping on with the next generation pre trained model is huge gains in image understanding, video understanding, and audio. Right now, the model can hear and see the world just like any of you. And with all the tools and other agents it can talk to, we’re going to see a huge unlock for many different application layers once multimodal agents arrive.” — xAI Team

“ xAI team is currently working heavily on coding models. Right now, the main focus is training a specialized coding model that will be both fast and smart. I believe we’ll share it with you guys in a few weeks. That’s exciting. Second ,after coding, we all see that the main weakness of Grok 4 is its multimodal capabilities. In fact, it was so bad that Grok was effectively looking at the world while squinting through glass trying to see blurry features and make sense of them. The most immediate improvement we’re stepping on with the next generation pre trained model is huge gains in image understanding, video understanding, and audio. Right now, the model can hear and see the world just like any of you. And with all the tools and other agents it can talk to, we’re going to see a huge unlock for many different application layers once multimodal agents arrive.” — xAI Team

Apurv Kochara

88,142 görüntüleme • 6 ay önce

is live 🚨 > Psyop tactics X DR marketing X mathematics everything here and more to build our psywar Models This is just the beta, the scale in which our models will enhance will be legendary. And ONLY for marketing/VSL purposes. Operating without psywar intel will from now on will be negligent to your potential as a founder & marketer... the amount of data this is being trained on is unfathomable. literally every singe data point from my firm + a insane computational prediction engine + some more [redacted] stuff I can't disclose for privacy reasons. 3+ psywar manuals worth of data, hundreds of case studies for the top neuro data sources hundreds of case studies and mapping data for stimuli response patterns ungodly amount of DR data and testing there is also no shot anyone can make something close to the level of this engine will output data. the Information this has been built on has been lost data, BH data, sourced from the farthest corners of the internet and even FARTHER THAN THAT + Our own Psywar firm's data The edge this will give is immeasurable and I promise you I will make this shit work and connect with all the automations you need it to > this is just the beta, once I can turn this into a full model. everything will go exponential, so do not forget about this. Try out our tools now and see how you like it. We are Just getting started. You do NOT want to miss out.

is live 🚨 > Psyop tactics X DR marketing X mathematics everything here and more to build our psywar Models This is just the beta, the scale in which our models will enhance will be legendary. And ONLY for marketing/VSL purposes. Operating without psywar intel will from now on will be negligent to your potential as a founder & marketer... the amount of data this is being trained on is unfathomable. literally every singe data point from my firm + a insane computational prediction engine + some more [redacted] stuff I can't disclose for privacy reasons. 3+ psywar manuals worth of data, hundreds of case studies for the top neuro data sources hundreds of case studies and mapping data for stimuli response patterns ungodly amount of DR data and testing there is also no shot anyone can make something close to the level of this engine will output data. the Information this has been built on has been lost data, BH data, sourced from the farthest corners of the internet and even FARTHER THAN THAT + Our own Psywar firm's data The edge this will give is immeasurable and I promise you I will make this shit work and connect with all the automations you need it to > this is just the beta, once I can turn this into a full model. everything will go exponential, so do not forget about this. Try out our tools now and see how you like it. We are Just getting started. You do NOT want to miss out.

Krma

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𝐃𝐞𝐬𝐭𝐫𝐚 𝐎𝐂𝐀𝐈 (𝐎𝐧𝐞-𝐂𝐥𝐢𝐜𝐤 𝐀𝐈) 𝐌𝐨𝐝𝐞𝐥𝐬 - $DSYNC 𝐄𝐚𝐫𝐥𝐲 𝐀𝐜𝐜𝐞𝐬𝐬 𝐏𝐫𝐞𝐯𝐢𝐞𝐰 We are training and building a wide range of AI models for the Destra OCAI platform. Today, we will be showcasing an early preview of one of the initial models we are working on—a bullish/bearish prediction model designed to 𝐟𝐨𝐫𝐞𝐜𝐚𝐬𝐭 𝐭𝐡𝐞 𝐦𝐚𝐫𝐤𝐞𝐭 𝐝𝐢𝐫𝐞𝐜𝐭𝐢𝐨𝐧 𝐨𝐟 𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐭𝐨𝐤𝐞𝐧𝐬. This is just one of the initial models we are working on; as we progress, the models will become more complex and intricate. In this behind-the-scenes video, we demonstrate how the model predicts the future market trend of the BTC-USD pair with an 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐨𝐟 𝟔𝟓%. This is the barebones structure of the models we are training, which will soon be available to users with a single click through the Destra OCAI platform. The OCAI platform includes a wide array of Destra Exclusive AI models, specifically designed to simplify life for crypto audiences, that are envisioned and built from scratch at Destra Labs.

Destra Network

37,150 görüntüleme • 2 yıl önce

Not the flashiest demos, but what’s under the hood represents a foundational shift for general-purpose robotics. World models are the next-gen foundation of Physical AI, not the VLM backbones found in typical VLAs. DreamZero is a 14B-parameter World Action Model (WAM) by NVIDIA that treats robotics as a joint video-and-action prediction task. Unlike traditional Vision-Language-Action (VLA) models that map images directly to motor commands, DreamZero leverages a pretrained video diffusion backbone to predict future world states and actions simultaneously. - achieves 2× better zero-shot generalization to unseen tasks and environments compared to state-of-the-art VLAs. - learns effectively from heterogeneous, non-repetitive data (500 hours), breaking the need for thousands of repeated demonstrations. - adapts to new robot embodiments with just 30 minutes of play data. - enables 7Hz closed-loop control via system optimizations and "DreamZero-Flash," making high-capacity diffusion models viable for real-time use.

Not the flashiest demos, but what’s under the hood represents a foundational shift for general-purpose robotics. World models are the next-gen foundation of Physical AI, not the VLM backbones found in typical VLAs. DreamZero is a 14B-parameter World Action Model (WAM) by NVIDIA that treats robotics as a joint video-and-action prediction task. Unlike traditional Vision-Language-Action (VLA) models that map images directly to motor commands, DreamZero leverages a pretrained video diffusion backbone to predict future world states and actions simultaneously. - achieves 2× better zero-shot generalization to unseen tasks and environments compared to state-of-the-art VLAs. - learns effectively from heterogeneous, non-repetitive data (500 hours), breaking the need for thousands of repeated demonstrations. - adapts to new robot embodiments with just 30 minutes of play data. - enables 7Hz closed-loop control via system optimizations and "DreamZero-Flash," making high-capacity diffusion models viable for real-time use.

The Humanoid Hub

35,204 görüntüleme • 7 ay önce

I tested GLM 5.2 on three real builds. A horror house game. A 3D stealth game. A Remotion marketing video. The results are exactly what I expect from Chinese open source coding models right now. The horror house has broken game logic. Collect all three keys, complete the objective, and the front door still won't open. The stealth game shipped with lighting so dark you can't see your own character. The Remotion video is rough. GLM 5.2 is faster than 5.1 and 10x cheaper than the frontier. But Opus 4.8 and GPT 5.5 are still in a different league for serious coding work. Chinese models keep missing the attention to detail that matters. I MISS FABLE 5 ALREADY. Full review live now.

I tested GLM 5.2 on three real builds. A horror house game. A 3D stealth game. A Remotion marketing video. The results are exactly what I expect from Chinese open source coding models right now. The horror house has broken game logic. Collect all three keys, complete the objective, and the front door still won't open. The stealth game shipped with lighting so dark you can't see your own character. The Remotion video is rough. GLM 5.2 is faster than 5.1 and 10x cheaper than the frontier. But Opus 4.8 and GPT 5.5 are still in a different league for serious coding work. Chinese models keep missing the attention to detail that matters. I MISS FABLE 5 ALREADY. Full review live now.

BridgeMind

24,681 görüntüleme • 3 ay önce