Sharing Insights on AI, Online business & Productivity | Helping you leverage AI to grow & monetize | DM for collaboration | [email protected]๐Ÿ“ค

Joined January 2022
This is crazyyy!
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A robot completes an action. But does it know the task is actually finished? SenseNova-RoboRSI documents where embodied AI still fails: misjudging success, forgetting completed subtasks, undoing earlier progress, and struggling with irreversible mistakes. The research is open here: arxiv.org/abs/2610.22471
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We release the most comprehensive ๐—ฒ๐—บ๐—ฝ๐—ถ๐—ฟ๐—ถ๐—ฐ๐—ฎ๐—น ๐˜€๐˜๐˜‚๐—ฑ๐˜† ๐—ผ๐—ณ ๐—š๐—ฃ๐—ง-๐Ÿฒ-๐—”๐˜€๐˜๐—ฟ๐—ฎโ€™๐˜€ ๐—ฒ๐—บ๐—ฏ๐—ผ๐—ฑ๐—ถ๐—ฒ๐—ฑ ๐—ถ๐—ป๐˜๐—ฒ๐—น๐—น๐—ถ๐—ด๐—ฒ๐—ป๐—ฐ๐—ฒ across 12 simulation benchmarks and 8 real-robot platforms, together with ๐—ฆ๐—ฒ๐—ป๐˜€๐—ฒ๐—ก๐—ผ๐˜ƒ๐—ฎ-๐—ฅ๐—ผ๐—ฏ๐—ผ๐—ฅ๐—ฆ๐—œ: an open-source harness framework and a 27B model that runs at ~1/50 the cost of GPT-6-Astra. Here's what we found: ๐Ÿ”น ๐—ง๐—ต๐—ฒ ๐—ฐ๐—ฎ๐—ฝ๐—ฎ๐—ฏ๐—ถ๐—น๐—ถ๐˜๐˜† ๐—ฐ๐—ฒ๐—ถ๐—น๐—ถ๐—ป๐—ด ๐—ถ๐˜€ ๐—ฎ๐—ฐ๐˜๐˜‚๐—ฎ๐—น๐—น๐˜† ๐—บ๐˜‚๐—ฐ๐—ต ๐—ต๐—ถ๐—ด๐—ต๐—ฒ๐—ฟ. Under a unified protocol (ICL + up to 10ร— the interaction budget), GPT-6-Astra achieves clear SOTA across all 12 benchmarks: RoboDojo: 27.6% at 1ร— โ†’ 81.9% at 10ร—, DexJoCo: 87.3% vs. 40.3% prior SOTA, while the LIBERO benchmarks are pushed to saturation. ๐Ÿ”น ๐—ง๐—ผ๐—ผ ๐—บ๐˜‚๐—ฐ๐—ต ๐—ฏ๐˜‚๐—ฐ๐—ธ ๐—ณ๐—ผ๐—ฟ ๐˜๐—ต๐—ฒ ๐—ฏ๐—ฎ๐—ป๐—ด. Even a simple task like placing pens in a holder costs ~$55 in inference. ๐Ÿ”น ๐—ง๐—ต๐—ฒ ๐˜„๐—ผ๐—ฟ๐—น๐—ฑ ๐—ฑ๐—ผ๐—ฒ๐˜€๐—ปโ€™๐˜ ๐˜„๐—ฎ๐—ถ๐˜. Time matters as real robots overheat, and an egg burned while the model was still thinking. Our takeaway: GPT-6-Astra has largely crossed the threshold of ๐—ฒ๐—บ๐—ฏ๐—ผ๐—ฑ๐—ถ๐—ฒ๐—ฑ ๐—ฐ๐—ผ๐—บ๐—ฝ๐—ฒ๐˜๐—ฒ๐—ป๐—ฐ๐—ฒ, but ๐—ฝ๐—ฟ๐—ฎ๐—ฐ๐˜๐—ถ๐—ฐ๐—ฎ๐—น ๐—ฒ๐—บ๐—ฏ๐—ผ๐—ฑ๐—ถ๐—ฒ๐—ฑ ๐—ถ๐—ป๐˜๐—ฒ๐—น๐—น๐—ถ๐—ด๐—ฒ๐—ป๐—ฐ๐—ฒ is not here yet. ๐ŸŒ Project page: sensenova.ai/has-gpt-6-astraโ€ฆ ๐Ÿ“„ Report: sensenova.ai/gpt-6/assets/doโ€ฆ ๐Ÿ’ป Code & model: github.com/OpenSenseNova/Senโ€ฆ
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12 simulation benchmarks. 8 real robot platforms. One open-source embodied AI harness. Dockerized environments. Unified HTTP API. Interchangeable models and policies. Plus a 27B preview model. Technical report: arxiv.org/abs/2610.22471
๐Ÿค Paid partnership
We release the most comprehensive ๐—ฒ๐—บ๐—ฝ๐—ถ๐—ฟ๐—ถ๐—ฐ๐—ฎ๐—น ๐˜€๐˜๐˜‚๐—ฑ๐˜† ๐—ผ๐—ณ ๐—š๐—ฃ๐—ง-๐Ÿฒ-๐—”๐˜€๐˜๐—ฟ๐—ฎโ€™๐˜€ ๐—ฒ๐—บ๐—ฏ๐—ผ๐—ฑ๐—ถ๐—ฒ๐—ฑ ๐—ถ๐—ป๐˜๐—ฒ๐—น๐—น๐—ถ๐—ด๐—ฒ๐—ป๐—ฐ๐—ฒ across 12 simulation benchmarks and 8 real-robot platforms, together with ๐—ฆ๐—ฒ๐—ป๐˜€๐—ฒ๐—ก๐—ผ๐˜ƒ๐—ฎ-๐—ฅ๐—ผ๐—ฏ๐—ผ๐—ฅ๐—ฆ๐—œ: an open-source harness framework and a 27B model that runs at ~1/50 the cost of GPT-6-Astra. Here's what we found: ๐Ÿ”น ๐—ง๐—ต๐—ฒ ๐—ฐ๐—ฎ๐—ฝ๐—ฎ๐—ฏ๐—ถ๐—น๐—ถ๐˜๐˜† ๐—ฐ๐—ฒ๐—ถ๐—น๐—ถ๐—ป๐—ด ๐—ถ๐˜€ ๐—ฎ๐—ฐ๐˜๐˜‚๐—ฎ๐—น๐—น๐˜† ๐—บ๐˜‚๐—ฐ๐—ต ๐—ต๐—ถ๐—ด๐—ต๐—ฒ๐—ฟ. Under a unified protocol (ICL + up to 10ร— the interaction budget), GPT-6-Astra achieves clear SOTA across all 12 benchmarks: RoboDojo: 27.6% at 1ร— โ†’ 81.9% at 10ร—, DexJoCo: 87.3% vs. 40.3% prior SOTA, while the LIBERO benchmarks are pushed to saturation. ๐Ÿ”น ๐—ง๐—ผ๐—ผ ๐—บ๐˜‚๐—ฐ๐—ต ๐—ฏ๐˜‚๐—ฐ๐—ธ ๐—ณ๐—ผ๐—ฟ ๐˜๐—ต๐—ฒ ๐—ฏ๐—ฎ๐—ป๐—ด. Even a simple task like placing pens in a holder costs ~$55 in inference. ๐Ÿ”น ๐—ง๐—ต๐—ฒ ๐˜„๐—ผ๐—ฟ๐—น๐—ฑ ๐—ฑ๐—ผ๐—ฒ๐˜€๐—ปโ€™๐˜ ๐˜„๐—ฎ๐—ถ๐˜. Time matters as real robots overheat, and an egg burned while the model was still thinking. Our takeaway: GPT-6-Astra has largely crossed the threshold of ๐—ฒ๐—บ๐—ฏ๐—ผ๐—ฑ๐—ถ๐—ฒ๐—ฑ ๐—ฐ๐—ผ๐—บ๐—ฝ๐—ฒ๐˜๐—ฒ๐—ป๐—ฐ๐—ฒ, but ๐—ฝ๐—ฟ๐—ฎ๐—ฐ๐˜๐—ถ๐—ฐ๐—ฎ๐—น ๐—ฒ๐—บ๐—ฏ๐—ผ๐—ฑ๐—ถ๐—ฒ๐—ฑ ๐—ถ๐—ป๐˜๐—ฒ๐—น๐—น๐—ถ๐—ด๐—ฒ๐—ป๐—ฐ๐—ฒ is not here yet. ๐ŸŒ Project page: sensenova.ai/has-gpt-6-astraโ€ฆ ๐Ÿ“„ Report: sensenova.ai/gpt-6/assets/doโ€ฆ ๐Ÿ’ป Code & model: github.com/OpenSenseNova/Senโ€ฆ
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D-Coder retweeted
Okay, this is where the world-model argument starts to make sense. The deep learning pioneers from Maluuba are back solving one of the biggest problems in the industry. They unveiled SKY-001, the first engineering world model. SKY-001 isnโ€™t just generating something that looks 3D. It simulates different designs, breaks the object down into parts and sub-assemblies, works through the hierarchy, then rebuilds it into something that can actually open in CAD. Plus, the results compared to frontier LLMs are hard to ignore, including claims of being 150x cheaper and faster. That feels much closer to how real engineering work happens. With this release, theyโ€™re also building toward autonomous manufacturing and reimagining what manufacturing firms could look like. If this approach keeps scaling beyond CAD and BIM, the interesting question wonโ€™t be โ€œwhich LLM is better?โ€ Itโ€™ll be whether some of these long-horizon problems should be solved with a completely different kind of model.
Introducing SKY-001, the first Engineering World Model. On CAD and BIM tasks, it beats GPT-6 Astra and every frontier model we tested: -> 51-125x faster - > 150-210x cheaper -> Higher IOU than all frontier models SKY-001 lets you dream a physical object before you build it. 18 months of work. Built by the team out of Maluuba's deep learning lab (acq. by @MSRResearch). The first โ€˜AI General Engineerโ€™ will be the backbone for Autonomous Manufacturing.
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D-Coder retweeted
CapCutโ€™s Black Friday promotion is live: 60% off Pro, 40% off Ultra, Oct 20โ€“30, CapCut Desktop only. A moody product shot starts the idea; a Halloween ad needs a sequence. Bring AI-generated storyboards into CapCut PCโ€™s multi-track timeline and develop visuals, text, and audio together. Review each sceneโ€™s role and refine the flow as the seasonal concept takes shape in motion. #capcutpc #capcutmarketing #capcutsale #capcutblackfriday
๐Ÿค Paid partnership ยท ๐Ÿค– Made with AI
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Your monthly subscription might cost more than your next coffee, but this Dreamina deal is just $0.29 for a full month of Basic. โ˜• Iโ€™m one of the 1,000 creators officially selected by Dreamina to share this promotion, and I wanted to pass it along. Remember, the special price is only available through my dedicated creator link. The next post shows you how to get there. dreamina.capcut.com/pricing/โ€ฆ #DreaminaPartner #DreaminaOnly29Cents
๐Ÿค Paid partnership ยท ๐Ÿค– Made with AI
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Can stored experience make a coding agent more reliable? In MemT 1.0-Nanoโ€™s SWE-bench Verified evaluation, average success increased from 66.07% without experience to 69.73% with native memory in the harness-triggered setting. Tasks solved in all three runs increased from 54.00% to 62.80%. The result that stands out: more consistent execution when earlier programming experience is available. huggingface.co/collections/Mโ€ฆ
Can large language models retain information from past interactions and reuse it in the next task? Introducing MemT-1.0: Towards Memory-Native Evolution. This time, we've built memory directly into the model architecture. Our MemT-1.0-Nano and MemT-1.0-Flash make persistent memory part of model computation, carrying information across interactions.
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wait, you can change how it divides the page and keep the rest of the parser? yep. that's the OXR detail i like: layout analysis is a separate, replaceable model, recognition and reading order stay with OXR, and the 1.3B model still wins olmOCR-Bench, OmniDocBench and DocParsingBench. speed test, 100 pages, lone RTX PRO 6000: 101s vs PaddleOCR-VL-1.6's 144.7s. also the quickest open-source parser they tested, btw. useful when your application needs a different definition of where one region ends. github.com/SoMarkAI/OXR
๐Ÿค Paid partnership
Beyond OCR. Meet OXR. Only 1.3B. Open source. #1 on olmOCR-Bench, OmniDocBench & DocParsingBench๐Ÿ† #1 in parsing speed among open-source modelsโšก RSI-powered. A first in document parsing. Turn documents into Agent-Friendly data. If OXR helps, give us a star on GitHub! ๐ŸŒŸ
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Wait, 122B parameters and it can run on a single NVIDIA DGX? Thatโ€™s the detail that makes Ornith-1.5-122B much more interesting than another open-weight model drop. Add MIT licensing, multiple quantized variants, and support across Ollama, Hermes Agent, OpenCode and more, and this starts to feel built for people who actually want to use the model, not just benchmark it.
Aloha!๐ŸŒบ Meet Ornith-1.5-122B: No Tradeoff Between Strong and Fast Stronger. Faster. Leaner. Ornith-1.5-122B delivers frontier-level capabilities with exceptional inference efficiency. With superior instruction following, stronger task decomposition, and deeper understanding of real-world workflows, it achieves state-of-the-art performance among open-source models of comparable size, while running on a single NVIDIA DGX card!! Open weights under the MIT license. Coding โœ…SWE-bench Verified: 0.845 โœ…TB2.1: 0.813 โœ…CyberGym: 0.800 โœ…NL2Repo: 0.512 Agentic โœ…HLE: 0.446 โœ…BrowseComp: 0.785 โœ…MCP-Atlas: 0.746 โœ…Toolathlon: 0.708 ๐Ÿค—Hugging Face: huggingface.co/ornith-ai/Ornโ€ฆ We canโ€™t wait to see what you build with Ornith-1.5-122B.
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Imagine you're planning a Tesla Roadster campaign. You have a visual concept, a mood, and a story you want the audience to experience. How do you turn that brief into a finished marketing video efficiently? I tested CapCut Web ร— Codex as a new way to approach the task. Instead of treating AI as a standalone generation tool, I used natural-language instructions to communicate the creative goal and let Codex call CapCut to create the video. This connects creative planning with execution in a more seamless workflow. For marketers and content creators, that means a more approachable way to experiment with ideas and bring campaigns to life. #capcutweb #capcutchatgptplugin #capcutcodex #capcutmarketing
๐Ÿค Paid partnership ยท ๐Ÿค– Made with AI
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CapCutโ€™s Black Friday deal went live today: get 60% off Pro and 40% off Ultra, on CapCut Desktop only. The promotion runs October 20โ€“30. That trending video where a turning fan blade reveals a different outfit? You can create it in CapCut and use it in Black Friday marketing. A promising AI-generated shot in the sequence may still have a visual detail worth revisiting. Local Inpainting in CapCut PC gives me a way to revise that selected area. I can assess the updated shot within the existing sequence and decide whether it works better alongside the surrounding footage. Itโ€™s a useful option when a visual needs a focused revision before joining the finished ad. #capcutpc #capcutmarketing #capcutsale #capcutblackfriday
๐Ÿค Paid partnership ยท ๐Ÿค– Made with AI
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Itโ€™s official! CapCut Black Friday is LIVE TODAY! Get 60% OFF Pro and 40% OFF Ultra, Oct 20โ€“30, exclusively on CapCut Desktop. Got a creative product idea inspired by the Jean Phil trend? CapCut PC helps you turn AI-generated concepts into marketing videos with room for real editing, refinement, and finishing. #capcutpc #capcutblackfriday
๐Ÿค Paid partnership ยท ๐Ÿค– Made with AI
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Wait, 122B parameters and it can run on a single NVIDIA DGX? Thatโ€™s the detail that makes Ornith-1.5-122B much more interesting than another open-weight model drop. Add MIT licensing, multiple quantized variants, and support across Ollama, Hermes Agent, OpenCode and more, and this starts to feel built for people who actually want to use the model, not just benchmark it.
Aloha!๐ŸŒบ Meet Ornith-1.5-122B: No Tradeoff Between Strong and Fast Stronger. Faster. Leaner. Ornith-1.5-122B delivers frontier-level capabilities with exceptional inference efficiency. With superior instruction following, stronger task decomposition, and deeper understanding of real-world workflows, it achieves state-of-the-art performance among open-source models of comparable size, while running on a single NVIDIA DGX card!! Open weights under the MIT license. Coding โœ…SWE-bench Verified: 0.845 โœ…TB2.1: 0.813 โœ…CyberGym: 0.800 โœ…NL2Repo: 0.512 Agentic โœ…HLE: 0.446 โœ…BrowseComp: 0.785 โœ…MCP-Atlas: 0.746 โœ…Toolathlon: 0.708 ๐Ÿค—Hugging Face: huggingface.co/ornith-ai/Ornโ€ฆ We canโ€™t wait to see what you build with Ornith-1.5-122B.
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What if your AI assistant started with a button, not an app? Thatโ€™s the part of SpeakON that caught my attention. A thought comes to mind, you press the button and say it. From there, the agent can help turn that into a reminder, email, or scheduled task. Less time navigating to start the request, with review and confirmation before the reminder, email, or calendar action is completed. That feels much closer to how a personal agent should fit into everyday life.
๐Ÿค Paid partnership
we crushed a keyboard. its retirement party got a little out of hand. for years, it was there for every email, every plan, every โ€œjust one more thing.โ€ a loyal colleague. a very loud colleague. but your ideas move faster than your fingers. and somewhere between knowing what you want to do and typing it all out, a quick task becomes another thing taking up your afternoon. meet SpeakON AI Agent. start with what you need, in your own words. less time behind a blinking cursor. more room to move things forward. the keyboard had a good run. now, letโ€™s give your voice a turn. THE LAST KEYSTROKE is here. watch the film, then tell us: whatโ€™s the first task youโ€™d hand over? speakon.app/x Disclaimer: The calling feature is still in beta and will be available soon. All other Agent features are available now, with ongoing improvements.
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Okay, this is where the world-model argument starts to make sense. The deep learning pioneers from Maluuba are back solving one of the biggest problems in the industry. They unveiled SKY-001, the first engineering world model. SKY-001 isnโ€™t just generating something that looks 3D. It simulates different designs, breaks the object down into parts and sub-assemblies, works through the hierarchy, then rebuilds it into something that can actually open in CAD. Plus, the results compared to frontier LLMs are hard to ignore, including claims of being 150x cheaper and faster. That feels much closer to how real engineering work happens. With this release, theyโ€™re also building toward autonomous manufacturing and reimagining what manufacturing firms could look like. If this approach keeps scaling beyond CAD and BIM, the interesting question wonโ€™t be โ€œwhich LLM is better?โ€ Itโ€™ll be whether some of these long-horizon problems should be solved with a completely different kind of model.
Introducing SKY-001, the first Engineering World Model. On CAD and BIM tasks, it beats GPT-6 Astra and every frontier model we tested: -> 51-125x faster - > 150-210x cheaper -> Higher IOU than all frontier models SKY-001 lets you dream a physical object before you build it. 18 months of work. Built by the team out of Maluuba's deep learning lab (acq. by @MSRResearch). The first โ€˜AI General Engineerโ€™ will be the backbone for Autonomous Manufacturing.
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Imagine you're planning a Tesla Roadster campaign. You have a visual concept, a mood, and a story you want the audience to experience. How do you turn that brief into a finished marketing video efficiently? I tested CapCut Web ร— Codex as a new way to approach the task. Instead of treating AI as a standalone generation tool, I used natural-language instructions to communicate the creative goal and let Codex call CapCut to create the video. This connects creative planning with execution in a more seamless workflow. For marketers and content creators, that means a more approachable way to experiment with ideas and bring campaigns to life. #capcutweb #capcutchatgptplugin #capcutcodex #capcutmarketing
๐Ÿค Paid partnership ยท ๐Ÿค– Made with AI
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D-Coder retweeted
Viggle's JST-2 can take one full-body photo of you and build the room you were standing in, in 3D, with you in it. then you spin the camera round yourself. going to need a tidier room before I try this
Today we are releasing JST-2, the world's first full-stack physics-native world foundation model. Many world models learn in pixel/video space. We went back to first principles and built the entire model stack in 3D. Generate a 3D scene in seconds and expand it in real time. Create characters with motion built in, all through one general architecture.
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D-Coder retweeted
STOP telling ChatGPT: โ€œEdit my photo.โ€ That vague prompt is exactly why your results can end up looking fake, overprocessed, or nothing like you. Use these 7 prompts instead: ๐Ÿงต
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