When I was 13, I lost my mom to breast cancer. I still wonder if it could have been prevented. When she found the lump, she did everything right. She went to her primary care doctor, and he told her it was nothing. My mom wasn’t convinced, so she went to a women’s health specialist for a second opinion and was told the same thing. A year passed before she saw a third doctor who was shocked that nothing had been done and ordered tests the same day. By the time she was diagnosed, the cancer had already spread. For the next three years, she went through chemo, surgery, and radiation. Specialist after specialist, asking the same questions again and again. After she passed away, there were two thoughts I couldn’t out of my head: (1) Somewhere, a doctor must have seen a case like my mom’s and caught it early. (2) Nothing that doctor learned had any way to reach her. Healthcare runs in my family. All four of my sisters are nurses or pharmacists, and my fiancée spent the pandemic working in a multi-organ transplant recovery ward. I’ve seen how hard clinicians work, and how much of that work requires understanding the patient and recognizing patterns from past cases. That’s why a doctor can practice for 30 years and get better every year, while a hospital can treat millions of patients without getting much better at treating the next one. What’s missing in healthcare is the infrastructure for it to learn from its own experience. If we don’t examine the cause and outcome of every interaction, how do we get better at knowing which signs to watch for? I spent years at Google and Upwork building AI from human expertise, then eventually left big tech to work on the one problem I couldn’t stop thinking about. I started a company called Concurrence (@concurrencehq) and raised $24M to build data and AI systems that enable healthcare orgs to learn from every patient they treat. Nothing learned is lost, and it reaches the next patient who needs it. We’ve already supported care for over 10 million patients worldwide. My mom deserved a doctor who had seen her case a thousand times. Every patient does.

Introducing the Holiday Buzz Suite. Viral holiday videos for your product or service, in minutes. 🍁 Holiday Director creates viral ads 🎅 Mix & Match: pick a scene, character, vibe 🎃 93 Halloween viral presets More views = more sales. No editing! Live now, and limited time to get 50% off BuzzHooks for life!

We release the most comprehensive 𝗲𝗺𝗽𝗶𝗿𝗶𝗰𝗮𝗹 𝘀𝘁𝘂𝗱𝘆 𝗼𝗳 𝗚𝗣𝗧-𝟲-𝗔𝘀𝘁𝗿𝗮’𝘀 𝗲𝗺𝗯𝗼𝗱𝗶𝗲𝗱 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 across 12 simulation benchmarks and 8 real-robot platforms, together with 𝗦𝗲𝗻𝘀𝗲𝗡𝗼𝘃𝗮-𝗥𝗼𝗯𝗼𝗥𝗦𝗜: 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: 🔹 𝗧𝗵𝗲 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗰𝗲𝗶𝗹𝗶𝗻𝗴 𝗶𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗺𝘂𝗰𝗵 𝗵𝗶𝗴𝗵𝗲𝗿. Under a unified protocol (ICL + up to 10× the interaction budget), GPT-6-Astra achieves clear SOTA across all 12 benchmarks: RoboDojo: 27.6% at 1× → 81.9% at 10×, DexJoCo: 87.3% vs. 40.3% prior SOTA, while the LIBERO benchmarks are pushed to saturation. 🔹 𝗧𝗼𝗼 𝗺𝘂𝗰𝗵 𝗯𝘂𝗰𝗸 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗯𝗮𝗻𝗴. Even a simple task like placing pens in a holder costs ~$55 in inference. 🔹 𝗧𝗵𝗲 𝘄𝗼𝗿𝗹𝗱 𝗱𝗼𝗲𝘀𝗻’𝘁 𝘄𝗮𝗶𝘁. 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 𝗲𝗺𝗯𝗼𝗱𝗶𝗲𝗱 𝗰𝗼𝗺𝗽𝗲𝘁𝗲𝗻𝗰𝗲, but 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗲𝗺𝗯𝗼𝗱𝗶𝗲𝗱 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 is not here yet. 🌐 Project page: sensenova.ai/has-gpt-6-astra… 📄 Report: sensenova.ai/gpt-6/assets/do… 💻 Code & model: github.com/OpenSenseNova/Sen…