Five Checks I Run Before Shipping Generative Images to Production
I outline a practical preflight for generative image pipelines—rights, consistency, safety, provenance, and human review—so creative work survives contact with users.
Video, 3D, robots, physical AI
I outline a practical preflight for generative image pipelines—rights, consistency, safety, provenance, and human review—so creative work survives contact with users.
Tashi Zhixang unveiled AWE3.0, a non-simulated, non-VLA embodied model. I read the filing; it claims general-purpose capability without teleoperation or simulation tricks.
SenseTime’s SenseNova-MARS claims agentic multimodal search. Here is the preflight I run before letting any tool-using vision model near creator IP.
Musk opens X's algo, calls it 'terrible' but promises updates. I note this transparency is rare for Musk, signaling a shift in how social platforms handle their core AI engines.
I read the UC Berkeley research. It frames a dishwasher robot as an extension topic for the 2025-2026 AI industry cycle.
I read Tencent’s release on Hunyuan Image 2.1. The framework claims alignment with human intent across 24 dimensions to decode complex instructions. I think this shifts the burden of proof from users to the model's architecture.
I see Ant Group backing a robotic hand startup, signaling that physical embodiment is the next frontier for their 2025-2026 AI strategy.
UniToken unifies image-text encoding, targeting 2025-26 AI timelines. I note the vague 'all-around' claim lacks specific benchmark evidence for true multimodal mastery.
I watched a pure on-device LLM hit mass production in just 10 months, shattering speed records and reshaping the 2025 automotive AI landscape.
I read the 2nd China Embodied Intelligence Conference summary covering 2025-2026 extensions. In practice, lab demos don't fix on-call pain. This adds to our timeline tracking.