Booster T2 is now available.
Smart. Powerful. Built to Perform.
As a new flagship embodied development platform, Booster T2 delivers up to 2070 TFLOPS of computing power, the highest in its class for bipedal humanoid robots. Combined with a high-output, high-DOF body, it enables sharper motion perception, stronger understanding, and reliable execution in complex scenarios.
From real-time perception to rapid response, from agile movement to precise manipulation, Booster T2 combines powerful computing performance and reliable hardware with Booster Studio's integrated development platform, unlocking limitless possibilities for developers.
Search "Booster Robotics" to visit the official website and order now.
COMRADES, we have successfully reverse engineered a unitree GO2 battery. I am shocked at how simple their detection mechanism is. You just connect one resistor to 2 pins. And BOOM. You're all set!
Chinese people never leave a chance to amaze me man!
Blog coming soon about this!
We built the best chip design engine in the world!! 🥳🥳
@archgen_ is currently ranked #1 on the @WeAreHRT / Partcl Macro Placement Challenge 2026 leaderboard.
Macro placement is one of the hardest problems in physical design.
It involves placing large fixed-size blocks such as SRAMs, IPs, and analog macros on a chip floorplan while balancing, wirelength, density, congestion, routability, timing and constraints.
After months of research and iteration our submission reached a verified rank-1 with an average proxy cost of 0.9507 across the IBM benchmark suite.
@naveen_venk and @JishnuMada86596 burned the midnight oil to build an optimization flow that combined fast local repair, multi-start search, congestion-aware ranking, GPU-accelerated candidate generation and strict legality checks to reach the top spot. (detailed blog in the comments)
Grateful to Madhusudan S, Abhishek Lal, and Anant Gulati for their valuable suggestions and inputs to help us overcome issues in EDA algorithms, traditional macro placement algorithms and GPU optimisation.
If you are working on physical design and want to understand how AI, self learning agents, loops, and GPU-accelerated optimisation can improve your flows please feel to reach out to us.
Thank you @Willschips, Vamshi Balanaga and the Partcl team for organising this competition.
#PhysicalDesign #EDA #ChipDesign #VLSI #AIforEDA #Semiconductors #Placement #ArchGen #HardwareDesign