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Training Camp Notebook 8/24: Rookies stack late‑camp reps as their climb accelerates https://www.raiders.com/news/training-camp-notebook-8-24-rookies-stack-late-camp-reps-as-their-climb-accelerates

Lonic: Algorithm-Hardware Co-Design for Energy-Efficient Fully Local Online SNN Training with INT4 Precision
Peilin Chen, Xiaoxuan Yang
https://arxiv.org/abs/2608.12500 https://arxiv.org/pdf/2608.12500 https://arxiv.org/html/2608.12500
arXiv:2608.12500v1 Announce Type: new
Abstract: Spiking neural networks (SNNs) have recently attracted increasing attention as an energy-efficient learning paradigm. Existing works also propose temporally and fully local online SNN training algorithms to address memory and computation overhead. However, they do not consider whether the algorithmic advantages can be effectively translated into real-device efficiency. To address this challenge, we present Lonic, an algorithm-hardware co-design for energy-efficient and scalable fully local online supervised SNN learning. On the algorithm side, we implement an INT4 low-precision training algorithm for fully local online SNN learning while maintaining accuracy. On the hardware side, to leverage the benefits of the proposed algorithm, we introduce reconfigurable multiplier-free integer PE arrays, dual-optimization zero-gating strategy, temporal prefix-accelerated local learning dataflow, and low-precision weight movement to significantly improve training efficiency. Compared to Apple M4 and Nvidia V100 GPUs, Lonic achieves average energy efficiency improvements of 17.44x and 66.28x, respectively, along with speedups of 3.25x and 1.02x, respectively. Moreover, Lonic achieves 15.95x (14.64x) and 1.52x (7.28x) energy efficiency (area efficiency) over ASIC TPU-like and H2Learn accelerators, respectively. The code for Lonic is available at https://github.com/peilin-chen/Lonic.
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Sources: AfterQuery, which sells coding and finance training data to AI labs, has hit a valuation of $3.2B, up from $300M in April, and is profitable (Anna Tong/Forbes)
http://www.forbes.com/sites/annatong/2026/09/01/afterquery-becomes-yc…

AfterQuery Becomes YC’s Fastest Unicorn At $3.2 Billion
Twenty three year-old Spencer Mateega pivoted his YC startup into the fastest unicorn in the accelerator’s history, fueling AI's shift toward human reasoning data.

Digital Twin Modeling of a Highly Automated Agricultural Tractor
Clay Hallman, Lukas Pindl, Timo Oksanen
https://arxiv.org/abs/2607.19912 https://arxiv.org/pdf/2607.19912 https://arxiv.org/html/2607.19912
arXiv:2607.19912v1 Announce Type: new
Abstract: In efforts to increase research efficiency and availability, a digital twin of our research tractor (AMX G-trac) is created, focusing especially on the CAN communication for data reading and actuation command following the ISOBUS protocol. Mevea Simulation Software is utilized as the foundation, providing the kinematic model and visuals, while Python is used to read and write CAN messages over a Kvaser CanKing virtual CAN channel. Various performance tests involving straight line and turning behavior are performed in both the digital twin simulation and in the real world to measure similarity. Results indicate that the Mevea model behaves very comparable in its lateral dynamics, often within 5-10 percent, but requires better data to fully capture the longitudinal aspects like acceleration. The final model described in this paper sets the table for a second iteration to include more tractor functions such as hydraulics and tractor-implement dynamics.
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