• Ukraine war is reportedly deploying fully-autonomous AI drones: in the terminal attack phase the drones switch to on-board vision models after jammers cut the radio link, allowing them to recognize and strike targets without human confirmation; usage is said to exceed 10 000 drones per month on the Ukrainian side alone. (CBC article: https://www.cbc.ca/amp/1.7130368)

• Stephen Wolfram published a new essay proposing a minimal, computational model that explains why biological evolution works and how similar adaptive processes can be engineered in other domains. (https://writings.stephenwolfram.com/2024/05/why-does-biological-evolution-work-a-minimal-model-for-biological-evolution-and-other-adaptive-processes/)

• A long-form argument was advanced for replacing digital neural-network accelerators with ultra-high-frequency analog computers: by operating at 0.5–1 THz (far-infrared) and using FPAA/dpASP reconfigurable analog arrays, a garage-built chip in 300 nm CMOS could allegedly outperform all the world’s GPUs for certain adaptive-learning tasks while costing <$10.

• The analog-computing advocate claimed that only ~1 000 primitive circuit blocks are needed to approximate complex dynamical-system attractors, and that combinatorial modulation at THz frequencies yields “virtually infinite entropy,” eliminating the need for superconducting or thermodynamic computing schemes such as those proposed by Extropic.

• Counter-arguments stressed the versatility and low marginal cost of digital software plus general-purpose hardware, questioned the scalability and R&D investment required for domain-specific analog processors, and noted that reconfigurable devices (FPGAs/FPAAs) remain niche because of IP monopolies, toolchain complexity, and market inertia rather than fundamental technical limits.