• Deep-learning stack debate: TensorFlow (with new TPU support), Deeplearning4j (tight Spark integration for Hadoop-scale workloads), and Keras (clean API, active community) were compared; Keras was recommended for most users unless Spark scalability is critical.  
• GPU ecosystem status: NVIDIA/CUDA remains dominant; OpenCL support in TensorFlow is uncertain, and Deeplearning4j currently exposes only NVIDIA back-ends.  
• Hardware curiosity: participants expressed interest in obtaining Google’s TPU and noted a contact who performs silicon reverse-engineering.  
• Low-cost high-speed camera sourcing: Casio Exilim EX-FC100 (224×64 @ 1 000 fps) was suggested as a cheap option, while the Kickstarter FPS1000 was noted as nearing shipment.  
• “Visual microphone” replication: a plan was outlined to reproduce MIT’s patented algorithm that recovers audio from high-speed video of subtle object vibrations (e.g., crisp packet or plant leaf) using 700×700 @ 2 200 fps footage.  
• Lightning Network innovation: the post “Blinded outsourcing of channel monitoring” was shared as a new idea for trust-minimized watch-towers.  
• Electronics sourcing: participants sought ready-made LVDS/differential clock modules for high-speed logic, with the Analog Devices ADA2200 proposed as a possible solution.