Generalist’s new GEN-1 robot model boosts task success from 64% to 99%, cuts execution time threefold, and exhibits emergent physical commonsense by handling unexpected situations, thanks to training on over 500,000 hours of human‑captured motion data, signaling a scaling‑driven leap in embodied AI.
This article investigates how mixing data from mathematical reasoning, code generation, and general instruction-following tasks influences supervised fine‑tuning of LLaMA models, revealing distinct scaling curves, resource‑dependent performance conflicts, and a two‑stage DMT strategy that mitigates catastrophic forgetting while boosting overall capability.
This essay traces humanity's technological journey—from early agricultural and industrial revolutions through the rise of computing, cloud, and artificial intelligence—highlighting how demand, generalization, cost reduction, and safety shape each breakthrough and drive societal transformation.
From the agricultural revolution to the industrial age and now the AI era, this essay traces how societal needs drive technological breakthroughs, examines the stages of technology maturation—generalization, cost reduction, and safety—and explains how computing power, data, and cloud infrastructure enable the intelligent, scalable systems of today.
QTSDB is a distributed time‑series database built on InfluxDB that adds cluster capabilities such as horizontal scaling, replica fault‑tolerance, sharding, meta‑service coordination, and comprehensive read/write processing, while also detailing failure recovery and node expansion strategies.
The article details how Alibaba tackled explosive data growth by migrating from a single Oracle instance to a multi‑source architecture using horizontal and vertical sharding, MySQL clusters, KV stores, and proprietary tools such as Erosa, Otter, and Cobar to achieve high availability, consistency, and performance across multiple IDC sites.
