On July 27, at the Kimi K3 open day, Moonshot AI released not only the model weights and technical report but also three core infrastructure technologies — the high-performance communication library MoonEP, the KDA linear-attention kernel FlashKDA, and the distributed RL sandbox AgentEnv co-developed with KVCache.ai. Kimi K3 is a 2.8-trillion-parameter MoE model that activates only 16 of 896 experts per inference, so the central engineering challenge is: how do you keep training and communication stable at extreme sparsity? MoonEP delivers near-linear scaling for fine-grained expert-parallel (EP) communication even under imbalanced loads, so very large EP domains no longer stall when a few hot experts create a bottleneck. FlashKDA provides a Triton/CUDA-level implementation of KDA, achieving a 1.72x–2.22x prefill speedup over the flash-linear-attention baseline on H20, and can be dropped in as a backend replacement. AgentEnv offers a high-fidelity, fast-fork sandbox that supports the large-scale parallel agent workloads in K3's post-training pipeline. Together, the three pieces cover "communication — kernels — sandbox", the three engineering corners most often overlooked by the open-source community, allowing anyone with comparable hardware to reproduce a K3-class training pipeline. Combined with architectural innovations such as a 3:1 KDA + AttnRes hybrid, Quantile Balancing in Stable LatentMoE, and Per-Head Muon, Kimi K3 improves scaling efficiency by 2.5x over K2 under constrained compute — and open-sourcing the model is only part of the story. For the first time, a Chinese frontier model is laying out every engineering detail of "how to train a 2.8T model" on the table.