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Bera Buddies claimed Kimi K3 agents found 19 Redis zero-days in 90 minutes and built an RCE exploit in 27 minutes — and Redis confirmed the bugs. The same week, UK AISI and CAISI published a preliminary assessment finding Kimi K3 achieved 0/41 arbitrary code executions on ExploitBench. Both claims are accurate. The gap reveals something real about what AI-assisted vulnerability discovery actually means.
Read more →Moonshot AI announced Kimi K3 on July 16, claiming "the world's first open 3T-class model" at 2.8 trillion total parameters — with weights delayed until July 27. The architecture uses a 16-of-896 expert MoE with Kimi Delta Attention and MXFP4 quantization-aware training, keeping active inference cost near a 50B model while scaling total capacity nearly three-fold over K2.
Read more →Kimi K2.7 Code became the first open-weight model selectable in GitHub Copilot's model picker on July 1. Moonshot AI's 1-trillion-parameter MoE joins Claude and Gemini in GitHub's hosted offering — but unlike those, its weights are public. The move is less about this specific model and more about what it signals: the line between open-weight and enterprise product is getting thinner.
Read more →Moonshot AI's Kimi K2.7-Code is a 1-trillion-parameter MoE coding model that improves on its predecessor while using 30% fewer reasoning tokens. The reasoning-token efficiency story is the interesting part: the model has been explicitly tuned to stop overthinking, and the benchmarks suggest it works.
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