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🤖 AI Agents Weekly: Kimi K3, DeepSeek-V4-Flash API, GPT-5.6 Price Cuts, Inkling-Small, YC's QM Harness, Gemini Robotics 2, Codex Security CLI, and More

Moonshot AI released Kimi K3, a 2.8T-parameter open-weight mixture-of-experts model with native vision, a 1-million-token context window, and 104B active parameters per token, achieving frontier-level performance on long-horizon coding, agentic, reasoning, and vision tasks, trailing only Claude Fable 5 and GPT-5.6 Sol among models evaluated. The model uses Kimi Delta Attention, Attention Residuals, and Stable LatentMoE, with million-token agentic reinforcement learning and multiple reasoning-effort levels.

read1 min views1 publishedAug 1, 2026
🤖 AI Agents Weekly: Kimi K3, DeepSeek-V4-Flash API, GPT-5.6 Price Cuts, Inkling-Small, YC's QM Harness, Gemini Robotics 2, Codex Security CLI, and More
Image: Nlp (auto-discovered)

Kimi K3, DeepSeek-V4-Flash API, GPT-5.6 Price Cuts, Inkling-Small, YC's QM Harness, Gemini Robotics 2, Codex Security CLI, and More

In today's issue:

Moonshot open-sources Kimi K3

DeepSeek ships V4-Flash agent API

OpenAI cuts GPT-5.6 prices 80%

Thinking Machines drops Inkling-Small

Google launches Gemini Robotics 2

OpenAI open-sources Codex Security CLI

YC open-sources its QM agent harness

Microsoft Foundry adds tool search

Moonshot releases agent RL infra

Nous adds wake word to Hermes

Cursor lands on iPad

ResearchArena probes AI R&D sabotage

HANDBOOK.md tests long policy files

Study exposes coding agent harness effects

SlopCodeBench stress-tests Opus 5

And all the top AI dev news, papers, and tools.

Top Stories #

Moonshot Open-Sources Kimi K3

Moonshot AI released Kimi K3, a 2.8T-parameter open-weight MoE model with native vision that lands closer to the closed frontier than any prior open release.

Architecture: Combines Kimi Delta Attention, Attention Residuals, and Stable LatentMoE, activating 16 of 896 routed experts and 104B parameters per token.Scale and context: Ships a 1-million-token context window and roughly 2.5x better scaling efficiency than Kimi K2.Agentic post-training: Uses million-token agentic RL with persistent rollout and sandbox state, plus multiple reasoning-effort levels for long-horizon execution.Where it lands: Frontier-level on long-horizon coding, agentic, reasoning, and vision tasks, trailing only Claude Fable 5 and GPT-5.6 Sol among models evaluated.

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