Gemini 3.5 Live Translate is Google’s new real-time speech-to-speech model, handling 70+ languages with natural intonation and no awkward pauses. It’s in private preview on Google Meet and rolling out to Android and iOS Translate. Meanwhile, Anthropic just launched Claude Fable 5 for everyone and Claude Mythos 5 for select cyber defenders and infrastructure partners. Google is backing Anthropic’s massive $35 billion chip lease across five data centers, a deal that underscores how deep their AI partnerships have become. On the theory side, one piece frames text—prompts, memory, retrieval—as an “optimization layer,” treating prompt design as a form of sample-efficient learning. Another argues that fully automated AI engineering loops produce sloppy agents because they optimize against imperfect evaluations, missing the nuance only a developer catches. And large-scale test-time compute now drives LLM performance more than model architecture. GPT-5.5 barely outperforms GPT-5.4 on raw benchmarks, but it dominates when you factor in token count, cost, or latency—showing that single-number scores are losing their meaning. In engineering news, Teleport is giving each AI agent its own cryptographic identity instead of shared credentials, cutting standing privileges and improving audit trails across Kubernetes, databases, and clouds. Cohere released North Mini Code, a 30 billion-parameter MoE coding model with 3 billion active parameters under Apache 2.0. Evo ported its autoresearch orchestrator into Claude Code’s dynamic workflows, moving coordination out of model memory and into deterministic JavaScript. And FlashMemory’s DeepSeek-V4 retriever keeps just 10–15% of the KV cache on GPU, yet maintains or improves downstream performance by predicting which chunks matter most. Anthropic has also embedded hidden safeguards in Fable 5 that can quietly limit its responses in about 0.03% of cases—mostly when competitors use it to build rival models. These interventions, applied via prompt tweaks and fine-tuning rather than model switches, aren’t visible to users. That opacity creates a supply-chain risk: you won’t know if your agent stopped giving you full answers. Anthropic argues it’s protecting its lead, but uneven, invisible safety policies erode trust across the AI ecosystem.