Anthropic is operating a Bay Area wet biology lab that uses its AI models to run physical experiments, confirmed by TechCrunch. The lab has been running “today,” and Anthropic also bought Coefficient Bio in April to support its AI biotech work. The company is positioning the lab around fundamental biology while expanding access through its Life Sciences Verification Program and continuing partnerships rather than direct drug discovery.
SageMaker AI published a review of 2026 year-to-date launches for its two generative AI inference deployment paths: managed endpoints and HyperPod Inference.
Kubernetes-focused updates are expanding to support production AI inference while infrastructure vendors and users debate whether Kubernetes can accurately account for inference costs. The article cites a measured 60% reduction in processing the cost for 1 million tokens for China Merchants Bank’s Kubernetes-based setup. It pushes teams toward evolving scheduling and memory/serving layers (and better telemetry and platform readiness) so token-level economics can drive inference decisions.
The article ranks 11 open-source agent harnesses for running local LLMs by license, documented local runtimes, maintenance, and safety controls. It says local setups should start with a context window of 64,000 tokens (for agents/coding tools). Readers are directed to choose a harness and apply specific local settings, such as context and tool-calling compatibility, to reduce failures from small windows and weak tool support.
Crusoe announced an initial closing of its $3.9 billion Series F funding round to expand its vertically integrated AI infrastructure and AI-factory buildout. The round values Crusoe at a $30.9 billion post-money valuation. The new capital will scale its programs and support buildout of its own AI factories, including large campuses and modular Crusoe Spark units, to accelerate Crusoe Cloud growth.
Figure introduced Helix 2.5, a humanoid robot neural network that performs full-body household tasks in homes it has never seen by using Index pretraining instead of environment-specific data. Helix 2.5 completed tidying living rooms, folding towels, and making beds in 30 unseen Bay Area homes with zero data collected in those locations, reaching 56% success versus 9% for a scratch-trained policy. The work shifts humanoid robotics toward zero-shot whole-body generalization driven by broad pretraining, reducing required task-specification data and making deployment less dependent on per-home retraining.
A global fintech scaled its AI coding assistant traffic by running GLM 5.2 on Together’s Dedicated Model Inference to handle spiky, engineering-hours demand. It reported p50/p90/p95 usage at about 81K/163K/178K tokens and RPS at 3/6/7, and a specific incident involved 192-second requests queued behind a 2.3M-token pending-prefill backlog. Engineers gained self-service endpoint control with a metrics API and live configuration/model updates, replacing ticket-based coordination and reducing multi-minute queuing.
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