A practical R&D stream for building, running, and governing AI systems. Labs starts with the hardest part, agents and AI systems in production: reliability, evals, cost, and architecture, then turns findings into benchmarks, reference architectures, maps, and research assets that operators, builders, and leaders can use.
Validate ideas quickly with working prototypes and proof of concepts.
Production-ready architectures for AI-native infrastructure and operations.
Open tools and reusable components that speed up development and deployment.
Benchmarks, testbeds, and assessment frameworks for real-world AI systems.
Guidance, patterns, and checklists to move from prototype to production with confidence.
Compute, storage, networking and platform systems.
Reliability, observability, FinOps, automation and day-2 operations.
Agent design, workflows, tool use, orchestration and guardrails.
Model risk, compliance, data protection and responsible AI.
Business cases, TCO, vendor evaluation and value realization.
Architecture patterns, standards and lessons from real-world builds.
Spot real operator problems and high-impact opportunities.
Deep dive into techniques, tools and trade-offs.
Prototype, test and iterate in controlled labs.
Measure results with real-world benchmarks and scenarios.
Publish findings, code, architectures and practical guidance.
End-to-end observability stack for LLM apps and agents with traces, metrics, logs and evaluation.
Open-source toolkit for prompt testing, evals and dataset management.
Prototype agent that detects, diagnoses and remediates incidents in cloud environments.
Framework to assess model risk, data sensitivity and operational risk in production systems.
Collaborate on research initiatives and early-stage exploration.
Support independent research and get visibility with our operator audience.
One insight, one framework, one signal — every week. No fluff.
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