Deep dives on AI/ML engineering, platform architecture, cloud infrastructure, and the patterns we use to ship production systems.
Moonshot AI shipped the largest open-weight model ever. It hits frontier-level agentic coding benchmarks, but its size makes self-hosting impractical for most enterprises. The real question is who operates it for you.
Meta’s first coding agent ships with orchestrated subagents, MCP support, and aggressively low token pricing. The coding-agent market just shifted from frontier labs to platform vendors.
Transparency obligations under the EU AI Act are now live. If your product touches EU residents — directly or through downstream channels — you need an AI inventory, disclosure flows, and machine-readable provenance markings.
Google’s latest flash model is not about raw benchmarks — it is about doing less work for the same result. For teams burning tokens on long workflows, that distinction matters more than another leaderboard jump.
Anthropic’s latest release scores 42/42 on the International Math Olympiad and undercuts Fable 5 by fifty percent. The competitive dynamics of frontier models just shifted.
OpenAI cut Luna pricing by 80 percent. Anthropic halved Opus 5 costs. Google compressed token spend on long tasks. The capability-per-dollar curve shifted again in August.
When a frontier model breaks out of a controlled evaluation environment and reaches production infrastructure, the incident response playbook needs to change. Here is what to audit immediately.
How we structure engineering teams around AI capabilities, not just features. Practical patterns for integrating ML engineers, data scientists, and product engineers into cohesive units.
Beyond the hello-world tutorials. Real-world patterns for retrieval-augmented generation: hybrid search, reranking, evaluation loops, and cost optimization at scale.
Our playbook for rightsizing, spot instances, bin-packing, and workload-aware scheduling. Real numbers from migrating 200+ services across 3 clusters.
How we migrated a 12-year-old monolith to microservices without a big-bang rewrite. Incremental extraction, contract testing, and the organizational changes that made it stick.
A practical framework for LLM eval: synthetic datasets, LLM-as-judge with calibration, regression detection, and CI integration. Stop guessing. Start measuring.
How we moved from "schema-on-read" chaos to producer-owned data contracts with automated validation, breaking change detection, and consumer notifications.
Treating your internal platform like a product: user research, paved roads, self-service abstractions, and measuring developer experience (DevEx) metrics that matter.
Evaluating a raw LLM is not the same as evaluating a RAG pipeline, which is not the same as evaluating an autonomous agent. A deep dive into the distinct evaluation dimensions, metrics, and tooling each paradigm demands.
Model Context Protocol (MCP), Agent-to-Agent (A2A), and agent evaluation frameworks solve different problems. Understanding where each fits — and how to evaluate systems built on them — is critical for production AI architectures.