Long-form essays from the engineers shipping AI inside payers, hospitals, energy operators and proptech platforms. Written for technology leaders who care more about what runs in production than what trended last week.
How to build medical imaging pipelines that handle storage, AI inference, and compliance at scale: DICOM, model integration, and the controls a clinical deployment needs.
How to design clinical decision support that avoids alert fatigue: relevance, specificity, and the controls that keep alerts trusted and acted on, not dismissed.
Techniques for de-identifying healthcare data at scale for analytics and AI: methods, re-identification risk, and the controls a compliant de-identification pipeline needs.
How to manage patient consent for healthcare AI: capturing, enforcing, and honoring consent across data uses, with the patterns and pitfalls a compliant system needs.
Where AI delivers real ROI in healthcare revenue cycle management, coding, denials, prior auth, and where it does not, with the controls a production deployment needs.
What FHIR-native architecture means and how it delivers real healthcare interoperability: using FHIR as the model, not a translation layer, plus the controls it needs.
How to build a healthcare data lake that governs PHI at petabyte scale: access control, lineage, de-identification, and the controls a compliant platform needs.
What ambient clinical intelligence actually requires in production beyond AI scribe demos: accuracy, clinician workflow, compliance, and the controls that make it safe.
How to build an AWS backup and disaster recovery plan that actually works: RPO/RTO targets, tested restores, and the controls that turn backups into real recovery.
How AWS PrivateLink enables private B2B SaaS integrations without exposing services to the internet: architecture, patterns, and the controls a production setup needs.
What Amazon Bedrock Knowledge Bases offer for managed RAG, where they save effort, and where their limits mean you need a custom retrieval pipeline instead.
The real tradeoffs between AWS Glue and self-managed Spark: control, cost, operational burden, and how to choose the right one for your data workloads.
A 2026 comparison of Karpenter and Cluster Autoscaler on EKS: how each scales nodes, their tradeoffs in flexibility and cost, and how to choose for your cluster.
How AWS Cost Anomaly Detection catches cost spikes before the monthly bill: setup, monitors, alerting, and the practice that turns surprises into early signals.
How Amazon EventBridge serves as the backbone of event-driven AWS architectures: decoupling, routing, schema, and the controls a production event bus needs.
How AWS Control Tower sets up multi-account governance the right way: guardrails, account factory, and where it fits versus a hand-built landing zone.
Why data architectures that work today break at 10x scale, the assumptions that fail, and how to design for the next order of magnitude without over-building today.
How to run Spot Instances in production safely: handling interruptions, diversification, and the resilience patterns that capture up to 70% savings without outages.
A deep dive into AWS networking, VPCs, Transit Gateway, and PrivateLink, and how to choose the right connectivity pattern for your architecture and scale.
A decision framework for choosing between Aurora, RDS, and DynamoDB: access patterns, scale, consistency, and cost, matched to what your workload actually needs.
How to use AWS Organizations and Service Control Policies for account-level governance: structure, guardrails, and the controls a production multi-account setup needs.
How to design cloud architectures for portability without over-investing in cloud-agnosticism: where lock-in matters, what to abstract, and the controls an exit needs.
A clear-eyed look at service meshes in 2026: what they solve, the complexity they add, when they are worth it, and how to decide for your architecture.
The hidden costs of monolith-to-microservices migration nobody warns you about: distributed complexity, data, and the disciplined, incremental approach that works.
One long-form essay every other Wednesday. Written by the engineers shipping production AI for our clients, not by a content team. No promotional emails. Unsubscribe in one click.
