HexTechGuide is the independent engineering journal of Hex Data Technologies, publishing implementation notes, architecture research, and field reports from the layer where AI infrastructure meets production operations.
An architecture review of confidential AI systems that protect sensitive data during computation using hardware-rooted trust, trusted execution environments, remote attestation, encrypted model delivery, and policy-based key management.
Read research →Published for engineers, architects, directors, and technical leaders responsible for production systems.
An architecture review of confidential AI systems that protect sensitive data during computation using hardware-rooted trust, trusted execution environments, remote attestation, encrypted model delivery, and policy-based key management.
An architecture review of using Retrieval-Augmented Generation (RAG) to build adaptive cyberdeception platforms that generate realistic responses, synthetic assets, and high-fidelity interaction telemetry without exposing production environments.
An architecture review of runtime skill injection and adaptive AI agent platforms that evolve through deployment-time capabilities rather than static post-training updates.
An architecture review of collaborative threat intelligence platforms that preserve organizational privacy through federated learning, graph-based analytics, causal reasoning, and privacy-preserving data sharing.
A vendor-neutral production checklist for operating GPU inference workloads with Kubernetes scheduling, runtime health probes, secrets, observability, scaling, and rollback planning.
Designing a lightweight publishing platform does not always require a database or content management system. This article explores a flat-file architecture using Flask, Markdown, and in-memory caching to build a fast, maintainable engineering publication.
A production guide for deploying Python WSGI applications on managed shared hosting platforms using Flask, Passenger, clean routing, predictable directory structures, and efficient static asset delivery.
Gateways, RAG, inference APIs, model serving, and GPU workloads.
Azure, Terraform, identity, networking, storage, and private endpoints.
AKS, node pools, probes, ingress, scheduling, and recovery.
Flask, FastAPI, WSGI, automation, and deployment behavior.
Logs, metrics, tracing, health checks, incidents, and runbooks.
Secrets, RBAC, TLS, network policy, hardening, and least privilege.
Design decisions, constraints, patterns, and enterprise tradeoffs.
Failures, regressions, recoveries, and operational lessons.
Engineering credibility is earned through implementation.
HexTechGuide exists to document the technical layer beneath enterprise AI: architecture choices, implementation constraints, operational failures, production code patterns, and recovery practices.
Hex Data Technologies provides advisory and delivery for organizations operating mission-critical AI, cloud, Kubernetes, and reliability systems. HexTechGuide is the public research layer.