The engineering disciplines that define HexTechGuide.
HexTechGuide organizes technical publishing around long-term engineering disciplines, not temporary tools, vendors, models, or framework names.
Each research area represents a production system concern: how software is designed, deployed, secured, observed, scaled, and recovered when it runs in real environments.
AI Infrastructure
The systems layer behind production AI workloads: GPU inference, model serving, embedding services, RAG pipelines, runtime selection, AI gateways, vector stores, request routing, observability, scaling, and cost control.
Core questions
How should inference platforms be operated? How should GPU workloads be scheduled? How do teams manage latency, memory pressure, cold starts, batching, runtime upgrades, and model lifecycle changes?
Current publications
Operating GPU Inference at Scale
Runtime Skill Injection for Adaptive AI Agent Platforms
Security Architecture
The security patterns required to protect AI and cloud systems under real operational pressure: confidential computing, trusted execution, remote attestation, cyberdeception, threat detection, secrets, identity, runtime isolation, and data protection.
Core questions
How do organizations protect data in use? How should trust be established before key release? How can teams detect attackers without exposing real systems? How can threat intelligence improve without centralizing sensitive telemetry?
Cloud Architecture
Durable design patterns for cloud systems: networking, identity, private connectivity, landing zones, storage, disaster recovery, cost constraints, hybrid architecture, and secure service boundaries.
Core questions
How should production environments be segmented? Where should identity boundaries exist? How do teams design for private access, recovery, observability, and long-term operational cost?
Current publications
Cloud Architecture notes will be added as this research area expands.
Kubernetes
Kubernetes as an operating environment: scheduling, node pools, probes, ingress, storage, autoscaling, controllers, operators, Helm, workload isolation, and cluster failure modes.
Core questions
Why do pods fail to schedule? When should taints and tolerations be used? How should readiness differ from liveness? What does a safe node drain require? How do teams recover from upgrade, storage, and networking failures?
Current publications
Kubernetes appears across several AI Infrastructure and Incident Analysis publications. Dedicated Kubernetes notes will be added as the archive expands.
Python Systems
Python applications as production systems: Flask, FastAPI, WSGI, background jobs, automation scripts, managed hosting, packaging, caching, deployment behavior, and performance tuning.
Core questions
How should a Python application be deployed reliably? What does the WSGI layer do? Where should static assets live? How should content systems avoid unnecessary databases without sacrificing maintainability?
Reliability Engineering
The operational discipline of keeping systems healthy: observability, SLOs, SLIs, alerting, incident response, dashboards, health checks, runbooks, rollback planning, capacity management, and recovery procedures.
Core questions
What should be measured? When should alerts fire? How do teams distinguish a slow startup from a failed workload? How should recovery steps be documented before incidents happen?
Current publications
Reliability patterns appear across AI Infrastructure, Kubernetes, and Security Architecture notes. Dedicated incident and reliability research will be added as the publication grows.
Architecture Reviews
Reviews of platform designs, architectural patterns, system boundaries, implementation trade-offs, and operational consequences. These articles evaluate how systems should be structured rather than how a single product is configured.
Core questions
What is the trust boundary? What should be replaceable? Where does state live? What are the scaling constraints? Which failure modes are acceptable, and which require redesign?
Incident Analysis
Failure-driven engineering research: startup probe failures, GPU memory exhaustion, image pull errors, DNS issues, TLS failures, node drain problems, storage faults, autoscaling surprises, and recovery work.
Core questions
What failed? Why did it fail? What signals were missed? What made recovery slower? What system design change would prevent the same failure from recurring?
Editorial position
Incident Analysis is one of the most important future areas for HexTechGuide because production failures teach engineering lessons that generic tutorials rarely capture.
Most production problems cross domain boundaries.
A GPU inference incident may involve AI Infrastructure, Kubernetes, Cloud Architecture, Reliability Engineering, and Security Architecture at the same time. HexTechGuide keeps research areas stable while allowing individual publications to span multiple disciplines.
This structure keeps the publication organized around durable engineering principles rather than short-lived technology labels.