Comprehensive guides for building reliable systems and effective incident response processes.
28 articles, frameworks, and resources
Build a world-class incident management process. Learn frameworks for detection, response, communication, and learning from incidents to build more reliable systems.
Track system availability and uptime percentage. Essential for SLAs, reliability, and customer trust.
Measure how quickly your team restores service after an incident. A key DORA metric that indicates your organization's resilience.
Track the percentage of deployments that result in failures, rollbacks, or hotfixes. Essential for balancing speed with stability.
Track the percentage of failed requests. Critical for reliability, user experience, and incident detection.
A structured template for blameless incident analysis with timeline, root cause, and action items.
A battle-tested framework for handling production incidents—from the first alert to the blameless post-mortem. Includes severity classification, escalation playbooks, communication templates, and lessons from real outages.
AI is shifting from a feature-layer add-on to an operations-layer control plane: AI agents and AI-powered observability are being productized and funded, while engineering leaders confront the maintenance tax of AI-generated code and AI-accelerated change.
Ransomware attack playbook for CTOs: decisions, containment, recovery, and insurance
Operational resilience for CTOs: Meeting FCA and DORA without turning engineering into paperwork
AI is rapidly shifting from conversational assistants to agentic systems that execute tasks (browsing, coding, security research), pushing companies to redesign workflows, service models, and...
AI is shifting from a feature layer to an operational actor, driving new approaches to observability, incident response, and cybersecurity governance as cost and scale pressures collide.
Observability is shifting from "monitoring your stack" to "running the business": cloud-native network visibility, multi-CDN telemetry, and AI-driven operations are pushing CTOs toward unified, dat...
AI is moving from "app layer innovation" to "end-to-end operational constraint," where power availability, runtime isolation (Wasm), and autonomous optimization (agents/RL) become first-class archi...
Engineering organizations are moving from generic "scale out" tactics to explicit latency budgets and priority-aware load control, treating performance as a product feature and resilience as a policy problem, not just an engineering concern.
Platform engineering is moving into a "second phase": organizations are standardizing internal developer platforms while pairing them with unified observability and automated incident response under increasing regulatory and sovereignty constraints.
Engineering organizations are operationalizing AI—from coding agents and AI-assisted onboarding to AI observability—just as policy and legal pressure increases around AI outputs and platform risk.
Most CTOs I meet can describe their calendar, but not their job. That's not a knock-it's just what happens when the role is a moving target.
Most CTOs I talk to aren't worried about whether AI "works." They're worried about what happens when it works just enough to get embedded into core workflows—support, underwriting, sales ops, security...
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