Blog — Page 14 of 53
Browsing page 14 of 53 — 634 articles and counting.
Testing Architecture for TypeScript Backends: Unit Tests, Integration Tests, and a Testing Strategy That Actually Scales
A practical guide to testing TypeScript backends at scale: revisiting the testing pyramid, unit testing with Vitest and dependency injection, real integration tests with Testcontainers, and the tradeoffs between mocking and integration approaches that shape a testing strategy that survives a growing codebase.
AI FinOps for Engineering Teams: Cost Observability, Budget Enforcement, and Runaway Detection for LLM Infrastructure
How engineering teams build cost governance for AI infrastructure, covering per-team token budget enforcement, real-time cost dashboards, rogue agent detection, shadow AI spend, zombie resource cleanup, and multi-model billing consolidation.
GDPR Engineering for SaaS Teams: Consent Management, Data Subject Requests, and Privacy-by-Design Architecture
The engineering patterns behind GDPR compliance for SaaS products: consent storage and versioning, DSAR automation pipelines, right-to-erasure cascades, Article 30 records, and privacy-by-design architecture in TypeScript.
Implementing Enterprise SSO for Your SaaS: SAML, OIDC, SCIM, and Directory Sync for Multi-Tenant Applications
A complete engineering guide to SAML 2.0 SP-initiated and IdP-initiated flows, OIDC for enterprise, SCIM 2.0 directory sync, multi-tenant identity routing, and production considerations including certificate rotation and just-in-time provisioning.
MCP Security in Production: Protocol Vulnerabilities, Tenant Isolation Patterns, and Hardening AI Agent Tool Connections
66% of MCP servers have code smells and 14.4% contain bug patterns. Asana's tenant isolation flaw hit 1,000+ enterprises. This is what the MCP attack surface actually looks like and how to harden it.
Securing Your AI Supply Chain: How a Trusted Library Compromised a $10 Billion Startup and What It Means for Your Stack
The Mercor/LiteLLM incident exposed a class of vulnerability most engineering teams have not yet addressed: AI-specific dependency risk. This article covers dependency pinning, SBOM generation, automated vulnerability scanning in CI/CD, and the practical controls that would have caught the attack before it reached production.
From No-Code to Production Code: A Technical Migration Guide for Founders Who've Outgrown Bubble, Webflow, and Glide
A practical guide for founders hitting the ceiling on Bubble, Webflow, or Glide. Covers the signals that tell you it's time to migrate, how to pick a production stack, how to move your data without downtime, and a realistic timeline and budget framework for the rewrite.
Building Runtime Governance for AI Agents: Pre-Execution Validation, Permission Boundaries, and Behavioral Drift Detection in Production
How to build a governance middleware layer that intercepts every agent action before execution, validates it against policy, and either allows, denies, or escalates to a human. Covers five production failure modes with TypeScript implementation for each governance control.
The 5 Runtime Failure Modes That Kill AI Agent Deployments Before the Model Is the Problem
88% of AI agent projects never reach production, and model quality is rarely the reason. Covers 5 named runtime failure modes that kill deployed agents, with detection signals, real incident examples, and TypeScript countermeasures for each.
Why Your Engineering Team Is Shipping Slower Than 6 Months Ago
Engineering velocity declines at seed-to-Series-A startups for predictable, diagnosable reasons. Process debt, unclear ownership, hiring mistakes, burnout, and architectural bottlenecks all compound. Here is a diagnostic framework you can run in one afternoon, plus a tradeoffs table for each intervention.
Securing Agentic AI in Production: Permission Scoping, M2M Traffic Monitoring, and Preventing the Next Kiro-Style Outage
47% of organizations have delayed AI agent releases due to API security concerns. This guide covers the concrete engineering patterns that fix the problem: permission scope boundaries, M2M traffic observability, and human approval gates for high-risk agent actions.
The AI Ratchet Effect: Why Giving Your Engineering Team AI Tools Made Them Work Harder, Not Smarter
67% of engineers who adopted AI tools in 2025 worked more hours by year-end, not fewer. This is the AI ratchet effect: management converts every productivity gain into a permanently higher baseline. Here is how it happens, why it is worse at startups, and what a sustainable AI adoption cadence actually looks like.