Blog — Page 15 of 53
Browsing page 15 of 53 — 634 articles and counting.
Why Your Vibe-Coded MVP Starts Breaking at 60 Days: Security Patterns, Architecture Fixes, and a Production Readiness Checklist
The Lovable BOLA incident exposed 8 million users' data through a 48-day unpatched vulnerability. Here are the five security failure patterns that show up in nearly every AI-generated codebase, with TypeScript fixes and a 15-point production readiness checklist.
AI Supply Chain Security: How Open-Source Dependencies Become Attack Vectors in LLM Applications
How the AI toolchain layer (LiteLLM, LangChain, LlamaIndex, etc.) introduces supply chain attack vectors distinct from traditional OSS risk, with a TypeScript implementation for dependency validation and monitoring, multi-agent cascade risk analysis, and a production lockdown checklist.
Measuring AI Tooling ROI for Engineering Teams: Adoption Metrics, Productivity Baselines, and the Framework Your Board Actually Wants
86% of engineering leaders cannot tell their boards which AI tools deliver value. Here is a practical measurement framework: baselines, adoption metrics, per-developer ROI, controlled experiments, and how to present findings without fabricating precision.
The 7 Signs Your Technical Founder Has Become the Engineering Bottleneck
A hands-on technical founder is an asset at zero to one. At ten engineers and Series A, the same behavior becomes the primary velocity constraint for the entire company. Here are the seven signals that the transition is overdue.
Context Engineering for Production AI Applications: System Prompts, Memory Architecture, and Retrieval Strategies That Actually Scale
Context engineering is the discipline of deciding what information reaches your LLM and when. This guide covers system prompt versioning, memory hierarchies, retrieval-augmented context assembly, token budget allocation, and the architectural patterns that separate prototype AI apps from production-grade ones.
Why 88% of AI Agent Projects Never Reach Production (and What the 12% Do Differently)
IDC and Digital Applied 2026 data show 79% of enterprises have adopted AI agents, but only 11% have them in production. This article examines the governance, audit, and architecture gaps that kill agent projects after pilot, and what the teams that ship actually build.
Designing a Circuit Breaker: Failure Detection, State Machines, and Cascading Failure Prevention in Distributed Systems
A deep dive into circuit breaker design: the three-state machine mechanics, failure detection strategies, timeout calibration, the relationship with retry policies and bulkheads, and how to choose between library-level and infrastructure-level circuit breaking.
EU AI Act Compliance for Engineering Teams: Risk Classification, Technical Documentation, and Building Audit-Ready AI Systems Before August 2026
A practical engineering guide to EU AI Act compliance before the August 2, 2026 enforcement deadline. Covers risk classification, Annex IV technical documentation, logging architecture for agent decisions, human oversight patterns, and a realistic timeline for teams starting now.
Decision Debt Is Killing Your Series A: How Missing Architecture Decision Records Cost More Than Technical Debt
Decision debt is the undocumented reasoning behind your architecture. Unlike technical debt, it compounds invisibly at every leadership transition, compliance review, and due diligence event. Here is how to name it, measure it, and retroactively fix it before it kills a deal or a new CTO.
Migrating From No-Code to Production Code: Architecture Decisions, Data Migration, and Scaling Beyond Platform Limits
A founder's guide to escaping Bubble, Webflow, or Glide. Covers the decision framework for when to migrate, how to choose your new stack, incremental migration patterns that avoid the big rewrite, data migration strategy, and production hardening.
Graceful Degradation for AI Features: Fallback Strategies, Timeout Budgets, and Keeping Your App Alive When LLMs Fail
LLM outages are not edge cases. This guide covers fallback chains across cached responses, simpler models, and rule-based logic, timeout budget allocation across multi-step pipelines, feature-level circuit breakers, and detecting quality degradation before users notice.
Designing a Customer Data Platform: Event Collection, Identity Resolution, and Unified Customer Profiles at Scale
A practical guide to the internal architecture of a customer data platform. Covers event collection SDKs, identity resolution algorithms, profile merge logic, audience segmentation, and GDPR right-to-deletion.