Blog — Page 46 of 53
Browsing page 46 of 53 — 634 articles and counting.
Edge-First Architecture with Cloudflare: D1, R2, KV, and Queues Working Together in Production
A unified architecture guide for building full-stack applications on Cloudflare's edge platform. Covers when to use each primitive, how they compose in a real application, data flow patterns, consistency tradeoffs, and production gotchas with a concrete TypeScript example.
End-to-End Type Safety in Full-Stack TypeScript: From Database to UI Without Runtime Surprises
TypeScript doesn't prevent runtime type errors by itself. Learn how to close the gaps across the full stack using Drizzle, Hono RPC, Zod, and shared schemas so your types survive all the way from the database to your React components.
React Performance Patterns: Memoization, Virtualization, and Bundle Splitting for Production Apps
A practical guide to React performance optimization that actually matters in production. Covers measurement with React DevTools and web vitals, memoization tradeoffs, list virtualization with TanStack Virtual, code splitting with React.lazy, and bundle analysis.
AI Guardrails in Production: Content Filtering, Output Validation, and Safety Layers for LLM Applications
Most teams ship LLM features with a system prompt and hope for the best. This article covers the full guardrails stack for production: input validation, prompt injection detection, output filtering, structured output validation with Zod, PII detection, topic boundaries, and cost guardrails, layered without destroying latency.
Embedding Models Compared: OpenAI, Cohere, and Open-Source Models for Production Search
Choosing an embedding model affects retrieval quality, cost, latency, and operational risk in ways most teams underestimate. This guide compares OpenAI text-embedding-3, Cohere embed-v3, and open-source options including sentence-transformers, nomic-embed, and BGE, with TypeScript examples and a concrete decision framework.
Prompt Engineering for Production: Versioning, Testing, and Deployment Pipelines
Prompt engineering in production is a software discipline, not a creative exercise. This guide covers versioning strategies, testing pipelines, A/B testing, deployment patterns, and observability for teams running prompts at scale.
Chaos Engineering for Startups: Breaking Things on Purpose Before Production Does It for You
Chaos engineering is not a Netflix luxury. Startups benefit disproportionately because their blast radius is smaller and their redundancy is thinner. This covers the principles, how to run your first experiments, practical tooling, hypothesis design, blast radius control, and how to build a chaos practice that does not terrify your team.
Infrastructure as Code for Startups: Terraform, Pulumi, and SST Compared
A practical comparison of Terraform, Pulumi, and SST for startups. Covers state management, team workflow, CI/CD integration, and a decision framework for picking the right IaC tool based on your stack, team size, and cloud provider.
Secrets Management for Startup Engineering Teams: From .env Files to Production Vault
A practical guide to secrets management maturity for engineering teams. Covers why .env files break down at scale, the full maturity ladder from encrypted dotenv through cloud-native secret stores, TypeScript access patterns, rotation without downtime, CI/CD injection, and Cloudflare Workers secrets.
Running Effective Architecture Reviews: A Lightweight Process for Growing Engineering Teams
Most architecture reviews happen too late, involve too many people, and produce no binding decisions. This covers a lightweight process for triggering reviews at the right time, running them in 30 minutes, and connecting them to ADRs so the decisions actually stick.
Scaling from 3 to 30 Engineers: The Processes That Help and the Bureaucracy That Kills
Growing an engineering team from 3 to 30 people breaks things in predictable ways at predictable sizes. This guide covers what breaks at each inflection point, which processes to introduce and when, and which ones to avoid until you actually need them.
Bloom Filters, Count-Min Sketch, and HyperLogLog: Probabilistic Data Structures for Production Systems
When exact answers cost too much memory or too many round trips, probabilistic data structures trade a small, tunable error rate for massive space and speed savings. This article covers how Bloom filters, Count-Min Sketch, and HyperLogLog work internally, how to implement them in TypeScript, and how to tune them for real workloads.