Blog — Page 38 of 53
Browsing page 38 of 53 — 634 articles and counting.
Write-Ahead Logs: How Databases and Distributed Systems Guarantee Durability
A deep-dive into the write-ahead log pattern that underpins crash recovery, replication, and point-in-time restore in PostgreSQL, SQLite, Kafka, etcd, and beyond. Covers WAL structure, checkpointing, log compaction, fsync cost, group commit, and production monitoring.
gRPC in TypeScript: Protocol Buffers, Streaming RPCs, and Service-to-Service Communication in Production
A practical guide to gRPC in TypeScript services. Covers protobuf schema design, code generation with buf, implementing unary and streaming RPCs with @grpc/grpc-js, interceptors, deadline propagation, and load balancing.
Progressive Web Apps in 2026: Service Workers, Push Notifications, and Offline-First Patterns
A practical guide to building production PWAs with service worker lifecycle management, caching strategies, push notifications, and offline-first IndexedDB patterns in TypeScript.
Real-Time Collaboration in Web Apps: Yjs, Liveblocks, and Conflict-Free Editing in Production
A practical deep-dive into building real-time collaborative editing features: comparing Yjs CRDTs, Liveblocks managed service, and PartyKit/Durable Objects, with TypeScript code for presence, persistence, and production concerns like document growth and garbage collection.
Building a Conversational AI Platform: Multi-Turn Dialogue, Session Management, and Persona Consistency in Production
A production architecture guide for conversational AI systems: session state, multi-turn dialogue tracking, persona consistency, streaming with interruption, cost management, and TypeScript patterns that hold up beyond the demo.
LLM Evaluation Datasets: Building, Curating, and Versioning Gold-Standard Test Sets for Production AI
Most teams judge LLM quality by vibes. This guide covers how to build proper evaluation datasets from production logs, annotate them with inter-rater agreement, version them, and wire them into CI/CD regression gates.
Model Context Protocol (MCP) in Practice: Building Interoperable Tool Integrations for AI Agents
MCP is Anthropic's open standard for connecting AI models to external tools and data sources. This guide covers the architecture, how it differs from raw function calling, building a custom MCP server in TypeScript, authentication, testing, and when MCP is actually worth the overhead.
Running AI Inference at the Edge: Cloudflare Workers AI, WebGPU, and Small Model Deployment Strategies
A practical guide to running AI inference at the edge instead of calling centralized APIs. Covers Cloudflare Workers AI bindings, WebGPU client-side inference, small model quantization, latency vs accuracy tradeoffs, hybrid architectures, cost analysis, and caching strategies with TypeScript examples.
PostgreSQL Performance Tuning in Production: Query Plans, Index Strategy, and Connection Management
A practical guide to PostgreSQL performance for production systems. Covers EXPLAIN ANALYZE, index types, covering indexes, connection pooling with PgBouncer, configuration tuning, and autovacuum.
Production Readiness Reviews: A Pre-Launch Checklist for Startup Engineering Teams
A practical guide to production readiness reviews for startup engineering teams. Covers reliability, observability, security, data, deployment, operational readiness, and dependencies, with a concrete checklist and guidance on when to skip vs when to treat items as non-negotiable.
Service Mesh in Practice: Istio, Linkerd, and When Your Startup Actually Needs One
A practical guide to service meshes for startup engineering teams. Covers what a service mesh actually does, the sidecar proxy model, Istio vs Linkerd head-to-head with real resource numbers, ambient mesh, eBPF alternatives, and an opinionated decision framework based on team size and compliance requirements.
SOC 2 Compliance for Engineering Teams: Controls, Evidence Collection, and Automation Without Slowing Down
A practical engineering guide to SOC 2 compliance. Covers Trust Service Criteria, Type I vs Type II, which controls engineering teams own, evidence collection automation from GitHub and AWS, infrastructure-as-code as compliance evidence, common audit mistakes, and tools like Vanta and Drata.