Blog — Page 35 of 53
Browsing page 35 of 53 — 634 articles and counting.
Network Security for Cloud-Native Startups: VPCs, Security Groups, Zero Trust, and Practical Hardening for Production Infrastructure
A practical guide to network security for startup engineering teams on AWS and GCP: VPC design, security groups, NACLs, mTLS, zero trust, DNS security, egress filtering, and a hardening checklist with Terraform examples.
Communicating Technical Strategy to Non-Technical Stakeholders: Board Presentations, Investor Updates, and Executive Communication for CTOs
A practical guide for CTOs on translating technical strategy into language that resonates with boards, investors, and non-technical founders. Covers board deck structure, framing technical debt as business risk, before/after translation examples, and templates for recurring updates.
The Fractional CTO Engagement Playbook: First 30 Days, Technical Assessment, and Delivering a Roadmap
What a fractional CTO engagement actually looks like in practice: the 30-day assessment framework, how to audit a codebase you didn't build, how to prioritize findings founders can act on, and the failure modes that make these engagements fall flat.
Designing a Logging and Log Aggregation Pipeline: Collection, Transport, Storage, and Search at Scale
A practical guide to building a production logging pipeline from structured log emission through Kafka transport to Elasticsearch, ClickHouse, or Loki, covering indexing strategies, retention policies, and query patterns.
Designing a Tenant-Aware AI Gateway: Per-Customer Model Routing, Token Quotas, and Cost Attribution in Multi-Tenant SaaS
How to architect an AI gateway layer for multi-tenant SaaS that routes requests to different LLM providers per tenant, enforces per-customer token budgets, and attributes costs accurately.
Designing an Object Storage System: Blob Storage, Metadata Indexing, and Erasure Coding at Scale
How object storage systems like S3 and R2 work under the hood. Covers the separation of metadata and data planes, consistent hashing for blob placement, erasure coding for durability without full replication overhead, metadata indexing for listing and prefix queries, and the tradeoffs between strong consistency and availability.
Building a White-Label SaaS Platform: Tenant Theming, Custom Domains, and Dynamic Configuration in Next.js
How to architect a white-label SaaS platform in Next.js where each tenant gets isolated branding, a custom domain, and dynamic configuration without forking your codebase.
Dependency Injection in TypeScript: Containers, Scoped Services, and Testing Patterns for Production
A practical guide to dependency injection in TypeScript backends, covering manual constructor injection, lightweight containers like tsyringe and InversifyJS, framework-native DI in NestJS, request-scoped services in serverless, and testing patterns that avoid excessive mocking.
Implementing Full-Text Search in Your SaaS: Postgres, Meilisearch, and Typesense Compared
A practical comparison of Postgres full-text search, Meilisearch, and Typesense for SaaS applications. Covers TypeScript integration, indexing strategies, relevance tuning, and a decision framework for when to graduate from Postgres to a dedicated search engine.
React Streaming and Suspense in Production: Selective Hydration, Out-of-Order Streaming, and Loading Patterns in Next.js App Router
A deep dive into React's streaming architecture for production apps. Covers how selective hydration works, out-of-order streaming with the App Router, practical loading.tsx and Suspense boundary patterns, waterfall avoidance, and how to measure streaming performance.
Building a Real-Time ML Feature Store: Stream Processing, Feature Pipelines, and Online Inference
A production-focused deep dive into ML feature store architecture: dual-compute pipelines, online/offline stores, point-in-time correctness, feature serving latency, and operational tradeoffs.
Building an AI-Powered Anomaly Detection System: Statistical Baselines, ML Models, and Real-Time Alerting in Production
How to build anomaly detection that works beyond toy examples: statistical baselines, isolation forests, autoencoders, feature engineering for time-series data, alert fatigue management, and production streaming deployment.