Blog — Page 40 of 53
Browsing page 40 of 53 — 634 articles and counting.
Building a Tool-Use Layer for LLM Agents: Function Calling, Schema Validation, and Error Recovery
Most function calling implementations are glue code, not architecture. This guide covers building a durable tool-use layer: provider abstraction across OpenAI and Anthropic, schema-first design with Zod, pre-execution sandboxing for dangerous tools, agent-loop retry strategies, and observability for tool execution chains.
Building an AI-Powered Customer Support System: Intent Classification, Retrieval, and Human Handoff in Production
A practical architecture for AI customer support: intent classification with embeddings, RAG over a knowledge base, confidence-gated human handoff, conversation state management, and the production concerns nobody writes about.
LLM Memory and Context Management: Sliding Windows, Summarization, and Long-Term Recall for Production Agents
How to manage memory and context in LLM-powered applications and agents. Covers sliding window truncation, conversation summarization pipelines, vector store long-term memory, hierarchical memory architectures, and token budget management with TypeScript examples.
Container Security in Production: Image Scanning, Runtime Policies, and Supply Chain Integrity
A practical guide to the full container security lifecycle, from building minimal images and scanning in CI/CD to runtime policies, supply chain integrity, and secrets management.
GitOps in Practice: Declarative Deployments with ArgoCD, Flux, and Reconciliation Loops
GitOps is not just a deployment pattern. It is a contract between your Git history and your cluster state. This guide covers the real mechanics of ArgoCD and Flux, repo structure, Kustomize and Helm overlays, drift detection, secret management with Sealed Secrets and External Secrets Operator, multi-environment promotion, and the cases where GitOps adds more friction than it removes.
Monitoring and Alerting Strategy for Startups: What to Alert On, How to Reduce Noise, and When to Page
A practical guide for small engineering teams: building a signal-over-noise alerting system using the alert pyramid, SLI-based thresholds, runbooks, and concrete TypeScript health check patterns.
Building a Platform Engineering Team: Internal Developer Platforms, Golden Paths, and Developer Self-Service
When and how to build a platform engineering team at a startup or scale-up. Covers the internal developer platform model, golden paths vs. guardrails, the platform product mindset, and the common mistakes that cause platform teams to fail.
Engineering Onboarding at Startups: From First Commit to Productive Contributor in Two Weeks
A practical guide to engineering onboarding at startups with 3-30 engineers. Covers environment automation, progressive task design, buddy systems, documentation strategy, and how to measure whether onboarding is actually working.
Technical Due Diligence: What Investors Actually Evaluate in Your Engineering Organization
Investors and acquirers evaluate far more than codebase quality during technical due diligence. This guide covers every dimension they examine: architecture scalability, CI/CD maturity, test coverage, dependency health, security posture, bus factor, tech debt, infrastructure costs, and documentation. Includes a self-assessment checklist founders can run before fundraising.
Designing a Data Pipeline: ETL vs ELT, Batch vs Streaming, and Orchestration Patterns for Production
A practical comparison of ETL vs ELT, batch vs streaming, and orchestration tools for production data pipelines. Covers schema evolution, backfill strategies, exactly-once semantics, and monitoring with TypeScript code examples.
Designing a Geospatial Search System: Spatial Indexes, Proximity Queries, and Location-Based Services at Scale
A deep-dive into spatial indexing strategies, proximity query internals, and the architecture behind location-based services that handle millions of location updates per second.
Designing a Recommendation Engine: Collaborative Filtering, Embeddings, and Real-Time Personalization
A deep dive into recommendation system architecture: collaborative filtering approaches, content-based methods, embedding-based retrieval, the cold start problem, real-time vs batch scoring, A/B testing quality, and the infrastructure required to serve recommendations at low latency in production.