EM
Embeddings • Apr 02, 2026
Common Failure Modes: embedding model evaluation
The mistakes teams should identify before launch. how to test embeddings for search, clustering, deduplication, and recommendations
Articles
AI engineering notes from real workflow work
Practical writing on agentic systems, orchestration, automation design, and the operating patterns behind reliable AI delivery.
AI
AI Infrastructure • Apr 02, 2026
Implementation Checklist: cost-aware AI architecture
A practical checklist for building and reviewing the workflow. how routing, caching, batching, and evals control model spend
BA
Backend Engineering • Apr 02, 2026
Evaluation Strategy: API rate limits
How to measure quality, reliability, and operational readiness. how throttling, retries, queues, and budgets prevent overload
ML
ML Engineering • Apr 01, 2026
Deployment Playbook: fine-tuning workflows
How to prepare the workflow for CI/CD and production operations. how to prepare data, choose adapters, test outputs, and track model versions
DE
DevOps • Apr 01, 2026
Tooling Map: container hardening
A map of useful tools, libraries, and platform decisions. how to package application runtimes without leaking secrets or state
FR
Frontend Engineering • Apr 01, 2026
DropTicks Field Notes: design systems for AI products
Opinionated engineering notes for practical AI and software delivery. how reusable components keep AI workstations consistent
EM
Embeddings • Mar 31, 2026
Implementation Checklist: embedding model evaluation
A practical checklist for building and reviewing the workflow. how to test embeddings for search, clustering, deduplication, and recommendations
AI
AI Infrastructure • Mar 31, 2026
Architecture Guide: cost-aware AI architecture
A system-design view for planning production implementation. how routing, caching, batching, and evals control model spend
BA
Backend Engineering • Mar 31, 2026
Common Failure Modes: API rate limits
The mistakes teams should identify before launch. how throttling, retries, queues, and budgets prevent overload
ML
ML Engineering • Mar 31, 2026
Security Review: fine-tuning workflows
The security and permission concerns that should be reviewed. how to prepare data, choose adapters, test outputs, and track model versions
DE
DevOps • Mar 30, 2026
Team Workflow: container hardening
How engineering, product, and operations teams should collaborate. how to package application runtimes without leaking secrets or state
FR
Frontend Engineering • Mar 30, 2026
Scaling Notes: design systems for AI products
How the workflow changes as traffic, users, or data volume grows. how reusable components keep AI workstations consistent