17 articles exploring architecture
Comprehensive Apache Airflow analysis: open source and every managed vendor (Astronomer, Cloud Composer, MWAA), pricing at three scales, Airflow 3.0 migration, operational pain points, AI workflows, and a decision matrix for choosing the right deployment.
Comprehensive Dagster analysis: pricing, asset-centric orchestration, AI/ML pipelines, learning curve, integrations, and why treating data as the product gives teams lineage and governance that task-centric orchestrators cannot match.
Comprehensive Prefect analysis: pricing, scaling, deployment architecture, integrations, learning curve, and why its dynamic Python-native control flow fits AI agent workflows better than static DAG orchestrators.
Most teams adopt a dedicated vector database before the production signals that justify it arrive. A decision framework for when pgvector stops being enough.
Benchmark-driven comparison of the 2026 document parsing landscape - LiteParse, LlamaParse, Unstructured, Docling, PyMuPDF, Google Document AI - with Python code, failure modes, and an async routing architecture.
Framework choice is no longer about developer ergonomics. In an AI-driven era, the winners will be frameworks that resist entropy, enforce constraints, and scale safely under continuous AI modification.
For 30 years, language choice was driven by developer productivity. AI changes the equation. When machines generate code, verification matters more than velocity.
PostgreSQL's built-in full-text search can handle global search for most SaaS applications. Learn when Postgres is enough and when Elasticsearch makes sense.
Stop over-engineering AI infrastructure. PostgreSQL already has everything you need: pgvector for embeddings, pgai for automation, TimeScaleDB for metrics. Build faster by using what you have.
Production AI systems are 98% harness, 2% model. New research reveals why architecture, permissions, and safety matter more than model capability - and how to build systems that actually work.
After running all three in production: 20-criteria breakdown of real migration costs, team overhead, backfill behaviour, and which orchestrator survives 500+ pipelines.
How to build a SaaS metrics stack that produces ARR, MRR, churn, LTV, and CAC you can actually defend - with SQL, Python, and the right source-of-truth hierarchy.
Most AI coding tool comparisons still reward the wrong things. A workflow-first breakdown of Claude Code, Cursor, Copilot, Windsurf, and Antigravity through the lens that actually matters: how teams ship under real constraints.
AI-generated code feels fast, but the maintenance cost appears later. Why AI creates locally correct but globally fragile systems, and the engineering standards that fix it.
A battle-tested blueprint for Claude Code projects that stay predictable at 50+ files - folder layout, .claudeignore, skills architecture, and 3 ready-to-copy templates.
A CTO's honest account of building a personal portfolio site from scratch - the decisions that made sense at the time, the bugs that didn't, and what I'd do differently.
From a simple JSON formatter to a 400+ tool developer platform serving 100K+ users - the complete engineering journey covering architecture, zero-backend design, performance, and deployment.