Software development articles
Articles on software strategy, architecture, product planning, AI systems, cost, delivery timelines, infrastructure choices, and the tradeoffs behind building reliable digital products.
From ideas to implementation
Clear technical thinking. Work that can ship.
Technical guides
A focused archive for software architecture, AI systems, SaaS planning, pricing, timelines, retrieval, infrastructure, and implementation decisions.
Choosing an LLM provider is a product decision, not just a technical preference. OpenAI, Anthropic, and open-source models each fit different workloads, and the right answer often depends on privacy, cost, latency, and tooling more than on which model scores best on a benchmark.
Read article ->Vector database choice shapes the cost, scaling profile, and operational burden of every RAG and agent system you build. Pinecone, Weaviate, and pgvector cover most production cases; the right pick depends on volume, hybrid search needs, operational maturity, and how tightly the vectors live alongside your existing data.
Read article ->AI is finally moving the needle in e-commerce, but only when it is applied to the workflows that actually drive revenue. Personalized discovery, smarter support, dynamic pricing, and operational automation each have real, measurable upside, and each one has implementation traps that can quietly burn budget if the architecture is wrong.
Read article ->Cloud cost optimization is not about cutting every bill as low as possible. For SaaS companies, the goal is to keep infrastructure efficient while protecting reliability, performance, security, and the product velocity needed for growth.
Read article ->A useful MVP is not the smallest possible product. It is the smallest version that can test the right business risk with real users.
Read article ->A real SaaS MVP is rarely a two-week project. Done properly, it takes between two and six months from kickoff to a paying-customer launch, with the exact duration shaped by scope, integrations, billing complexity, and how clearly the product is defined before code begins.
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