Technical deep-dives, honest cost analyses, and lessons learned from shipping production software for 14+ years.
Engineering CultureThe "full stack developer" was a useful concept when stacks were simple. In 2026, the frontend alone is as complex as an entire stack was in 2015. Specialization is not optional anymore.
Backend EngineeringWe ran identical API implementations in Go and Node.js on the same hardware with real-world payloads. The results are more nuanced than "Go is faster." Here is the complete data.
BusinessAgencies quote long timelines because their structure demands it. Here is how our two-person operation consistently delivers in weeks what traditional shops estimate in months.
ArchitectureWe will be the first to admit that hexagonal architecture is overengineered for most projects. But the alternative -- no architecture -- is worse. Here is our pragmatic middle ground.
BusinessWe have rescued 14 projects from failed offshore development in the past 3 years. The pattern is always the same: the bottom-barrel rate ends up costing 2-3x what domestic development would have cost. Here is why.
Backend EngineeringPostgreSQL can handle full-text search, JSON documents, time-series data, vector embeddings, and pub/sub messaging. Here is when to use Postgres for everything and when to reach for a specialized database.
AI EngineeringWhile everyone debates GPT-4o vs Claude, we quietly moved most of our production workloads to Gemini Flash Lite. The performance-to-cost ratio is unmatched for structured tasks.
Mobile DevelopmentAfter shipping 11 Flutter and 8 React Native apps over the past 3 years, we have a clear perspective on which framework wins in 2026. The answer depends on exactly two questions.
AI EngineeringRAG sounds simple in tutorials. In production, it adds 3-5 layers of hidden costs that most teams do not budget for. Here is a breakdown from 6 production RAG systems we maintain.
BusinessFounders consistently underestimate what software costs. Here is a transparent breakdown of real project budgets, from five-thousand-dollar MVPs to two-hundred-thousand-dollar platforms.
AI EngineeringClaude Opus 4.5 is the most expensive mainstream LLM on the market. We broke down exactly when you should use it versus cheaper alternatives, with real cost data from production workloads.
ArchitectureMicroservices solve problems that most small and mid-size business applications do not have. We switched back to modular monoliths and our delivery speed doubled.
AI EngineeringWe audit about 2 RAG-based chatbots per month for clients unhappy with their AI. The same 7 mistakes appear in almost every one. Here is what they are and how to fix them.
AI EngineeringTraditional OCR pipelines were fragile, expensive to maintain, and terrible at understanding context. Multimodal LLMs replaced our entire document processing stack in under a year.
AI EngineeringEveryone thinks self-hosting an LLM will save money. We ran the numbers on 4 real client projects and found the break-even point is further away than most teams think.
AI EngineeringThe default advice is "just use GPT-4 with good prompts." That works until your API bill hits five figures. Here is a decision framework based on sixty-plus AI features we have shipped.
AI ToolsWe gave 6 senior developers Claude Code for 30 days and tracked everything. The results surprised us: AI-assisted developers were faster, but not in the ways we expected.
AI EngineeringMost AI agent demos are impressive. Most AI agents in production are disasters. After building 8 production agents, we have identified the patterns that separate agents that work from agents that hallucinate their way into support tickets.
Frontend EngineeringServer-side rendering added complexity we did not need. For 80% of our projects, static export with client-side hydration delivers better performance, lower costs, and simpler ops.
Engineering CultureVibe coding is more than a meme. We tracked our team for 90 days and found that developers who embraced flow-first practices shipped features 3x faster with fewer bugs. Here is the methodology behind the results.
Product StrategyMost MVPs are built to validate an idea, but their architecture ensures they can never scale. We break down the structural decisions that doom prototypes and how to build MVPs that actually graduate.
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