What we learn building APIs, mobile apps, AI and web platforms for SMEs across Latin America.
A network timeout does not mean the payment failed: it can mean the client lost the response to a payment that went through. How to implement an idempotency key in Spring Boot that covers that case, not just the duplicate charge.
Read →The concrete problems with Provider that pushed us to Riverpod: BuildContext coupling, implicit providers that are hard to test, and a real provider migrated end to end.
Read →We compare Cloud Run and GKE Autopilot on what actually changes an architecture decision: cold starts, networking control, and the point where a cluster starts to earn its keep.
Read →SSE handles a large share of real-time updates with a fraction of the operational overhead of a WebSocket server. We compare data flow direction, automatic reconnection, and compatibility with existing HTTP infrastructure, with Node.js examples.
Read →Astro hydrates nothing by default: every interactive component is an explicit decision. We cover client:load, client:idle, client:visible, and the most common mistake, hydrating components that never needed client-side JavaScript.
Read →Managed Identity solves authentication between Azure resources without storing credentials, but only once you understand system vs. user-assigned identity and the RBAC errors around it.
Read →A concrete GitHub Actions pipeline to ECS Fargate: build, push to ECR, task definition updates, and a rolling zero-downtime deploy, with OIDC, health checks, and automatic rollback.
Read →pipe() respects data-flow backpressure, but not error propagation or a Transform that buffers chunks on its own. We look at why that saturates an ingestion microservice's memory and how pipeline() fixes the lifecycle.
Read →GlobalScope has no lifecycle, and that is exactly what causes the leak. We walk through the Job hierarchy, the real difference between lifecycleScope and viewModelScope, and how a misused SupervisorJob can hide exceptions.
Read →Swift's @Observable macro tracks state at the property level, not the object level. We cover the real impact on view recomposition and when to use @State vs @Bindable.
Read →Minimal APIs strips away ceremony, but it won't organize your project for you. We cover grouping endpoints, where validation filters belong, and when it's time to go back to controllers.
Read →Without a readinessProbe and a startupProbe, a pod can look healthy and still cause 502s on every rolling deploy. We cover the three probes, their defaults, and how to size them for slow JVM and .NET startups.
Read →Signals does not replace RxJS, it solves a different problem. We cover when to use each, how they interoperate through toSignal and toObservable, and the anti-patterns we keep seeing when teams port RxJS-heavy code.
Read →LLMs generate methods, imports, and parameters that sound real but do not exist. We walk through the five signs that give away a code hallucination before it costs hours of debugging.
Read →How we structure mid-to-large Flutter projects by feature, using Cubit for state and get_it for dependency injection. With code, decision criteria, and the mistakes that break the separation in practice.
Read →An LLM can write code that compiles and passes its own tests without that meaning it is ready to ship. Here are the four checks we run before the client sees it.
Read →Most projects don't need microservices yet. We walk through what they actually cost and the five objective signals that genuinely justify splitting the monolith.
Read →Nine articles describing failures in our own product. It is not humility: it is the only way a good number means anything.
Read →No test covers the scenario where your program is not running. And that turned out to be one of our most expensive failure modes.
Read →If the shape of Cerberus is what loses money, a system with the opposite shape should make it. We built it. The result was a profit factor of 1.04 over seven and a half years: a coin flip.
Read →The URL you compile into the binary is immutable for life: the client pastes it once and never touches it again. That constraint decides the whole architecture.
Read →We lowered the limit from $60 to $20 expecting less risk. Maximum drawdown went from $224 to $2,331. A stop inside normal market noise does the opposite of what you think.
Read →Most robots mix entry logic with protection logic. When the strategy fails, the protection fails with it. Here is how we split the two.
Read →Every close attempt we sent was rejected. The lesson: if your protection lives only in your code, it does not exist when you need it most.
Read →Same configuration, same data, same window. Only the simulator fill model changes. And the result changes sign. A backtest like that cannot support any decision.
Read →The reference bot we studied closed almost 99 % of its cycles green. It also carried $64,077 of floating loss. A high win rate is not an edge: it is a knob you can turn.
Read →A volatility brake, a flip-close and a regime switch. All three sounded good. All three came out worse than not having them. This is what happens when you measure one at a time instead of stacking.
Read →The real workflow for integrating Claude into daily development: planning, block-by-block generation, diff review, tests with real semantics, and documentation. No hype — what actually works.
Read →Java 21 shipped stable virtual threads and Spring Boot 3.2 enables them with one line. What actually changes in your API, what doesn't, and the pinning problem nobody tells you about.
Read →Not every project needs microservices from day one. The modular monolith solves the real problems of the traditional monolith without microservices' operational complexity — and keeps the door open to migrate later.
Read →The architecture decisions you make in the first few weeks determine how much the project hurts at month 6. How to organize domain modules, separate layers, and keep clean boundaries in NestJS.
Read →AI-generated code can look perfect and still break production. This checklist covers the 6 points you need to verify before approving any LLM-generated PR.
Read →AI can refactor code with precision, but it can also silently break production. Here are the types of changes where you can trust it — and where you need to verify carefully.
Read →AI-generated code usually compiles and fails code review. The difference is not in the model — it is in how you describe the work. Five concrete practices that move the needle.
Read →Tuurt builds custom software, integrates AI where it adds verifiable value, and develops 2D and mobile games. This is what you will find in this blog and how we work.
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