Structured tutorials and reference knowledge—organized for learning and lookup
Current Situation Analysis We inherited a payment processing service that looked healthy on dashboards but collapsed under load. The architecture followed the standard "scale-out" dogma: Kubernetes Horizontal Pod Autoscaler (HPA) based on CPU, auto-scaling database read replicas, and aggressive ret...
Current Situation Analysis At scale, LLM API costs don't scale linearly with users. They scale with redundancy. Most engineering teams optimize at the prompt level: trimming whitespace, switching to cheaper models, or implementing basic string caching. This is tactical theater.
Current Situation Analysis Most microservice tutorials teach you to split a monolith by drawing boxes around domain boundaries and connecting them with synchronous HTTP or gRPC calls. This creates a distributed monolith disguised as modern architecture.
Current Situation Analysis Marketing teams at SaaS companies demand hyper-personalized landing pages. They want dynamic headlines, segment-specific feature lists, and localized social proof. Engineering teams respond with static site generation (SSG). This creates a fundamental conflict.
Current Situation Analysis Local LLM deployment has matured past the "run it in a terminal" phase, but production teams still hit the same wall: naive implementations collapse under concurrent load.
Current Situation Analysis When we audited our flagship mobile application at scale (2.1M DAU across iOS 17 and Android 14), the performance profile was textbook mid-tier React Native: acceptable on developer devices, catastrophic in production. Cold starts hovered at 1.8 seconds on Android 14.
Optimizing Dense LLM Inference on Trillium TPUs: A Production-Grade vLLM Deployment Guide Current Situation Analysis The industry is currently experiencing a structural shift in how large language m...
Current Situation Analysis Static onboarding flows are conversion killers. When a user signs up, their intent varies wildly: some want to import existing data, others want to build a dashboard from scratch, and some are just browsing.
Current Situation Analysis At scale, LLM integration is rarely an API problem; it's an information theory problem. Most engineering teams treat the context window as a bucket: they dump chat history, RAG results, and system instructions into the payload and pray the model ignores the noise.
Current Situation Analysis AI usage metering is typically treated as a synchronous post-request hook. You fire a request to an LLM, wait for the response, parse the token count, and log it. This works in development.
Current Situation Analysis Most engineering teams treat AI feature pricing as a post-execution accounting problem. They ship a model, count tokens in a background worker, multiply by a static rate card, and reconcile the invoice at month-end. This approach worked when AI was a novelty.
