Structured tutorials and reference knowledge—organized for learning and lookup
Current Situation Analysis Most engineering teams treat LLM fine-tuning as a research exercise rather than a production pipeline. I've audited dozens of failed fine-tuning projects at FAANG scale, and the failure modes are identical: 1. Bloated Training Costs: Teams use vanilla transformers.
Current Situation Analysis Most engineering teams select open-source LLMs using a flawed heuristic: they pick the model with the highest score on MMLU or GSM8K, deploy it in FP16 via a generic Docker container, and pray the GPU bill doesn't bankrupt the project.
Current Situation Analysis When we migrated our internal coding assistant and customer support summarization pipeline from cloud APIs to on-prem hardware, we expected cost savings. We didn't expect the engineering debt. The standard tutorial approach fails immediately under production load.
Current Situation Analysis Most engineering teams implement Zero Trust by purchasing a commercial SASE platform, routing all internal traffic through a centralized broker, and calling it secure. This works for branch offices. It collapses in Kubernetes.
Current Situation Analysis Cache stampedes are not theoretical edge cases. They are the primary cause of production outages in read-heavy microservices.
Current Situation Analysis When we migrated our event ingestion pipeline to handle 50,000 writes per second, PostgreSQL 16 started hemorrhaging. The architecture was "standard": a Go 1.21 service using pgx v5, hitting a managed RDS instance with pg_bouncer in transaction mode.
Current Situation Analysis You deploy three exporters, spin up Prometheus, attach Grafana, and call it a day. It works until you hit 40 microservices. Then the cardinality explodes. Every pod scrapes /metrics every 15 seconds. Network connections multiply. Prometheus starts dropping samples.
Current Situation Analysis When I took over platform engineering for a high-throughput payment processing cluster, our deployment pipeline was bleeding money and engineer time. We were running Argo Rollouts 1.5.3 with static canary steps: 10%, 25%, 50%, 100%.
Current Situation Analysis Most engineering teams treat Docker multi-stage builds as a size optimization tool. They copy source code, install dependencies, build artifacts, and copy the result to a minimal runtime image.
Current Situation Analysis At scale, GitHub Actions YAML stops being a configuration file and becomes a maintenance liability. We manage 340+ microservices across a monorepo and polyrepo hybrid.
Current Situation Analysis AI metering is rarely a first-class citizen in architecture reviews. Most engineering teams treat token counting as a logging concern, attaching a simple counter to the API response and writing it to the primary database.
Current Situation Analysis Most AI SaaS products die by a thousand token cuts. You build a feature, integrate the OpenAI SDK, and ship. Then the traffic spikes. Your bill hits $4,200/month for 15,000 active users. Your P99 latency creeps past 2.
