Learning Paths
Knowledge Base
Structured tutorials and reference knowledge—organized for learning and lookup
How We Slashed RAG Eval Costs by 94% and Caught 99.8% of Hallucinations Using Adaptive Tri-Vector Evaluation
Current Situation Analysis At FAANG scale, RAG evaluation is not a "nice-to-have"; it's the gatekeeper of production stability. When our team first adopted RAG for the internal knowledge assistant serving 40,000 engineers, we followed the standard playbook: generate a golden dataset, run RAGAS metr...
Autonomous Constraint Evolution: Engineering Self-Optimizing Agent Frameworks
Autonomous Constraint Evolution: Engineering Self-Optimizing Agent Frameworks Current Situation Analysis Modern AI agent architectures treat the behavioral harness—the collection of system instructi...
In Q2 2026, we benchmarked React 20.0.0 and Svelte 5.0.0 across 12 desktop UI workloads: Svelte deli
In Q2 2026, we benchmarked React 20.0.0 and Svelte 5.0.0 across 12 desktop UI workloads: Svelte delivered 42% faster first-contentful-paint (FCP) and 37% lower memory overhead at 10,000 DOM nodes, but...
Referral Architecture: Cutting Fraud by 99.2% and Latency to 14ms with PostgreSQL 17 and Idempotent Event Streams
Current Situation Analysis When I joined the growth engineering team at a Series B fintech, our referral program was bleeding money and stalling signups. We were losing $42,000 per month to Sybil attacks and self-referral loops.
How We Cut LLM Serving Costs by 62% and TTFT by 71% with KV-Cache-Aware Routing
Current Situation Analysis Most teams deploying LLMs in production treat serving infrastructure like traditional stateless APIs. You spin up vLLM pods, put a round-robin load balancer in front, and pray the GPU memory holds up. This approach works in benchmarks and fails in production.
How I Cut LLM Inference Costs by 82% and Latency by 64% Using Adaptive Mixed-Precision Routing
Current Situation Analysis - Real-world problem: Serving 70B+ parameter models at production scale demands a brutal trade-off between accuracy, latency, and GPU spend.
The Idempotency-First Pattern: How I Reduced System Design Interview Failures by 80% and Cut Retry Storms by 94%
Current Situation Analysis Most system design interview prep is fundamentally broken. Candidates spend hours memorizing CAP theorem trade-offs and drawing boxes for "Load Balancer" and "Cache.