Archive: 2026/03

Vibe-coded MVPs get you to market fast, but they collapse under real user load. Learn the exact user thresholds, red flags, and steps to transition safely to production engineering before technical debt destroys your startup.

Sliding windows and memory tokens let large language models handle hundreds of thousands of tokens without crashing. Here’s how they work-and why they’re the real reason today’s AI can understand long documents.

Security KPIs for LLM programs measure real risks like prompt injection and data leakage - not uptime or accuracy. Learn the exact metrics enterprises use to stop AI attacks before they happen.

Corpus diversity in LLM training isn't about quantity-it's about quality. Models trained on balanced, multi-domain, multilingual data outperform larger models on narrow datasets, using less energy and generalizing better to unseen tasks.

Hybrid recurrent-transformer designs combine the efficiency of Mamba with the reasoning power of attention to solve long-context bottlenecks in large language models. They're already powering production systems like Hunyuan-TurboS and AMD-HybridLM.