AI & LLM

The economics of Large Language Models present a brutal reality: GPT-4-level performance costs $0.03 per 1K tokens for input and $0.06 for output. Run that at scale—say, 10 million daily queries—and you’re burning $900,000 monthly. But here’s what’s fascinating: researchers have discovered that a 1.3B parameter model, properly distilled from a 175B teacher, can match 95% of its predecessor’s performance on specific tasks while costing 0.1% to run. This isn’t magic. It’s knowledge distillation—a technique that has evolved from Geoffrey Hinton’s 2015 “dark knowledge” paper into a sophisticated ecosystem of methods that compress frontier AI capabilities into models small enough to run on your laptop. ...

The quadratic complexity of self-attention has haunted transformer architecture since its inception. As context windows expanded from 2K to 1M tokens, the O(N²) attention computation transformed from an annoyance into an existential bottleneck. Yet a counterintuitive discovery emerged in 2025-2026: computing only 5-20% of attention weights can match or exceed full attention performance. This isn’t compression with acceptable loss—it’s the revelation that transformers have been computing billions of unnecessary operations. The mathematics behind this phenomenon, and the engineering that exploits it, represents one of the most significant advances in LLM efficiency. ...

Traditional approaches to controlling Large Language Model behavior have followed two well-worn paths: prompt engineering at the input level, and fine-tuning or RLHF at the weight level. But what if we could modify how a model “thinks” in real-time, without changing its weights or crafting the perfect prompt? Representation Engineering (RepE) offers exactly this capability—a paradigm that treats internal activations, rather than neurons or circuits, as the fundamental unit of analysis and control. ...

In April 2025, Meta’s Llama 4 Scout achieved something previously thought impossible: processing 10 million tokens in a single context window. To put this in perspective, that’s roughly 20 novels, 40 hours of video, or an entire mid-sized codebase—all in one prompt. The secret behind this breakthrough isn’t a revolutionary new model architecture or exotic hardware. It’s a clever distributed computing technique called Ring Attention that fundamentally rethinks how we compute attention across multiple GPUs. ...