Two years ago, running a quality language model cost $15 per million tokens. Today, you can get comparable output for $0.15. That’s a 100x reduction. This isn’t incremental improvement — it’s a phase transition…
Mixture of Experts (MoE) is the architectural trick that broke the scaling laws. Instead of activating every parameter for every token, MoE models route each input to a small subset of specialized “expert” networks….
Over-parameterized neural networks routinely achieve near-identical performance after losing 90% of their weights. Network pruning reveals something surprising: most parameters carry zero meaningful signal. The question is why structure emerges more reliably from absence…
What if you could combine the strengths of two models without retraining? Create a model that writes code like Model A but reasons like Model B? This is model merging, and it works. Model…
Running a 30 billion parameter model on a gaming PC used to be a pipe dream. Now it’s routine. The techniques that made this possible—quantization, memory optimization, and efficient inference—are transforming what’s accessible to…
The race to Artificial Superintelligence has a clear consensus strategy: scale. More parameters. More data. More compute. Build a bigger model and intelligence will emerge. The evidence so far seems to support this. GPT-4…
Every reasoning error an LLM makes traces back to a false binary choice. Not some of them. All of them. This is a strong claim. Here’s why it holds. The Pattern Ask a model…
Every time you start a conversation with an LLM, you lose everything. The model doesn’t remember you. It doesn’t know your preferences, your expertise level, your communication style, or the fifty previous conversations where…