An implementation guide for llama3 from scratch using JAX in 100 lines of code, covering model architecture, initialization, and training on Shakespeare dataset. The implementation focuses on pure functional programming principles with JAX's unique features like xla, jit, and vmap for optimized performance.
Novel research demonstrates how large language models can improve their forecasting abilities through self-play and outcome-driven fine-tuning, achieving 7-10% better prediction accuracy without human-curated samples. The approach brings smaller models (Phi-4 14B and DeepSeek-R1 14B) to performance levels comparable to GPT-4 in forecasting tasks.
A detailed walkthrough of building a budget-friendly AI workstation with 48GB VRAM for running local LLMs, costing around 1700 euros using second-hand Tesla P40 GPUs. The setup enables running various AI models locally, achieving 5-15 tokens per second depending on model size, while maintaining independence from cloud-based AI services.
A detailed analysis comparing large language models to psychic cold reading techniques reveals striking parallels in how both create illusions of intelligence through statistical responses and subjective validation. The author argues that LLMs are mathematical models producing statistically plausible outputs rather than demonstrating true intelligence, suggesting many AI applications may be unintentionally replicating classic mentalist techniques.