Archify has the agent write a typed JSON IR and lets a deterministic compiler turn it into a self-contained HTML artifact. I read the repository end to end and built four diagrams with it, watching what the validator refuses, why deliver, visual-check, and human review are kept apart, and what changes when this blog’s own mermaid is ported over.
Paper notes on The Llama 3 Herd of Models: 15T tokens, a 405B flagship, 128K context, and a design philosophy of 'managing complexity' (dense over MoE, SFT → rejection sampling → DPO instead of PPO) — the paper where an open-weight model first reaches GPT-4-class performance. It completes Llama 2's skeleton through scale, data, and simplicity.
Paper notes on Llama 2 covering its commercial license, a base model scaled to 2T tokens with 4k context and GQA, and the Llama 2-Chat alignment pipeline (SFT → two reward models → iterative RLHF with rejection sampling + PPO → GAtt). It fuses LLaMA's open base lineage with InstructGPT's RLHF at production quality.
Paper notes on LLaMA covering its core strategy — train smaller models for longer on publicly available data, optimized for inference — the result where a 13B model beats 175B GPT-3, the RMSNorm/SwiGLU/RoPE architecture changes, the 2048-A100 training scale, and how it kicked off the open LLM ecosystem.
An architectural analysis of Paperclip, an open-source control plane that operates AI agents as a single virtual company under an org chart, budget, and governance structure.