This post highlights the first book that pulls together language modeling, inference optimization, reinforcement learning, system scaling, agentic AI, retrieval-augmented generation, memory, environments, and benchmarks in one volume. It then points you to paperswithcode.co’s “most cited” list and recommends reading the top ten papers, coding them, and writing about your findings.

OpenMed developed a pipeline that transforms protein concepts into codon-optimized DNA sequences. They compared various transformer architectures for codon-level language modeling, finding CodonRoBERTa-large-v2 to outperform others in biological relevance. The project includes detailed results and runnable code for each stage.