AI research, translated intoengineering decisions
Understand what influential papers actually claim, how the system works, where it breaks, and what changes when you operate it in production.
Research library
Each briefing separates the paper’s evidence from our production interpretation. Coverage spans Model architecture, Retrieval, Training systems, Agents, Inference systems, Alignment.
The Transformer
Attention Is All You Need
Transformers model relationships between tokens directly with attention, making sequence training far more parallel than recurrent architectures.
Read the engineering briefingRAG
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
RAG gives a generator access to an external, updateable document memory instead of relying only on facts stored in model weights.
Read the engineering briefingFlashAttention
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
FlashAttention speeds up exact attention by minimizing expensive reads and writes between accelerator memory levels.
Read the engineering briefingReAct
ReAct: Synergizing Reasoning and Acting in Language Models
ReAct turns tool use into a feedback loop: reason, act, observe, and revise.
Read the engineering briefingvLLM and PagedAttention
Efficient Memory Management for Large Language Model Serving with PagedAttention
PagedAttention makes LLM serving more efficient by allocating KV-cache memory in blocks instead of reserving large contiguous regions per request.
Read the engineering briefingDPO
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
DPO converts preference alignment into a supervised-style classification objective over chosen and rejected responses.
Read the engineering briefingHow to use these briefings
Start with the one-sentence model, inspect the architecture, then challenge the claims using the limitations. The production takeaways are editorial interpretation; the linked paper remains the authority for experimental methods and results.