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Papers

# Title Topic
1 Beyond RAG: How Chomsky's I-Language and Compiler Design Converge on Knowledge Graphs LLVM-style IR, Chomsky's I-language, BFO ontology, grammar-first design
2 What Is Knowledge Engineering, Really? A working definition built around elicitation, evaluation, and 0→1 modeling in messy domains
3 Fine-Tuning LLMs Will Restructure Your Data Science Team How fine-tuning replaces annotation pipelines and the NN-optimization role; the new "fine-tuning analyst"
4 Why Domain-Specific Language AI Features Fail The customer-discovery process for niche language AI, and why a Lean Startup approach is required
5 Language AI Evaluation 101: Know Your User Why simplistic Ground Truth produces misleading accuracy metrics; cognitive empathy as the iteration loop
6 Hyper-Local Community Funding: A DAO Alternative to CDFIs Local digital tokens and DAOs as a delivery mechanism for under-served-neighborhood capital
7 CV: Knowledge Engineering in Messy Domains The IR-compile pattern across clinical trials, legal billing, maritime construction, narrative gaming, and geographic inequality