A comp-sci foundation applied across AI systems architecture, full-stack development, and neuro-symbolic research. These are the tools I reach for when building systems that need to work.
Hybrid architectures combining neural inference with symbolic reasoning, constraint systems, and knowledge graphs.
Router design, tool contracts, state machines, retry/fallback logic. The harness that makes LLMs production-ready.
Prompt engineering, fine-tuning strategies, hallucination mitigation, evaluation frameworks, and model selection.
Structured memory for grounded reasoning. Entity extraction, relationship modeling, graph-based retrieval.
Symbolic postprocessing, constraint satisfaction, schema enforcement, recursive validation loops.
Research briefs, architecture documentation, position papers, glossaries. Translating complexity into clarity.
Full-stack development. React, Node.js, async patterns, type-safe architectures, build systems.
Data processing, ML pipelines, scripting, API development. FastAPI, pandas, numpy.
Component architecture, hooks, state management, performance optimization, SSR/SSG.
Semantic markup, responsive design, CSS Grid/Flexbox, animations, accessibility-first development.
Server-side JavaScript, REST APIs, middleware, real-time systems, CLI tooling.
Relational modeling, query optimization, migrations. PostgreSQL, SQLite.
Interactive data visualizations, force-directed graphs, custom chart types, SVG manipulation.
Structuring complex data for human comprehension. Glossaries, taxonomies, interactive explorers.
Distilling dense technical domains into structured, navigable outputs. Benchmarks, comparisons, evidence mapping.
Branching strategies, CI/CD integration, collaborative workflows, monorepo management.
Static site deployment, serverless functions, edge computing, preview environments.
Automated deployments, static hosting, CI pipelines, workflow automation.
Database architecture, schema design, automation, API integration. Knowledge management systems.
Write in implementable components. State model, router loop, tool contracts, validators, memory graph, eval hooks. Label it "neurosymbolic" after it works, not before.
The model handles interpretation. Everything else — routing, validation, state, memory, scoring — is explicit, deterministic, and auditable. Reliability lives in the harness.
Logic doesn't guarantee truth. It guarantees procedural correctness: constraints enforced, validators run, fallbacks triggered. That's the confidence inference alone can't provide.