Foundations for a Fluid, AI-Augmented Mind-Mapping System
Creative thinking is nonlinear, associative, and often emergent. Yet most tools constrain us to rigid lists, boxes, and outlines.
Creative thinking is nonlinear, associative, and often emergent. Yet most tools constrain us to rigid lists, boxes, and outlines.
A new design paradigm is emerging where engineering meets language models — shifting from deterministic problem-solving to collaborative, interpretive dialogue with hybrid intelligence.
This essay explores how Named Entity Recognition (NER) transforms raw text into structured meaning — and how, when paired with knowledge graphs, it reveals the hidden architecture of language. From identifying entities to connecting them, we uncover how language becomes a web of relationships machines can navigate, reason with, and grow from.
Designed a scalable AI inference pipeline with Python and Go, combining FastAPI for model serving and a Go-based reverse proxy for orchestration. Implemented modular stages: preprocessing, tokenization, inference, postprocessing, and monitoring. Enabled secure, observable, and resilient AI delivery with centralized routing, load balancing, and metrics instrumentation.
Designed a scalable AI inference pipeline with Python and Go, combining FastAPI for model serving and a Go-based reverse proxy for orchestration. Implemented modular stages: preprocessing, tokenization, inference, postprocessing, and monitoring. Enabled secure, observable, and resilient AI delivery with centralized routing, load balancing, and metrics instrumentation.