by Jaepil Jeong | August 25, 2026
A database is not truly unified merely because every result fits in one container. We introduce the typed carrier algebra behind UQA Engine, show why document sets, scored postings, ranked views, SQL bags, join tuples, and graph matches must retain different laws, and trace those distinctions into the Rust optimizer and runtime.
Read Postby Jaepil Jeong | April 10, 2026
Between models and hardware lies an invisible structural gap — a data layer that should exist but doesn't. We examine why the current AI infrastructure stack is fragmented, why this is a structural problem rather than a transitional one, and what conditions a proper solution must satisfy. We then present UQA and the Cognica engine as one concrete response to these conditions.
Read Postby Jaepil Jeong | March 26, 2026
Graph databases solve relationship-heavy problems elegantly, but adding a separate graph system alongside your relational database creates operational complexity. We explain how Cognica integrates graph queries into its unified algebra, enabling Cypher and SQL to compose in a single transaction without data duplication.
Read Postby Jaepil Jeong | March 25, 2026
A cosine similarity of 0.85 tells you an angle, not a probability. We show how to transform vector similarity scores into calibrated relevance probabilities using distributional statistics that ANN indexes already compute — completing the probabilistic unification of text and vector retrieval.
Read Postby Jaepil Jeong | February 23, 2026
Sigmoid is not a design choice — it is a mathematical theorem. We show why the sigmoid function is the unique valid transform for converting BM25 scores to probabilities, completing Robertson's Probability Ranking Principle after 50 years.
Read Postby Jaepil Jeong | February 1, 2026
Modern search systems struggle to combine lexical matching with semantic understanding. We explore how we built a probabilistic ranking framework in Cognica Database that transforms BM25 scores into calibrated probabilities, enabling principled fusion of text and vector search results.
Read Postby Jaepil Jeong | January 19, 2026
The essential infrastructure that makes Copy-and-Patch JIT development and debugging practical. We explore the multi-architecture disassembler for validation and software CPU emulator for cross-platform testing and debugging.
Read Postby Jaepil Jeong | January 17, 2026
How Cognica Database Engine breaks the JIT compilation latency barrier. We explore Copy-and-Patch JIT compilation, a technique that achieves 2-10x speedup over interpretation while keeping compilation time under one millisecond per kilobyte of bytecode.
Read Postby Tim Yang | December 23, 2025
We examine the database architecture changes required by on-device AI. Just as SQLite was the answer for on-device computing, on-device AI requires a new database that integrates transactions, analytics, full-text search, and vector search. We explain why Cognica works identically on-device and on servers.
Read Postby Tim Yang | December 9, 2025
This article provides a technical analysis of why legal case search is challenging in the legal services market. We examine the structural characteristics of legal case data and the limitations of existing distributed architectures (RDB + ElasticSearch + Vector DB), and explain why integrated search based on a single database is necessary.
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