Insight · Plateer AI Labs

Article series

LLM Inside

The core ideas you need to make sense of an LLM, taken one at a time.

4 articles

The GS Certification Diary

Three weeks of reading 80 defect reports and rebuilding the code, the errors, and the manual

5 articles

The Harness Diary

Designing the execution environment an AI agent works in, from the ground up

10 articles

The Ontology Diary

Starting from the questions, building a knowledge graph, then working on quality and search

13 articles

All articles

Tech Note2026.09.21

Kubernetes Three-Node HA Setup and Failover Testing

Testing pod recovery in a three-node K3s cluster: local-path volume constraints, migration to shared storage, recovery results, and validation limits.

박예원

Tech NoteLLM Inside2026.09.17

Why is a long context window not memory? (Part 3)

A context window is working space for one request. Separating parameters, chat history, memory, and RAG shows why selection matters as much as length.

김해수

Tech NoteThe Ontology Diary2026.09.15

The ontology build and search improvement journey, Part 1 — Why we removed the LLM from the automatic build

How we separated the LLM from the automatic ontology build and found silent document loss, damaged Korean meaning, and false paths created by value nodes.

김진수

Tech NoteLLM Inside2026.09.10

What changes when an LLM has more parameters? (Part 2)

Parameters are trained numbers, not stored facts. Model size only makes sense alongside data, compute, post-training, and inference cost.

김해수

Field Report2026.09.06

Financial institutions ask about operations before AI agents: questions from the field

Financial institutions now ask whether AI can operate safely, not simply whether it works. Field insights on document quality, closed networks, auditability, business-built agents, and governance.

PPlateer AI Labs

InsightInsight from our research and the field

The tools our agent built for itself were not canvas nodes