Zero-Dependency 기반 AI 출력 품질 검증 CLI 도구 구현
pip install self-audit: A Zero-Dependency CLI for AI Output Quality
n-gram 매칭에서 Neural-based 평가로의 LLM 정량 측정 패러다임 전환
How We Actually Measure Whether an LLM's Output Is Good - BLEU, COMET and BLEURT
단순 Classifier에서 Decision-Support Tool로의 아키텍처 전환
What Building an AI Detector Taught Me About Machine Learning
Bootstrap Resampling을 통한 LLM Eval Metric의 통계적 신뢰 구간 확보
Bootstrap confidence intervals for your LLM eval metrics
Agent Skill 도입으로 하위 모델 성능 최대 +23.1%p 향상
Anthropic, OpenAI, or Cursor model for your agent skills? 7 learnings from running 880 evals (including Opus 4.7)
월 성장률 50% 달성 중인 Agentic Commerce 최적화 플랫폼 설계
Wildcard (YC W25) Is Hiring a Founding Applied ML Engineer
Kimi 2.6 도입으로 Baseline 75% 달성 및 Skill Uplift 17.2pp 유지
Evaluating Kimi 2.5 vs Kimi 2.6: What happens to agent skills when the model gets smarter?
LLM Agent Eval의 Overfitting 방지를 위한 Trace-Verdict 결합 구조 설계
Goodhart's Law Comes for Your Agent Evals: Why Your Green Dashboard Stops Meaning Anything
Anonymized Peer Review를 통한 LLM Self-Preference Bias 제거
LLM Self-Preference Bias: How Anonymized Peer Review Fixes It
OpenTelemetry 기반 AI Observability 플랫폼 Currai를 통한 LLM Pipeline 최적화
AI Observability for Lovable Apps: Monitor, Test, and Improve Prompts with Currai
Deterministic Behavioral Grading을 통한 LLM Rule Compliance 검증
Your AGENTS.md is valid. Your agent still breaks the rules.
Power Analysis 도입을 통한 LLM Eval set 크기 최적화 및 5% Regression 탐지력 확보
Power analysis for LLM evals: how big does your eval set need to be to catch a 5% regression?
OLMES 표준 기반의 체크포인트별 고해상도 LLM 평가 워크벤치 구축
olmo-eval: An evaluation workbench for the model development loop
Frontier LLM의 Adversarial Framing 하 Tool-use 능력 상실 및 Agentic Regression 발견
I Tested Claude Opus 4, GPT-4.1, GPT-4o, Sonnet 4, and Gemini 2.5 Pro on 10 Adversarial Scenarios. They All Broke on the Same One.
7개 세부 지표와 Veto Gate 기반의 AI Agent 품질 평가 프레임워크
The 7 things KaiCalls grades on eligible real calls
200+ Task 기반 LLM 평가 표준화를 통한 Regression Detection 체계 구축
What is an LLM evaluation harness? A deep dive into lm-eval-harness
LLM의 Worldview Context 결여 해결을 위한 Eval-driven 설계 전략
AI models are missing religious context. Builders should treat that as an eval problem.
Agentic Skills 도입을 통한 UI Accessibility 통과율 12%에서 86%로 개선
AI-generated accessibility, an update — frontier models still fail, but skills change the game
정적 벤치마크 탈피를 통한 LLM 평가 신뢰성 확보 전략
Why Your LLM Evals Are Lying to You
4개 모델 Council과 Git 로그를 통한 AI Agent 검증 아키텍처 설계
We built a 4-model Council to certify AI agents — every decision is in git