Tagged articles

Evidence

4 articles · Page 1 of 1

Sep 9, 2026 · Artificial Intelligence

Three Graphs, Three Jobs: Loop, Task Graph & Plugin Runtime in Agent Systems

This article argues that complex Agent systems must separate three distinct structures: Loop handles node-level convergence, Task Graph manages work dependencies and coordination, and Plugin Runtime binds capabilities like models and tools; mixing them into a monolithic Agent leads to invisible boundaries and unrecoverable failures.

Agent ArchitectureComponent GraphContext

0 likes · 17 min read

Three Graphs, Three Jobs: Loop, Task Graph & Plugin Runtime in Agent Systems

Smart Workplace Lab

Apr 19, 2026 · Industry Insights

How to Turn AI-Boosted Productivity into Visible Performance Metrics

This article presents a practical framework for documenting AI‑enhanced work contributions, introducing a weekly performance‑evidence matrix that quantifies decision density, risk interception, and asset accumulation, along with communication scripts tailored to different manager types and step‑by‑step SOPs for archiving proof, helping professionals turn speed gains into measurable performance value.

AIEvidenceMetrics

0 likes · 7 min read

How to Turn AI-Boosted Productivity into Visible Performance Metrics

Model Perspective

Nov 12, 2022 · Fundamentals

Understanding Bayes’ Theorem: From Basics to Real-World Applications

This article demystifies Bayes’ theorem by deriving it from probability multiplication rules, explaining its components—prior, likelihood, posterior, and evidence—and illustrating its significance with intuitive examples, while also discussing its role in sequential data analysis and model updating.

Bayes theoremEvidenceposterior

0 likes · 5 min read

Understanding Bayes’ Theorem: From Basics to Real-World Applications

Model Perspective

Nov 9, 2022 · Fundamentals

Understanding Bayes’ Theorem: From Basics to Real-World Applications

This article introduces Bayes’ theorem, explains its derivation from probability multiplication rules, clarifies the meanings of prior, likelihood, posterior, and evidence, and illustrates why these components differ, providing intuitive examples and highlighting its importance for sequential data analysis.

Bayes theoremEvidencelikelihood

0 likes · 6 min read