Feb 17, 2026
How HGC-Net Differs from Today’s Interpretability Approaches
Interpretability in deep learning has evolved significantly over the past decade. From post-hoc explanation methods to c...
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Feb 09, 2026
HGC-Net: Making the Black Box Explicit Instead of Pretending It Doesn’t Exist
One of the most persistent problems in deep learning is not performance it is opacity. Modern models can classify, predi...
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Feb 06, 2026
Hybrid Semantic Bottlenecks: Bridging Interpretability and Performance in Deep Learning
Deep learning models have achieved remarkable success across a wide range of tasks, particularly in computer vision. How...
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Feb 03, 2026
Legacy-First Design: Designing Software That Survives Time
Most software systems are designed with one implicit assumption:someone will always be there to maintain them.Teams will...
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Jan 30, 2026
Raygon: Making Dependency, Intent, and Evolution Explicit
Most software systems don’t fail because of bugs. They fail because of invisible dependency.Contracts are consumed...
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Architecture
Jan 26, 2026
Why Most AI Memory Systems Are Fundamentally Broken (And How Adaptive Retention Fixes It)
The Hidden Problem Nobody Talks About: Memory That Never ForgetsMost AI systems today suffer from the same silent flaw:&...
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Jan 19, 2026
Survival in Abandoned State: Why Software Should Make Sense Even When No One Maintains It
Most software systems are designed under an implicit assumption:someone will always be there to maintain them.New featur...
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Architecture
Jan 12, 2026
Why Long-Term Memory Architectures Matter for Energy Efficiency in AI Systems
IntroductionWhen discussing artificial intelligence systems, especially those based on large language models (LLMs), ene...
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CLI
Jan 11, 2026
GrimleyTK: Declarative Data Architecture as a First-Class Concern
Modern software systems rarely fail because of missing features. They fail because data ownership becomes unclear,...
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Architecture
Jan 10, 2026
Legacy-First Design: Treating Time as a First-Class Architectural Constraint
Modern software architecture is remarkably good at solving today’s problems. It is far less effective at surviving tomor...
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