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What Is Model Sharding, and Why Does It Matter for Home AI Memory Limits?
Model sharding spreads required state across devices so one accelerator need not hold the whole model, trading memory feasibility for coordination overhead.
What Is Context Rot, and When Does It Matter in Long Local AI Sessions?
Context rot is declining information-use reliability as active context grows, even while the relevant tokens remain inside the model window.
What Is a Vector Index Tombstone, and Why Does It Matter After File Deletion?
A vector tombstone is a logical deletion marker that keeps removed records out of results until the index can safely clean or rebuild the underlying structure.
What Is Retrieval Grounding Confidence, and When Does It Matter in Local RAG?
Grounding confidence estimates whether retrieved evidence sufficiently supports an answer, rather than treating a high retrieval score as proof of correctness.
What Is an AI Agent Execution Budget, and Why Does It Matter on a Home Server?
An execution budget is an enforced resource envelope for one agent run, combining logical counters, deadlines, and host limits with a defined stop policy.
How Does Content-Defined Chunking Reduce Duplicate Backup Data?
Content-defined chunking anchors boundaries to local bytes, letting backups resynchronize after edits and reuse unchanged chunks across file versions.
How Does Columnar Storage Accelerate Home Sensor Analytics?
Columnar storage speeds sensor analytics by reading only needed fields, compressing similar values, skipping ranges, and processing values in efficient batches.
How Does Time-Series Downsampling Shrink Long-Term Smart Home Data?
Downsampling replaces dense old readings with coarser summaries, shrinking history while preserving selected trends, peaks, counts, and other useful signals.
