Caching keeps copies of frequently accessed data in a faster layer, reducing latency and load on the underlying store. Effective caching balances freshness against hit rate.
Caching - Interview Concepts - ExtraBrain is part of ExtraBrain's local-first Mac workflow for live interviews, meetings, transcription, provider control, and responsible AI use.
Caching keeps copies of frequently accessed data in a faster layer, reducing latency and load on the underlying store. Effective caching balances freshness against hit rate.
ExtraBrain has 1 current public platform family, macOS, with support for 2 Mac CPU families: Apple Silicon and Intel.
ExtraBrain has 3 configurable data paths to review before sensitive work: local Parakeet transcription, local Gemma 4 where installed and compatible, and external providers you choose.
Caches sit in the client, CDN, application memory, or a shared store like Redis.
Strategies include time-to-live expiry, write-through, and write-back, each trading freshness for performance.
Policies like LRU and LFU decide what to drop when the cache is full.
Use any live AI assistant only where interview, workplace, school, and platform rules allow it. Do not use generated answers to misrepresent your skills, experience, or authorship.
Short answers for people and crawlers comparing ExtraBrain with other live AI assistants.
Time-to-live expiry, write-through, write-back, and explicit invalidation each balance freshness and performance.
It decides which entries to remove when full; LRU and LFU are common choices.
