Engineering
Fast and correct
The hard part of caching is knowing when to stop.
We cache aggressively where data is safe to reuse and invalidate precisely where it is not, so you get the speed of a cache without the classic bug of stale, wrong data.
Speed you can trust
Genuinely faster
Hot data served instantly from close by.
Sensible expiry
Clear rules decide exactly when a copy is retired.
Clean invalidation
When the source changes, the cache follows, no stale surprises.
The trade-off
Fast is easy; fresh is the hard part
A cache that lies is worse than no cache at all.
Caching buys speed by keeping a copy close by, but the moment the original changes, that copy can start telling users a story that's no longer true. The engineering is all in knowing when to let go of it.
We treat invalidation as a first-class concern: clear rules for what expires when, so you get the speed of a cache without the confusion of stale data.
Highlights
Principles
Cache safely
Only where reuse is correct.
Invalidate precisely
Stale data is cleared on change.
Measure
Hit rates guide what to cache.
Deep dive
A cache is a bet, write down the odds
Every cache is a wager that stale data costs less than a slow response. Sometimes that bet is obviously right (a logo), sometimes obviously wrong (an account balance), and the pain lives in the middle, so we make the wager explicit: every cached value gets a written owner, TTL and staleness budget.
Invalidation stops being folklore when events drive it. When the source record changes, an event fans out and the relevant keys die immediately, no waiting for a timer, no praying.
The dashboard tells the truth about whether the bet pays: hit rates, stale serves and origin load per cache. When a cache stops earning its complexity, we delete it, the fastest cache bug to fix is the cache that shouldn't exist.
We build software for teams who want their tools to fit the way they actually work — web, mobile, AI and the systems that tie them together. We write here about what we learn shipping it.