Ask HN: What Are You Working On? (April 2026)

Currently PostgreSQL, Generic Docker services and GarageFs is supported, Backup and disaster recovery included. Aimed at smaller setups that do not need the complexity of the big Hyperscalers.

Curious about the user-created puzzles feature you're working toward - that's a classic community moat play. Once players can make and share puzzles, your distribution essentially scales with your most engaged users.

I'm building ad-vertly.ai - marketing agents for solo founders. One thing I find fascinating with puzzle/game products is that the word-of-mouth loop is already there (people share scores, show friends), but it rarely gets deliberately amplified. Are you doing anything intentional to capture that organic sharing energy, beyond the stats sharing you just built?

The core is a four-layer microkernel: a Bloom-filter-backed sparse Markov graph (FNV-1a hashing, 500-node cap with LRU eviction), a streaming anomaly detector using Welford's online algorithm for per-state mean/variance in O(1), and a typed event emitter that fires when Z-score thresholds cross.

Under 2ms per track() call. ~11kB gzipped. Nine typed behavioral states — from Conviction Buyer (trajectory Z The motivation: every behavioral analytics stack I've evaluated has median 3–15 minute round-trip latency. Users make churn decisions in under 3 seconds. That gap isn't a data quality problem — it's a physics problem. The only solution is to put the model in the same process as the behavior.

Cold-start is solved with Blueprint JSON — a declarative prior you describe once, the engine boots calibrated on the first track() call. No warm-up period.

Core is AGPLv3. Happy to dig into the Markov eviction policy or the Welford implementation if anyone's curious.