Projects

Jim Gray's 1985 process pair rebuilt on one laptop and killed 420 times to measure the checkpoint tradeoff he described and never tested. Checkpointing every request costs 25% of throughput, lost work is N/2, and the 1974 formula for the best interval lands 20x too high because it assumes lost work gets recomputed.

Why: Built as the first stop in rederiving fault tolerance from the original papers. Two things I did not expect: Gray's own fix, client replay, closes the lost-work hole for free, while a durable log costs 99% of throughput because fsync() on macOS does not wait for the disk. And my first takeover result was wrong: macOS AirPlay listens on port 5000 and was accepting the client's reconnects, which is the gray-failure problem in miniature.

A crash-durable task store for AI agents that guarantees a side effect happens exactly once, even under kill -9 at any point. 500 randomized crashes produced 0 double charges; a naive implementation under the identical harness produced 121.

Why: Built because the MCP Tasks spec defines a store interface and ships only an in-memory implementation, leaving persistence to implementers, and an agent that charges a card twice is a real failure rather than a theoretical one. Peak 5,202 tasks/s in Python and 7,345 in a Go port, both hitting the ceiling at 8 workers, which traced the wall to Postgres's commit path by elimination.

Semantic search over primary-source philosophy that classifies whether a passage argues for or against a claim, not merely whether it is about the topic. On a held-out set written after the fix, 20 of 21 queries surfaced the philosopher who actually holds the position.

Why: Built after finding that embedding search matches vocabulary and imagery rather than the conclusion an argument reaches, so a query about free will returns everyone who mentions it, on either side, undifferentiated. Fixed with HyDE query expansion over a from-scratch HNSW index, and validated on queries written afterwards so the result could not be tuned into existence.

A metrics ingest pipeline that survives the label explosion that OOM-kills a naive one, proven by exit code. Same 20M-sample workload in the same 256 MB container: the naive ingester dies at 8M samples; Peras completes all 20M at 1.67M samples/s with ~3 MB of heap.

Why: Built after measuring, while instrumenting Hapax, what one high-cardinality label costs: adding task_id took an export from 22 series to 30,019 and the payload up 1,488x. Peras is the ingest-side defense: it attributes a breach to the exploding label dimension at runtime and aggregates it away instead of crashing, and CI re-proves the die-vs-survive contrast on every push.