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The per-element Rhai sandbox caps (max_string_size 64 KiB, max_array_size / max_map_size 10 000) do NOT bound the *materialized* size: Rhai shares strings and arrays by Rc, so a 10 000-element array of one aliased 64 KiB string is cheap to build (~10 000 ops, far under the 1 M op budget) yet dealiases to ~640 MiB of distinct JSON. Every Dynamic→JSON conversion deep-copies the aliases; the HTTP response body path had NO size cap at all, and the KV/docs value caps run only AFTER full materialization — so a handful of anonymous requests could OOM the node (32 concurrent × ~640 MiB ≈ 20 GiB) on the stated consumer-hardware target. Add a byte-budgeted materializer that bails the moment the budget is exceeded, so the transient allocation is bounded regardless of aliasing: - bridge.rs: `dynamic_to_json_capped(value, max) -> Result<Json, JsonSizeError>` (charges a running budget) + `MAX_JSON_MATERIALIZE_BYTES` (16 MiB hard rail, far above the 256 KiB business caps). `dynamic_to_json` stays infallible for best-effort paths (logging) but is now internally bounded — it collapses an over-limit value to a marker string instead of OOMing. - Route every user-value boundary through the fail-closed capped form: KV set/set_if (+ the CAS `expected`), docs create/update/find, secrets set, http request body, workflow input, `json::stringify`, and the HTTP RESPONSE body (previously the one fully-uncapped exit → now a 500). `invoke` args and the pubsub/queue message materializers get the same byte budget in place. - `impl From<JsonSizeError> for Box<EvalAltResult>` so SDK sites protect themselves with a bare `?`. Pinned by bridge unit tests: an aliased 10 000×64 KiB array errors on the budget rather than materializing, and the infallible wrapper yields a marker instead of OOMing. Workspace 914 + journeys 157/157 green. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
PiCloud
A lightweight, self-hosted, event-driven serverless compute platform. Upload a Rhai script, get an HTTP endpoint. Designed to run on a single modest server with no idle CPU cost, and to scale out to a small cluster when you need it.
Status: Phase 1 — MVP scaffolding in progress.
The authoritative design lives in
serverless_cloud_blueprint.md.
Why
Existing serverless platforms are either cloud-locked, heavyweight, or both. PiCloud aims for the opposite end of the spectrum: one binary, one database, one reverse proxy — running on hardware you already own.
Architecture (one paragraph)
PiCloud splits into three logical services — manager (control plane: scripts, schedules, dashboard), orchestrator (per-node event ingress and dispatch), and executor (per-node Rhai sandbox) — each backed by a *-core Rust library. In MVP they run in a single process; in cluster mode they run as three binaries with one manager and one orchestrator + executor per node. Caddy fronts everything; PostgreSQL is the single source of truth.
See CLAUDE.md for working notes and serverless_cloud_blueprint.md for the full design.
Quick Start
Coming as scaffolding lands. For now:
# Rust toolchain (pinned via rust-toolchain.toml)
cargo check --workspace
# Run the all-in-one MVP binary (once main.rs is wired up)
cargo run -p picloud
Repository Layout
crates/
shared/ cross-cutting types
executor-core/ Rhai engine + sandbox
orchestrator-core/ event ingress, dispatch
manager-core/ control plane
picloud/ MVP all-in-one binary
picloud-{manager,orchestrator,executor}/ cluster-mode binaries (skeleton)
dashboard/ SvelteKit
caddy/ Caddyfile
docker/ Dockerfiles
docs/
git-workflow.md Trunk-based workflow
Contributing
See docs/git-workflow.md for the branching and commit conventions. TL;DR: trunk-based, short-lived branches, Conventional Commits, no force-pushing main.
License
TBD.