when + input templating at run time
Wire the two pure M1 modules into the orchestrator's execute path:
- Before running a step, build a `RunContext` from the run input + every prior
succeeded step's output (`load_run_step_outputs`, read fresh at execution
time so a step sees all upstream results).
- `workflow_expr::eval` the step's `when` condition (if any): false → the step
is `skipped` — never executed — and counts as satisfied-but-empty for its
dependents (a new `StepOutcome::Skipped`, written in `complete_step_and_advance`
then advanced; `compute_advance` already treats skipped as satisfying deps).
- `workflow_template::resolve` the step's `input` against the context: an
exact single `{{ ref }}` preserves the referenced JSON type, an embedded ref
interpolates as text; a missing reference is a hard step failure (a definition
bug surfaced, not hidden), never a silent null.
`when` and templates were already parse-validated at apply time (M1); this is
the runtime half.
Tests (DB-gated, end-to-end via a scripted fake executor):
- `when_false_skips_step` — b skipped, never executed, omitted from run output
- `step_output_flows_into_downstream_input` — a's `{n:7}` feeds b's input,
type-preserved for a bare ref and interpolated in a larger string
- `missing_input_ref_fails_the_step` — an unresolved ref fails b → run fails
Verified: cargo fmt, clippy -D warnings clean, 448 manager-core lib tests,
11 workflow_orchestrator DB tests (3 new).
Co-Authored-By: Claude Opus 4.8 <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.