Yes — that changes the design a lot, in a good way. If **castle is not a general platform**, but a **curated machine for you + an AI assistant**, then the ontology should be optimized for: * few concepts * strong defaults * predictable placement * limited extension paths * easy generation from natural language That means castle should **not** try to model all possible machine semantics. Ubuntu already does that. Castle should define a **small opinionated overlay** on top of Ubuntu. So I’d stop trying to find the perfect universal ontology and instead design a **usefully incomplete one**. ## The design principle Castle should answer: > What are the few things I commonly want the machine to have or do? From your examples, those are roughly: * a **tool** * a **site** * a **service** * a **worker/daemon** * a **job** * a **store/broker** * maybe a **library/project** That suggests the core user-facing abstraction should not be “artifact” or “namespace” or even “runtime.” It should be something more like: ## Castle’s main abstraction: `unit` A **unit** is a thing the castle machine knows how to host. That thing may be: * a Python tool * a static site * a web service * a daemon * a scheduled job * a database * a broker Then beneath that, castle can have a smaller set of internal dimensions. --- # A much simpler castle model I think the practical model is: ```text unit kind stack uses ``` Where: ## `unit` The named thing on the machine. Examples: * `reddit-tool` * `docs-site` * `api` * `transform-worker` * `morning-search` * `postgres` * `mqtt` ## `kind` The machine role. Examples: * `tool` * `site` * `service` * `worker` * `job` * `store` * `broker` ## `stack` How it is built/run. Examples: * `python` * `rust` * `react-static` * `fastapi` * `shell` This is where language/build/runtime conventions collapse into one curated choice. ## `uses` What castle subsystems or other units it depends on. Examples: * `postgres` * `neo4j` * `mqtt` * `caddy` * `reddit` * `web` This is much closer to how you actually want to talk to the AI. --- # Why this is better You don’t want to say: > create a deployment of kind service using runtime python with artifact class program and lifecycle systemd You want to say: > add a Python service that does X and uses Postgres So castle should be shaped around **intentional unit kinds**, not abstract machine theory. The machine theory should stay underneath as implementation. --- # What castle can standardize Because castle is curated, you can hardcode a lot: ## Always true on castle * Ubuntu * systemd * Caddy * standard directory layout * standard logging * standard service supervision * standard way to expose commands on PATH * standard way to run Python via `uv` * standard way to build JS via `pnpm` * standard way to define dependencies between units That means users and AI do **not** need to specify those every time. This is the key simplification move. --- # Recommended fixed subsystems I’d make these built-in castle subsystems: * **python** via `uv` * **rust** via `cargo` * **node/react** via `pnpm` * **java** via one standard choice * **caddy** * **systemd** * **postgres** * **mqtt** * maybe **neo4j** * maybe **web-search ingestion** as a castle-native capability Then the schema does not need to model arbitrary runtimes. It only needs to reference these known capabilities. --- # Strong defaults for placement You said castle can always use well-known spots. Good. Do that aggressively. For example: ```text /castle/units//src /castle/units//build /castle/units//env /castle/units//state /castle/units//config /castle/units//logs ``` And for exposure: ```text /castle/bin/ /castle/sites/ /castle/data/ ``` Then castle itself can generate: * symlinks into `/usr/local/bin` * systemd units * Caddy config * env files * state dirs So the schema does not need to talk much about placement, because placement is mostly implied. --- # The actual user-facing kinds I’d recommend just these: ## 1. `tool` A command you can run from anywhere. Examples: * Python CLI * Rust CLI * shell helper Defaults: * exposed on PATH * no long-running process * build/install conventions by stack Example: ```yaml units: summarize-reddit: kind: tool stack: python ``` --- ## 2. `site` A human-facing HTTP site served by Caddy. Examples: * React static site * docs site * maybe proxied app frontend later Defaults: * served by Caddy * standard root dir * standard local hostname or port Example: ```yaml units: castle-web: kind: site stack: react-static port: 8080 ``` --- ## 3. `service` A long-running HTTP or TCP process supervised by systemd. Examples: * FastAPI app * Rust API * Java backend Defaults: * systemd-managed * logs to journal * optional Caddy exposure if HTTP * standard environment/config paths Example: ```yaml units: reddit-api: kind: service stack: fastapi port: 9090 uses: [postgres, neo4j, reddit] ``` --- ## 4. `worker` A long-running non-HTTP daemon, often connected to broker/store inputs. Examples: * MQTT consumer * pubsub transformer * sync loop * queue processor Defaults: * systemd-managed * usually not publicly exposed * can subscribe to broker/topic or poll/store Example: ```yaml units: topic-transformer: kind: worker stack: python uses: [mqtt] subscribe: channel-b publish: channel-y ``` This kind seems especially important for your goals. --- ## 5. `job` A scheduled or one-shot task. Examples: * morning search * report generation * backup * fetch and write JSON Defaults: * systemd timer * writes to known output locations * can use web-search capability Example: ```yaml units: morning-z-search: kind: job stack: python schedule: daily@08:00 uses: [web] outputs: - /castle/shared/search/z/ ``` --- ## 6. `store` A built-in stateful service. Examples: * Postgres * Neo4j Defaults: * castle manages lifecycle and placement * other units reference by name Example: ```yaml units: postgres: kind: store engine: postgres neo4j: kind: store engine: neo4j ``` You may even decide these are so built-in they don’t need to be declared unless customized. --- ## 7. `broker` A built-in messaging service. Examples: * MQTT Example: ```yaml units: mqtt: kind: broker engine: mqtt ``` Again, maybe built-in by default. --- # The schema can now be tiny Instead of a universal schema, use a compact one with per-kind defaults. Example: ```yaml units: reddit-tool: kind: tool stack: python description: CLI for querying reddit summaries castle-web: kind: site stack: react-static port: 8080 reddit-api: kind: service stack: fastapi port: 9090 uses: [postgres, neo4j, reddit] topic-transformer: kind: worker stack: python uses: [mqtt] subscribe: channel-b publish: channel-y morning-z-search: kind: job stack: python schedule: "daily@08:00" uses: [web] output_dir: /castle/shared/search/z ``` That is probably closer to the right level. --- # What `stack` should mean now Since castle is curated, `stack` can absorb a lot: Instead of: * build system * runtime * conventions * entrypoint rules just use a known stack name. Examples: * `python` * `fastapi` * `rust` * `react-static` * `java-service` * `shell` Each stack implies: * where source goes * how to build * how to run * what files are expected * how dependencies are installed This is much simpler than separately modeling build/runtime/deployment for every case. So for castle: > **stack = curated implementation pattern** That’s probably the right definition. --- # What `uses` should mean This is another very valuable simplification. Instead of asking the user to define bindings and interfaces, let them say what the unit uses. Examples: * `postgres` * `neo4j` * `mqtt` * `caddy` * `web` * `reddit` * another unit name Then castle knows how to wire it. Example: ```yaml uses: [postgres, neo4j, reddit] ``` could imply: * env vars for local connection strings * service ordering in systemd * generated client config * AI awareness that those capabilities are available This is much more useful than formal dependency graphs. --- # Under the hood Internally, castle can still map to: * artifacts * runtimes * systemd * Caddy * mount namespaces * env files * directories But that should be hidden. The AI assistant should primarily think in terms of: * **What kind of unit is this?** * **What stack should it use?** * **What does it use?** * **What should it be named?** That’s enough to generate the machine config. --- # So the real castle ontology may be ## User-facing ```text unit kind stack uses ``` ## Machine-facing ```text source artifact runtime deployment placement ``` This split is important. You were trying to find one ontology for both humans and machines. You probably want two: * a **simple command ontology** for you + AI * a **lower-level implementation ontology** for castle internals That resolves a lot of the tension. --- # Example natural language mapping “Add a python tool that does X” becomes: ```yaml name: x-tool kind: tool stack: python ``` “Make a web service that does Y” becomes: ```yaml name: y-service kind: service stack: fastapi ``` “Make a daemon that watches pubsub channel B and transforms the data and puts it on Y” becomes: ```yaml name: b-to-y kind: worker stack: python uses: [mqtt] subscribe: B publish: Y ``` “Every morning, do a web search for Z and put the results in PATH as json files” becomes: ```yaml name: z-search kind: job stack: python uses: [web] schedule: daily@08:00 output_dir: /castle/shared/search/z format: json ``` This is very manageable. --- # Recommendation I would make castle intentionally opinionated around: * **7 unit kinds max** * **5–8 stacks max** * **a handful of built-in capabilities in `uses`** * **fixed placement conventions** * **automatic generation of systemd/Caddy/config** That seems like the right balance of simplicity and extensibility for what you want. The next useful step is to draft the **minimal v1 castle schema** with only those user-facing fields and strong defaults.