typingssdkhardware

Typings and SDK patterns for Raspberry Pi HATs: designing safe TypeScript interfaces

ttypescript

2026-01-26

10 min read

Design TypeScript typings and SDK patterns for Raspberry Pi HATs that deliver autocomplete, runtime validation, and safe APIs in 2026.

Hook: When hardware promises power but developers get friction

Your team just bought a fleet of Raspberry Pi 5 boards with the new AI HAT+ 2 (late 2025 heralded a wave of production-ready Pi AI HATs). Great — until app developers open their editors and face undocumented register maps, fragile runtime errors, and no autocomplete. The result: slow integration, runtime crashes, and fragile hacks that undermine the whole point of adding dedicated hardware.

Why typings and SDK design matter in 2026

In 2026 the Raspberry Pi ecosystem is more diverse than ever: AI accelerators, sensor HATs, multi‑bus I/O expanders, and vendor-specific boards ship prebuilt images and native drivers. With Node.js v20+ and a growing number of teams deploying TypeScript at scale for edge and cloud, the right TypeScript typings and higher-level SDKs unlock three things developers crave:

Autocomplete and discoverability in editors (VS Code, WebStorm).
Runtime safety — validate sensors, capabilities and inputs before performing hardware actions.
Maintainability — clear boundaries between low-level bindings and high-level domain APIs.

Principles for robust HAT SDKs

Single source of truth: keep type shapes and runtime validation together (or auto-generate one from the other).
Separation of concerns: low-level bindings (native or I2C/SPI) vs high-level SDK that presents domain primitives.
Typed capability negotiation: represent device features as discriminated unions so code can safely branch on what a HAT supports.
Ergonomic factory: return an SDK instance with precise generics so callers get exact typings for their hardware model and connected sensors.
Runtime validation: verify connected hardware and configuration, fail fast on mismatch.

Practical pattern: typed HAT descriptors and factories

Start by describing HATs as typed constant objects. Use as const and TypeScript's inference to derive literal types that drive autocomplete.

// src/hatDescriptors.ts
export const AI_HAT_V2 = {
  id: "ai-hat-v2",
  model: "AI HAT+ 2",
  vendor: "ExampleCorp",
  buses: ["i2c", "spi"],
  capabilities: ["neural-accelerator", "temperature", "fan-control"] as const,
  i2cAddress: 0x48,
} as const;

export type HatDescriptor = typeof AI_HAT_V2;

With that descriptor we can create a generic SDK factory whose return type depends on the descriptor shape. That yields precise autocomplete for methods and properties related to present capabilities.

// src/createHat.ts
import { HatDescriptor } from "./hatDescriptors";

export function createHat(desc: D) {
  return {
    descriptor: desc,
    async readTemperature(this: { descriptor: D }) {
      if (!desc.capabilities.includes("temperature" as const)) {
        throw new Error("Temperature not supported by this HAT");
      }
      // low-level i2c read...
      return 42.3; // number
    },
    async runModel(this: { descriptor: D }, buffer: Uint8Array) {
      if (!desc.capabilities.includes("neural-accelerator" as const)) {
        throw new Error("No neural accelerator");
      }
      // delegate to native runtime / model runner
      return { latencyMs: 12 } as const;
    },
  } as const;
}

// Usage in app code
const hat = createHat(AI_HAT_V2);
// hat.

Why this pattern helps

Autocomplete: editors infer exact methods available for the selected descriptor.
Type-level safety: generic constraint prevents calling unsupported APIs at compile-time where possible.
Minimal runtime checks: callsites still validate on initialization, then rely on types for day-to-day safety.

Runtime validation: don’t trust the wiring

Hardware differs. A HAT advertised as supporting multiple sensors may be disconnected or misconfigured. Use runtime validators to check what’s actually connected. In 2026, zod remains a solid choice for runtime schema + type inference, and many teams use it to keep runtime schemas close to TypeScript types.

// src/schemas.ts
import { z } from "zod";

export const HatProbeSchema = z.object({
  id: z.string(),
  model: z.string(),
  i2cAddress: z.number().optional(),
  capabilities: z.array(z.enum(["neural-accelerator", "temperature", "fan-control"]))
});

export type HatProbe = z.infer;

// src/probe.ts
import { HatProbeSchema } from "./schemas";

export async function probeDevice(i2cBus: any) {
  // run a quick I2C read to get identify register
  const raw = await i2cBus.readDeviceId();
  const parsed = {
    id: raw.id,
    model: raw.model,
    i2cAddress: raw.addr,
    capabilities: raw.flags,
  };

  const result = HatProbeSchema.safeParse(parsed);
  if (!result.success) {
    throw new Error(`Invalid device response: ${result.error.message}`);
  }
  return result.data;
}

Keep the schema centralized. Use z.infer to get the TypeScript type for downstream code, removing duplication between type definitions and runtime checks.

Typed capability negotiation

Once you’ve probed the device, map runtime capabilities into a typed SDK. A discriminated union gives you exhaustive checks and great editor hints.

// src/capabilities.ts
export type Capability =
  | { kind: "neural-accelerator"; maxOps: number }
  | { kind: "temperature"; unit: "C" | "F" }
  | { kind: "fan-control"; pwmRange: [number, number] };

export function toCapabilities(raw: string[]): Capability[] {
  // map flags to rich capabilities
  return raw.map(flag => {
    if (flag === "neural-accelerator") return { kind: "neural-accelerator", maxOps: 1024 } as const;
    if (flag === "temperature") return { kind: "temperature", unit: "C" } as const;
    return { kind: "fan-control", pwmRange: [0, 255] } as const;
  });
}

Low-level bindings: native vs pure JS

Many HATs require native drivers. For Node.js in 2026, prefer N-API bindings (node-addon-api) or Rust + Neon for stable ABI across Node versions and architectures (ARM64 for Raspberry Pi 5). Key considerations:

Publish prebuilt binaries for common Node versions and CPU architectures (arm64, amd64). Use GitHub Actions to build and publish — this kind of CI and prebuild matrix is now standard in cloud and edge projects such as micro‑factory logistics field reports that document artifact pipelines.
Fallback to a pure JS path if native bindings are unavailable — with reduced performance but better compatibility.
Expose a tiny, well-typed JS entry that the TypeScript SDK wraps.

package.json exports and types

Export both runtime and types clearly so consumers get correct resolution and autocompletion:

{
  "name": "@example/ai-hat-sdk",
  "version": "1.2.0",
  "main": "./dist/index.cjs.js",
  "module": "./dist/index.esm.js",
  "types": "./dist/index.d.ts",
  "exports": {
    ".": {
      "import": "./dist/index.esm.js",
      "require": "./dist/index.cjs.js",
      "types": "./dist/index.d.ts"
    },
    "./native": {
      "default": "./dist/native.node"
    }
  }
}

Generating and publishing typings (.d.ts)

Build step should emit declarations and maps. In tsconfig.json:

{
  "compilerOptions": {
    "declaration": true,
    "declarationMap": true,
    "emitDeclarationOnly": false,
    "outDir": "dist",
    "module": "ES2020",
    "target": "ES2020",
    "skipLibCheck": true
  }
}

Publish the generated .d.ts files alongside your JS bundles. If you maintain multiple entry points or conditional types for older TS versions, use the typesVersions field in package.json to map consumer TypeScript versions to proper declaration bundles.

Developer ergonomics: typed events, commands and examples

Good SDKs make common patterns trivial. Use typed event emitters and template literal types to model commands and parameters.

// src/events.ts
import { EventEmitter } from "events";

export type HatEvents = {
  "temperature": (value: number) => void;
  "model:loaded": (meta: { sizeMB: number }) => void;
};

export class TypedEmitter extends (EventEmitter as new() => EventEmitter) {
  on(event: K, cb: HatEvents[K]) {
    return super.on(event as string, cb as any);
  }
}

Provide short, runnable examples in the README and inline JSDoc on exported functions so both docs and editor tooltips help developers onboard quickly. For remote teams and documentation workflows that accelerate onboarding, consider practices from projects like Mongoose.Cloud on remote-first productivity which show how to keep docs and CI connected for distributed teams.

Testing strategies for 2026

Types need tests too. Use these practices:

tsd for assertion tests on exported types (ensures your public types don’t regress)
Unit tests for runtime logic and validation (vitest/jest)
Hardware-in-the-loop (HIL) tests on actual Pi 5 devices. Use self-hosted GitHub Actions runners or an internal device farm to run smoke tests against prebuilt images — some teams pair device farms with offline-first test tablets and kiosks like the NovaPad Pro approach.
Mock drivers to validate behavior on CI where hardware is unavailable.

Versioning and backward compatibility

Hardware adds complexity to semver. Follow these guidelines:

Patch: bugfixes that don’t change types or behavior.
Minor: small API additions and optional capabilities (feature flags in types should be optional union branches).
Major: breaking changes to public typings or binary ABI changes. Publish clear migration guides and deprecation warnings at runtime.

Security and runtime safety

Protect against misconfiguration and privilege misuse:

Validate all inputs to native calls.
Run as non-root where possible; document required capabilities clearly.
Validate probes and abort on mismatch rather than performing destructive operations. For broader operational safety guidance in high-risk contexts see field reports on micro-factory logistics which include operational checklists for device farms and artifact handling.

Case study: designing an AI HAT SDK in 2026

I recently advised a team integrating the AI HAT+ 2 into a fleet of edge devices. Here’s a condensed playbook we used — practical, repeatable, and suited to the 2026 landscape.

Descriptor-first: Create JSON-like descriptors for each HAT model (id, buses, capabilities, quirks). Commit them to source.
Schema-driven probing: Use zod schemas to parse the device identity and capabilities returned from an I2C probe.
Dual-path runtime: Implement a high-performance native path (N-API) and a fallback JS path. Export a single typed API that abstracts over both.
CI and prebuilds: Build native binaries for Node.js 18–22 and for arm64/amd64. Publish prebuilds to GitHub Releases and wire preinstall scripts to fetch correct binaries — this prebuild matrix is part of modern edge and hosting pipelines covered in edge hosting trends.
Typed factory: Return an SDK instance typed to the detected descriptor so app code gets exact autocomplete for supported operations.

// high-level usage example for app developers
import { autoDetectHat } from "@example/ai-hat-sdk";

async function start() {
  const hat = await autoDetectHat(); // returns correctly typed instance
  if (hat.descriptor.capabilities.includes("temperature" as const)) {
    const t = await hat.readTemperature();
    console.log("Temp:", t);
  }
  if (hat.descriptor.capabilities.includes("neural-accelerator" as const)) {
    await hat.loadModel("/models/face-detect.bin");
    const r = await hat.runModel(sampleBuffer);
    console.log(r.latencyMs);
  }
}

Trends and predictions for 2026–2027

Several trends from late 2025 into 2026 reshape how we design HAT typings and SDKs:

Edge AI HATs proliferate: more vendors ship accelerators; expect converging driver APIs and richer metadata (model formats, quantization support).
WASM + WASI on-device: SDKs will commonly include WASM runners for vendor-agnostic model execution; design your types to represent WASM-backed runtimes too, given the crossover with low-latency on-device compute patterns.
Prebuilt universal binaries: builders are standardizing artifact matrices for Node and WASM; CI pipelines for prebuilds will be expected artifacts in npm packages.
Tooling improves: new tools in 2025–2026 help generate runtime validators from TypeScript types and vice versa — adopt those to reduce duplication and improve developer experience similar to what is being discussed in evolution of cloud and edge patterns.

Checklist: ship a production-ready HAT TypeScript SDK

Describe each HAT with a typed descriptor (use const assertions).
Create centralized zod (or equivalent) schemas and derive TS types via z.infer.
Expose a generic create/auto-detect factory for precise autocomplete.
Publish .d.ts alongside JS bundles and set package.json exports/types fields.
Provide native prebuilds + pure JS fallback and document platform requirements.
Run tsd tests and a hardware-in-the-loop test matrix for Pi 5 and common OS images.
Use semantic versioning and provide migration guides for breaking changes.

Final takeaways (actionable)

Start with descriptors — they drive both runtime checks and TypeScript types.
Centralize validation using zod or similar, and derive types from schemas to avoid drift.
Return typed SDK instances so app devs get autocomplete and compile-time safety tailored to the actual HAT.
Publish clear artifacts: JS bundles, .d.ts files, and prebuilt native binaries for popular Node versions and ARM architectures.

The most successful hardware SDKs in 2026 are the ones that treat developer experience as a first-class design requirement: precise types + reliable runtime checks = faster integrations and fewer field incidents.

Call to action

If you’re designing a HAT SDK or migrating an existing driver to TypeScript for Raspberry Pi fleets, start by open-sourcing your descriptor and schema files. Want a checklist tailored to your HAT and CI constraints? Reach out or clone the reference repo I maintain that contains example descriptors, zod schemas, CI workflows for prebuilds, and a playground to exercise typed SDKs on a Raspberry Pi 5. Ship safer hardware integrations with confidence. For practical notes on distributing artifacts and running device farms you may find reports on micro-factory logistics and CI matrices useful.

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