WebAssembly
Rust
C
C++
AssemblyScript
Emscripten
WASI
JavaScript
TypeScript
Wasm-Pack
Web Workers
Node.js
Deno
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Supported AI models on Workik
GPT 5.2 Codex, GPT 5.2, GPT 5.1 Codex, GPT 5.1, GPT 5 Mini, GPT 5
Gemini 3.1 Pro, Gemini 3 Flash, Gemini 3 Pro, Gemini 2.5 Pro
Claude 4.6 sonnet, Claude 4.5 Sonnet, Claude 4.5 Haiku, Claude 4 Sonnet
Deepseek Reasoner, Deepseek Chat, Deepseek R1(High)
Grok 4.1 Fast, Grok 4, Grok Code Fast 1
Models availability might vary based on your plan on Workik
Features
Generate WIT Definitions
Use AI to generate canonical WIT interfaces from modules or specs, reducing contract drift and manual mapping errors.
Enforce Cross-Language Contracts
AI maintains stable interface contracts across languages and runtimes, preventing ABI mismatches and breaking changes.
Produce Host Bindings
AI generates ready-to-use host bindings for JavaScript, Go, & Wasm runtimes with consistent types.
Validate Interfaces Early
AI validates WIT schemas against toolchain rules & flags structural issues before compilation or runtime execution
TESTIMONIALS
Real Stories, Real Results with Workik
"We run Wasm microservices everywhere, and Workik AI’s interface checks have saved us from so many sneaky ABI issues. Total game-changer."
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Liam Morris
Cloud Platform Architect
"Interop used to slow everything down. With Workik AI, I spin up WIT files, test interfaces, and hook Rust, Go, and JS together in minutes."
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Jerry Cooper
Full-Stack Developer
"Our plugin system lives or dies on clean interfaces. Workik AI made multi-module linking and component-model updates surprisingly painless."
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Donna Grant
Systems & Tooling Engineer
What are the most popular use cases of Workik AI WebAssembly Interface Generator for developers?
AI can assist developers with a wide range of WebAssembly Interface tasks, including but not limited to:
* Generating WIT files from Rust, Go, JS, C, or existing bindings to accelerate component development.
* Creating clean host bindings for browsers, Deno, Wasmtime, and Wasmer with runtime-ready signatures.
* Validating WIT definitions to detect ABI mismatches, incorrect layouts, and signature inconsistencies.
* Migrating legacy glue code (JS, C headers, wasm-bindgen outputs) into modern Component Model–compatible WIT.
* Designing cross-language interop workflows that allow Rust ↔ JS ↔ Go modules to share structured types.
* Auto-generating interface tests to confirm that imports, exports, and capabilities behave consistently across runtimes.
What types of context can I provide for WebAssembly Interfaces Generation?
Adding context is optional. Adequate context helps AI personalize output and produce more precise WebAssembly Interface definitions. You can add:
* Repository context by connecting GitHub, GitLab, or Bitbucket.
* Languages, frameworks, and toolchains, such as Rust + cargo-component, TinyGo, AssemblyScript, or Emscripten.
* Existing code files like lib.rs, C headers, TS bindings, or .wit packages.
* Database schemas or APIs, if your Wasm modules expose data or call external services requiring structured WIT types.
* Common functions or utility modules so AI can infer patterns for consistent type modeling and capability boundaries.
Can the generator help migrate manual bindings or older glue code to modern WIT-based interfaces?
Yes. AI can analyze wasm-bindgen outputs, JS shims, C headers, or Go FFI signatures and produce equivalent WIT definitions. This is especially useful when moving existing WebAssembly workloads toward the Component Model without rewriting everything from scratch.
How does AI improve cross-language interop when building WebAssembly components?
AI generates consistent interfaces for Rust, Go, JS, C, or TinyGo modules, ensuring they agree on type layout and semantics. For example, if your Rust module exports an Image record, AI generates compatible bindings for Deno, Wasmtime, or browser hosts so each environment decodes the structure correctly.
What does the generator do to prevent ABI mismatches, alignment issues, or signature inconsistencies?
AI validates WIT signatures against toolchain rules and checks for incorrect integer widths, struct packing differences, missing results, or misaligned fields. This prevents common runtime crashes like “unreachable” traps caused by incorrectly interpreted memory.
Does the generator support async WIT functions, streaming types, or callback patterns?
Yes. AI can model async functions using futures, define streaming interfaces for incremental processing, and generate callback signatures for event-driven workloads. For example, network functions like fetch(url) can be represented using async-friendly WIT that works across browsers and WASI runtimes.
How does AI help maintain and scale large modular interfaces or plugin ecosystems?
As applications grow, interface packages become harder to keep consistent. AI can analyze multiple WIT packages, consolidate duplicated types, enforce naming conventions, and restructure interfaces into modular units. This is especially helpful in plugin systems where each module exposes capabilities like log, transform, or auth.
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