Sakoa is a progressive systems and application language I’ve been designing and implementing from scratch. The pitch I keep coming back to:

TypeScript’s approachability, Rust’s safety, Zig’s explicitness, Go’s simplicity, Swift/Kotlin’s ergonomics, and ML-style types where they actually help.

The interesting bits aren’t the syntax — that’s deliberately boring (braces, no semicolons, types after names, inference everywhere it doesn’t hurt). The interesting bits are the choices underneath: a first-class effect system, three memory modes instead of one borrow checker, a clean type vs actor split, structured concurrency, parallel for as an expression, and a MIR with multiple backends (native via C, JS, WebAssembly text, embedded) all driven from one CLI and one compiler that also powers the LSP.

type PaymentStatus =
  | Pending
  | Paid { at: DateTime }
  | Failed { reason: String }
  | Refunded { amount: Money }

fn describe(status: PaymentStatus) -> String {
  match status {
    Pending => "Waiting for payment"
    Paid { at } => "Paid at ${at}"
    Failed { reason } => "Failed: ${reason}"
    Refunded { amount } => "Refunded ${amount}"
  }
}

Effects in the type system

Functions declare what they touch. A pure function has no uses clause; anything that talks to the outside world says so on its signature.

fn calculateTax(income: Money) -> Money

fn saveUser(user: User) uses db -> Result<(), DbError>

fn sendEmail(email: Email) uses net, env -> Result<(), EmailError>

Effects propagate through call sites and are inferred locally, but become required at API and package boundaries — the compiler answers “can this transitively touch the network?” without anyone having to grep. The neat part: effects also apply to property getters, so a field-looking access that secretly hits the database is forced to surface that.

type User = {
  id: UserId

  get avatarUrl uses db -> Url {
    return db.users.avatarUrl(id)
  }
}

Three memory modes, not one borrow checker

Most modern languages pick a single memory model and live with the consequences. Sakoa picks three and lets you opt into the right one per value.

let user = User { id: UserId("1"), name: "Daz", email: "..." }
let buffer = boxed Buffer.withCapacity(4096)
let session = shared Session.new()

Default — value semantics with move/copy depending on the type. No annotations, no borrow checker yelling at you for writing a script.
boxed — uniquely owned heap value, deterministic drop. Think Rust Box<T> without the lifetime ceremony.
shared — reference-counted or GC’d depending on backend, used for graph-like app state.
unsafe — allowed, but visually fenced and visible in diffs.

Resource lifetimes are explicit too. Owned values get a drop block, and ad-hoc resources can be scoped with using:

type FileHandle = {
  fd: raw.FileDescriptor

  drop {
    unsafe { raw.close(fd) }
  }
}

using connection = try Database.connect(env.databaseUrl()) {
  try connection.migrate()
  try connection.seed()
}

`type` vs `actor`: data and concurrency are different things

A perpetual frustration with OO-flavoured languages is that “object” is overloaded — sometimes it’s data, sometimes it’s a concurrency boundary, and you can’t tell from the spelling. Sakoa splits them.

type Cart = {
  items: List<CartItem>
}

actor CartStore {
  mut carts: Map<UserId, Cart> = Map.empty()

  fn addItem(userId: UserId, item: CartItem) {
    mut cart = carts.get(userId) ?? Cart { items: [] }
    cart.items.push(item)
    carts[userId] = cart
  }
}

A type is a value: copy it, move it, serialize it, pattern-match on it. An actor is a live concurrency boundary with isolated state and message-passing semantics — crossing it is async at the call site, and data races are impossible in safe code.

let store = CartStore()
await store.addItem(userId, item)

Structured concurrency, parallel-for as an expression

Tasks are scoped. If the parent exits, children are cancelled. There are no orphaned futures sitting around with no parent.

async fn loadDashboard(userId: UserId) -> Dashboard {
  let userTask = task fetchUser(userId)
  let feedTask = task fetchFeed(userId)
  let notesTask = task fetchNotifications(userId)

  return Dashboard {
    user: try await userTask,
    feed: try await feedTask,
    notifications: try await notesTask,
  }
}

For data parallelism, parallel for is an expression that produces ordered results — the ordering is preserved across the parallel scheduler, and shared counters use std.atomic so the runtime knows when to apply atomic ops vs ordinary stores.

import std.atomic as atomic

fn main() -> Int {
  let total = atomic.new(0)

  let squares = parallel for value in range(0, 8) where value % 2 == 0 {
    value * value
  }

  parallel for value in range(1, 5) {
    atomic.add(lend total, value)
  }

  println("squares = ${squares.length}, total = ${atomic.load(lend total)}")
  0
}

Note lend total at the call site — borrowing is visible in the caller, not just the callee. You can’t accidentally hand out a mutable reference; you have to write the word.

Tests, properties, and benches are syntax

There’s no testing framework to import. test, bench, and property declarations are language constructs, discovered and run by the same toolchain that builds your code, with deterministic property generation/shrinking and beside-test snapshots.

import std.thread as thread

test "thread worker can be spawned and joined" {
  let spawned = thread.spawn<Int>(countBatch)

  match spawned {
    Ok { value } => {
      let joined = await thread.join(value)
      expect joined == Ok { value: 21 }
    }
    Err { error } => expect false,
  }
}

bench "parallel heavy loop" {
  parallel for value in range(0, 64) { cpuHeavy(value) }.length
}

One compiler, one MIR, four targets

The compiler lowers Sakoa to a typed MIR (mid-level IR) that the test runner interprets directly — so sakoa test and sakoa run execute through the same MIR that the backends consume. There are four artifact contracts:

native — generated C plus a compiled executable from the platform C toolchain (no rustc dependency at runtime).
js — runnable JavaScript, useful for shipping Sakoa code to the web.
wasm — WebAssembly text format, with a clear story for shared-memory and host-import contracts.
embedded — a manifest plus C source for embedded toolchains.

Backend diagnostics fail the build before writing any artifact when a feature isn’t supported on the chosen target. So if your code uses std.thread and you ask for --target wasm, you get a clean error pointing at the call site, not a half-broken .wasm file.

sakoa build --release counter.sakoa
sakoa build --target wasm examples/wasm
sakoa build --target js --output build/counter.js counter.sakoa
sakoa profile --format json counter.sakoa
sakoa explain E0500

The same compiler powers the LSP and VSCode extension, so editor diagnostics, hovers, completions, semantic tokens, and code actions are always exactly what sakoa check produces. Nothing drifts.

Motivation

I’ve been collecting opinions about programming languages for years and never had a place to actually try them out together. Sakoa is that place: an effect system that doesn’t make you annotate everything, three memory modes instead of one, structured concurrency by default, value/actor as a real distinction, deterministic resource cleanup, sum types and pattern matching that pull their weight, and tests/benches/properties as part of the grammar instead of an afterthought library.

The other goal is tooling cohesion. Most languages I love have an LSP that lags the compiler, a formatter that disagrees with the linter, and a package manager whose error messages have nothing to do with anything else. Sakoa is built so a single sakoa binary owns the lifecycle — and a single Rust crate is the source of truth for what the language is. The CLI, the LSP, the formatter, the test runner, and the docs generator all consume that crate. Nothing can drift, because there’s nowhere for it to drift to.

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