Blorp Language

# Blorp - Overview Source: [https://blorp-lang.org/](https://blorp-lang.org/) A low\-friction, high\-performance language for code you can trust\. [Blorp by Example](https://blorp-lang.org/docs) ``` GOALS = [ ("confidence", ["pure functions", "explicit effects"]), ("speed", ["native code", "structured concurrency"]), ("approachability", ["small syntax", "direct control flow"]), ("durability", ["typed failure", "safe bounds"]), ] pure func format_goal(goal: (String, List[String])) -> String: (name, features) = goal "${name}: ${features.join(", ")}" func main(args: List[String]): pitch = GOALS .map(format_goal) .join("\n") print(pitch) ``` ## Features Blorp keeps the language surface direct while making effects, failure, and concurrency easier to reason about\. - **Readable syntax**Indentation, keyword operators, and method\-style calls keep code easy to scan\. - **Static safety**Strong types, checked imports, explicit fallibility, and exhaustive`match`make mistakes harder to hide\. - **Purity tracking**`pure func`separates deterministic logic from code that can perform I/O\. - **Value semantics**Assignment behaves like an independent value, while ARC/COW keeps sharing efficient\. - **Typed absence and failure**`Option`,`Result`,`match`, and`?=`put uncertainty in the type flow\. - **Structured concurrency**Scoped tasks, joins, timeouts, and channels keep concurrent work bounded\. - **Compile\-time bounds**Fixed dimensions let the compiler prove safe indexing for arrays, vectors, and matrices\. - **Native performance**Blorp compiles to C while keeping performance work visible\. - **Tool\-friendly design**Stable formatting and explicit effects make human and AI\-written code easier to review\. ## Performance Blorp compiles to C, so idiomatic Blorp code is intended to run within range of hand\-written C\. Below is a recent benchmark snapshot from the Blorp benchmark suite, run on an M4 MacBook Air\. It's not meant to be authoritative, but shows broadly where blorp is positioned\. BenchmarkBlorpCGoPythonnumeric\_loop0\.1242s0\.1215s \(1\.0x\)0\.1726s \(1\.4x\)5\.1754s \(41\.7x\)fib0\.1979s0\.1970s \(1\.0x\)0\.2600s \(1\.3x\)7\.6289s \(38\.5x\)string0\.1169s0\.1071s \(0\.9x\)0\.1681s \(1\.4x\)0\.1323s \(1\.1x\)array\_sum0\.0011s0\.0005s \(0\.5x\)0\.0045s \(4\.1x\)0\.0957s \(87\.0x\)array\_ops0\.0069s0\.0056s \(0\.8x\)0\.0179s \(2\.6x\)0\.4976s \(72\.1x\)dict\_ops0\.1370s\-0\.1340s \(1\.0x\)0\.3506s \(2\.6x\)list\_ops0\.1236s\-0\.2074s \(1\.7x\)0\.4296s \(3\.5x\)set\_ops0\.2573s\-0\.5509s \(2\.1x\)0\.2332s \(0\.9x\)threaded\_cpu\_map0\.0150s0\.0110s \(0\.7x\)0\.0191s \(1\.3x\)0\.9889s \(65\.9x\)channel\_pipeline0\.0262s0\.0345s \(1\.3x\)0\.0092s \(0\.4x\)0\.1952s \(7\.5x\)sleep\_fanout0\.0084s0\.0118s \(1\.4x\)0\.0061s \(0\.7x\)0\.0376s \(4\.5x\)options0\.0148s\-\-\-simd0\.1302s0\.1071s \(0\.8x\)\-\-nbody0\.0539s0\.0495s \(0\.9x\)0\.0489s \(0\.9x\)3\.0480s \(56\.5x\)binary\_trees0\.1217s0\.1125s \(0\.9x\)0\.1158s \(1\.0x\)0\.6813s \(5\.6x\)fannkuch0\.3254s0\.1819s \(0\.6x\)0\.1493s \(0\.5x\)2\.6348s \(8\.1x\)spectral\_norm0\.0160s0\.0106s \(0\.7x\)0\.0140s \(0\.9x\)0\.9081s \(56\.8x\)mandelbrot0\.0020s0\.0021s \(1\.0x\)0\.0195s \(9\.8x\)0\.0590s \(29\.5x\)knucleotide0\.0231s\-0\.0153s \(0\.7x\)0\.0601s \(2\.6x\)reverse\_complement0\.0003s\-0\.0001s \(0\.3x\)0\.0022s \(7\.3x\) Run details: in\-process BENCH markers, 52 binaries compiled up front, 4 benchmark threads, Apple clang 21\.0\.0, Go 1\.26\.3, Python 3\.14\.4, and Python concurrency 3\.14\.4\. Comparison cells show time plus the benchmark suite's reported factor relative to Blorp\. A dash means that runner was not reported for that benchmark\.[See performance tools](https://blorp-lang.org/docs/performance-tools) ## Technical Details Blorp is meant to feel direct at the surface while keeping the compiler's safety and runtime choices explicit\. - **Static Types**The compiler checks imports, calls,`match`exhaustiveness, and fallible values before code reaches the C backend\. - **Hindley\-Milner\-Style Inference**Local names usually do not need annotations; types flow from literals, calls, branches, and generic uses\. - **Value Semantics**Assignment and updates behave like independent values, while`ARC`and`COW`keep common sharing cheap\. - **Perceus Ownership**The compiler lowers ownership with Perceus\-style dup/drop and reuse analysis, then runtime reference counts preserve source semantics\. - **Native Output**Typed programs lower through`Core IR`to generated C, then a C compiler produces a native binary\.