VITALIS — GITHUB
Open Source · v44 Public Release
A from-scratch AI-native programming language built entirely in Rust. Cranelift JIT + AOT compilation, SIMD vectorization, SSA-form IR, pattern matching, closures, traits, generics, async/await, structured concurrency, Hindley-Milner type inference, algebraic effects, macros — designed from day one for machine learning, autonomous code evolution, and neuromorphic computing. 39-phase roadmap to v300: the world's first language with full neuromorphic ISA emulation.
Compiler Pipeline
10 StagesLanguage Features
v21fn map<T, U>(list: [T], f: fn(T) -> U) -> [U] {
let result = []
for item in list {
result.push(f(item))
}
result
}
// Monomorphization: compiled to native code
let doubled = map([1, 2, 3], |x| x * 2)async fn fetch_data(url: str) -> Result {
let response = await http.get(url)
await response.json()
}
async fn pipeline() {
let (tx, rx) = channel()
spawn(async { tx.send(await fetch_data("...")) })
let data = await rx.recv()
println(data)
}trait Trainable {
fn forward(self, input: Tensor) -> Tensor
fn backward(self, grad: Tensor) -> Tensor
}
struct Linear { w: Tensor, b: Tensor }
impl Trainable for Linear {
fn forward(self, x) { self.w.matmul(x) + self.b }
fn backward(self, g) { self.w.T().matmul(g) }
}let device = GpuDevice.new()
let buf_a = device.create_buffer([1.0, 2.0, 3.0])
let buf_b = device.create_buffer([4.0, 5.0, 6.0])
// Launch compute pipeline
let pipeline = device.compute_pipeline("vector_add")
let result = pipeline.dispatch(buf_a, buf_b, 3)
println("Result:", result.read()) // [5.0, 7.0, 9.0]Evolution Timeline
v1 → v300v1–v10
- ▸Lexer, Parser, AST (30+ node types)
- ▸Variables, functions, closures, lambdas
- ▸Pattern matching, pipe operator
- ▸Basic type checker, scope resolution
- ▸Cranelift JIT codegen backend
- ▸412+ stdlib builtins
- ▸SIMD F64x4 vectorization (AVX2)
v11–v17
- ▸Structs, enums, impl blocks, traits
- ▸Type aliases, self keyword, methods
- ▸Try/catch, throw expressions
- ▸SSA-form IR with optimizations
- ▸C FFI bridge (64 exports)
- ▸Evolution engine + quantum UCB
- ▸ML built-ins, tensor ops
v18–v20
- ▸Hotpath engine (44 native ops)
- ▸Advanced math (FFT, matrix ops)
- ▸Quantum math primitives
- ▸Meta-evolution strategies
- ▸Predictive JIT optimizer
- ▸Delta Debug integration
- ▸Engram memory storage
v22
- ▸Ownership / borrow checker
- ▸Incremental compilation + caching
- ▸Full trait dispatch with vtables
- ▸Debug Adapter Protocol (DAP)
- ▸Interactive REPL with commands
- ▸Lifetime annotations + region analysis
- ▸Effect system + capability types
- ▸Hot-reload with file watching
- ▸Self-hosted compiler bootstrap (Stage 0/1/2)
- ▸Native AOT compilation (standalone executables)
- ▸Cross-compilation (x86-64, AArch64, RISC-V)
v21
- ▸Async/await runtime + channels
- ▸Generics with monomorphization
- ▸Package manager + SemVer resolver
- ▸LSP server (diagnostics, completion, hover)
- ▸WebAssembly target (WASM builder)
- ▸GPU compute (buffers, kernels, pipelines)
v23
- ▸Non-lexical lifetimes (NLL)
- ▸CFG builder + liveness analysis
- ▸Borrow regions at CFG points
- ▸Conflict detection for mut/shared
v24
- ▸Algebraic effect handlers
- ▸Resume/abort continuations
- ▸Pattern exhaustiveness (Maranget)
- ▸Or-patterns + redundancy detection
- ▸Or/Tuple pattern AST extensions
v25
- ▸AST-based code formatter
- ▸Static linter — 17 lint rules
- ▸Refinement / dependent types
- ▸Constraint solver + subtype checking
- ▸Span derives Copy (zero-cost)
v26
- ▸Hygienic macro expansion
- ▸Derive macros (Debug, Clone, PartialEq)
- ▸Compile-time const evaluation
- ▸Lazy iterator protocol + generators
- ▸13 adapters, state-machine lowering
v27
- ▸Structured concurrency primitives
- ▸Mutex, RwLock, MPSC channels, Select
- ▸Hindley-Milner type inference (Algorithm W)
- ▸Union / intersection types
- ▸Documentation generation (MD/HTML)
v28–v34
- ▸24 algorithm libraries (graph, crypto, ML, quantum, etc.)
- ▸Bioinformatics, chemistry, neuromorphic computing
- ▸Signal processing, compression, string algorithms
- ▸Sorting algorithms, combinatorial optimization
- ▸Probability, numerical methods, geometry
v35–v40
- ▸GPU compute pipelines & kernels
- ▸Advanced evolution strategies (DE, PSO, CMA-ES)
- ▸NSGA-II multi-objective optimization
- ▸Novelty search & MAP-Elites
- ▸Island model parallel evolution
- ▸Security & scoring modules
v41
- ▸WASM AOT compilation
- ▸WASI system interface support
- ▸Component model (wit-component)
- ▸Browser shim generation
- ▸Dead-code elimination & tree-shaking
v42
- ▸Package registry protocol
- ▸Distributed compilation (work-stealing)
- ▸Content-addressable build cache
- ▸Hermetic reproducible builds
v43
- ▸Formal verification — pre/post contracts
- ▸Symbolic execution engine
- ▸SMT constraint generation
- ▸IDE features — refactoring, coverage, call graphs
- ▸Cyclomatic complexity analysis
v44
- ▸Neural architecture search (NAS)
- ▸SuperNet & ENAS controller
- ▸Continual learning — EWC, progressive nets
- ▸Federated learning — FedAvg, differential privacy
- ▸Gossip protocol for decentralized training
v45–v47
- ▸Tracing garbage collector — generational, concurrent
- ▸M:N green threading — work-stealing scheduler
- ▸Embedded SQL database — B-tree, ACID, query planner
- ▸LSM-Tree key-value store — compaction, bloom filters
- ▸Raft consensus protocol — leader election, log replication
- ▸CRDTs, distributed sagas, causal consistency
v48–v49
- ▸Polyhedral loop optimizer — tiling, skewing
- ▸Thread pool runtime — fork-join parallelism
- ▸3-tier JIT — interpreter → baseline → optimizing + OSR
- ▸Pi/Sigma dependent types — type-level computation
- ▸Tactic-based proof assistant — induction, rewriting
v51–v53
- ▸Record-replay time-travel debugger
- ▸OpenTelemetry tracing — distributed spans & metrics
- ▸Package ecosystem v2 — verified publishers, CDN
- ▸Doc site generator — search, versioning, API docs
- ▸Jupyter kernel — interactive notebooks, rich output
- ▸Web playground — browser WASM execution
v54–v56
- ▸HLS — high-level synthesis to RTL
- ▸FPGA compilation backend — bitstream, place & route
- ▸Bare-metal embedded — no_std, interrupt handlers
- ▸RTOS kernel — priority scheduling, IPC
- ▸Cloud-native deployment — containers, serverless
- ▸Service mesh — load balancing, circuit breakers, mTLS
v57–v59
- ▸LLM-assisted error messages & fix suggestions
- ▸Intelligent error recovery — continuation parsing
- ▸Computer vision — CNNs, object detection, transforms
- ▸Audio processing — spectrograms, speech synthesis
- ▸Verified compilation — correctness proofs
- ▸Abstract interpretation — value analysis, widening
v60
- ▸Self-Hosting v2 — compiler rewritten in Vitalis
- ▸Multi-stage meta-compiler (quasi-quotation, staging)
- ▸Stage 0/1/2 fixpoint bootstrap
- ▸Compiler-compiler from grammar spec
v61–v70
- ▸Type system unification (Union, Intersection, Never)
- ▸JIT symbol bridge — 196 symbols from 32 modules
- ▸Optimizer hardening — CSE, strength reduction, fixpoint
- ▸LSP wire protocol + AOT linker integration
- ▸Evolution mutation strategies + meta-evolution
- ▸Self-optimizer pass wiring with RL
- ▸Autonomous agent execution loops
v71–v80
- ▸GPU-accelerated 2D renderer + retained widget tree
- ▸Input/event system + theming + accessibility
- ▸Tensor type system in AST/IR/type-checker
- ▸Autodiff core (reverse-mode AD tape)
- ▸Differentiable control flow
- ▸GPU kernel lowering (CUDA/Metal/Vulkan codegen)
v81–v100
- ▸Package registry + supply-chain security
- ▸Runtime hardening + database primitives
- ▸Formal spec + verified type safety
- ▸Quantum IR research track + backend
- ▸Self-evolution safety rails
- ▸Autonomous improvement lab + fixpoint validation
v101–v141
- ▸Toolchain CLI (LSP, DAP, formatter, linter)
- ▸Closures, enums, generics, strings, for-loops
- ▸WASM, GPU, tensor, autograd pipeline bridges
- ▸JIT cache, memory hardening, AOT optimizer
- ▸Compiler hardening — parser, type checker, codegen
- ▸Safety pipeline — ownership + lifetimes wiring
- ▸Copy propagation, LICM, function inlining
v142–v200
- ▸Runtime observability & audit
- ▸Production security & hardening
- ▸Advanced self-evolution
- ▸AI-native language features
- ▸Persistent storage + network protocol stack
- ▸Concurrency v2 + type system evolution
- ▸Ecosystem milestone v200
v201–v300 (Neuromorphic Arc)
- ▸Neuromorphic core engine — LIF, Izhikevich, STDP
- ▸Loihi 3 ISA & core architecture emulation
- ▸Advanced SNN training + von Neumann elimination
- ▸Brain-inspired architectures — cortical columns, HTM
- ▸GPU-accelerated neuromorphic + applications
- ▸Quantum-neuromorphic hybrid compute
- ▸Self-evolving neuromorphic + v300 milestone
- ▸Target: 4,800+ tests · 192+ modules · 650+ builtins
Vitalis vs Python Performance
BenchmarksCranelift JIT + SIMD vectorization vs CPython 3.12 — single-threaded, best of 5 runs, same algorithms.
| Benchmark | Vitalis | Python | Speedup |
|---|---|---|---|
| Fibonacci(35) | 28ms | 2,840ms | 101× |
| Matrix 1024×1024 mul | 42ms | 3,100ms | 74× |
| Sort 1M integers | 68ms | 1,250ms | 18× |
| String concat 100K | 4ms | 380ms | 95× |
| Neural net forward | 12ms | 890ms | 74× |
| JSON parse 10MB | 35ms | 1,620ms | 46× |
| Regex match 1M lines | 22ms | 410ms | 19× |
| Binary tree depth 25 | 15ms | 4,200ms | 280× |
Core Capabilities
Cranelift JIT Compilation
Compiles directly to native x86-64 machine code via the Cranelift code generator (same backend as Wasmtime). No interpreter overhead — every function executes as real machine instructions.
SIMD Vectorization (AVX2)
F64x4 vector operations compiled to AVX2 instructions. Dot products, fused multiply-add, reduction ops — all executing 4 floats per cycle on modern CPUs.
Pattern Matching & Pipes
Exhaustive pattern matching on enums and structs. Pipe operator (|>) for functional data transformation chains. Destructuring, guards, and wildcard patterns.
Self-Evolving Code Engine
Built-in evolution system that can mutate, evaluate, and select code improvements autonomously. Thompson sampling strategies, quantum UCB for exploration/exploitation.
Async/Await Runtime (v21)
Cooperative async runtime with channels, futures, and a task scheduler. Async functions compile to state machines — no heap allocation per await point.
Native AOT Compilation (v22)
Compile Vitalis programs to standalone executables with no JIT dependency. Cross-compile to ARM64 and RISC-V 64 targets from any host platform.
Effect System & Lifetimes (v22)
Static effect system with capability-based security. Region-based lifetime analysis tracks borrow scopes. Algebraic effects for composable side-effect management.
Hot Reload & Bootstrap (v22)
Live code reloading with file watching and incremental recompilation. Self-hosted compiler bootstrap pipeline with Stage 0/1/2 cross-validation.
Structured Concurrency (v27)
Mutex, RwLock, MPSC channels, Select multiplexing, WaitGroup, atomic operations. Scoped tasks with lifecycle management and DFS-based deadlock detection.
Hindley-Milner Inference (v27)
Full Algorithm W type inference with let-polymorphism. Unification, bidirectional checking, union/intersection types, and flow-sensitive narrowing.
Hygienic Macros (v26)
Token-tree macro expansion with hygiene scoping. Built-in derive macros for Debug, Clone, PartialEq, Default. Fragment specifiers and repetition expansion.
Algebraic Effects & NLL (v23-v24)
Non-lexical lifetimes with CFG-based borrow regions. Algebraic effect handlers with resume/abort continuations. Pattern exhaustiveness via Maranget algorithm.
WASM AOT & Distributed Builds (v41-v42)
WebAssembly AOT compilation with WASI support, component model, and tree-shaking. Distributed build system with content-addressable cache and hermetic reproducible builds.
Formal Verification & IDE (v43)
Pre/post contracts, symbolic execution, SMT constraint generation. Automated refactoring, code coverage analysis, call graph construction, and cyclomatic complexity metrics.
NAS & Federated Learning (v44)
Neural architecture search with SuperNet and ENAS controller. Continual learning via EWC and progressive nets. Federated training with differential privacy and gossip protocol.
Systems Programming (v45–v49)
Tracing GC, M:N green threads, embedded SQL database, LSM-Tree KV store, Raft consensus, CRDTs, polyhedral loop optimizer, 3-tier JIT with OSR, and dependent types with proof assistant.
Platform & DevEx (v51–v56)
Time-travel debugger, OpenTelemetry tracing, package ecosystem v2, doc site generator, Jupyter kernel, web playground, HLS/FPGA targets, bare-metal embedded, RTOS, cloud-native deployment.
AI-Native Compiler (v57–v59)
LLM-assisted error messages with fix suggestions, intelligent error recovery, computer vision and audio processing modules, verified compilation with correctness proofs, abstract interpretation.
Self-Hosting & Meta-Compiler (v60)
Compiler rewrites itself in Vitalis. Multi-stage meta-programming with quasi-quotation, staging annotations, and compiler plugins. Stage 0→1→2 fixpoint bootstrap.
Foundation & Evolution (v61–v100)
Type system unification, JIT symbol bridge, optimizer hardening (CSE, strength reduction), LSP wire protocol, AOT linker, self-evolution with RL-driven pass planning, GUI toolkit, formal verification, quantum IR.
Neuromorphic Arc (v201–v300)
Full neuromorphic ISA emulation — spiking neural networks, Loihi 3 instruction set, STDP learning, brain-inspired architectures, quantum-neuromorphic hybrid, GPU-accelerated spike processing, self-evolving topologies.
Source Inventory
192 modules · 158,000+ LOCWhy a Custom Language for AI?
Why not just use Python?
Python is 88× slower on average. For an AI system that needs to evaluate thousands of code mutations per hour, that overhead is unacceptable. Vitalis compiles to native machine code.
Why not use an existing compiled language?
No existing language has built-in evolution primitives, ML operators as first-class operations, or a compiler designed to JIT-compile AI-generated code safely in a sandbox.
Can it replace Python for ML?
For the Infinity system, yes — it already does. Vitalis handles all code evolution, sandbox evaluation, and performance-critical paths. Python remains for the FastAPI web layer.
Is it production-ready?
4,308 tests passing, 59 major versions, deployed in production powering the Infinity autonomous AI system. The compiler itself is 158,000+ lines of battle-tested Rust with AOT compilation, FPGA targets, verified compilation, and a 39-phase roadmap to v300 — the neuromorphic programming language.
Grand Vision: Vitalis v300
Neuromorphic MilestoneVitalis v300 aims to be the world's first programming language with full neuromorphic ISA emulation — a language that compiles spiking neural networks to native hardware, eliminates the von Neumann bottleneck, and bridges classical, quantum, and neuromorphic compute paradigms.
Loihi 3 Emulation
Full Intel Loihi 3 instruction set — soma, synapse, axon, dendrite — on commodity GPU/gaming PC hardware.
Eliminates von Neumann
Processing-in-memory, zero-copy spike propagation, and memory-compute fusion eliminate the data movement bottleneck.
Spikes Natively
Priority-queue event system, multi-compartment neurons, conductance synapses, population dynamics as first-class primitives.
Learns Biologically
STDP, surrogate gradients, BPTT-SNN, neuromodulation, hippocampal consolidation, and sleep replay.
Brain-Inspired
Predictive coding, HTM, cortical columns, neural oscillations, and spike-based attention mechanisms.
GPU-Accelerated
Batch spike propagation, sparse CSR synapse processing, mixed-precision INT8/FP16, multi-GPU partitioning.
Self-Evolving
NEAT v2 for SNNs, spike-driven RL, curiosity exploration, meta-learning, and autonomously evolving topologies.
Bridges Paradigms
SNN↔ANN conversion, hybrid inference, neural ODEs, and quantum-neuromorphic interface for unified compute.
Verified Safety
Formal verification, certified inference, adversarial robustness, fairness auditing, and safety rails for all SNN models.
Scales Everywhere
WASM neuromorphic, edge computing, cloud deployment, distributed clusters, cross-platform x86/ARM/RISC-V targets.