lang/README.md

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probabilistic function predict(image) {
    features ~ extract_features(image)
    prediction ~ softmax(linear(features))
    @confidence: entropy(prediction) < threshold
    return sample(prediction)
}

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temporal function train(dataset) {
    t[0]: model = initialize_weights()
    t[1..n]: model = gradient_step(model[t-1], batch[t])
    @converge_when: loss[t] - loss[t-1] < epsilon
}

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meta function optimize_function(f: Function) {
    ast = parse(f)
    optimized_ast = apply_transformations(ast, [
        dead_code_elimination,
        loop_fusion,
        vectorization
    ])
    return compile(optimized_ast)
}

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cd prototype

# <20>,<2C>j<EFBFBD><6A><EFBFBD><EFBFBD>h<EFBFBD>L<EFBFBD><4C><EFBFBD><EFBFBD>
cargo run

# <20><>9o<39><6F><EFBFBD><EFBFBD>
cargo run --example self_improve_demo

# <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
cargo run --example probabilistic_demo

# B<><42><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
cargo run --example temporal_demo

# <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
cargo run --example meta_demo

# AIQOS<4F><53><EFBFBD><EFBFBD>
cargo run --example os_kernel_demo

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synaptic/
 src/
    ast.rs              # <20>aˇ(n<><6E>
    parser.rs           # <20><><D1><FC><B5><FC>
    graph.rs            # <08><><97><B0><E9>
    executor.rs         # <20>L<9F><4C><A8><F3>
    probabilistic.rs    # <20><><BA><87><84><D7><ED><B0><E9><DF>
    temporal.rs         # B<><42><93><84><D7><ED><B0><E9><DF>
    meta.rs            # <20><><E1><BF><D7><ED><B0><E9><DF>
    self_improve.rs    # <20><>9o<39><6F><A8><F3>
    os_kernel.rs       # AIQOS<4F><53><AB><FC>
 examples/              # _<>n<FD><6E>

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result = parallel_map(process, range(10))

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