Open Source License Commercial Use Guide

Understanding Commercial Rights in Open Source Licenses

📋 Legend

✅ Free: Commercial use is completely free without restrictions

⚠️ Conditional: Commercial use allowed with specific requirements

❌ Not Free: Commercial use prohibited or requires separate licensing

❓ Varies: Check specific license terms

💻 Traditional Software Licenses

Permissive Licenses (Business-Friendly)

License	Commercial Use	Key Requirements	Notes
Apache-2.0	✅ Free	Attribution, state changes, include license	Very permissive, patent grant included
MIT	✅ Free	Include copyright notice and license	One of the most permissive licenses
BSD-2-clause	✅ Free	Include copyright notice	Very permissive
BSD-3-clause	✅ Free	Include copyright notice, no endorsement	Slightly more restrictive than 2-clause
BSD-3-clause-clear	✅ Free	Same as BSD-3 but clarifies no patent rights	Patent rights explicitly not granted
ISC	✅ Free	Include copyright notice	Similar to MIT, very permissive
Unlicense	✅ Free	None - public domain dedication	No restrictions whatsoever
CC0-1.0	✅ Free	None - public domain dedication	Creative Commons "No Rights Reserved"
WTFPL	✅ Free	None	"Do What The F*** You Want" - no restrictions
Zlib	✅ Free	Don't misrepresent origin	Very permissive
PostgreSQL	✅ Free	Include copyright notice	Similar to MIT
BSL-1.0	✅ Free	Include license text if source distributed	Boost Software License - very permissive
PDDL	✅ Free	None - public domain	Public Domain Dedication License
NCSA	✅ Free	Include copyright and license	Similar to BSD/MIT

Copyleft Licenses (Conditional)

License	Commercial Use	Key Requirements	Notes
GPL-2.0/3.0	⚠️ Conditional	Must open source entire work	Strong copyleft - viral license
AGPL-3.0	⚠️ Conditional	Like GPL + network use = distribution	Strictest copyleft
LGPL-2.1/3.0	⚠️ Conditional	Share LGPL library mods, allow relinking	Lesser GPL - library-friendly
MPL-2.0	⚠️ Conditional	Share modifications of MPL code only	File-level copyleft
EPL-1.0/2.0	⚠️ Conditional	Share modifications, patent grant	Eclipse Public License
AFL-3.0	⚠️ Conditional	Attribution, share source if distributed	Academic Free License
OSL-3.0	⚠️ Conditional	Share source if distributed, attribution	Open Software License
ECL-2.0	⚠️ Conditional	Attribution, state changes	Educational Community License
MS-PL	⚠️ Conditional	Include license, share source if distributed	Microsoft Public License
EUPL-1.1/1.2	⚠️ Conditional	Share modifications	European Union Public License
Artistic-2.0	⚠️ Conditional	Attribution, share modifications	Perl's license
OFL-1.1	⚠️ Conditional	Keep font name, share modifications	Open Font License
GFDL	⚠️ Conditional	Include license, preserve sections	GNU Free Documentation License
LPPL-1.3c	⚠️ Conditional	Rename if modified	LaTeX Project Public License

🎨 Creative Commons Licenses

License	Commercial Use	Key Requirements	Notes
CC-BY-2.0/2.5/3.0/4.0	⚠️ Conditional	Attribution required	Creative Commons with attribution
CC-BY-SA-3.0/4.0	⚠️ Conditional	Attribution + share-alike	Must use same license for derivatives
CC-BY-ND-4.0	⚠️ Conditional	Attribution, no derivatives	Can't modify the work
CC-BY-NC-2.0/3.0/4.0	❌ Not Free	Non-commercial only	Commercial use prohibited
CC-BY-NC-SA-2.0/3.0/4.0	❌ Not Free	Non-commercial + share-alike	Commercial use prohibited
CC-BY-NC-ND-3.0/4.0	❌ Not Free	Non-commercial + no derivatives	Most restrictive CC license

🤖 AI Model Licenses

License	Commercial Use	Key Requirements	Notes
Llama2	⚠️ Conditional	Free up to 700M users, attribution	Meta's Llama 2 license
Llama3/3.1/3.2/3.3	⚠️ Conditional	Free up to 700M users, attribution	Updated Meta license
Llama4	⚠️ Conditional	Assumed similar to Llama3	Not yet released
Gemma	⚠️ Conditional	Prohibited uses list, attribution	Google's Gemma license
OpenRAIL	⚠️ Conditional	Use restrictions, attribution	Responsible AI License
OpenRAIL++	⚠️ Conditional	More restrictions than OpenRAIL	Enhanced responsible use
CreativeML-OpenRAIL-M	⚠️ Conditional	Use restrictions, attribution	For ML models
BigScience-OpenRAIL-M	⚠️ Conditional	Use restrictions, share license	BLOOM model license
BigScience-BLOOM-RAIL-1.0	⚠️ Conditional	Specific BLOOM restrictions	Original BLOOM license
BigCode-OpenRAIL-M	⚠️ Conditional	Code generation restrictions	For code models
DeepFloyd-IF-License	❌ Not Free	Research only by default	Commercial license needed
Apple-AMLA	⚠️ Conditional	Apple's specific terms	Apple Machine Learning
Apple-ASCL	⚠️ Conditional	Apple sample code terms	Limited to Apple platforms
Intel-Research	❌ Not Free	Research purposes only	Intel's research license

📊 Data Licenses

License	Commercial Use	Key Requirements	Notes
CDLA-Permissive-1.0/2.0	✅ Free	Attribution for data	Community Data License
CDLA-Sharing-1.0	⚠️ Conditional	Share data improvements	Copyleft for data
ODC-By	⚠️ Conditional	Attribution for databases	Open Data Commons
ODbL	⚠️ Conditional	Share-alike for databases	Open Database License
C-UDA	⚠️ Conditional	Computational use only	Computational Use of Data
Etalab-2.0	✅ Free	Attribution	French government open license

🔍 Generic/Other Licenses

License	Commercial Use	Key Requirements	Notes
Other	❓ Varies	Check specific license	Not a standard license
CC	❓ Varies	Depends on CC variant	Generic Creative Commons
BSD	✅ Free	Usually permissive	Generic BSD reference
GPL	⚠️ Conditional	Strong copyleft	Generic GPL reference
LGPL	⚠️ Conditional	Weak copyleft	Generic LGPL reference
LGPL-LR	⚠️ Conditional	LGPL with special exceptions	Language resources variant

⚠️ Important Notes

1. Always read the full license text - This guide provides general guidance but specific versions may have additional terms
2. "Conditional" means you CAN use commercially BUT must follow requirements - These aren't necessarily restrictive
3. Attribution is usually simple - Just credit the original authors/source
4. Copyleft/Share-alike - You must license derivatives under the same terms
5. Patent grants - Some licenses (Apache, EPL) explicitly grant patent rights
6. Compatibility - Not all licenses can be combined in one project
7. AI/ML licenses often have use-case restrictions - Even if commercial use is allowed, certain uses may be prohibited

🏆 Quick Reference Guide

Most Business-Friendly Licenses

• MIT - Minimal requirements, maximum freedom
• Apache-2.0 - Patent protection included
• BSD variants - Simple and permissive
• ISC - MIT-style simplicity
• Unlicense - Public domain dedication
• CC0-1.0 - No rights reserved

Decision Tree for Choosing a License

• Want maximum freedom? → MIT or Apache-2.0
• Building a library? → LGPL or MPL-2.0
• Want improvements shared back? → GPL
• Running a SaaS? → AGPL (if you want to enforce sharing)
• Publishing content? → CC-BY (attribution only)
• Using AI models? → Check user count limits carefully

Most Restrictive for Commercial Use

• AGPL-3.0 - Requires source disclosure even for SaaS
• CC-BY-NC-ND - No commercial use, no modifications
• Research-only licenses - Intel-Research, DeepFloyd-IF
• Any "NC" (non-commercial) license

💡 Pro Tips

1. For startups: Stick to MIT or Apache-2.0 for maximum flexibility
2. For open source projects: Consider your goals - permissive for adoption, copyleft for contributions
3. For using AI models: Always check the user/revenue thresholds
4. For content creators: CC-BY allows commercial use with attribution
5. For enterprises: Create an approved license list to streamline decisions

---

Last updated: June 2025

Disclaimer: This guide is for informational purposes only and does not constitute legal advice. Always consult with a legal professional for specific licensing questions.

C++ Template Parameter Packs with sequence

C++ Template Parameter Packs with sequence

Simple Explanation

sequence<x,x,x> is a template that holds a compile-time list of integer values. It's like a

tuple but for numbers only, and the values are fixed at compile time. It's commonly used in

template metaprogramming to pass multiple integer parameters to other templates.

Simple Usage Example

.

#include <iostream>

// A simple sequence template that holds compile-time integers

template<int... Values>

struct sequence {

static constexpr int size = sizeof...(Values);

// Function to print all values

static void print() {

((std::cout << Values << " "), ...); // C++17 fold expression

std::cout << std::endl;

}

};

// Template that uses sequence to create arrays

template<typename T, int... Dims>

class MultiArray {

private:

static constexpr int total_size = (Dims * ...); // multiply all dimensions

T data[total_size];

public:

MultiArray() {

std::cout << "Created array with dimensions: ";

((std::cout << Dims << "x"), ...);

std::cout << " = " << total_size << " elements" << std::endl;

}

static constexpr int get_total_size() { return total_size; }

};

int main() {

// Create sequences with different values

sequence<1, 8> block_warps; // 1 block, 8 warps

sequence<16, 16, 8> tensor_dims; // 16x16x8 tensor

sequence<2, 4, 8, 16> powers_of_2; // sequence of powers

std::cout << "Block warps: ";

block_warps.print(); // Output: 1 8

std::cout << "Tensor dims: ";

tensor_dims.print(); // Output: 16 16 8

std::cout << "Powers of 2: ";

powers_of_2.print(); // Output: 2 4 8 16

// Use sequence values as template parameters

MultiArray<float, 10, 20> matrix2d; // 10x20 = 200 elements

MultiArray<int, 4, 4, 4> tensor3d; // 4x4x4 = 64 elements

return 0;

}

..

Output:

  Block warps: 1 8

  Tensor dims: 16 16 8

  Powers of 2: 2 4 8 16

  Created array with dimensions: 10x20x = 200 elements

  Created array with dimensions: 4x4x4x = 64 elements

Thank you. 🙏

C++ Temporary Function Objects Pattern

A C++ pattern where you create a temporary object and immediately call its function operator in

one line. The syntax Class<Template>{constructor_args}(function_args) creates an object, uses

it, then destroys it automatically.


Simple Usage Example

.

#include <iostream>

#include <vector>

// A simple function object that adds a value to each element

template<typename T>

class AddValue {

private:

T value;

public:

AddValue(T val) : value(val) {}

void operator()(std::vector<T>& arr) {

for(auto& x : arr) {

x += value;

}

}

};

int main() {

std::vector<int> numbers = {1, 2, 3, 4, 5};

// Pattern: Class<Template>{args}(target)

AddValue<int>{10}(numbers); // Add 10 to each element

// Print result

for(int x : numbers) {

std::cout << x << " "; // Output: 11 12 13 14 15

}

return 0;

}

..

This pattern is commonly used in libraries for one-time operations like filling arrays, applying

transformations, or configuring objects without creating persistent variables.

Thank you! 🙇🏻‍♂️

C++ Variadic Template example code for studying

 

Example 1: Basic Variadic Template

#include <iostream>

// 1. Simple function that accepts any number of arguments
template <typename... Args>
void print_simple(Args... args) {
    std::cout << "Number of arguments: " << sizeof...(args) << std::endl;
}

int main() {
    print_simple();                    // 0 arguments
    print_simple(1);                   // 1 argument
    print_simple(1, 2.5, "hello");    // 3 arguments
    return 0;
}

Example 2: Print All Arguments (Recursion)

#include <iostream>

// Base case: no arguments left
void print() {
    std::cout << std::endl;
}

// Recursive case: print first, then rest
template <typename First, typename... Rest>
void print(First first, Rest... rest) {
    std::cout << first << " ";
    print(rest...);  // Call with remaining arguments
}

int main() {
    print(1, 2, 3);                        // Output: 1 2 3
    print("Hello", 42, 3.14, 'A');        // Output: Hello 42 3.14 A
    return 0;
}

Example 3: Simple Integer Sequence

#include <iostream>

// Our own simple Sequence class
template <int... Values>
struct Sequence {
    static constexpr int size = sizeof...(Values);
    
    static void print() {
        std::cout << "Sequence contains " << size << " values: ";
        ((std::cout << Values << " "), ...);  // C++17 fold expression
        std::cout << std::endl;
    }
};

int main() {
    using Seq1 = Sequence<1, 2, 3>;
    using Seq2 = Sequence<10>;
    using Seq3 = Sequence<>;  // empty
    
    Seq1::print();  // Output: Sequence contains 3 values: 1 2 3
    Seq2::print();  // Output: Sequence contains 1 values: 10
    Seq3::print();  // Output: Sequence contains 0 values:
    
    std::cout << "Seq1 size: " << Seq1::size << std::endl;  // Output: 3
    
    return 0;
}

Example 4: Sequence with Array Access

#include <iostream>
#include <array>

template <int... Values>
struct Sequence {
    static constexpr int size = sizeof...(Values);
    static constexpr std::array<int, size> values = {Values...};
    
    static constexpr int at(int index) {
        return values[index];
    }
    
    static void print_all() {
        for (int i = 0; i < size; ++i) {
            std::cout << "Index " << i << ": " << at(i) << std::endl;
        }
    }
};

int main() {
    using MySeq = Sequence<10, 20, 30, 40>;
    
    std::cout << "Size: " << MySeq::size << std::endl;
    std::cout << "Element at index 2: " << MySeq::at(2) << std::endl;
    
    MySeq::print_all();
    
    return 0;
}

Example 5: Type Sequence (Like std::tuple)

#include <iostream>
#include <typeinfo>

// Store multiple types
template <typename... Types>
struct TypeList {
    static constexpr int size = sizeof...(Types);
};

// Helper to print type names
template <typename T>
void print_type() {
    std::cout << typeid(T).name() << " ";
}

template <typename... Types>
void print_types() {
    std::cout << "Types (" << sizeof...(Types) << "): ";
    ((print_type<Types>()), ...);
    std::cout << std::endl;
}

int main() {
    using MyTypes = TypeList<int, double, char>;
    std::cout << "Number of types: " << MyTypes::size << std::endl;
    
    print_types<int, double, char>();
    print_types<std::string>();
    
    return 0;
}

Example 6: CK-Style Sequence Usage

#include <iostream>

// Simplified CK-style Sequence
template <int... Is>
struct Sequence {
    static constexpr int Size() { return sizeof...(Is); }
    
    template <int N>
    static constexpr int At() {
        constexpr int arr[] = {Is...};
        return arr[N];
    }
};

// Alias for convenience
template <int... Is>
using S = Sequence<Is...>;

// Function that uses sequences for dimension mapping
template <int... InputDims, int... OutputDims>
void describe_mapping(S<InputDims...>, S<OutputDims...>) {
    std::cout << "Mapping: ";
    std::cout << "Input dims [";
    ((std::cout << InputDims << " "), ...);
    std::cout << "] -> Output dims [";
    ((std::cout << OutputDims << " "), ...);
    std::cout << "]" << std::endl;
}

int main() {
    // Define dimension indices
    S<0> batch_dim{};      // Dimension 0: batch
    S<1> height_dim{};     // Dimension 1: height
    S<2> width_dim{};      // Dimension 2: width
    S<3> channel_dim{};    // Dimension 3: channel
    
    // Example: NHWC to NCHW transformation mapping
    describe_mapping(S<0,1,2,3>{}, S<0,3,1,2>{});
    // Output: Mapping: Input dims [0 1 2 3 ] -> Output dims [0 3 1 2 ]
    
    // Example: Merge height and width
    describe_mapping(S<0,1,2,3>{}, S<0,1,2>{});
    // Output: Mapping: Input dims [0 1 2 3 ] -> Output dims [0 1 2 ]
    
    // Access sequence properties
    using NHWC = S<0,1,2,3>;
    std::cout << "NHWC dimensions: " << NHWC::Size() << std::endl;
    std::cout << "Height dimension index: " << NHWC::At<1>() << std::endl;
    
    return 0;
}

Example 7: Advanced - Tensor Transformation Simulator

#include <iostream>
#include <vector>

template <int... Dims>
struct Sequence {
    static constexpr int size = sizeof...(Dims);
    static constexpr int dims[size] = {Dims...};
};

template <int... Is>
using S = Sequence<Is...>;

// Simplified tensor descriptor
struct TensorDesc {
    std::vector<int> shape;
    std::vector<std::string> dim_names;
    
    TensorDesc(std::initializer_list<int> s, std::initializer_list<std::string> n)
        : shape(s), dim_names(n) {}
    
    void print() const {
        std::cout << "Tensor shape: ";
        for (size_t i = 0; i < shape.size(); ++i) {
            std::cout << dim_names[i] << "=" << shape[i] << " ";
        }
        std::cout << std::endl;
    }
};

// Transform function that uses sequences
template <int... InputIndices, int... OutputIndices>
TensorDesc transform_tensor(const TensorDesc& input,
                           S<InputIndices...> in_mapping,
                           S<OutputIndices...> out_mapping) {
    std::vector<int> new_shape;
    std::vector<std::string> new_names;
    
    // Simple pass-through transformation
    constexpr int in_dims[] = {InputIndices...};
    for (int idx : in_dims) {
        new_shape.push_back(input.shape[idx]);
        new_names.push_back(input.dim_names[idx]);
    }
    
    return TensorDesc{new_shape, new_names};
}

int main() {
    // Create NHWC tensor
    TensorDesc input_tensor({32, 224, 224, 3}, {"N", "H", "W", "C"});
    std::cout << "Input ";
    input_tensor.print();
    
    // Transform to NCH (drop W dimension)
    auto output = transform_tensor(input_tensor, 
                                  S<0,3,1>{},    // Take N, C, H
                                  S<0,1,2>{});   // Map to positions 0,1,2
    std::cout << "Output ";
    output.print();
    
    // Another example: reorder to NCHW
    auto reordered = transform_tensor(input_tensor,
                                     S<0,3,1,2>{},  // N,C,H,W order
                                     S<0,1,2,3>{}); // Same positions
    std::cout << "Reordered ";
    reordered.print();
    
    return 0;
}

Key Concepts to Remember:

1. ... in template: Declares a parameter pack that can accept any number of arguments
2. ... after variable: Expands the parameter pack
3. sizeof...(): Gets the number of elements in a pack
4. Sequence: A compile-time container of values (usually integers)
5. In CK: Sequences are used to specify which dimensions to transform and how to map them