#rust #build #productivity #systems

https://github.com/dimensionhq/fleet

#shell #bash #bash_script #devops #linux #systems_engineering

https://github.com/luischaparroc/holberton-system_engineering-devops

#shell #compiler #garbage_collection #native #programming_language #system_programming #systems #webassembly

https://github.com/titzer/virgil

#other #algorithms #bioinformatics #computational_biology #computational_physics #computer_architecture #computer_science #computer_vision #database_systems #databases #deep_learning #embedded_systems #machine_learning #quantum_computing #reinforcement_learning #robotics #security #systems #web_development

This collection of computer science courses offers a wide range of topics, from introductory programming to advanced specialized fields like machine learning, blockchain, and quantum computing. Here’s a simple summary of the benefits You can learn various aspects of computer science, including programming, algorithms, data structures, computer systems, software engineering, artificial intelligence, machine learning, and more.
- **Diverse Resources** There are courses on specific areas such as blockchain development, quantum computing, computational finance, and robotics, which can help you specialize in your area of interest.
- **Practical Skills** Most of these resources are available online for free, making quality education accessible to everyone.

Overall, this collection is a valuable resource for anyone looking to learn or deepen their knowledge in computer science.

https://github.com/Developer-Y/cs-video-courses

#c_lang #avx2 #c99 #cpu_inference #deep_learning #from_scratch #inference_engine #kimi_k3 #linear_attention #llm #llm_inference #machine_learning #memory_efficient #mixture_of_experts #moe #mxfp4 #quantization #simd #systems_programming #transformer #zero_dependencies

kimi-k3-in-c runs the Kimi K3 language model on a CPU without a GPU or machine learning framework. It supports text generation and multi-turn text chat on Linux, macOS, and Windows. By loading model data from disk as needed, it can run the full model with limited memory, making it useful on machines that cannot hold the model in RAM. It requires roughly 1.7 TB of free storage, and generation can be slow, especially with less memory or slower disks.

https://github.com/FareedKhan-dev/kimi-k3-in-c