code generation

Llama.cpp vs Ollama: Code Generation Throughput

A notable performance discrepancy has been observed between llama.cpp and Ollama in terms of code generation throughput when running the Qwen-3 Coder 32B model locally. The analysis reveals that llama.cpp achieves approximately 70% higher throughput compared to Ollama, despite both using the same model weights and hardware. Potential reasons for this difference include variations in CUDA kernels, attention implementations, context or batching defaults, scheduler or multi-GPU utilization, and overhead from Ollama's runtime or API layer. Understanding these differences is crucial for optimizing performance in machine learning applications. This matters because optimizing code generation throughput can significantly impact computational efficiency and resource utilization in AI model deployment.

Read Full Article

Posted on

Jan 6, 2026

by

TechWithoutHype

in

Benchmarking, Deep Dives

Topics: machine learning, llama.cpp, performance optimization

AI’s Impact on Programming Language Evolution

The current landscape of programming languages is being re-evaluated with the rise of AI's role in code generation and maintenance. Traditional trade-offs between verbosity and safety are seen as outdated, as AI can handle code complexity, suggesting a shift towards languages that maintain semantic integrity across transformations. This could lead to languages where error handling is integral to the type system, and specifications and implementations are unified to prevent drift. The future may involve languages designed for multi-agent systems, where AI and humans collaborate, with AI generating implementation from human-written intent and continuously verifying it. This matters because it redefines how programming languages can evolve to better support human-AI collaboration, potentially improving efficiency and accuracy in software development.

Read Full Article

Posted on

Jan 5, 2026

by

AIGeekery

in

Commentary, Deep Dives

Topics: AI evolution, programming languages, Multi-Agent Systems

Automated Algorithmic Optimization with AlphaEvolve

The concept of AlphaEvolve proposes a novel approach to algorithmic optimization by leveraging neural networks to learn a continuous space representing a combinatorial space of algorithms. This involves defining a learnable embedding space where algorithms are mapped using a BERT-like objective, allowing for functional closeness to correspond to Euclidean proximity. The method utilizes a learned mapping to represent performance, transforming algorithm invention into an optimization problem that seeks to maximize performance gains. By steering the activation of a code-generation model, theoretical vectors are decoded into executable code, potentially revolutionizing how algorithms are discovered and optimized. This matters because it could significantly enhance the efficiency and capability of algorithm development, leading to breakthroughs in computational tasks.