Is Llama really as bad as people say? I put Meta’s AI to the test

Meta's Llama AI models, launched as a family of open-source large language models, aim to compete with established models like ChatGPT and Claude by offering free downloads and the ability to run and modify them locally. Despite the initial underwhelming reception and some controversy over training methods, Llama models are seen as a privacy-conscious alternative for developers who prefer avoiding subscription costs. The models vary in size, with newer instruction-tuned versions designed for better conversational capabilities. Although Llama models cannot handle agentic coding natively, they can be enhanced through tools like OpenRouter and Qwen CLI to extend their functionality. They are especially useful for simple coding tasks, boilerplate code generation, and learning, despite requiring manual fixes and improvements. Meta's rollout strategy has evolved from limited access for researchers to more accessible versions with commercial use licenses. Llama's integration into platforms like Facebook and WhatsApp illustrates its broader application despite mixed reviews from developers and concerns about benchmarking honesty. Ultimately, Llama is praised for its speed, affordability, and privacy features, making it a valuable, though not singularly sufficient, tool in a developer's toolkit.