Software 3.1? – AI Functions (blog.mikegchambers.com)

Andrej Karpathy’s concept of Software 3.1 has emerged with the introduction of AI Functions by Strands Labs, marking a significant evolution in how developers interact with AI in coding. While Software 1.0 and 2.0 primarily rely on human-written code and neural network-generated weights, Software 3.0 is about prompting large language models (LLMs) to generate code based on plain language specifications. AI Functions advance this model by integrating AI directly into runtime environments, where the LLM not only generates code but also executes it and validates the output through built-in post-conditions. This shift allows for seamless generation of native Python objects, like DataFrames and models, rather than just JSON strings that require parsing. The significance of AI Functions lies in their ability to enhance the software development lifecycle with continuous verification and dynamic execution. By attaching post-conditions that validate output with every function call, the framework establishes an automated feedback loop, empowering the LLM to learn and improve with each attempt without needing human inspection before deployment. With these advancements, developers can produce robust applications that incorporate real-time AI capabilities, shifting the focus from crafting precise prompts to designing effective validation criteria. This not only streamlines development processes but also raises important considerations around monitoring and observability as AI takes a more integrated role in runtime environments.