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The article uses tissue as a metaphor to argue that as the cost of large models decreases, AI usage will shift from reuse to consumption, Agents and software may become disposable consumables, and real feedback and verification will become scarce.
This article discusses how with the evolution of AI Agent capabilities, Skill (skill/workflow encapsulation) may replace traditional software as a more important application form. In the future, Agents will dynamically combine Skills around tasks, while software will retreat to the background as infrastructure.
This paper introduces Code2LoRA, a hypernetwork-based method to generate adapters for code language models, addressing challenges under software evolution.
Code2LoRA introduces a hypernetwork that generates LoRA adapters from a repository in a single forward pass, allowing frozen code LLMs to adapt to repository context without extra tokens, and supporting evolving codebases efficiently. It also delivers RepoPeftBench, a benchmark for repo-conditioned code modeling.