Now, you can start a coding agent and proceed in two ways: turn the implementation into a specification, and then in a new session ask the agent to reimplement it, possibly forcing specific qualities, like: make it faster, or make the implementation incredibly easy to follow and understand (that’s a good trick to end with an implementation very far from others, given the fact that a lot of code seems to be designed for the opposite goal), or more modular, or resolve this fundamental limitation of the original implementation: all hints that will make it much simpler to significantly diverge from the original design. LLMs, when used in this way, don’t produce copies of what they saw in the past, but yet at the end you can use an agent to verify carefully if there is any violation, and if any, replace the occurrences with novel code.
Легендарный музыкант рассказал об отношении КГБ к рокерам17:53
。新收录的资料对此有专业解读
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GNU and the AI reimplementationsantirez 1 hour ago. 2193 views. Those who cannot remember the past are condemned to repeat it. A sentence that I never really liked, and what is happening with AI, about software projects reimplementations, shows all the limits of such an idea. Many people are protesting the fairness of rewriting existing projects using AI. But, a good portion of such people, during the 90s, were already in the field: they followed the final part (started in the ‘80s) of the deeds of Richard Stallman, when he and his followers were reimplementing the UNIX userspace for the GNU project. The same people that now are against AI rewrites, back then, cheered for the GNU project actions (rightly, from my point of view – I cheered too).