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The third installment of "Embedded AI in Practice Notes" documents one of the author's attempts after the failure of a new system: the author spent about a week building a legacy project maintenance skill library with only five skills and roughly seven hundred lines of code, and got it working in real changes. The workflow sets up two manual gates: twenty-one assumptions must be confirmed one by one, and modification plans must be explicitly approved. The first replay validation generated forty-five test cases, with zero compilation errors and zero new static check issues. The author suggests starting with maintenance-type work and placing manual confirmation at decision points such as assumptions and plans.
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After the new system failed, a legacy project maintenance requirement brought AI down to earthDevelopments
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The author builds an AI skill library for maintaining legacy projects
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Oct 18
Alibaba Cloud Developer Community AI
After the new system failed, a legacy project maintenance requirement brought AI down to earth
The third installment of "Embedded AI Implementation Notes" records that after the new system failed, the author spent about a week building a legacy project maintenance skill library with only five skills and around seven hundred lines of code, and ran it successfully in a real change. The process sets up two manual gates: twenty-one assumptions confirmed one by one, and the modification plan must be explicitly approved; the first replay verification generated forty-five test cases, with zero compilation errors and zero new issues from static checks. The author suggests starting with maintenance-type work and placing manual confirmation at decision points such as assumptions and plans.