To achieve testability in modular software we need to leverage the Dependency Inversion Principle (DIP) up to the level of application architecture. That was the reasoning behind introducing the concept of Infrastructure-Modules. When we extract all the infrastructure code from Business-Modules, they can stay focused on the business logic and have no design/compile-time dependencies on any untestable infrastructure code. As a side effect, we achieve Separation of Concerns.
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近日近健太明确表示，“当前必须降低盈亏平衡点，以抵御市场低迷期”。言下之意，研发该做还是做，业务该砍还得砍，否则无法抵御风险。当下而言，“止血”更重要。
The threat extends beyond accidental errors. When AI writes the software, the attack surface shifts: an adversary who can poison training data or compromise the model’s API can inject subtle vulnerabilities into every system that AI touches. These are not hypothetical risks. Supply chain attacks are already among the most damaging in cybersecurity, and AI-generated code creates a new supply chain at a scale that did not previously exist. Traditional code review cannot reliably detect deliberately subtle vulnerabilities, and a determined adversary can study the test suite and plant bugs specifically designed to evade it. A formal specification is the defense: it defines what “correct” means independently of the AI that produced the code. When something breaks, you know exactly which assumption failed, and so does the auditor.
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