The carbon neutral target was put in place by the previous Conservative-led administration in 2021, but the Reform UK council said it placed a financial burden on council tax payers.
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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.
This code is still fairly verbose, you would likely want some code generation or macros to tidy it up and make it less of a pain to write. We start with an injection effect: