In an underpowered statistical study, a claim that two experimental conditions did not differ are not persuasive.
The claim is not "two experimental conditions did not differ". The claim is "The data do not show evidence that the experimental conditions did differ".
Of course the critical part is not the numbers, but what they mean.
So, what does the evidence mean?
The author interprets it to mean that there is no difference. They state this several times:
"46% EXACT PERMUTATION TEST P-VALUE (ONE-SIDED, H₁: CLAUDE MEAN > HISTORICAL)[...] What this p-value tells us is There's nothing unusual about the Claude group."
"74% ONE-SIDED P-VALUE (H₁: CLAUDE MORE LIKELY ABOVE MEDIAN) Fisher's exact test asks: if we split all releases at the historical median (0.74 sev/10c), are these Claude releases significantly buggy than previous releases (more likely to land above the median)? With a p-value of 74%, the answer is a decisive no. "
In an under-powered study, when a P value is above your alpha level cutoff (.05, .01, whatever was chosen) you can't distinguish between "no effect" and "could be an effect, but I didn't see one".
The two examples you bring are not claims of absence of evidence, but claims of evidence of absence. The author takes the result as evidence that there is no effect. As I wrote, the author shouldn't do that, because indeed you cannot distinguish between "no effect exists" and "no effect observed". But again, these are (wrong) claims for evidence of absence.
The author can absolutely claim: I did these statistical tests, and none showed evidence that there is an effect. Absence of evidence. It's not a claim that there will never be evidence. Just that there is none from these tests.
Edit: To convert the absence of evidence into evidence for absence, indeed you need to understand the statistical power of your test, and how it is affected by alternate hypotheses. And for that, without having done the math, having only two data points seems very thin.
