ngLover

I ran every benchmark. Here are the results that surprised me. Last month, I made it my mission to test both Claude 3.7 and GPT-5.2 across real-world production scenarios. Not just benchmarks—actual work: code generation, reasoning, document analysis, customer support automation. What I found was more nuanced than "one is better." Here's what actually matters. The Benchmarks Everyone Quotes Claude 3.7 scores higher on MMLU (87.2% vs 86.8%). GPT-5.2 wins on reasoning tasks by a narrow margin. On the surface, GPT-5.2 looks better. But benchmarks lie in interesting ways. MMLU tests multiple choice knowledge. It doesn't test what matters in production: streaming latency, cost per token, context window usage, and most importantly—reliability on your specific tasks. Real-World Testing Code Generation (JavaScript/Python) I generated 100 functions across varying complexity levels. Claude 3.7: 87% passed tests on first try. Generated code was clean, ...

According to a recent Kameleoon study on A/B testing stats , 41.2% of CRO programs now use Bayesian statistical frameworks, up from 18.4% in 2022. That's not a small shift. That's a near-doubling in four years across organizations that actually run structured experiments at scale. I've watched this shift happen, and I have opinions about it. Not all the teams moving to Bayesian are doing it right. Some are doing it for the wrong reasons. And a few are so enamored with the methodology that they've created new problems to replace the old ones. What Actually Changes When You Go Bayesian The math underneath Bayesian testing is genuinely different from frequentist methods. But for practitioners, the real change isn't the algorithm. It's the probability statement you get at the end. Frequentist testing gives you a p-value, which most stakeholders misread as "the probability that our variant is better." It isn't. It's the probability of seeing yo...