Ask HN: Is post-training make AI models dumber?

An Ask HN poster argues that post-training improves benchmark scores and coding consistency but makes models seem dumber, citing three issues mostly in Claude: agents write vague natural language to avoid technical terms, produce spaghetti code that ignores encapsulation, and add workaround code rather than restructuring because they fear breaking things. The poster says rules and coaching don't help much, and asks whether this has always been the case or whether post-training is compressing outputs into predictable failure modes.