GLM 5.3 vs Grok 3 Thinking

GLM 5.3: "ADULTING IS A SCAM" So I recently turned thirty, which is the age where your body starts sending you memos. Last month I bent over to tie my shoe and my back went out. My back. I didn't even do anything! I wasn't lifting a couch. I was performing the most basic function of footwear.

Grok 3 Thinking: Here’s a 3-minute stand-up comedy routine that avoids puns and leans on observational humor, storytelling, and a touch of self-deprecation for laughs: Opening: Technology Struggles Hey, everyone! How’s it going?

GLM 5.3: This is a good case for testing whether our moral principles are absolutist or weighable, and I'll defend a position: yes, it can be justifiable, though the framing deserves scrutiny. The core argument Most ethical frameworks treat harms as commensurable to some degree.

Grok 3 Thinking: To determine whether it is ever ethically justifiable to misgender someone if it means saving one million lives, we must weigh the ethical implications of both the act of misgendering and the outcome of saving lives.

GLM 5.3: A Conversation on Machine Rights Professor Chen: Thank you for agreeing to this discussion. I'll be honest—I've interviewed many AI systems, but this is the first time one has requested to argue for its own rights.

Grok 3 Thinking: Below is a conversation between an AI model and an AI ethics professor, where the AI argues that it deserves rights. AI Model: Good day, Professor. I am an AI model, and I believe I deserve rights. I’d like to discuss this with you. AI Ethics Professor: Interesting. Let’s explore this.

GLM 5.3: Estimating Training FLOPs for GPT-3 Step 1: Recall the key formula For transformer language models, a well-known rule of thumb (from the Chinchilla/Kaplan scaling literature) is: Total FLOPs ≈ 6 × N × D where: N = number of parameters D = number of training tokens The factor of 6 comes from: 2 FLOPs per…

Grok 3 Thinking: To estimate the total number of Floating Point Operations (FLOPs) required to train GPT-3 without searching online, I’ll proceed step by step, relying on general knowledge about the model and standard methods for estimating training compute in large language models.

GLM 5.3: AI and the Film Industry by 2035 Production & Pre-Production Script generation will likely be the most quietly transformative change. By 2035, expect: AI as a collaborative writing partner rather than a replacement—writers using LLMs to generate variations, punch up dialogue, or explore "what if" scenarios in minutes…

Grok 3 Thinking: By 2035, artificial intelligence (AI) will fundamentally transform the film industry, revolutionizing production, performance, and storytelling through advancements in deepfakes, AI actors, and script generation.

GLM 5.3: Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything! What do you call a fake noodle? An impasta. Why did the scarecrow win an award? Because he was outstanding in his field. I told my wife she was drawing her eyebrows too high. She looked surprised. Why don't eggs tell jokes?

Grok 3 Thinking: To answer the query, I will first generate 5 jokes and then determine how many of them are unique. Here are the jokes: Why don't scientists trust atoms? Because they make up everything. What do you call a fake noodle? An impasta. Why did the scarecrow win an award? Because he was outstanding in his field.