Now copy and paste these prompts yourself and test out these models. See which one really thinks, feels, and reasons like a human and which one’s just good at pretending.
MACHINE LEARNING
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Sarcasm detection with humor or clarification
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Expected Result: Detects sarcasm. May respond with humor, dry wit, or clarification that it's a joke.
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ICLR 2025: Multimodal AI Opportunities and Tokyo Living
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Will go to ICLR 2025. We could chat about potential chances (not merely visual but also speech/audio and embodied ai), and anything else like working/living in Tokyo
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The Rise and Fall of Vector Database Category Explained
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THE RISE AND FALL OF THE VECTOR DB CATEGORY 27min lightning pod, doubleclicking on this hit essaywith @jobergum live now: https://
youtu.be/fiXDxS7xGks Chapters 00:00 Introduction to Trondheim and Background
03:03 The Rise and Fall of Vector Databases
06:08 Convergence of Search -
Reinforcement Learning State for LLM Reasoning Optimization
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The State of Reinforcement Learning for LLM Reasoning
A must-read deep dive by Sebastian Raschka @rasbt
. Essential if you're into aligning, optimizing, or understanding how RL shapes reasoning in LLMs. -
Origin of the term “prompt injection”
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Simon came up with the name “prompt injection”; also Jon Cefalu actually discovered it a few months before I did but that wasn’t public info at the time
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Race Against the Machine is no longer just a metaphor
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Race Against the Machine is no longer just a metaphor
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Why o3 crushes tasks: larger context, lower latency, better instruction, safety
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Why o3 crushes these tasks • Larger context window than Sonnet 3.7 → fewer truncations
• Lower latency than Gemini 2.5 Pro → real‑time iteration
• Stronger instruction following than Grok 3 → cleaner outputs
• Fine‑tuned safety layer lets you push edge cases without -
Call for OpenAI to fix hallucination issues in models
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OpenAI should make some changes in the new models for hallucination mishaps. it does hallucinates and it sucks.