Toss up between Gemini Pro 2.5 and ChatGPT 4o right now. Claude Sonnet 3.5 from June 2024 also still good
@thatroblennon
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o1 Pro vs Other AI Models: Capabilities and Strengths Comparison
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It's just a smart model that's good at stuff. o3 writes a little too short and uses tables too much. The other reasoning models are heavily optimized for code. But o1 Pro will do stuff like deep research + write you a personalized playbook to help you execute a specific task.
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GPT-o1 Pro: Preferred Tool Beyond Content and Coding
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GPT-o1 Pro is still one of my favorites. Not for content. Not for coding. But for almost everything else.
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Teaching Through Real Prompting Challenges and Use Cases
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I want to teach everything through the lens of real prompting challenges and use cases. And include lots of prompts. If that sounds cool, stay tuned. Gonna be awesome.
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Focusing on Basics and Intermediate Prompting Concepts
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This time I'm tackling basics/intermediate concepts first. Not just the advanced stuff that many know me for. I've learned a lot in the past few years, having spent thousands of hours experimenting with prompting at this point.
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Revisiting Prompting Material for New Updated Course and Workshops
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BUT — I *am* revisiting a lot of that material right now though. That, and material from my advanced prompting course from 2023 and Build Powerful GPTs in 2024. Why? I've decided that putting out an updated prompting course with some live workshops is long overdue.
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Pausing Thought Prompting Course Due to Uncertain Effectiveness
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I still think thought prompting has potential, but I put the Thought Prompting course away without finishing it. I didn't want to teach people something that was just as likely to make their prompts worse as it was to make them better.
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AI Self-Correction: More Thinking Yields Bigger Failures and Successes
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What about thinking harder? Thinking more? Couldn't the AI be taught to self-correct? Well, according to the paper, more thinkg results in more spectacular results on both sides. Bigger failures.
Bigger successes too.
It cuts both ways. -
Thinking helps with medium tasks but fails on hard puzzles
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For medium tasks, the first answer is often wrong, so thinking further helps it get to the correct answer. With hard tasks (or puzzles in the case of this research), it never manages to figure it out regardless.
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Reasoning Models Overthinking Easy Tasks
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When a thinking/reasoning model encounters an easy task, something funny happens. The model finds correct answer/plan early, but then it keeps thinking and sometimes talks itself out of it. Overthinking, lol. It's trained on human data, after all.