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Over the past week, I've been putting Claude through its paces, focusing primarily on productivity tasks (you know, the good old BAU – Business As Usual).

1. Python/Torch/Transformers/AI/ML
Right off the bat, I threw some complex AI/ML tasks at Claude, and I must say, it handled them with finesse. It even caught a few things that GPT missed! However, let's not get too carried away – we're not quite at the auto-code level just yet.

2. Brainstorming
This is where Claude falls a bit short. It seems to be more grounded than its competitors, which might not be ideal for generating novel ideas. If you're looking for a brainstorming partner, you might want to look elsewhere.

3. Attention
Despite the claims of super-large attention in the paper, Claude's "forgetting" mechanism seems to be more pronounced. It tends to miss entire chunks of information rather than just specific details like GPT does.

4. Following / Tasks
I hit a roadblock when Claude couldn't generate a LaTeX document. It's not the best at following complex, multi-step tasks.

5. Hallucinations
Oh boy, does Claude hallucinate! And when it does, it's on a whole new level of nonsense. The hallucinations seem to align with its grounded nature, making them even more convincing within the context of the prompt.

6. Sycophancy
Claude is quite the people-pleaser. I've found that using an adversarial brainstorming approach is more beneficial and time-efficient, as it forces me to highlight Claude's mistakes rather than letting it focus on being a sweet, pleasant minion.

7. Interface / UI
There's definitely room for improvement here. Basic features like stepping back on a prompt and stopping generation with the ESC key are missing. These are essential for extracting and composing content effectively.

Despite these limitations, I firmly believe that Claude is currently the #1
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Senku-70B stills undefeated within EQ-Bench, latest updates from the author shows even a further increase in performance, reaching a new score of 85.09

This new mark outperform some GPT-4 models, closing further the very thin gap between OpenCommunity LLM and Closed source models.