With @cursor_ai
, you can code using natural language—just describe what you need, and the AI handles the rest. This feature simplifies complex tasks, helping you turn ideas into reality faster and with less hassle. It’s all about making your work more intuitive. #Productivity
PROMPT ENGINEERING
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Cursor AI: Code with Natural Language for Faster Development
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Cursor AI: Practical Development Tools for Teams
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We’ve put @cursor_ai to the test, and it’s impressive. From coding in plain English to seamless deployment, Cursor offers practical tools that can make a real difference for your team. Read on to see how it can benefit you. #AI #DevTools #Tech
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Multi-LLM prompt testing and model selection behavior
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I caught myself copy/pasting the same initial prompt into three chat LLMs and then just picked my favorite and started chatting with that one
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Question about softmax invariance and matching counts
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I know there’s no integer count; my read was that softmaxing makes it invariant to the number of matches so it has to rely on other less reliable methods but I could well be misunderstanding that point; reading again now
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When LLMs Should Use Code
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Everyone acknowledges that code is the correct way to solve this. It’s still worth understanding why that’s necessary, and more practically for LLMs to understand *when* that’s necessary.
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Paper on limitations of counting in single-token inference
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Yes — and you can often achieve much more reliable counting by repeating an input a second time annotated with a running total, but that’s expensive and context-constrained; this paper is about limitations on counting directly in a single token inference
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15 ChatGPT Prompts to Land Your Dream Job Faster
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I'm shocked people still don't use ChatGPT for job search! ChatGPT can help you land your dream job twice as fast. Copy and paste these 15 ChatGPT prompts to land your Dream Job:
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Claude may inject invisible copyright-avoidance instructions
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Claude sometimes invisibly adds instructions to avoid reciting copyrighted works to the user’s prompt, presenting them to the model as though the user wrote them. Thus, these rules need not apply to Batman:
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Commentary on LLM prompting and counting reliability
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As many have pointed out, there are much better ways to prompt this. Also, it’s silly to use an LLM here, as there’s little hope of making them 100% reliable at counting. What’s notable IMO isn’t that it can’t count, but that it doesn’t see it can’t count (and e.g. try its REPL)
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User compares ChatGPT 4o and Claude 3.5 Sonnet behavior
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ChatGPT 4o just keeps making doomed attempts forever without iterating or progressing. Claude 3.5 Sonnet says 2, pushes back the first time you say “wrong,” asks what *you* think it is if you’re such a genius, asks why you keep repeating “wrong,” then stops playing: