191–200 of 358 posts
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I suggest you try enabling "Ignore search results" from the model dropdown for these types of questions. The web results can be distracting for the model for Leetcode-type questions.
I see you've had to suggest this a few times in this thread, and in my experience I would agree with the suggestion. I wonder if you can have a simple gpt model decide automatically when ignoring search results would improve the result and do it automatically.
I tried this question and GPT4 did way way better to getting closer to a final answer. Phind was horribly wrong. I can't help but think something seems off with your eval given just how badly Phind did on this. I want to make an interactive plot in Colab where I can show X axis is interest rate of a 15 year mortgage. Y axis is the relative advantage of buying a house vs. renting in terms of total net worth at 15 year…
Seemed to give plausible results for me: https://www.phind.com/search?cache=lswmiuewv2l33jt337dgrsho
Chat gpt actually implements it
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"We do have issues with consistency sometimes" That's a strange statement. Having issues with consistency means that sometimes the output is wrong. What does it mean to have issues with consistency sometimes ? You're either consistent or you're not.
There's a difference between models that are incompetent and aren't capable of getting the right answers ever and models that are capable of getting the right answer but may not do so every time. The Phind Model is in the latter camp. Consistency issues can be caused by a wide range of factors from inference hyperparameters to prompting.
I just spent a few minutes doing a comparison between Phind and GPT-4 for a very high-level question on a distributed job queue. I gave them both the same fairly vague sketch of a kind of system I would like to build. Here are my impressions: In the positives of Phind: * Phind was able, even eager, to recommend specific libraries relevant to the implementation. The recommendations matched my own research. GPT-4 takes…
> * Phind provides copious relevant sources including github, stackoverflow and others. This is a major advantage, especially if you use these AI assistants as a jumping off ground for further research. Did you find them to be correct?
I also spent about 20 minutes on this which is why I mentioned this is a first impression. I'll leave it to researchers to develop a "relevancy" metric and objectively apply it.
In my experience, the sources were sufficiently relevant based on its responses. They were about as relevant as equivalent Google queries. Some tiny, tiny niggles, like I was explicit I wanted it to recommend approaches in Go and for one reference I recall related to distributed locking mechanisms it provided a reference to an implementation in Java. However, that is completely fine for me since the context was more about the locking on the database side and not really the implementation in a specific language.
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We support custom instructions at https://phind.com/profile .
I’m trying to get it to answer only in executable Python. I used the template with instructions I use for my system prompt on gpt4. And I tried using the additional context field for the same. It gets to writing the expected code but it still wants to include formatted headings instead of commenting those out so the entire response is executable Python. As a follow up I provided an example heading with the hash out f…
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It's because we haven't updated the Phind Model to support function calling yet but we're working on it.
Can you share what your long term monetization model is? I'm noticing Phind is free to use right now.