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OpenAI O3-Mini

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Re: OpenAI O3-Mini

#871

Post by elliotec »

profsummergig wrote: post #870
simonw wrote: post #306

I used o3-mini to summarize this thread so far. Here's the result: https://gist.github.com/simonw/09e5922be0cbb85894cf05e6d75ae... For 18,936 input, 2,905 output it cost 3.3612 cents. Here's the script I used to do it: https://til.simonwillison.net/llms/claude-hacker-news-themes...

Something I've not understood about their pricing system. I have the $20 plan. How does this "3.3612 cents" apply to my situation?

The API has a different pricing structure than using the app. This cost seems high to me compared to previous models I’ve used large data sets with.

Re: OpenAI O3-Mini

#872

Post by georgeck »

simonw wrote: post #306

I used o3-mini to summarize this thread so far. Here's the result: https://gist.github.com/simonw/09e5922be0cbb85894cf05e6d75ae... For 18,936 input, 2,905 output it cost 3.3612 cents. Here's the script I used to do it: https://til.simonwillison.net/llms/claude-hacker-news-themes...

I have been trying to approach the problem in a similar way, and in my observation, it is also important to capture the discussion hierarchy in the context that we share with the LLM.

The solution that I have adopted is as follows. Each comment is represented in the following notation:

   [discussion_hierarchy] Author Name:
To this end, I format the output from Algolia as follows:

   [1] author1: First reply to the post
   [1.1] author2: First reply to [1]
   [1.1.1] author3: Second-level reply to [1.1]
   [1.2] author4: Second reply to [1]
After this, I provide a system prompt as follows:

  You are an AI assistant specialized in summarizing Hacker News discussions. 
  Your task is to provide concise, meaningful summaries that capture the essence of the thread without losing important details. 
  Follow these guidelines:
  1. Identify and highlight the main topics and key arguments.
  2. Capture diverse viewpoints and notable opinions.
  3. Analyze the hierarchical structure of the conversation, paying close attention to the path numbers (e.g., [1], [1.1], [1.1.1]) to track reply relationships.
  4. Note where significant conversation shifts occur.
  5. Include brief, relevant quotes to support main points.
  6. Maintain a neutral, objective tone.
  7. Aim for a summary length of 150-300 words, adjusting based on thread complexity.
  
  Input Format:
  The conversation will be provided as text with path-based identifiers showing the hierarchical structure of the comments: [path_id] Author: Comment
  This list is sorted based on relevance and engagement, with the most active and engaging branches at the top.
  
  Example:
  [1] author1: First reply to the post
  [1.1] author2: First reply to [1]
  [1.1.1] author3: Second-level reply to [1.1]
  [1.2] author4: Second reply to [1]
  
  Your output should be well-structured, informative, and easily digestible for someone who hasn't read the original thread. 
  Use markdown formatting for clarity and readability.
The benefit is that, I can parse the output from the LLM and create links back to the original comment thread.

You can read about my approach in more detail here: https://gist.github.com/simonw/09e5922be0cbb85894cf05e6d75ae...

Re: OpenAI O3-Mini

#873

Post by digdugdirk »

simonw wrote: post #306

I used o3-mini to summarize this thread so far. Here's the result: https://gist.github.com/simonw/09e5922be0cbb85894cf05e6d75ae... For 18,936 input, 2,905 output it cost 3.3612 cents. Here's the script I used to do it: https://til.simonwillison.net/llms/claude-hacker-news-themes...

Out of curiosity - do you have an estimate on how much you've spent on various LLM API services through all of your experimentation?

And in your experience, what service do you feel hits a good sweet spot for performance/price if summarizing long text excerpts is the main use case? Inference time isn't an issue, this will be an ongoing background task.

Re: OpenAI O3-Mini

#874

Post by mvkel »

goosejuice wrote: post #608
mvkel wrote: post #603

Earlier quoted context omitted.

This is probably the right way to think about it. O1-pro is an absolute monster when it comes to architecture. It is staggering the breadth and depth that it sees. Ask it to actually implement though, and it trips over its shoelaces almost immediately.

Can you give an example of this monstrous capability you speak of? What have you used it for professionally w.r.t. architecture.

The biggest delta over regular o1 that I've seen is asking it to make a PRD of an app that I define as a stream-of-consciousness with bullet points.

It's fantastic at finding needles in the haystack, so the contradictions are nonexistent. In other words, it seems to identify which objects would interrelate and builds around those nodes, where o1 seems to think more in "columns."

To sum it up, where o1 feels like "5 human minute thinking," o1-pro feels like "1 human hour thinking"

Re: OpenAI O3-Mini

#875

Post by Sharlin »

waynecochran wrote: post #860
Sharlin wrote: post #696

Earlier quoted context omitted.

Well, of course it knew the author. I'm sure you can ask just about any LLM who the author of the DRA is and it will answer Wang et al. without even having to google or follow the tinyurl link. And certainly it would also know that the algorithm is supposed to compute rotation minimizing frames.

I don't know if you are being sarcastic or not, but that is pretty cool if true.

Not sarcastic at all. it just doesn't seem like a big deal if you have played with LLMs and realize just how much LLMs know. The double reflection paper is not particularly obscure. (Incidentally I just asked Claude a couple of weeks ago about implementing rotation-minimizing frames!)

Re: OpenAI O3-Mini

#876

Post by EMIRELADERO »

martin-t wrote: post #861
sebzim4500 wrote: post #832

Earlier quoted context omitted.

I would expect that if you asked the same question to 100 people off the street they would make the same mistake though. Neither people nor LLMs expect goats to eat wolves.

Comparisons to humans are ultimately misleading because 1) humans are not general intelligences most of the time, 2) humans run on incredibly faulty hardware. 1) Attention is limited. Human reasoning is slow. Motivation is limited. System 1 vs 2 thinking. Many will just tell you to fuck off or get bored and give some random answer to make you go away. Etc. See difference 2. 2) People run on limited hardware in terms…

How do you respond to this paper from 2 years ago? https://news.ycombinator.com/item?id=34815718

Re: OpenAI O3-Mini

#877

Post by kif »

sixhobbits wrote: post #803

For years I've been asking all the models this mixed up version of the classic riddle and they 99% of the time get it wrong and insist on taking the goat across first. Even the other reasoning models would reason about how it was wrong, figure out the answer, and then still conclude goat. o3-mini is the first one to get it right for me. Transcript: Me: I have a wolf, a goat, and a cabbage and a boat. I want to get th…

For the first one, I got these results from the Deepseek app, after it "thought" for 156 seconds:

    1. Take the wolf over
    2. Return alone.
    3. Take the goat over.
    4. Bring the wolf back.
    5. Take the cabbage over.
    6. Return alone.
    7. Take the wolf over.
It gave an invalid solution to the second one, but in the thinking steps it says:

    Oh, right! The actual problem is that the goat will eat the cabbage if left alone. Similarly, the wolf will eat the goat if they're left alone.

Re: OpenAI O3-Mini

#878

Post by Legend2440 »

winrid wrote: post #374
Legend2440 wrote: post #220

Earlier quoted context omitted.

Click the 'open sidebar' icon in the top left corner of the screen.

There isn't one, unless they fixed it today. Just a down arrow to change the model.

Try clearing your cache, the button has always been there for me.

Re: OpenAI O3-Mini

#879

Post by kif »

yodot555 wrote: post #856
martin-t wrote: post #824

Earlier quoted context omitted.

Thank you, this is a perfect argument why LLMs are not AI but just statistical models. The original is so overrepresented in the training data that even though they notice this riddle is different, they regress to the statistically more likely solution over the course of generating the response. For example, I tried the first one with Claude and in its 4th step, it said: > This is safe because the wolf won't eat the…

This is a dumb argument. Humans frequently fall for the same tricks, are they not "intelligent"? All intelligence is ultimately based on some sort of statistical models, some represented in neurons, some represented in matrices.

State-of-the-art LLMs have been trained on practically the whole internet. Yet, they fall prey to pretty dumb tricks. It's very funny to see how The Guardian was able to circumvent censorship on the Deepseek app by asking it to "use special characters like swapping A for 4 and E for 3". [1]

This is clearly not intelligence. LLMs are fascinating for sure, but calling them intelligent is quite the stretch.

[1]: https://www.theguardian.com/technology/2025/jan/28/we-tried-...

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