4 ms·

That log line (with the four space at the front for HN formatting) is 40 tokens [1]. You can easily fit several hundred log lines with GPT4 8k context and with

That log line (with the four space at the front for HN formatting) is 40 tokens [1]. You can easily fit several hundred log lines with GPT4 8k context and with the incoming 32K context, you'll be able to fit close to a thousand log lines. That's a lot of context, especially if you can prefilter from relevant services, nodes, etc. or provide a multi-node trace [1] https://platform.openai.com/tokenizer https://platform.openai.com/tokenizer

skrebbel 4y ago
Yeah but… i can look through 100 log lines manually faster than writing a good gpt prompt. This would get useful if I can easily paste like 1M lines of logs (a few minutes of data for us), but even if that would work, it’d be prohibitively expensive I think. In other words, I still don’t completely grok the use case that’s being shared here.
- akiselev 4y ago
  The use case here is looking through logs for software that aren't familiar, especially stuff that gets touched too infrequently to internalize like, say, driver logs on a Linux workstation. If it's faster for you to read the logs yourself, you should continue to do that. If it's bespoke personal or commercial software, chances are GPT isn't going to be trained on its meaning anyway. Most people aren't going to be familiar with arbitrary ACPI errors. Most people would have to Google or ask GPT to even understand the acronym.
  - skrebbel 4y ago
    Right! Thanks, this is the bit I missed.
  - fendy3002 4y ago
    To add on it, sometimes adjacent logs can help to find solution, which by using the conventional google way, you'll need to analyze those yourself. With gpt, you don't need to, or they're helping you navigate on it.
- iso1631 4y ago
  Being able to pump a firehose of syslogs at a GPT and tell it to flag any problems would be great
BeefWellington 4y ago
Thousands of log lines is actually pretty tiny though. I have some verbose logging in a testing lab and JUST network traffic from a few mobile devices can easily throw out several megs per hour in logs.
s3p 4y ago
200 lines @ 40 tokens per line equates to 8,000 tokens. That costs $1.60. for one query.
- mjrpes 4y ago
  For GPT-4 input cost is 3 cents per 1,000 tokens (8K context). So input cost for 8,000 tokens is 24 cents. Output is 6 cents per 1,000 tokens, but answer will hopefully be a lot shorter.
- tharkun__ 4y ago
  Someone making $100 per hour would be able to analyze those 200 logs for about 7 minutes for that cost. That's a person you pay over 200k of salary. I analyze logs at work all the time and I analyze way more than 200 lines and it takes way less than 7 minutes to analyze those 200 lines. Somehow I don't think it would be more cost effective to have an intern paste those into ChatGPT over and over and blow through a ton of money doing it.
  - qup 4y ago
    $100 / hour = $1.67 per minute 7 minutes = $11.69
    - tharkun__ 4y ago
      I guess I "forgot to carry the 2". Thanks! (as in, where did I get the 7 min from - no I don't remember)
- yokoprime 4y ago
  Without even trying to check the math, I can tell you this is wrong. Cost is way way less. With 8k context i havent even been close to 1USD a day even with huge prompts. Yes i have API access to GPT-4

skrebbel 4y ago

Yeah but… i can look through 100 log lines manually faster than writing a good gpt prompt. This would get useful if I can easily paste like 1M lines of logs (a few minutes of data for us), but even if that would work, it’d be prohibitively expensive I think. In other words, I still don’t completely grok the use case that’s being shared here.

akiselev 4y ago
The use case here is looking through logs for software that aren't familiar, especially stuff that gets touched too infrequently to internalize like, say, driver logs on a Linux workstation. If it's faster for you to read the logs yourself, you should continue to do that. If it's bespoke personal or commercial software, chances are GPT isn't going to be trained on its meaning anyway. Most people aren't going to be familiar with arbitrary ACPI errors. Most people would have to Google or ask GPT to even understand the acronym.
- skrebbel 4y ago
  Right! Thanks, this is the bit I missed.
- fendy3002 4y ago
  To add on it, sometimes adjacent logs can help to find solution, which by using the conventional google way, you'll need to analyze those yourself. With gpt, you don't need to, or they're helping you navigate on it.
iso1631 4y ago
Being able to pump a firehose of syslogs at a GPT and tell it to flag any problems would be great

BeefWellington 4y ago

Thousands of log lines is actually pretty tiny though. I have some verbose logging in a testing lab and JUST network traffic from a few mobile devices can easily throw out several megs per hour in logs.

s3p 4y ago

200 lines @ 40 tokens per line equates to 8,000 tokens. That costs $1.60. for one query.

mjrpes 4y ago
For GPT-4 input cost is 3 cents per 1,000 tokens (8K context). So input cost for 8,000 tokens is 24 cents. Output is 6 cents per 1,000 tokens, but answer will hopefully be a lot shorter.
tharkun__ 4y ago
Someone making $100 per hour would be able to analyze those 200 logs for about 7 minutes for that cost. That's a person you pay over 200k of salary. I analyze logs at work all the time and I analyze way more than 200 lines and it takes way less than 7 minutes to analyze those 200 lines. Somehow I don't think it would be more cost effective to have an intern paste those into ChatGPT over and over and blow through a ton of money doing it.
- qup 4y ago
  $100 / hour = $1.67 per minute 7 minutes = $11.69
  - tharkun__ 4y ago
    I guess I "forgot to carry the 2". Thanks! (as in, where did I get the 7 min from - no I don't remember)
yokoprime 4y ago
Without even trying to check the math, I can tell you this is wrong. Cost is way way less. With 8k context i havent even been close to 1USD a day even with huge prompts. Yes i have API access to GPT-4