4 ms·

They're searching for multiple substrings in a single pass, regexes are the optimal solution for that.

by lou1306 7mo ago

noosphr 7mo ago
The issue isn't that regex are a solution to find a substring. The issue is that you shouldn't be looking for substrings in the first place. This has buttbuttin energy. Welcome to the 80s I guess.
- deleted 7mo ago
  [deleted]
- 8cvor6j844qw_d6 7mo ago
  Very likely vibe coded. I've seen Claude Code went with a regex approach for a similar sentiment-related task.
  - mr_00ff00 7mo ago
    My understanding of vibe coding is when someone doesn’t look at the code and just uses prompts until the app “looks and acts” correct. I doubt you are making regex and not looking at it, even if it was AI generated.
    - deleted 7mo ago
      [deleted]
- rdiddly 7mo ago
  Clbuttic!
- lou1306 7mo ago
  > The issue is that you shouldn't be looking for substrings in the first place. Why? They clearly just want to log conversations that are likely to display extreme user frustration with minimal overhead. They could do a full-blown NLP-driven sentiment analysis on every prompt but I reckon it would not be as cost-effective as this.
  - noosphr 7mo ago
    >Some people, when confronted with a problem, think “I know, I’ll use regular expressions.” Now they have two problems. The only time to use a regex is when searching with a human in the loop. All other uses are better handled some other way. >They could do a full-blown NLP-driven sentiment analysis on every prompt but I reckon it would not be as cost-effective as this. Every conversation is sent to an llm at least a thousand times the size of gpt2 which could one shot this nearly a decade ago.
    - lou1306 7mo ago
      > Every conversation is sent to an llm at least a thousand times the size of gpt2 which could one shot this nearly a decade ago. Yes, but that is _what the product does_. What we are talking about is _telemetry_.
      - deleted 7mo ago
        [deleted]
BoppreH 7mo ago
It's fast, but it'll miss a ton of cases. This feels like it would be better served by a prompt instruction, or an additional tiny neural network. And some of the entries are too short and will create false positives. It'll match the word "offset" ("ffs"), for example. EDIT: no it won't, I missed the \b. Still sounds weird to me.
- hk__2 7mo ago
  It’s fast and it matches 80% of the cases. There’s no point in overengineering it.
  - NitpickLawyer 7mo ago
    > There’s no point in overengineering it. I swear this whole thread about regexes is just fake rage at something, and I bet it'd be reversed had they used something heavier (omg, look they're using an LLM call where a simple regex would have worked, lul)...
- vharuck 7mo ago
  The pattern only matches if both ends are word boundaries. So "diffs" won't match, but "Oh, ffs!" will. It's also why they had to use the pattern "shit(ty|tiest)" instead of just "shit".
  - BoppreH 7mo ago
    You're right, I missed the \b's. Thanks for the correction.

They're searching for multiple substrings in a single pass, regexes are the optimal solution for that.

by lou1306 7mo ago

They're searching for multiple substrings in a single pass, regexes are the optimal solution for that.

The issue isn't that regex are a solution to find a substring. The issue is that you shouldn't be looking for substrings in the first place. This has buttbuttin energy. Welcome to the 80s I guess.

deleted 7mo ago
[deleted]
8cvor6j844qw_d6 7mo ago
Very likely vibe coded. I've seen Claude Code went with a regex approach for a similar sentiment-related task.
- mr_00ff00 7mo ago
  My understanding of vibe coding is when someone doesn’t look at the code and just uses prompts until the app “looks and acts” correct. I doubt you are making regex and not looking at it, even if it was AI generated.
  - deleted 7mo ago
    [deleted]
rdiddly 7mo ago
Clbuttic!
lou1306 7mo ago
> The issue is that you shouldn't be looking for substrings in the first place. Why? They clearly just want to log conversations that are likely to display extreme user frustration with minimal overhead. They could do a full-blown NLP-driven sentiment analysis on every prompt but I reckon it would not be as cost-effective as this.
- noosphr 7mo ago
  >Some people, when confronted with a problem, think “I know, I’ll use regular expressions.” Now they have two problems. The only time to use a regex is when searching with a human in the loop. All other uses are better handled some other way. >They could do a full-blown NLP-driven sentiment analysis on every prompt but I reckon it would not be as cost-effective as this. Every conversation is sent to an llm at least a thousand times the size of gpt2 which could one shot this nearly a decade ago.
  - lou1306 7mo ago
    > Every conversation is sent to an llm at least a thousand times the size of gpt2 which could one shot this nearly a decade ago. Yes, but that is _what the product does_. What we are talking about is _telemetry_.
    - deleted 7mo ago
      [deleted]

BoppreH 7mo ago

It's fast, but it'll miss a ton of cases. This feels like it would be better served by a prompt instruction, or an additional tiny neural network. And some of the entries are too short and will create false positives. It'll match the word "offset" ("ffs"), for example. EDIT: no it won't, I missed the \b. Still sounds weird to me.

hk__2 7mo ago
It’s fast and it matches 80% of the cases. There’s no point in overengineering it.
- NitpickLawyer 7mo ago
  > There’s no point in overengineering it. I swear this whole thread about regexes is just fake rage at something, and I bet it'd be reversed had they used something heavier (omg, look they're using an LLM call where a simple regex would have worked, lul)...
vharuck 7mo ago
The pattern only matches if both ends are word boundaries. So "diffs" won't match, but "Oh, ffs!" will. It's also why they had to use the pattern "shit(ty|tiest)" instead of just "shit".
- BoppreH 7mo ago
  You're right, I missed the \b's. Thanks for the correction.