Did you also forget to ignore case sensitivity back then?

The difference in response time - especially versus a regex running locally - is really difficult to express to someone who hasn't made much use of LLM calls in their natural language projects.

Someone said 10,000x slower, but that's off - in my experience - by about four orders of magnitude. And that's average, it gets much worse.

Now personally I would have maybe made a call through a "traditional" ML widget (scikit, numpy, spaCy, fastText, sentence-transformer, etc) but - for me anyway - that whole entire stack is Python. Transpiling all that to TS might be a maintenance burden I don't particularly feel like taking on. And on client facing code I'm not really sure it's even possible.

Oh it’s worse than that. This one ended up getting my account banned: https://github.com/anthropics/claude-code/issues/22284

Because they want it to be executed quickly and cheaply without blocking the workflow? Doesn’t seem very weird to me at all.

It's more like a truck company using people to transport some parts. I could be wrong here, but I bet this happens in Volvo's fabrics a lot.

A lot if things dont make sense until you involve scale. Regex could be good enough do give a general gist.

Well, regex doesn't hallucinate....right?

Because they actually want it to work 100% of the time and cost nothing.

what you are suggesting would be like a truck company using trucks to move things within the truck

Cloud hosted call centers using LLMs is one of my specialties. While I use an LLM for more nuanced sentiment analysis, I definitely use a list of keywords as a first level filter.

LLMs cost money, regular expressions are free. It really isn't so strange.

Good to have more than a hammer in your toolbox!

Don’t worry, they used an llm to generate the regex.

> That's like a truck company using horses to transport parts. Weird choice.

Easy way to claim more “horse power.”

Using some ML to derive a sentiment regex seems like a good actually?

This just proves its vibe coded because LLMs love writing solutions like that. I probably have a hundred examples just like it in my history.

Not everything done by claude-code is decided by LLM. They need the wrapper to be deterministic (or one-time generated) code?

because impact of WTF might be lost in the result of the analysis if you solely rely on LLM.

parsing WTF with regex also signifies the impact and reduces the noise in metrics

"determinism > non-determinism" when you are analysing the sentiment, why not make some things more deterministic.

Cool thing about this solution, is that you can evaluate LLM sentiment accuracy against regex based approach and analyse discrepancies

More like a car company transporting their shipments by truck. It's more efficient

I used regexes in a similar way but my implementation was vibecoded, hmmm, using your analysis Claude Code writes code by hand.

They had the problem of sentiment analysis. They use regexes.

You know the drill.

It's all regex anyways

Using regex with LLMs isn't uncommon at all.

Because they are engineers? The difference between an engineer and a hobbyist is an engineer has to optimize the cost.

As they say: any idiot can build a bridge that stands, only an engineer can build a bridge that barely stands.

The amount of trust and safety work that depends on google translate and the humble regex, beggars the imagination.

Asking a non deterministic software to act like a deterministic one (regex) can be a significantly higher use of tokens/compute for no benefit.

Some things will be much better with inference, others won’t be.

hmm not a terrible idea (I think).

You have a semi expensive process. But you want to keep particular known context out. So a quick and dirty search just in front of the expensive process. So instead of 'figure sentiment (20seconds)'. You have 'quick check sentiment (<1sec)' then do the 'figure sentiment v2 (5seconds)'. Now if it is just pure regex then your analogy would hold up just fine.

I could see me totally making a design choice like that.

it's like a faster than light spaceship company using horses. There's been infinite solutions to do this better even CPU only for years lol.

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

It's almost as if LLMs are unreliable

[dead]

WTF per minute strongly correlates to an increased token spending.

It may be decided at Anthropic at some moment to increase wtf/min metric, not decrease.

/feedback works for that i believe

>Everyone is commenting how this regex is actually a master optimization move by Anthropic

No? I'd say not even 50% of the comments are positive right now.

Continue?

I always type "please continue". I guess being polite is not a good idea.

The only time that function is used in the code is to log it.

    logEvent('tengu_input_prompt', { isNegative, isKeepGoing })

Makes me wonder what happens once flagged behind the api.

“Go on” works fine too

Did you notice a change in quality after you went foul?

I doubt they're all classified the same. I'd guess they're using this regex as a litmus test to check if something should be submitted at all, they can then do deeper analysis offline after the fact.

It's actually pretty common for old sysadmin code too..

You could always tell when a sysadmin started hacking up some software by the if-else nesting chains.

Nah, it's a hallmark of your average codebase in pre-LLM era.