[mromanuk]

I definitely like the LLM in the middle, it’s a nice way to circumvent the SEO machine and how Google has optimized writing in recent years. Removing all the cruft from a recipe is a brilliant case for an LLM. And I suspect more of this is coming: LLMs to filter. I mean, it would be nice to just read the recipe from HTML, but SEO has turned everything into an arms race.

[tines]

> Removing all the cruft from a recipe is a brilliant case for an LLM

Is it though, when the LLM might mutate the recipe unpredictably? I can't believe people trust probabilistic software for cases that cannot tolerate error.

[kccqzy]

I agree with you in general, but recipes are not a case where precision matters. I sometimes ask LLMs to give me a recipe and if it hallucinates something it will simply be taste bad. Not much different from a human-written recipe where the human has drastically different tastes than I do. Also you basically never apply the recipe blindly; you have intuition from years of cooking to know you need to adjust recipes to taste.

[Uehreka]

Hard disagree. I don’t have “years of cooking” experience to draw from necessarily. If I’m looking up a recipe it’s because I’m out of my comfort zone, and if the LLM version of the recipe says to add 1/2 cup of paprika I’m not gonna intuitively know that the right amount was actually 1 teaspoon. Well, at least until I eat the dish and realize it’s total garbage.

Also like, forget amounts, cook times are super important and not always intuitive. If you screw them up you have to throw out all your work and order take out.

[kccqzy]

All I'm arguing is that you should have the intuition to know the difference between 1/2 cup of paprika and a teaspoon. Okay maybe if you just graduated from college and haven't cooked much you could make such a mistake but realistically outside the tech bubble of HN you won't find people confusing 1/2 cup with a teaspoon. It's just intuitively wrong. An entire bottle of paprika I recently bought has only 60 grams.

And yes cook times are important but no, even for a human-written recipe you need the intuition to apply adjustments. A recipe might be written presuming a powerful gas burner but you have a cheap underpowered electric. Or the recipe asks for a convection oven but your oven doesn't have the feature. Or the recipe presumes a 1100W microwave but you have a 1600W one. You stand by the food while it cooks. You use a food thermometer if needed.

[tines]

Huh? You don't care if an LLM switches pounds to kilograms because... recipes might taste bad anyway????

[kccqzy]

Switching pounds with kilograms is off by a factor of two. Most people capable of cooking should have the intuition to know something is awfully wrong if you are off by a factor of two, especially since pounds and kilograms are fairly large units when it comes to cooking.

[whatevertrevor]

Not really an apt comparison.

For one an AI generated recipe could be something that no human could possibly like, whereas the human recipe comes with at least one recommendation (assuming good faith on the source, which you're doing anyway LLM or not).

Also an LLM may generate things that are downright inedible or even toxic, though the latter is probably unlikely even if possible.

I personally would never want to spend roughly an hour or so making bad food from a hallucinated recipe wasting my ingredients in the process, when I could have spent at most 2 extra minutes scrolling down to find the recommended recipe to avoid those issues. But to each their own I guess.

[joshvm]

There is a well-defined solution to this. Provide your recipes as a Recipe schema: https://schema.org/Recipe

Seems like most of the usual food blog plugins use it, because it allows search engines to report calories and star ratings without having to rely on a fuzzy parser. So while the experience sucks for users, search engines use the structured data to show carousels with overviews, calorie totals and stuff like that.

https://recipecard.io/blog/how-to-add-recipe-structured-data...

https://developers.google.com/search/docs/guides/intro-struc...

EDIT: Sure enough, if you look at the OPs recipe example, the schema is in the source. So for certain examples, you would probably be better off having the LLM identify that it's a recipe website (or other semantic content), extract the schema from the header and then parse/render it deterministically. This seems like one of those context-dependent things: getting an LLM to turn a bunch of JSON into markdown is fairly reliable. Getting it to extract that from an entire HTML page is potentially to clutter the context, but you could separate the two and have one agent summarise any of the steps in the blog that might be pertinent.

    {"@context":"https://schema.org/","@type":"Recipe","name":"Slowly Braised Lamb Ragu ...

[visarga]

I foreseen this a couple years ago. We already have web search tools in LLMs, and they are amazing when they chain multiple searches. But Spegel is a completely different take.

I think the ad blocker of the future will be a local LLM, small and efficient. Want to sort your timeline chronologically? Or want a different UI? Want some things removed, and others promoted? Hide low quality comments in a thread? All are possible with LLM in the middle, in either agent or proxy mode.

I bet this will be unpleasant for advertisers.

[yellow_lead]

LLM adds cruft, LLM removes cruft, never a miscommunication

[hirako2000]

Do you also like what it costs you to browse the web via an LLM potentially swallowing millions of tokens per minutes ?

[prophesi]

This seems like a suitable job for a small language model. Bit biased since I just read this paper[0]

[0] https://research.nvidia.com/labs/lpr/slm-agents/