51–59 of 59 posts
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No, obviously not. I mean having a regular web frontend with a fuzzy search form.
That's effectively what they built. The LLM is an implementation detail for how they got a version of fuzzy search to work.
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> See also "instagram is spying on you through your microphone". It's not, but I've seen people argue that it's OK for people to believe that because it supports their general (accurate) sentiment that targeted ads are creepy. I used to be sceptical of this claim but I have found it increasingly difficult to be sceptical after we found out last year that Facebook was exploiting flaws in Android in order to track your…
If they are using the microphone to target ads, show me the sales pitch that their ad sales people use to get customers to pay more for the benefits of that targeting. (I have a ton more arguments if that's not convinced enough for you, I collect them here: https://simonwillison.net/tags/microphone-ads-conspiracy/ )
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> No model should ever know Jon Snow’s phone number from a SaaS service, but this approach allows this sort of retrieval. This reads to me like they think that the response from the tool doesn’t go back to the LLM. I’ve not worked with tools but my understanding is that they’re a way to allow the LLM to request additional data from the client. Once the client executes the requested function, that response data then g…
I was confused by that too. I think I've figured it out. They're saying that a public LLM won't know the email address of Jon Snow, but they still want to be able to answer questions about their private SaaS data which DOES know that. Then they describe building a typical tool-based LLM system where the model can run searches against private data and round-trip the results through the model to generate chat responses…
It’s also funny how these tools push people into patterns by accident. You’d never consider sending a customer’s details to a 3rd party for them just to send them back, right? And there’s nothing stopping someone from just working more directly with the tool call response themselves but the libraries are setup so you lean into the LLM more than is required (I know you more than anyone appreciate that the value they add here is parsing the fuzzy instruction into a tool call - not the call itself).
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> See also "instagram is spying on you through your microphone". It's not, but I've seen people argue that it's OK for people to believe that because it supports their general (accurate) sentiment that targeted ads are creepy. I used to be sceptical of this claim but I have found it increasingly difficult to be sceptical after we found out last year that Facebook was exploiting flaws in Android in order to track your…
If they are using the microphone to target ads, show me the sales pitch that their ad sales people use to get customers to pay more for the benefits of that targeting. (I have a ton more arguments if that's not convinced enough for you, I collect them here: https://simonwillison.net/tags/microphone-ads-conspiracy/ )
Also, your own blog lists an leak from 2024 about a Facebook partner bragging about this ability[1]. You don't find the claim credible (and you might be right about that, I haven't looked into it), but I find it strange that you are asking for an example that your own website provides?
[1]: https://futurism.com/the-byte/facebook-partner-phones-listen...
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I get that the water stereotype is funny, but it gets tiring after a while (because it's not actually true).
AI most definitely uses more water than a traditional full text search because it is much more computationally expensive. The water figures are very overestimated, but the principle is true: using a super computer to do simple things uses more electricity, compute and therefore water than doing it in a traditional way.
It's a tool. The main question should be: is it useful? In the case of AI, sometimes yes, sometimes no.
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If they are using the microphone to target ads, show me the sales pitch that their ad sales people use to get customers to pay more for the benefits of that targeting. (I have a ton more arguments if that's not convinced enough for you, I collect them here: https://simonwillison.net/tags/microphone-ads-conspiracy/ )
I get your point, but can you point to a sales pitch which included "exploit security flaws in Android to improve tracking"? Probably not, but we know for a fact they did that. Also, your own blog lists an leak from 2024 about a Facebook partner bragging about this ability[1]. You don't find the claim credible (and you might be right about that, I haven't looked into it), but I find it strange that you are asking for…
Seriously: the entire idea there is that there was a vast global conspiracy to secretly spy on people to target ads which was blown wide open by THIS deck: https://www.documentcloud.org/documents/25051283-cmg-pitch-d...
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I was confused by that too. I think I've figured it out. They're saying that a public LLM won't know the email address of Jon Snow, but they still want to be able to answer questions about their private SaaS data which DOES know that. Then they describe building a typical tool-based LLM system where the model can run searches against private data and round-trip the results through the model to generate chat responses…
Makes sense. I agree that it’s probably a safe bet too. Not sure how customers would feel about it though. It’s also funny how these tools push people into patterns by accident. You’d never consider sending a customer’s details to a 3rd party for them just to send them back, right? And there’s nothing stopping someone from just working more directly with the tool call response themselves but the libraries are setup s…
I use hosted database providers and APIs like S3 all the time.
Sending customer details to a third party is fine if you trust them and have a financial relationship with them backed by legal agreements.
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AI most definitely uses more water than a traditional full text search because it is much more computationally expensive. The water figures are very overestimated, but the principle is true: using a super computer to do simple things uses more electricity, compute and therefore water than doing it in a traditional way.
Yes, and a YouTube video more than a text article. etc. etc. It's a tool. The main question should be: is it useful? In the case of AI, sometimes yes, sometimes no.
It's a tool, and using the wrong tool for the wrong job is just wasteful. And, usually, overly complicated and frail. So it's only losses.