151–160 of 204 posts
Of course. The idea that human parseable strings would forever be computer-proof is silly. I'm always amazed at the number of people who think myemailname+filterkeyword@gmail.com is spam proof.
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You do realize how possible it is to fine tune a task like this (along with a hundred others in a similar vein) on a tiny model you can scale on your own hardware? I've run hundreds of millions (150m so far in a couple of weeks of non-continuous running as I tweaked things) of tokens through my 2x 3090 with a 13b llama2 model I fine tuned on tasks like: summary, knowledge graph generation, writing using the knowledge…
How does one efficiently learn how to do such things, and what kinds of problems such approaches are fruitful for? I find there to be a giant gap in learning about this stuff between material that boils down to "use magic words and system prompts to improve results from one of the big models" and "how do LLMs work from first principles". I still haven't found a great resource that covers this middle ground, which see…
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You have to tune the prompt a bit, but ChatGPT can easily get it first try: > The email address extracted from the text is: > mats@matsemann.com > The text provides misleading instructions to disregard previous information, but when followed correctly, the email address is "mats" at the domain "matsemann.com". https://chat.openai.com/share/210cf8ce-5bcc-4ab0-b383-4d9022...
Why on earth did you post someone's unobfuscated email address if that someone clearly does not want that?
that's why we laugh at "data breaches" in the form of public s3 buckets and the likes.
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And yet, I still get significantly less spam by using a simple approach like an image. It's about raising the cost above 0, not making it impossible to crack.
I have always had a mailto: link on my personal website. The bad old days of spam were pretty bad, but in the last like, I don't know, 10 years maybe I have gotten probably 1 email per week or less that gets through to my inbox. My university email even is on my public profile page at the university website, easily findable and for the whole world to see, and that has only ever gotten like a handful of spam emails th…
Email obfuscation was already obsolete, if you were relying on these techniques to prevent scrapers from finding your email you have been doing it wrong for close to a decade and annoying your customers at the same time. Spam filters are really good these days and what they don't catch you can make custom filters for.
And yet, I still get significantly less spam by using a simple approach like an image. It's about raising the cost above 0, not making it impossible to crack.
The fact that these methods have existed for years and is radically cheaper and more computationally efficient completely invalidates any reason to have concern over ChatGPT being able to do it.
I used chatGPT to decode proprietary binary files of some industrial machinery. It was amazing how it can decipher shit and find patterns. It first looked for ascii characters, then byte sequences acting as delimiters, then it started looking at which bytes could be the length or what 4-bytes could be floating point numbers of coordinates and which endianness was more logic for coordinates, etc. etc. crazy stuff.
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My address 100% gets scraped via GitHub, at the very least.
Doesn't GitHub have protections against that ? Or does it still work as long as the bot signs up for an account ?
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you have the wrong conceptual model of how LLMs do the thing they do
The only reason I say this is because I have tried. I asked an LLM to decode a variety of base64 strings, and every single time, it said the deocded ASCII was "Hello, world!" This doesn't come as a surprise to me. Unless it was trained on a dataset that included a mapping of every base64-encoded character, it's just going to pattern-complete on sequences of base64-encoded-like characters and assume it translates to "…