HN user
chrisjtarry \at\ gmail.com
https://github.com/chris124567
If you put important Anthropic blog posts like the Fable announcement or J-Space through Pangram, you get 100% human written. Considering that the overwhelming majority of the code there is written by AI, I think this is an admission that AI writing is slop and AI code is pretty good.
reCAPTCHA is a great success story of security through obscurity because probably less than 100 people have reverse engineered it and much less than that have produced a working solver for it that doesn't require a headless browser. Snapchat would be another good example - almost no one is going to put in the work to understand this [0]. Most companies just half ass it though and accordingly achieve nothing with the obscurity at all besides worse performance.
[0] https://web.archive.org/web/20201128060507/https://hot3eed.g...
I wish we lived in a world where price wasn't the only way to filter out annoying passengers... if this experiment works maybe it will prove you can offer amenities more widely if you set high standards. I want an airline where at the end of the flight you rate the people next to you thumbs up or thumbs down and if someone gets >= 3 thumbs downs they are banned from the airline for 5 years. Eventually ordinary people would be able have the serene experience that only those in first class can presently enjoy as the people who get up thirty times to retrieve items from carry on or eat obnoxiously are removed.
This is an interesting idea... it'd be a fun side project to implement enough of a CSS engine to undo this
This is awesome and very fast especially given all the data displayed!
This wouldn't have helped here but there is a related field of research called hotel recognition. Many of these videos are filmed in hotels so being able to recognize if it was a Mariott or even better a non chain local hotel can be very helpful to investigators. They basically train CNNs that learn to pick up on the bathroom fixtures or kind of bedding used by different hotels. One researcher in particular has done a tone of work on this: https://scholar.google.com/citations?user=mNoB9SgAAAAJ&hl=en
I wonder if you could get the interior of every house from Zillow/realtor websites and then do something like this for every house in the country... Clearview for bedrooms?
I think he's referring to CPU mitigations: https://en.wikipedia.org/wiki/Transient_execution_CPU_vulner...
Protip: Settings -> Personalization -> Base style and tone -> Efficient largely solves this for ChatGPT
The malleability of the ciphertext matters because it enables certain circuit tagging attacks as the article explains. It means that the exit relay could confirm you are using a guard relay also controlled by them and thus discover your origin IP address.
There are many reasons that these cryptographic tagging attacks are a lot worse than just the timing correlation attacks that are possible if you control the guard and exit of a client: https://archive.torproject.org/websites/lists.torproject.org...
Nice article and especially so for including the parsing that most people just outsource. What's great about using an emulator is that you can also do fun things with the syscalls like implementing your own "virtual filesystem" instead of just translating directly to the x86_64 equivalent syscall: https://github.com/gamozolabs/fuzz_with_emus/blob/master/src... (not my code but basically something like this)
OK, I think the problem was that you are only supposed to input the user ID number. I just limited the form to numbers only and updated the description to make this more clear.
Hi, I just realized that the confusion here was that you are only supposed to input the numeric user ID. I just limited the form to numbers only and updated the description to make this more clear.
I am not familiar with Cinematch, is there a writeup about it? When training I used every input book and did not include ratings as a feature. In the future I want to experiment with treating 1 or 2 star ratings as negative feedback.
Can you access your profile page in incognito (ie is your account public)? Alternatively, if you have more than 5000 books in your shelf, that might break it. I just tried a number of users and I was able to import them all.
I did not add what you requested exactly because I think in many cases authors have written less popular books that people may not be aware of but if you try again you should see less highly repetitive things like 5 of the same series in a row in the results.
I just introduced something [1] which should give you much less repetitive recommendations if you want to try again.
[1] https://www.cs.cmu.edu/~jgc/publication/The_Use_MMR_Diversit...
Hi, I just added something called maximal marginal relevance which should give you much less repetitive recommendations.
Do you just adding the similar button that the input books have to the output books?
What do you think the probability that someone else read 15 books you also read is? It’s very unlikely unless they are all staples of a genre, part of the same series, or just extremely popular in general. 3-5 books is how much I would use on that page. I have found interesting accounts of medievalists, people who work at think tanks, etc with it.
Fake users I would agree should be filtered, but I don’t think filtering out users who gave it a bad review is necessarily the intended behavior. If I put in 3 semi obscure Russian history books, I am presumably looking for someone who is an expert in Russian history to see what else they read. In that case I don’t care if they didn’t like one of the books or not. Approximate matches would require something like LSH or cosine similarity of average input book embedding against average embedding of read books of every user which I think wouldn’t work well anyone for retrieving anyone with a moderately long interaction history.
Yeah, the latter are just because they are popular. If you have 3+ books you tend to get less random popular books included
You may find these better:
https://book.sv/similar?id=211570
Note 1: If you only provide one or two books, the model doesn't have a lot to work with and may include a handful of somewhat unrelated popular books in the results. If you want recommendations based on just one book, click the "Similar" button next to the book after adding it to the input book list on the recommendations page.
This is a result of the use of positional embeddings, which typically results in the final item being weighted very highly. The problem is that this information is shown to be very relevant to the task of predicting the next item interacted with. If you add more books the effect of this is somewhat diluted.
Thank you for the kind remarks. I would say your request is reasonable and it’s something I thought about but it’s worth noting that if you scroll down on a Goodreads book page you can see all of the users who gave it 1 star, 2 stars, etc (not just reviews, ratings too). And in fact Goodreads does not tell people this but these lists include private users. So someone looking to cause controversy would probably just find an incendiary book and look for everyone who gave it 5 stars. My data does not include people who marked their account as private or only visible to those who are signed in and also does not give information on stars. It is also very easy to remove your data if you are so inclined. I only log request URLs but I can tell that many thousands of people visited the site and less than 10 opted out despite “Remove My Data” being prominently displayed and referenced in the attached text to my post.
Also, I don’t use them but based on forum posts I read I think other services like LibraryThing or Storygraph do expose similar information about book readers.
You don't have to import your Goodreads profile. You can type titles and authors in the box and find books to add to the list that way.
I would agree the results are generally OK but do not feel magical in most cases (I think in some specific cases they do though). The results can be not great if you add books across many disciplines. For instance if you add "The Elements of Typographic Style" and "The Design of Everyday Things" (https://book.sv/#671857,18518), you do get "Grid Systems in Graphic Design" but under its German name "Rastersysteme für die visuelle Gestaltung."
Note 1: If you only provide one or two books, the model doesn't have a lot to work with and may include a handful of somewhat unrelated popular books in the results. If you want recommendations based on just one book, click the "Similar" button next to the book after adding it to the input book list on the recommendations page.
I use `fetch` on relative endpoints so that's odd. There shouldn't be any external API calls on my website other than whatever the Cloudflare captcha uses. I also use HTTPS-only in Chrome and did not experience any issues. I just tested Firefox with HTTPS-only on/off and Safari on my phone and I was able to import shelves for multiple users. Are you sure that you do not have any privacy settings on (can you access your shelf in Incognito mode)?
I use a Hetzner server with Ryzen 7 3700X and an SSD.
I think I could get the model to work with ONNX web but it'd be a 2GB download so the user experience wouldn't be too great. My Meilisearch index is ~40GB but I don't know how much that could be compressed down.
Here's how the similar page for books is generated, which I forgot to mention on the "how it works" page: https://gist.github.com/chris124567/8d06d64bfe827cb7f6121f93...
It's recommended that you put at least 3 books in. If you would like recommendations just based on one book, click the similar button on the book, it should take you to this page: https://book.sv/similar?id=297914