Hacker News: costco

New comment by costco in "Show HN: I built an SDK that scrambles HTML so scrapers get garbage"

costco — Thu, 12 Mar 2026 14:36:06 +0000

This is an interesting idea... it'd be a fun side project to implement enough of a CSS engine to undo this

New comment by costco in "Xweather Live – Interactive global vector weather map"

costco — Sun, 22 Feb 2026 22:48:53 +0000

This is awesome and very fast especially given all the data displayed!

New comment by costco in "Dark web agent spotted bedroom wall clue to rescue girl from abuse"

costco — Tue, 17 Feb 2026 18:05:26 +0000

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?

New comment by costco in "Why does SSH send 100 packets per keystroke?"

costco — Sun, 25 Jan 2026 21:23:23 +0000

I think he's referring to CPU mitigations: https://en.wikipedia.org/wiki/Transient_execution_CPU_vulner...

New comment by costco in "Court report detailing ChatGPT's involvement with a recent murder suicide [pdf]"

costco — Wed, 31 Dec 2025 19:09:46 +0000

Protip: Settings -> Personalization -> Base style and tone -> Efficient largely solves this for ChatGPT

New comment by costco in "Counter Galois Onion: Improved encryption for Tor circuit traffic"

costco — Wed, 03 Dec 2025 14:08:10 +0000

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...

A deep dive into QEMU: The Tiny Code Generator (TCG), part 1 (2021)

costco — Tue, 02 Dec 2025 13:42:43 +0000

Article URL: https://airbus-seclab.github.io/qemu_blog/tcg_p1.html

Comments URL: https://news.ycombinator.com/item?id=46121105

Points: 83

# Comments: 2

New comment by costco in "Building a Minimal Viable Armv7 Emulator from Scratch"

costco — Fri, 21 Nov 2025 15:45:01 +0000

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)

New comment by costco in "Show HN: I scraped 3B Goodreads reviews to train a better recommendation model"

costco — Sun, 09 Nov 2025 16:22:21 +0000

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.

New comment by costco in "Show HN: I scraped 3B Goodreads reviews to train a better recommendation model"

costco — Sun, 09 Nov 2025 16:21:33 +0000

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.

New comment by costco in "Show HN: I scraped 3B Goodreads reviews to train a better recommendation model"

costco — Sun, 09 Nov 2025 16:20:28 +0000

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.

New comment by costco in "Show HN: I scraped 3B Goodreads reviews to train a better recommendation model"

costco — Sun, 09 Nov 2025 04:00:13 +0000

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.

New comment by costco in "Show HN: I scraped 3B Goodreads reviews to train a better recommendation model"

costco — Sun, 09 Nov 2025 03:21:20 +0000

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.

New comment by costco in "Show HN: I scraped 3B Goodreads reviews to train a better recommendation model"

costco — Sun, 09 Nov 2025 03:17:43 +0000

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...

New comment by costco in "Show HN: I scraped 3B Goodreads reviews to train a better recommendation model"

costco — Sun, 09 Nov 2025 03:15:57 +0000

Hi, I just added something called maximal marginal relevance which should give you much less repetitive recommendations.

New comment by costco in "Show HN: I scraped 3B Goodreads reviews to train a better recommendation model"

costco — Sun, 09 Nov 2025 03:13:46 +0000

Do you just adding the similar button that the input books have to the output books?

New comment by costco in "Show HN: I scraped 3B Goodreads reviews to train a better recommendation model"

costco — Fri, 07 Nov 2025 11:24:01 +0000

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.

New comment by costco in "Show HN: I scraped 3B Goodreads reviews to train a better recommendation model"

costco — Fri, 07 Nov 2025 10:16:13 +0000

Yeah, the latter are just because they are popular. If you have 3+ books you tend to get less random popular books included

New comment by costco in "Show HN: I scraped 3B Goodreads reviews to train a better recommendation model"

costco — Fri, 07 Nov 2025 10:11:02 +0000

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.

New comment by costco in "Show HN: I scraped 3B Goodreads reviews to train a better recommendation model"

costco — Fri, 07 Nov 2025 10:05:24 +0000

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.