routerl

To be fair, most bot detection can't and isn't trying to catch 100% of bots. Generally the idea is to prevent repeated abuse, rather than stop every single instance of it.

In the case of the scalper bot you've linked, the goal of bot protection would be to make the bot's transactions so computationally expensive/slow that they miss out on most of their intended purchases.

I'm super interested in curating a dossier of these sites and experiences like yours, if only because we're genuinely interested in preventing issues like these and don't do the types of things that lead to these complaints, like IP-range profiling.

Disclosure: I work at friendly captcha.

> They all put up browser surveillance / captcha blockwalls to "stop bots" (even despite bots being legitimate shoppers these days!) that make the sites just straight up not work on most of my browser configs.

What do you mean by this? For example, we do escalating proof of work and I'm not sure how this would make a site not work, even for very exotic configs with high risk signal (e.g. spoofed user-agent, proxies, etc). It will definitely consume more CPU cycles, but I don't see how it would affect functionality.

Disclosure: I work for Friendly Captcha.

Have you thought about adding a proof of work challenge? Your bandwidth is spared and attackers get increasingly expensive computational puzzles.

Disclosure: I work for Friendly Captcha, which uses the above strategy.

It's less rare than you'd hope. Disclosure: I do developer relations at Friendly Captcha, and our widget is one of the scripts that uses generated offline audio (but with no microphone recording) and reads the buffer back on sites that otherwise have no use for audio.

We do this to check whether the browser is what it claims to be, not to track who you are. Similarly, font rendering, floating-point math results, WebGL renderer strings, and whether built-in functions are patched is all much harder to fake. These considerations determine how hard the proof-of-work puzzle should be, and site owners can see it as a risk score. We don't do cookies or browser storage, IPs aren't stored, and nothing gets used to recognize you on another site or on a later visit. The biggest players in automated traffic prevention can't (and usually don't) say this much, because of conflicts of interest between automated bot detection and their other products (e.g. ad sales and IP-profiling).

Your example is really fair though. I chatted with our engineers, prompted by this thread, and we found nothing in our detection that currently uses the audio hashes we collect, so we've started talking about removing the collection.

From the perspective of wanting to maintain the integrity of the American federal government, it seems like all this DOGE stuff (and the whole Trumpist movement in general) serves the purpose of a red team, in the cybersecurity sense; people with nebulous intent have gotten access to everything.

So now, if Americans care about the integrity of their government, there needs to be a blue team: how can this catastrophic level of access be dealt with, and how can it be safeguarded against in the future. Alas, I'm not seeing this perspective being enacted. The obvious security compromise is being allowed to stand and continue, usually on the basis that "separation of powers" and "checks and balances" are relied on to be effective; congress will stop this, or the courts will stop this. But we're watching these mechanisms fail.

So, what's the plan here? Where's the counter-offensive? We're watching a system being hacked, and I've yet to see anyone talk about a recovery plan, or a prevention plan.

It seems to be an article about all those "harmless" lies we tell students.

The vast majority of people think mathematics is about numbers, when it is actually about relations, and numbers are just some of the entities whose relations mathematics studies.

Nobody is born with this misconception; we teach it, and test it, and thereby ingrain it in the minds of every student, most of whom will never study mathematics at a level that makes them go "wait, what?". The overwhelming majority of people never get to this level.

I suspect this is also why statistics feels so counterintuitive to so many people, including me. The Monty Hall problem is only a problem to those who are naive about probability, which is most people, because most of us don't learn any of this stuff early enough to form long lasting, correct instincts.

It's not fair to students to bake "harmless" lies into their early education, as a way to simplify the topic such that it becomes more easily teachable. We've only done this because teaching is hard, and thus expensive. Education is expensive, at every step. It's not fair or productive to build a gate around proper education that makes it available only to those who can afford it at the level where the early misconceptions get corrected. Even those people end up spending a lot of cognitive capital on all those "wait, what?" moments, when their cognitive capital would be better spent elsewhere.

OP here, I'm adding a feature that will allow users to save specific words to lists, and export the lists in formats that can be imported to flashcard apps.

For anonymous users, I'm using OpenNMT, via Argos. Logged in users get DeepL translations, which correctly translates 气功师.

Thank you, and thanks for checking it out!

I use Pleco almost every day :)

I did! Jieba is the first step in my segmentation pipeline. As far as I can tell, Jieba's default config tends to work better for simplified, but in my case the custom dictionary I feed it has significantly more traditional entries than simplified entries, especially for historical terms and slang.

It supports traditional and simplified, as well as pinyin and bopomofo :)

It's already possible to switch instantly between pinyin and bopomofo, and I'm working on letting users switch between simplified/traditional, but this is also a non-trivial problem. For now, the app will follow the user's lead: if you enter traditional text, it will return traditional text, and same goes for simplified.

Thanks for the kind words, and the bug report!

The (awful and incorrect) translation you've pointed out comes from the segmenter being too greedy, not finding the (non-existent) word in any dictionary, and therefore dispatching the word to be machine translated, without context. This is the final fallback in the segmentation pipeline, to avoid displaying nothing at all, and my priority right now is making the segmentation pipeline more robust so this rarely (or never) happens, since it sometimes produces hilariously bad results!

'"Tradition" is a set of solutions for which we have forgotten the problems. Throw away the solution and you get the problem back.'

This is, by far, my most conservative opinion. Credit to Donald Kingsbury for the quote.

Honorable mention re: the same problem, "dogfooding"[0] is gone from the software industry, which is why users often feel like they're getting suckered by the companies they patronize; the decision makers, who don't themselves use the product, absolutely see the users as suckers.

[0] https://en.wikipedia.org/wiki/Eating_your_own_dog_food

Got it, thanks!

Could you post the text you used? This kind of thing goes straight into my unit tests.

I'm also working on showing all the pronunciations/definitions for a given hanzi, it should be ready later this week.

Thanks for the kind words!

I'm using Jieba[0] because it hits a nice balance of fast and accurate. But I'm initializing it with a custom dictionary (~800k entries), and have added several layers of heuristic post-segmentation. For example, Jieba tends to split up chengyu into two words, but I've decided they should be displayed as a single word, since chengyu are typically a single entry in dictionaries.

[0] https://github.com/fxsjy/jieba

Location: Toronto, Canada

Remote: Yes

Willing to relocate: No

Technologies:

- Backend: Typescript, Javascript, NextJS, React, Redux

- Frontend: Python, Django, PostgreSQL, Docker, Swift

Résumé/CV: Via e-mail

Email: [email protected]

I've submitted my latest work as a Show HN post at https://news.ycombinator.com/item?id=42936085

Seems like a straightforward case of malpractice. The guy had every ability to double check the hallucinated references, but didn't do so; he used AI to replace his own expertise, rather than augment it. I.e. he didn't "use AI to help write", he instead outsourced his work to AI with no oversight.

I can't get mad at this, it's so spot-on.

"Predictions for 2025 Personal Projects

Will start working on a novel semantic search engine for Chinese literature, combining their interest in both Chinese culture and search technologies"

Oh wow, I've been working on this for the past two months and haven't posted about it yet.

Yes.

Imho opinion, and I'm no expert, but this has been working well for me:

Segment the texts into chunks that make sense (i.e. into the lengths of text you'll want to find, whether this means chapters, sub-chapters, paragraphs, etc), create embeddings of each chunk, and store the resultant vectors in a vector database. Your search workflow will then be to create an embedding of your query, and perform a distance comparison (e.g. cosine similarity) which returns ranked results. This way you can now semantically search your texts.

Everything I've mentioned above is fairly easily doable with existing LLM libraries like langchain or llamaindex. For reference, this is an RAG workflow.

This write-up makes the erroneous assumption that he's choosing randomly. He himself says, in this same write-up, that he's choosing adversarially.

Nice write-up anyway, and yes, Ballmer is wrong.

It's not a "Chinese mythical book", it's one of the classical novels of Chinese civilization. Think of it as a cross between Lord of the Rings and The Iliad, but containing extensive references to ancient Chinese tales, culture, religion (especially Buddhism), and history (the central monk character in the story is based on a real, and revered, historical monk).

It also has beloved and well known characters who have featured in all kinds of Chinese stories and media for centuries: e.g. Erlang (no relation to the programming language), who is prominently in the opening cutscene, and is often found in stories accompanied by Nezha (who is so popular that generations of Chinese kids grew up hearing this[0] song and watching that show).

And this is by no means just a Chinese phenomenon. This story, Journey to the West, is a cultural keystone in all of Asia: Dragonball is very much based on Journey to the West[1], and "Son Goku" is just the Japanese pronunciation of the name "Sun Wukong", who is the monkey protagonist of this game. The two share many of their powers and characteristics, including flying around on a magic cloud and becoming powerful enough to challenge literal gods.

Finally, this is perhaps the first "postmodern" retelling of this extremely popular story. The game is called "black myth" because it is clearly darker and more serious than previous retellings of this story. For someone who knows this story well (i.e. basically anyone who grew up in Asia), it is a fresh version of an old classic. In this sense, this game is the equivalent of what The Witcher was for (mainly Eastern) Europeans; it takes legends, stories, superstitions you grew up hearing about (e.g. vampires, werewolves, etc) and breathes new life into them.

[0] https://m.youtube.com/watch?v=TG_KTrCetcM

[1] even down to having a cowardly pigman companion, Bajie, who is also in this game.

Orbital archeology would be an amazing subject for science fiction. Indiana Jones meets Gravity

Thanks for your perspective. I've known and lived with several people who have ADHD, and you've expressed exactly the perspective I expected to hear from them.

"Games as treatment" are a new frontier of "selling bullshit". This happened before in education, and it's now making its way into health care.

The problem, here, is that the users aren't any part of how these games are designed. Everything about them is just directed at making presentations to investors and licensing bodies.

In the real games industry, user testing is the apex of success: you know your users will enjoy and benefit from your design choices because your users have already enjoyed and benefited from your design choices. When this relationship doesn't hold, the game gets changed. Play testing is king.

This kind of thing is cynical, thoughtless, and testless. It shouldn't exist, and it's nothing more than the effort of some founders to gather funding from clueless agencies.

AFAIK several cloud services rely on requirements.txt for deployment.

Is there something wrong with pipenv[0] that I'm not aware of? It really feels like a full solution, I've been using it for years, and I can't understand why it isn't more widely used.

[0] pipenv.pypa.io

> Shok Jok. That name could pass for a Mandarin or Cantonese name.

These sounds do not exist in Mandarin. This would never be, or sound like, a Mandarin name. And no mention is made of tones, which are critical in Mandarin.

The article already smelled of a bovid's feces, but at this point I actually saw the bullshit.