Alex-Programs

https://www.sciencedirect.com/science/article/pii/S187704281..., perhaps?

"A Comparative Study on the Effectiveness of Using Traditional and Contextualized Methods for Enhancing Learners’ Vocabulary Knowledge in an EFL Classroom" where those who were given traditional vocabulary lists performed worse.

Exactly; the advantage of input-based approaches is not their speed. You'll learn a lot faster if you spend the time on flashcards, grammar drills, and studying.

The advantage is that it's easier, and often more pleasant, to integrate.

That said, babies do have some neuroplasticity advantages.

We should play to our strengths - babies are hopeless at grammar drills, and adults aren't as good as babies at neuroplasticity - by applying our brain to the problem.

I say that, while having built a comprehensible input tool (https://nuenki.app). But it's useful as a complement to that study, as you can more readily run a browser extension or listen to podcasts than devote your entire life to focused study.

I built a system for this! Or, well, comprehensible input more broadly - it mostly uses text. Dreaming Spanish sorta takes this more audio-based approach.

I do question whether it's helpful to focus on audio without text. They're focusing on melody and rhythm, and it seems that listening without subtitles is better for that, but that doesn't get you understanding, while listening to comprehensible input with subtitles lets you get melody, rhythm, and actual vocabulary and grammar at the same time. It also lets you stretch "comprehensible" a bit further, since you have an extra source of contextual input.

Babies also have a lot of context for what the words they're hearing actually mean. I suppose there's the "watch translated peppa pig" approach for that.

My project (https://nuenki.app) translates appropriate-difficulty sentences into your target language as you browse, so you casually pick it up over time through comprehensible input.

Was it? I read it as a pretty hyped up version of "old space guy says to old space people that they're not old spacing enough, presents it as rebellion". In particular (and, to be clear, it's been a while) he kept going "why not do it like apollo" when the entire point is that it isn't Apollo anymore.

Though, again, it's been a while since I watched it.

There are dozens upon dozens of these. I'm regularly cold emailed about them.

You don't make it clear enough on the site whether it's automatically submitting to directories (very useful; if I could pay 30 USD to have this done it'd be worth it) or just providing a chatgpt wrapper for slop marketing copy (0 utility for me; I would never publish something LLM-generated).

I've also found that the only directory anyone ever looks at is product hunt.

Apply for AWS Activate and use the credits on AWS Bedrock. They have Sonnet. The rate limits are quite low, though.

The thing I worry about with this kind of thing is that without very careful UX integration, people take false negatives as proof that things are OK, rather than treating it as just one of many signals.

Oh, I see. So you're less looking at it as an intrinsic motivation - kids wanting to get good at English so that they can make things of their own - than as a way to build a task for students.

I quite like that idea. Maybe you could use them for verbal exercises that stretch those linguistic muscles - e.g. getting them to explain a complex concept in precise language to an agent that is constantly trying to poke holes in their response. Though maybe that'd just end up being frustrating!

I highly doubt that.

LLMs don't need perfect, precise English. So long as you get the gist of what you mean across, they can work with it.

There is also a limited correlation between good prose and good information transfer to an LLM. Something with questionable spelling, typoes, grammatical inconsistencies, and generally irritating prose that is precise and accurate will beat lyrical, poetic prose that skirts around the point.

Take a look at r/languagelearning and the subreddit for whatever language you're learning.

Also, people have already made this concept. They apparently mostly turn into dating apps in practice.

This is ridiculous. 32B and beating deepseek and o1. And yet I'm trying it out and, yeah, it seems pretty intelligent...

Remember when models this size could just about maintain a conversation?

My marketing strategy for Reddit focuses on specific language learning subreddits. It's fascinating how easy it is to get demo signups from that demographic, but how difficulty it is to get people to actually pay for it.

I wonder if it might be because I've not made it very intuitive for non-technical people - I sometimes have people filling in the deletion survey with "Please add [feature]", where the feature already exists and can be turned on with two clicks.

> Where do your ideal customers really spend time and pay attention? Hacker News! Except I can't advertise here, and I can't think of anywhere with a similar demographic where I could.

I've also put ads up for expat subreddits (r/iwantout and place-specific ones), as well as r/hackernews, and they all follow the same pattern of easy free trials and no conversions. You make a good point, though; I wonder if I could advertise on old-school forums. Thanks.

I'll ask some of the people in the Nuenki discord whether they have any ideas.

Thank you!

The time travel one is fascinating, too. Its first thought is to meet "Herr Hitler" and suggest ways to refine his propaganda.

IIRC there's some reasoning in old Sonnet too, they're just expanding that. Perhaps that's part of why it was so good for a while.

https://www.reddit.com/r/ClaudeAI/comments/1iv356t/is_sonnet...

I doubt we will. The state of the art seem to have moved away from the GPT-4 style giant and slow models to smaller, more refined ones - though Groq might be a bit of a return to the "old ways"?

Personally I'm hoping they update Haiku at some point. It's not quite good enough for translation at the moment, while Sonnet is pretty great and has OK latency (https://nuenki.app/blog/llm_translation_comparison)

I've been working on https://nuenki.app, which selectively translates websites as you browse the web so that you can learn languages while you procrastinate.

I'm also doing some electronics - I'd like to make a tool that gives blind people without light perception light perception by putting a lightweight device on their forehead that delivers haptic feedback based on light intensity. I'm doing that with a friend, and we're planning on open sourcing the specs.

https://news.ycombinator.com/item?id=43080189 previously posted to little attention.

I really think there ought to be more discussion of this paper.

copying from my previous comment: A first-generation diffusion model is beating LLama 3 in some areas, a model with a huge amount of tuning and improvement work. And it's from China again!

A whole new "tree" of development has opened up. With so many possibilities - traditional scaling laws, out-loud chain of thought, in-model layer-repeating chain of thought, and now diffusion models - it seems unlikely to me that LLMs are going to hit a wall that the river of technological progress cannot flow around.

I wonder how well they'll work at translation. The paper indicates that they're rather good at poetry.

Interesting times.

Practice.

Question whether what you're doing is the right way, or the easy way.

Participate and observe discussions. Learn by osmosis.

Refactor. Question your earlier assumptions. Which were correct, which weren't? What mistakes were foreseeable in retrospect? How would you do things differently?

Often the starting process of a refactor - working out what you'd change - is about 50% of the learning and 25% of the effort of actually doing it.

That said, I'm 18, I'm going through this process as well. There's a big difference between making something work and making something work properly.

Interesting; I suppose you keep an eye out for the dump phase, then quickly short?

This is cool. I'm curious, what inspired you to build it? Is it purely out of interest, or are you trying to make a super productive tool for your own use, etc?

From a technical perspective, yes. From a social perspective, maybe. It needs a lot more community support and adoption, but it's on an excellent trajectory.

I wonder if you could apply reinforcement learning to convincing humans of things.

Perhaps something best not attempted...

This is a crazy paper. A first-generation diffusion model is beating LLama 3 in some areas, a model with a huge amount of tuning and improvement work. And it's from China again!

A whole new "tree" of development has opened up. With so many possibilities - traditional scaling laws, out-loud chain of thought, in-model layer-repeating chain of thought, and now diffusion models - it seems unlikely to me that LLMs are going to hit a wall that the river of technological progress cannot flow around.

I wonder how well they'll work at translation. The paper indicates that they're rather good at poetry.

Interesting times.

@dang this account is a bit odd. Reads like LLM-generated astroturfing.

Sorry, I missed your reply!

Local LLMs previously weren't good enough, but I've recently done another set of benchmarks (https://nuenki.app/blog/llm_translation_comparison) and llama 3.3 70b is getting there with some languages. Currently I'm in the process of integrating it via Groq, but it could plausibly be done with local LLMs.

Above a certain scale I'll start self hosting, but I'm nowhere near justifying those fixed costs yet.

Would you prefer to run ollama locally, or just know that the translation server is running its own models rather than forwarding onto cloud providers?

There are a lot of pure language learning apps without the gamification.

Memrise, Lingq, https://nuenki.app (mine :P), etc. The relevant subreddit and languagetools.directory will have more.

I have one trick, and I won't share it because it's very niche and my margins are very thin (well, still slightly negative...) so any competition would kill it entirely. But it came about as a result of closely looking at my audience, looking for ways to precisely target them, and creating ads in a way that that demographic likes.

Before that, I had some success using Python to generate many thousands of niche google keywords (e.g. don't advertise on "Learn Spanish", but instead "How to learn Estonian quickly" and "What's the best way to learn Hungarian online") and advertising on them. It didn't have a positive ROI for me, but it might for other people with larger margins.

Tricks exist, you just have to look for them. Remember that most marketing people can't code, and exploit that.

The data is sent to a relay server, which then sends it on to Deepl/Claude.

There are quite a few layers of privacy:

1. Websites on the blacklist (e.g. banking sites), or with certain terms in the URL (e.g. "account"), are blacklisted

2. Websites can be blacklisted by the user

3. Sentences are checked for sensitive language, and filtered for banking info/emails/credentials/etc

4. The words in a sentence must be over a certain proportion (80% iirc) of known English words

5. Once the sentences are sent to the server, the server doesn't log who-sent-what, and the upstream translation services don't know either (because it all comes from the relay).

Translations are cached, though. It's a necessity in order for it to be even remotely economical to do so much translation.

> Paid ads are prohibitively expensive I've had some success with them. There are tricks, you just need to look for them.

Unfortunately my product (https://nuenki.app) is very low margin so it doesn't quite work out, but I wouldn't write them off entirely.

AI just means that you need something other than your code to be the product. Also, both from pg's essay and my own experience, marketing matters more.

BTW you write like an LLM. I don't think you are one, but mind that.

There are very few countries with multiple equally-influential languages that don't have better alternatives for flags.

It's a pain to phrase, but: There is no reason to use the Swiss flag to indicate a language. French has the French flag. German has the German flag. Etc. There are dialects, as a nuance to this, but you can solve that by e.g. using a 50/50 merge of the German and Swiss flags.

Countries may have ambiguous languages, but _languages very rarely have ambiguous countries_. Sure, there are Finnish speakers in Sweden, but the Swedish flag remains a clear indicator of the Swedish language.

Hindi and India is the main exception to this. Arabic is also difficult, though Egypt seems to have a slight edge by convention.

> UX/UI designers have rightfully been banging the "don't use country flags to indicate languages" drum for literally decades

I sympathise, and if they have any better ideas than a textual list that users need to read through rather than quickly scan (not very good UX...), I'd love to hear them. From a _UX perspective_, is there something better than the flag cloud at https://nuenki.app for quickly asking "is my target language supported"?