Awesome, and yes, totally makes sense -- you are more learner-centric that way.
Having the full sentence context is actually one of the things I have been thinking about a lot -- this helps both the learner as well as the POS detection in Stanza. I always decided against, because I wanted to build agnostic flash-cards.
However, as your approach allows on-the-fly generation of flash cards, you always stay close to the learner progress. I could (e.g.) pick some Gutenberg fairy tales, allow the learner to read them in their target language and provide bi- and omni-directional translations across all languages. Creating flash cards from the source material keeps the learner in progress (context), allows to learn new words step-by-step (discovery), as well as providing a fun learning experience and measurable progress. Similarly, instead of fairy tales, we could use some series in combination with its subtitles. This allows video-progress. Awesome x2!
Sidenote: The awesome part about HN is that I get to chat with like-minded people and directly grasp some new inspiration. Probably I ought to visit some in-person hacker spaces :)
- How do you transform and enrich the data? How does your pipeline look?
- What are your key challenges?
- Which tools do you use? What is your 'stack'? (Stanze, wordfreq, Whisper, wn, ...)
Background: I am currently building a multi-lang vocabulary hub for language learning. The goal is to match core words/lemmas to their senses/concepts, and then be able to generate multi-language flash cards.
I am still stuck on the sense alignment and fingerprinting (example: should 'to shop', 'einkaufen', ' alışveriş yapmak' and 'go shopping' point to the same concept of 'shop'?), but in a later stage I want to allow user-submission and data enrichment for IPA, pictograms [1] and audio.
Use-case (the dream): I come back from language class, I input new vocab and I output new Anki cards that work across all my fluent languages.
Currently, I mostly find myself knee-deep in problems of linguistics, NLP, Python and getting an LLM to do exactly what I want. At the same time it is a super fun project, and really makes me feel the joy of programming again. LLMs are magic, time just flies by, and all the random projects I always wanted to do suddenly materialize.
For coding, I mostly use free Gemini and some deepseek-v4-flash via openrouter to keep a tight oversight and understand the problem space. Maybe this slows me down, but agentic code jsut does not align with me. Overall, I haven't spent more than 2 € in total.
So far, surprisingly, the biggest problem is the lack of high-quality, free input data (example: English has the Oxford 5000 words as core vocabulary, but it is difficult to find the same for e.g. Turkish).
2nd place is the lack of high-quality synsets/wordnets (cross-language is mostly incomplete), and the 3rd place is getting LLMs to reliable play to their strength (on paper, a LLM is the perfect tool to provide multi-lang sense equivalents)
I plan to do a full writeup sometimes, but first I need it to work :)
There was another article/blog on hackernews some time ago along the lines of 'I'm an editor, here is how I edit my friends texts' with some really good advice.
Unfortunately I can't find it anymore -- if someone knows which post I mean, I'd appreciate sharing it with me again.
Interesting approach. I am currently looking for jobs and went the 'career coaching' route for my CV. I did a few iterations with my coach until I got my current result (ideally I had a link):
I first looked at Canva templates, but apparently nowadays you are supposed to do black/white and no fancy designs for ATS readability. Then I tried it with Google Docs b/w resumee template, which kinda got me to write actual skills. Then I approached the coach, got her template and iterated, and then I also added some rules from here (https://principiae.be/pdfs/ECV-1.01.pdf).
I also involved ChatGPT to analyze job postings and to get the mix of keywords in my resumme right. Tools like https://tagcrowd.com/ also help with that. For example, I am targeting 'IT analyst' roles, and it does make sense that I have the word 'analysis' a few times in my CV.
E: mine is basically structured the following way
Name
Title
Summary
3x5 ATS keywords/skills specific to my profile and role
last ten years, also written in a way that 'gamifies' ATS: 'Year, worked as ROLE at Company, did XYZ'
--page 2--
Education (degree + grades)
Skills Training
Languages
Some more IT skills (programming languages, project management, ...)
E2: I obviously have no idea what I am doing, but I got three interview proposals for 10 applications, so I guess 30%.
Having the full sentence context is actually one of the things I have been thinking about a lot -- this helps both the learner as well as the POS detection in Stanza. I always decided against, because I wanted to build agnostic flash-cards.
However, as your approach allows on-the-fly generation of flash cards, you always stay close to the learner progress. I could (e.g.) pick some Gutenberg fairy tales, allow the learner to read them in their target language and provide bi- and omni-directional translations across all languages. Creating flash cards from the source material keeps the learner in progress (context), allows to learn new words step-by-step (discovery), as well as providing a fun learning experience and measurable progress. Similarly, instead of fairy tales, we could use some series in combination with its subtitles. This allows video-progress. Awesome x2!
Sidenote: The awesome part about HN is that I get to chat with like-minded people and directly grasp some new inspiration. Probably I ought to visit some in-person hacker spaces :)