Alternatives
Products that do what TUNiBrain–Korean Morphological Analyzer does
The next-gen Korean morphological analyzer
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Actually, it is yet another tool to learn a language through immersion (classic set of features, watch on youtube/netflix, read a website or book). It has a popup dictionary so you can click on a word and it will show the definition. Nothing new in that regard, except that it has been made with only Korean in mind and does not plan to extends to other languages. I was learning and still am learning Korean (now using my own tool to learn!). I initially made the tool for myself because none of the tools out there could correctly figure out what a word was in the text. And that's where the…
2023 · kimchi-reader.app
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I've been playing with embeddings and wanted to try out what results the embedding layer will produce based on just word-by-word input and addition / subtraction, beyond what many videos / papers mention (like the obvious king-man+woman=queen). So I built something that doesn't just give the first answer, but ranks the matches based on distance / cosine symmetry. I polished it a bit so that others can try it out, too. For now, I only have nouns (and some proper nouns) in the dataset, and pick the most common interpretation among the homographs. Also, it's case sensitive.
2025 · calc.datova.ai
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2017 · github.com
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Hi HN, Just wanted to share a tool i've been working on to help with my own study routine. It’s a browser extension called Lingoku. The idea is simple: we spend hours browsing the web in English every day. This tool replaces some of the english words with Japanese vocabulary based on your japanese level (Similar to Toucan, but with a better user experience). It’s basically an attempt to make the "i+1" method actually passive, you understand the sentence because it's mostly english, but you pick up Japanese words naturally from the context. It uses an LLM in the backend to make sure the…
Dec 2025 · lingoku.ai
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TLDR: I’ve made a transformer model and a wrapper library that segments text into meaningful semantic chunks. The current text splitting approaches rely on heuristics (although one can use neural embedder to group semantically related sentences). I propose a fully neural approach to semantic chunking. I took the base distilbert model and trained it on a bookcorpus to split concatenated text paragraphs into original paragraphs. Basically it’s a token classification task. Model fine-tuning took day and a half on a 2x1080ti. The library could be used as a text splitter module in a RAG system or…
2025 · github.com
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Hi HN, Working with multiple projects, I got tired of re-explaining our complex multi-node system to LLMs. Documentation helped, but plain text is hard to search without indexing and doesn't work across projects. I built Linggen to solve this. My Workflow: I use the Linggen VS Code extension to "init my day." It calls the Linggen MCP to load memory instantly. Linggen indexes all my docs like it’s remembering them—it is awesome. One click loads the full architectural context, removing the "cold start" problem. The Tech: Local-First: Rust + LanceDB. Code and embeddings stay on your machine. No…
Dec 2025 · github.com
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Hi HN, I made Lingo - the SQLite of semantic search. I'm a self-taught developer and researcher who left school at 16, and I've spent some time exploring a first-principles approach to system design for various frontier problems. In this case it's AI that challenges the 'bigger is better' transformer paradigm. Lingo is the first piece of that research, a high-performance linguistic database designed to run on-device. The full technical overview and manifesto is here: https://medium.com/@robm.antunes/bcd1e9752af6 The paper has been archived on Zenodo with a DOI:…
Sep 2025
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May 2026 · ezfurigana.com
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