Alternatives
Products that do what Help improve language coverage in Common Crawl does
Hey HN, the Common Crawl Foundation is trying to expand the coverage of our crawl to more languages, regions and cultures, and if you speak a language other than English (LOTE) you can help! By validating Language Identification data (LangID or LID): https://dynabench.org/tasks/text-language-identification By contributing urls for our seed crawl: https://github.com/commoncrawl/web-languages We're also organizing a Workshop on Multilingual Data Quality Signals (WMDQS) with MLCommons and EleutherAI where we have a call for papers open…
- 1TA
I built this tool because I wanted a way to just take a bunch of URLs or domains, and query their content in RAG applications. It takes away the pain of crawling, extracting content, chunking, vectorizing, and updating periodically. I'm curious to see if it can be useful to others. I meant to launch this six months ago but life got in the way...
2024 · embedding.io
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- 3IM
I built this project as a way to learn more about NLP by applying it to something weird and unsolved. The Voynich Manuscript is a 15th-century book written in an unknown script. No one’s been able to translate it, and many think it’s a hoax, a cipher, or a constructed language. I wasn’t trying to decode it — I just wanted to see: does it behave like a structured language? I stripped a handful of common suffix-like endings (aiin, dy, etc.) to isolate what looked like root forms. I know that’s a strong assumption — I call it out directly in the repo — but it helped clarify the clustering. From…
2025 · github.com
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- 6HW
TL;DR: Vector-based RAG performs poorly for many real-world applications like codebase chats, and you should consider 'language maps'. Part of our mission at Mutable.ai is to make it much easier for developers to build and understand software. One of the natural ways to do this is to create a codebase chat, that answer questions about your repo and help you build features. It might seem simple to plug in your codebase into a state-of-the-art LLM, but LLMs have two limitations that make human-level assistance with code difficult: 1. They currently have context windows that are too small to…
2024 · twitter.com
- 7TN
Here is a tool I built initially for myself to help with my German and Greek language studies. It started as a hack for creating Anki cards from native language audio. It extracts the words, finds their base forms (lemmas) and groups the examples by the lemma. At some point I realised that I have a transcription with word level timestamps that opens a lot of other opportunities. So I added a mode to click the first and last word in the transcript and it starts looping with the right gap and repeat count. Another feature I use a lot is selecting an audio fragment, sending a predefined prompt…
Jun 2026 · lingochunk.com
- 8DA
I've built an advanced RAG (Retrieval-Augmented Generation) pipeline from scratch to demystify the complex mechanics of modern LLM-powered Question Answering systems. This repository features: -- An implementation of a sub-question query engine from scratch to answer complex user questions. -- Illustrative explanations that unveil the inner workings of the system. -- An analysis of the challenges I faced while working with the system, like prompt engineering and cost estimation. -- Qualitative comparison with similar frameworks like LlamaIndex, offering a broader perspective. Key Takeaway:…
2023 · github.com
- 9IM
2023 · languageworldmap.com
- 10CL
2018 · littlelingua.eu
- 11FD
2014 · github.com
- 12IT
2023 · github.com
- 13IJ
Hi HackerNews, Lately, I have seen an explosion in posts offering paid APIs/services to get unstructured data into LLMs (i.e. langchain extract, ragflow, unstructured, unstract, just to name a few) and I have been largely disappointed by them, either because they fail to implement multimodal support, fail to give good context for "really tricky" PDFs / Word docs / Powerpoints, or are just plain difficult to use. In light of all these posts I figured I'd share my solution that has been working smoothly for me and my clients. I put it up on GitHub for free so you can check it…
2024 · github.com
- 14LA
G'day, HN! I'm one of the maintainers of `llm`. I've been working alongside a trusty group of contributors to bring this project to life, and we're now at a point where we're ready to share it with the world. Large language models (LLMs) are taking the computing world by storm due to their emergent abilities that allow them to perform a wide variety of tasks, including translation, summarization, code generation, and even some degree of reasoning. However, the ecosystem around LLMs is still in its infancy, and it can be difficult to get started with these models. `llm` is a one-stop shop for…
2023 · github.com
- 15CA
TLDR: I’m expanding the family of text-splitting Chonky models with new multilingual model. You can learn more about this neural approach in a previous post: https://news.ycombinator.com/item?id=43652968 Since the release of the first distilbert-based model I’ve released two more models based on a ModernBERT. All these models were pre-trained and fine-tuned primary on English texts. But recently mmBERT(https://huggingface.co/blog/mmbert) has been released. This model pre-trained on massive dataset that contains 1833 languages. So I had an idea of…
Oct 2025 · huggingface.co
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- 17LI
2018 · languagemodels.io
- 18IO
You can test translation quality here https://lingvanex.com/translate/
2025 · github.com
- 19VD
Discover, evaluate, and access relevant embeddings in your go-to framework. Skip all the infra for scraping, cleaning, indexing, and updating high-quality embeddings.
2023 · embedding.store
- 20GA
In the dark old days when I was uninspired and couldn't come up with a name for a project I'd hop onto my old pal Google Translate and see if a keyword associated with my name "sounded cool in German" If it did, and the domain was unused I'd scoop it up and use it. This process took a while, I'd try German, French, Russian, Armenian, Latvian, etc trying to find something cool that wasn't already being used. So I built http://www.catelus.com/ It translates your keyword into more than 30 different languages and checks if your-word-in-german.com and your-word-in-polish.com are…
2016
- 21AS
We explored a novel method to gauge the significance of tokens in prompts given to large language models, without needing direct model access. Essentially, we just did an ablation study on the prompt using cosine similarity of the embeddings as the measure. We got surprisingly promising results when comparing this really simple approach to integrated gradients. Curious to hear thoughts from the community!
2023 · heatmap.demos.watchful.io
- 22AE
2025 · github.com
- 23CA
Synthetic data generation is an essential step in training and evaluating LLMs/Agents/RAG pipelines, but tooling around this is still lacking. We're introducing Curator, an open-source library designed to streamline the data curation process. While there are many libraries to prompt LLMs, the semantics of generating synthetic data is different from prompting. For example, we need to process a large number of prompts (sometimes in millions or more) while accepting some failures, utilize several stages of prompting, incorporate human feedback, and filter out bad data using verifiers…
2025 · github.com
- 24LA
I recently submitted another project for using LLMs to correct errors and improve formatting of OCRed documents which was well received. The low cost and high quality/speed of the latest "value tier" models from OpenAI and Anthropic have made it possible to get compelling results at a very reasonable price in that application. It occured to me that the same approach taken there (namely, splitting documents into chunks and sending each chunk through a chain of LLM prompts that each take the output of the previous prompt and apply an additional layer of processing) could be easily applied…
2024 · github.com
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