AI · alternatives · 2026
24 alternatives to 3d Word Vectors with Neural Networks, T-SNE, and WebGL
3d Word Vectors with Neural Networks, T-SNE, and WebGL is an AI tool that visualizes word relationships in three-dimensional space using neural network embeddings and dimensionality reduction. It processes word vectors… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. 3d Word Vectors with Neural Networks, T-SNE, and WebGL launched in 2013; newer entries below may have overtaken it.
- 1
End-to-end deep dive of the project, spanning a large GPU cluster, distributed RocksDB, and terabytes of sharded HNSW.
2025 · blog.wilsonl.in · its alternatives →
- 2
First TTS model to support all 22 Indic languages + English
2024 · its alternatives →
- 3
Quantized word vectors that take 8x-16x less space than regular word vectors - agnusmaximus/Word2Bits
2018 · github.com · its alternatives →
- 4AS
2013 · insightdatascience.com · its alternatives →
- 5IM
Just a fun toy I wanted to make. I've been studying and playing around with language models lately and have always been intrigued by how words are processed by these models. Since the vectors generated by embedding models is in very high dimensional space, I thought it would be cool to reduce them to 3D vectors and visualise them myself. This is what I have so far!
2023 · seesaurus.com · its alternatives →
- 6
Here's a small demonstration of the fundamental aspects of the word-to-vec algorithm. It's implemented in a single python script and depends only on a single text file for training. It's not meant to be blazingly fast or anything, just a toy example to aid my understanding of how word vectors might be learnt from a corpus.
2023 · github.com · its alternatives →
- 7
I spent a lot of time and money on this rather big side project of mine that attempts to replicate the mechanistic interpretability research on proprietary LLMs that was quite popular this year and produced great research papers by Anthropic [1], OpenAI [2] and Deepmind [3]. I am quite proud of this project and since I consider myself the target audience for HackerNews did I think that maybe some of you would appreciate this open research replication as well. Happy to answer any questions or face any feedback. Cheers [1]…
2024 · github.com · its alternatives →
- 8
Arabic root finder with neural nets! Contribute to tb0yd/rootfinder development by creating an account on GitHub.
2018 · github.com · its alternatives →
- 9IM
As a grad student (and an ADHDer), I had trouble doing literature review systematically. To combat this, I made a website that finds similar papers using the meaning of the thing I am looking for. I used MixedBread's [^1] embedding model to generate vectors from the abstracts. I store and search similar vectors using Milvus [^2] and finally use Gradio [^3] to serve the frontend. I update the vector database weekly by pulling the metadata dataset from Kaggle [^4]. To speed up the search process on my free oracle instance, I binarise the embeddings and use Hamming distance as a metric. I would…
2024 · papermatch.mitanshu.tech · its alternatives →
- 10
Neural network 3D visualization framework, build interactive and intuitive model in browsers, support pre-trained deep learning models from TensorFlow, Keras, TensorFlow.js - GitHub - tensorspace-team/tensorspace: Neural network 3D visualization framework, build interactive and intuitive model in br
2018 · github.com · its alternatives →
- 11KG
Hi HN! My latest side project is knowledge graph that maps the French culinary network using data extracted from restaurant reviews from LeFooding.com. The project uses LLMs to extract structured information from unstructured text. Some technical aspects you may be interested in: - Used structured generation to reliably parse unstructured text into a consistent schema - Tested multiple models (Mistral-7B-v0.3, Llama3.2-3B, gpt4o-mini) for information extraction - Created an interactive visualization using gephi-lite and Retina (WebGL) - Built (with Claude) a simple Flask web app to clean and…
2025 · theophilecantelob.re · its alternatives →
- 12WS
We’ve trained a generative AI model to browse the web and answer questions/retrieve code snippets directly. Unlike ChatGPT, it has access to primary sources and is able to cite them when you hover over an answer (click on the text to go to the source being cited). We also show regular Bing results side-by-side with our AI answer. The model is an 11-billion parameter T5-derivative that has been fine-tuned on feedback given on hundreds of thousands of searches done (anonymously) on our platform. Giving the model web access lessens its burden to need to store a snapshot of human knowledge…
2022 · beta.sayhello.so · its alternatives →
- 13GC
Many important real-world datasets come in the form of graphs or networks: social networks, knowledge graphs, protein-interaction networks, the World Wide Web, etc. (just to name a few). Yet, until recently, very little attention has been devoted to the generalization of neural...
2016 · tkipf.github.io · its alternatives →
- 14
Hi HN! I've found this visualization tool immensely helpful over the years for getting an intuition for how an LLM "sees" some piece of text, and with a bit of elbow grease decided to move all compute to client side so I could make it publicly available. I've found it particularly useful for - Understanding exactly how repetition and patterns affect a small LM's ability to predict correctly - Understanding different tokenization patterns and how it affects model output - Getting a general sense of how "hard" different prediction tasks are for GPT-style models Known problems (that I probably…
2023 · perplexity.vercel.app · its alternatives →
- 15GA
2016 · erkaman.github.io · its alternatives →
- 16WA
2018 · wikipedia2vec.github.io · its alternatives →
- 17
A single / multi layer / recurrent neural network written in Golang. - made2591/go-perceptron-go
2017 · github.com · its alternatives →
- 18
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 · its alternatives →
- 19
Can a neural network learn to recognize doodles?
2016 · quickdraw.withgoogle.com · its alternatives →
- 20
Vectorizer.io▲158Fast and easy way to vectorize an image online
2016 · vectorizer.io · its alternatives →
- 21
Using word vectors to classify spam messages. Contribute to doodyparizada/word2vec-spam-filter development by creating an account on GitHub.
2017 · github.com · its alternatives →
- 22
Sonnet▲134A new library for constructing neural networks from DeepMind
2017 · deepmind.google · its alternatives →
- 23
sketch-rnn model trained to write kanji.
2018 · otoro.net · its alternatives →
- 24
💥 Fast State-of-the-Art Tokenizers optimized for Research and Production - huggingface/tokenizers
2020 · github.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →