Life & fun · September 21, 2017
Sense2vc – Semantic Analysis of the Reddit Hivemind
We parsed every comment posted Reddit in 2015 and 2019, and trained different word2vec models for each year.
Sense2vc – Semantic Analysis of the Reddit Hivemind launched on September 21, 2017 with 7 votes, #1,099 of 3,072 launches that month and more than 50% of that year's launches. The site was still online in September 2026, 9 years on.
What it does
We parsed every comment posted to Reddit in 2015 and 2019, and trained different word2vec models for each year. A lot's happened over the last four years, so many words, people or events have different associations. You can also try searching for a phrase that's more than the sum of its parts to see what the model thinks it means. Try your favourite band , slang words , technical things , or something totally randomfrom demos.explosion.ai
Does a similar job
all alternatives →- SMSense2vec model trained on all 2015 Reddit comments2016 · sense2vec.spacy.io · ▲225
- WAWikipedia2Vec – A tool for learning embeddings of words and entities2018 · wikipedia2vec.github.io · ▲105
UpvoticsApr 2026 · upvotics.com · ▲95Hey HN! I spent way too much time manually searching Reddit for mentions of problems my SaaS solved. I'd search keywords daily, miss conversations by hours, and get buried when I finally showed up. So I built Upvotics to automate the listening part. It monitors subreddits for keywords, tracks mentions as they happen, and surfaces conversations where people are already asking for what you're building. What it does: - Real-time keyword tracking across subreddits - User/subreddit engagement analytics (karma, post frequency, sentiment) - Rule checker (catches common removal triggers before…
Model2vec – Lightning-fast Static Embeddings for RAG/Semantic Search2024 · github.com · ▲28We’ve recently open-sourced Model2vec, a method to distill sentence transformers into static embeddings that outperform all previous approaches by a large margin on MTEB. Our new models set a new state-of-the-art for static embeddings. Main features: - Our best model (potion-base-8M) has only 8M parameters, which is ~30mb on disk - Inference is ~500x faster than the distilled base model (bge-base), on a CPU - New models can be distilled in 30 seconds on a CPU without requiring a dataset - just a vocabulary - Numpy-only inference: The packaged can be install the package with minimal…
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