Stars and mentions
Last 30 days, Oct 5 – Nov 3
Package downloads
Cumulative recorded downloads · since 2025-10-20
Across 10 known packages in repositories hemanth owns. Cumulative totals include recorded downloads before the selected range. Missing observations leave gaps; totals may be incomplete. PyPI excludes known mirrors.
Top repositories
hemanth’s repositories ranked by pull request authors, as on trending.
Contributes to
Repositories outside hemanth’s account where hemanth opened or reviewed pull requests in the past 365 days.
Who noticed hemanth
Everyone who starred or forked hemanth’s repositories since the archive began in 2011.
Notable accounts
Ordered by the stars their own repositories have received.
Biggest fans
Accounts that starred the most of these repositories.
Breakout weeks
Each repository’s best week of new stars, with the release published around it.
Achievements
Awards earned by the repositories hemanth owns. Select an award to embed its badge.
- Gold
- 51st place finishes
- Silver
- 02nd place finishes
- Bronze
- 13rd place finishes
- Awarded repositories
- 1Owned by hemanth now
Repositories
Ordered by gold, then silver, then bronze.
- functional-programming-jargonBest: 1st by stars, 3,706 stars501
Every award
Top-three finishes of these repositories in completed trending days and weeks, newest first.
A repository earns an award for finishing first, second or third in a completed trending day or week, ranked by new stars, new forks or pull request authors. Awards follow a repository through renames and transfers and count toward its current owner. Weeks run Monday to Sunday, UTC.
Mentions
Reverse-search XKCD comic panel, previously: - Vision LLM - VectorDB With EmbeddingGemma 2, we drop the VLM middleman: comic images (model.encode([img])) and text queries map directly into the same 768d space, 100% locally. https:// hemanth/xkcd-s earch …
EGM-2 trained with (MRL), you can slice vectors from 768d → 512d / 256d / 128d and L2-renormalize with minimal recall drop huge for IndexedDB storage in the browser. Updated: - webml-kit: hemanth/webml-kit - semantra: hemanth/semantra t.co
Reverse-search XKCD comic panel, previously: - Vision LLM - VectorDB With EmbeddingGemma 2, we drop the VLM middleman: comic images (model.encode([img])) and text queries map directly into the same 768d space, 100% locally. hemanth/xkcd-search t.co
Added first-class google/embeddinggemma-2 support to piragi across LanceDB and vector store backends: from piragi import Ragi kb = Ragi("./docs", embed_model="google/embeddinggemma-2") hemanth/piragi t.co