#autograd
Live, measured metrics for the hashtag #autograd from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #autograd
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Day-by-day usage
measured · fosstodon.org (Mastodon public tags API) · fetched 2026-07-28 12:56 UTC2 uses by 2 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · fosstodon.org (Mastodon public search API) · fetched 2026-07-28 12:56 UTCLive pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-07-28 12:56 UTCEverything below is measured over the latest 10 public posts (spanning ~13298 hours).
Posting hours (UTC)
Languages: Russian (5) · English (5)
Avg boosts / post: 0.2
Top of the latest posts
[Перевод] Языковая Модель без магии: Крошечная Language Model на чистом Node.js Мы создаем крошечную языковую модель с нуля на чистом Node.js без использования TensorFlow или PyTorch, реализуя нейроны, автоград, эмбеддинги, механизм самовни
Python: как один из самых медленных языков стал королем нейросетей Python. IT-курсы разрекламировали его как один из самых легких языков для входа в разработку, любители ИИ создают на нем свои первые нейросети, а некоторые сеньоры Java и C+
Why Distributed Training Is Hard: DTensor and the Costs of Abstraction 이 글은 PyTorch의 DTensor가 분산 학습에서 텐서의 배치 정보를 메타데이터로 관리해 올바른 그래디언트 계산을 보장하는 방식을 상세히 설명한다. 단순한 분산 처리 구현 시 발생하는 그래디언트 불일치 문제와 이를 해결하기 위한 여러 시도, 그리고 DTensor가 제공하는 추상화와 자동화 메커니즘
Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/autograd