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PrismaXAI/prism-pi

Fast pi rollout on consumer GPUs

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Momentum

How fast PrismaXAI/prism-pi is gaining stars, and where it lands on Trendshift and GitHub Trending

Activity and trending

Stars, forks and merged pull requests each day or month, lined up with the repository's rank on Trendshift Trending

Oct 29 – Nov 5, 2026

Stars +1 in 8 days

01
1

Forks +1 in 8 days

01
1

Merged PRs 0 in 8 days

01

Trendshift Trendingwhere the repository ranked each day it made the top 25

Not in the Trendshift top 25 in this period

Oct 29Oct 30Oct 31Nov 1Nov 2Nov 3Nov 4Nov 5
The left and right arrow keys step from day to day; Home and End go to the first and the last

Stars, forks, merged pull requests and Trendshift Trending rank, by day
DayStars gainedForks gainedMerged PRs
Thu, Oct 29, 2026+0+10
Fri, Oct 30, 2026+0+00
Sat, Oct 31, 2026+0+00
Sun, Nov 1, 2026+0+00
Mon, Nov 2, 2026+0+00
Tue, Nov 3, 2026+1+00
Wed, Nov 4, 2026+0+00
Thu, Nov 5, 2026+0+00
Total+1+10
  • 1#1 on the list
  • 7Rank that day
  • Not trending

Social mentions

Recent discussions about this repository across the web
  1. Nov 51 post
    • October was busy for PrismaX: • IROS: Shared dexterous-hand insights. A 1 cm mounting error can cut success by ~70%. • RoboCon SF: Packed Physical AI Salon and late-running teleop demos. • @castorhat…

      Read full on X, posted by @xAeth616

      @xAeth616 · x.com

  2. Nov 41 post
    • 1...机器人数据现在直接开放了。 不用找销售、也不用等名单,注册就能挑数据集下载。 想要特定任务、场景或者硬件的,也可以直接说,按你的要求去采。

      Read full on X, posted by @JiadongShe57877

      @JiadongShe57877 · x.com

  3. Nov 32 posts
    • We were at three events in October. 🤖 IROS in Pittsburgh: we shared field notes on dexterous hands and what mattered for our π0.5 demo. One finding was that a 1 cm mounting error cuts success rates…

      Read full on X, posted by @PrismaXai

      @PrismaXai · x.com

    • 这意味着π0.5现在可以运行在RTX 5080、5070Ti 等 16GB显卡上不再局限于 24GB 级别的设备。 我觉得这项优化的意义不只是省下显存。 当运行门槛降低,更多小型实验室和开发团队才有机会真正动手测试 Physical AI。 技术进步不一定是不断增加算力有时候把现有资源用得更聪明,同样能推动整个生态向前。

      Read full on X, posted by @fishfish5771

      @fishfish5771 · x.com

  4. Nov 21 post
    • What changed? Two unnecessary memory costs were addressed: Removed random FP32 backbone initialization before loading the real model. Stopped keeping PyTorch models in VRAM alongside compiled…

      Read full on X, posted by @shashivadhan77

      @shashivadhan77 · x.com

  5. Oct 311 post
    • In the physical world, near-misses can cause collisions, objects to move, and entire scenes to change. A small delay can create a much bigger problem.

      Read full on X, posted by @shipmaster_777

      @shipmaster_777 · x.com

  6. Oct 301 post
    • Part 3 is about memory. Two fixes took peak VRAM from about 22 GB to 11.2 GB so it now runs on 16 GB cards like the RTX 5080.

      Read full on X, posted by @PrismaXai

      @PrismaXai · x.com