npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2026 – Pkg Stats / Ryan Hefner

pi-tps-meter

v3.0.4

Published

Tokens per second meter for pi CLI — live TPS, rolling avg, mean, p95

Readme

pi-tps-meter

Tokens per second meter for pi CLI with sparkline visualization.

Install

pi install npm:pi-tps-meter

Features

During streaming — a smooth, animated gauge that fills with live speed (1/8-cell resolution, auto-scaled to your session's peak):

⠹ ▕███████▋···▏ 47 tps

After a message — a min-max normalized sparkline of your last 12 messages (newest on the right) plus aggregate stats:

▁▄▇▅▂▁▇█▅▃▆▇ 42 tps · μ 39 · p95 61

Color coding:

  • 🟢 Green: >50 tps (fast)
  • 🟡 Yellow: 20-50 tps (medium)
  • 🔴 Red: <20 tps (slow)

Accuracy

  • Uses the provider's real output token count (message.usage.output); the char/4 estimate is only a fallback for providers that don't report usage
  • Rate is measured from the first token, so time-to-first-token (network/queue latency) doesn't drag down the reported tps
  • Tracks thinking tokens (thinking_delta) alongside text, so models with extended thinking report accurate generation speed

Stats

  • avg: rolling 60-second window (resets older samples)
  • μ: all-time mean across the session
  • p95: 95th percentile, all-time (up to 500 samples)

Optimizations

  • Single shared 200ms timer, torn down on both message_end and agent_end (no runaway timer if a stream is aborted)
  • Fixed-size circular buffers (no allocations in the streaming repaint path)
  • Memoized sparkline (rebuilt once per message, not on every tick)
  • Insertion sort for p95 (cold path, runs once per message for ≤500 elements)

Author

Venkata Sai Chirasani

License

MIT