PluginTracker
Real install counts, usage data & feedback for WP plugins
What it does
PluginTracker reveals what the WordPress plugin repository doesn’t: your real active install count. It also provides real user-environment telemetry, including PHP and WP versions, active plugins and themes, deactivation reasons, server details, and more. Benchmark against competitors, analyze reviews and support tickets, and make smarter product decisions with real data instead of guesswork.
The numbers the directory hides: granular daily installs, keyword ranks, reviews, support health, competitor movement and opt-in telemetry for WordPress.org plugin authors. Free for 14 days, no card.
How many people really installed it this week. Which PHP version they run. Why they turned it off. The directory rounds it away or never asks — we tell you, every day. It floors your installs to one significant figure, keeps no history, and will not say why any of it moved. We read what it publishes every day and put the reasons beside the numbers — 19 tracker features, in four groups. Every number WordPress.org rounds away, tracked daily and kept as history you can scroll back through. You should not learn about a one-star review from a customer. Alerts fire the same day a signal moves. The busywork around reviews, tickets, and readme copy — drafted for you, always editable. Rank for the…from plugintracker.dev
Does the same job
all alternatives →
- WDWP Detective – Show what theme and plugins a WordPress site is using2017 · wpdetective.io · ▲76
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 16d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 26d ago · cactuscompute.com
Launched alongside, August 2026
the whole month →- TL
Life & fun · 10d ago · louisabraham.github.io
- SA
Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 16d ago · simedw.com