Taglert
Catch broken ad tracking before it costs you the client
What it does
A broken pixel can waste 23% of your ad budget and lose you clients. Taglert monitors your Meta pixels, CAPI event quality, and Google conversion tracking, and shows you what's broken before it costs you.
When tracking breaks, your ads keep spending and results drop. That's about 23% of the budget wasted, while the client wonders why sales are down. Taglert catches it first, across every account you run. Plug in your ad spend and how many accounts you run. The math is uncomfortable. And a pixel that's set up right delivers up to 18% better results on the same spend (Meta) . Connect your accounts and see what's actually broken. No credit card required. Taglert monitors the signals that actually break, across every client account, all the time. Catch pixels that stop firing or go unavailable before your client does. Get alerted the moment CAPI match quality drops below 6.0 and your matching…from taglert.app
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Scale8 - Tag Manager & Web Analytics2022 · ▲114Open-source | Tag Management | Analytics | JS Error Tracking
More growth this month
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AstraPixels▲267A pixel-art solar system at its real current positions.
Growth · 29d ago · astrapixels.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