Peloran - Sales Intelligence
Data-driven decisions that multiply your sales performance.
What it does
Data-driven decisions that multiply your sales performance. Peloran turns every visitor interaction into actionable customer intelligence — predicting churn, hesitation, value and CLV, then automatically activating personalized journeys across your store, email, WhatsApp and ads. Measure true incremental revenue with holdouts, discover proven strategies from similar stores, and let AI build campaigns for you. Built for e-commerce teams that want more revenue, not more dashboards.
From behavior to action. From data to revenue. All in real time. From behavior to action. From data to revenue. All in real time. Peloran brings together the data that normally lives in separate systems — customers, products, sales, discounts and real-time behavior — and connects them into a single behavioral data layer. This gives Peloran a much richer view of every customer: not just what they bought, but what they viewed, what they ignored, what they hesitated over, which products they prefer, how they respond to discounts and how their behavior changes over time. Raw data becomes customer intelligence. Peloran applies machine learning to behavioral, transactional and contextual signals…from peloran.com
Does the same job
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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 · 17d ago · simedw.com