SellerEdgeLab
Rank on Amazon page 1 without ads — for $10/mo
What it does
Amazon Sellers: What if finding a winning product was this simple? 1. Type any broad keyword (e.g. "kitchen", "dog", "plant"). 2. Hit RUN. 3. Done! SellerEdgeLab scans fresh Amazon market signals and instantly delivers: A feature-upgraded winning product with low competition & real buyer demand. A full, high-converting listing optimized for Amazon's search algorithm, buyer intent, SEO, GEO, and Alexa for Shopping! No outdated databases. Just fresh, on-demand data for only $10/month.
SellerEdgeLab is an affordable Amazon keyword research tool for FBA sellers. Estimate search volume, check competition, and find winning niches for just $10/month.
Use our search analytics to identify high-demand keyword opportunities with lower competition (0 to 30 top listings) . Launch on Amazon USA by selecting a modified or low-competition product and targeting the right keywords to reach Page 1 organically , without relying on heavy ad spend. Once SellerEdgeLab finds a winning product, try listing it via FBM first . Watch the magic happen as your listing hits Page 1 organically without any ads. Validate real buyer demand before ordering bulk inventory! Stop guessing. Make data-driven decisions to reach Page 1 without the ad bleed. Try a free scan today with SellerEdgeLab . Move past confusing spreadsheets. We provide clear, actionable metrics…from selleredgelab.com
Does the same job
all alternatives →More growth this month
the category →
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