Magdox
Know what your competitors changed, before your buyer does
What it does
Magdox watches your named competitors and tells you what actually changed. Pricing edits, hiring shifts, funding, reviews, ad spend. It drafts the battlecard your reps need and pushes it into your CRM. Digest to email, Slack or Teams. No dashboard to babysit.
Magdox watches competitor pricing, product, hiring and funding and keeps every check it makes, so you can read any tracked page on two dates side by side, with the date on both.
Magdox checks competitor pricing, product, hiring and funding on a schedule, daily or as often as hourly, and keeps every check it makes. Every line it writes carries the page it came from, the moment we read it, and a hash of what that page said - so a claim can still be checked months later. Set it up yourself in minutes, no sales call and no analyst headcount. Pick a competitor above to see exactly what a Magdox digest looks like. Email on every plan. Slack, Teams, CRM and webhooks on Track. Connect Magdox once, then ask in the tool you already have open. The answer comes from what Magdox actually observed, not from what a model remembers. Read-only . It can read your competitor data and…from magdox.io
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 · 16d ago · simedw.com