MetaLogic MailVexa
The one-time-fee, privacy-first desktop email verifier
What it does
MailVexa is a cross-platform desktop app (Windows + macOS) that cleans and verifies bulk email lists without uploading them to a server. Import CSV/XLSX/TXT and run multi-layer checks (format, disposable, MX, SMTP, mailbox, catch-all, SPF/DMARC), then export exactly the categories you need — Valid, Risky, Invalid, Unknown. Unlike SaaS verifiers, it's a one-time license: no monthly fees, no credit packs. Built-in proxy rotation and pause/resume make it ideal for agencies handling client data.
Clean your email lists without uploading them anywhere. MailVexa is a cross-platform desktop app that cuts bounce rates — pay once, own it forever, no monthly fees.
Remove invalid, risky, disposable and catch-all addresses — your list never leaves your machine. One-time license, no monthly SaaS fees, no recurring credits. Built for maximum speed. Three reasons people switch from online tools and never look back. Every invalid, disposable, or catch-all contact you send to chips away at deliverability, sender reputation, and your marketing budget — long before you notice the damage. A guided wizard walks you through import, cleanup, verification, review, and export — without ever leaving your desktop. A five-minute walkthrough of the full wizard — import to export — running natively on macOS. Multi-threaded engine that scales to your CPU, smart cleanup…from metalogicsoft.com
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Growth · 29d ago · astrapixels.com
Launched alongside, August 2026
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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