Trustia AI
Hardware-Agnostic Defense Autonomy Software Stack
What it does
Trustia is a 100% hardware-agnostic, dual-use autonomy software stack for Unmanned Ground Vehicles (UGVs) operating in GPS-denied environments. Featuring 1,276 verified CI/CD tests, Pose-Graph SLAM, Hybrid A* trajectory, and real-time edge AI threat perception (IEDs, landmines, trenches). Retrofits tactical and commercial fleets via CAN-Bus.
Trustia AI; şehir içi sivil Robotaksi filoları ve GPS sinyalinin bulunmadığı harekat sahalarında görev yapan İnsansız Savunma Robotları için V2X, 3D SLAM, 5 saniyelik yörünge tahmini ve Seviye 4 yerli otonomi yazılım platformudur. (İstanbul, Türkiye).
Şehir içi Robotaksi filoları ve GPS olmayan harekat sahalarındaki İnsansız Savunma Robotları için tam bağımsız yerli otonomi yazılımı. 128 Kanallı LiDAR, 4x HDR Kamera, 77GHz Radar ve 100 Hz CAN-FD aktüatör köprüsü ile donatılmış 16.000 satır özgün deterministik otonomi yazılımı. Trustia AI; donanım bağımsız evrensel bir otonomi beynidir. Standart CAN-Bus ve Drive-by-Wire haberleşmesi ile her türlü aracı tam otonom hale getirir. Binek ve elektrikli otomobiller için geliştirilen tak-çalıştır otonom sürüş beyni. Şehir içi yolcu taşımacılığı, servis hatları ve otonom taksi operasyonları. Elektronik harp ve uydu sinyalinin kesildiği çatışma alanlarında; 3D LiDAR SLAM ve sürü zekası ile görev…from trustia.com.tr
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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…
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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.
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