HAP Studio vs Trackshund

HAP is a design tool for creating and learning haptics. Create high-quality haptics using an intuitive visual editor or AI—no coding required. ■ Create Haptics Fine-tune intensity, sharpness, and timing with an intuitive visual editor while previewing your haptics in real time. * Fine-tune haptics with a visual editor * Generate haptics from natural language using AI * Generate haptics from Lottie JSON * Preview haptics in real time Export your haptics as **Swift, AHAP, or JSON** and easily integrate them into your app. ■ Use Haptics Access a library of 30 ready-to-use haptics available as a one-time purchase. Use them for buttons, notifications, success and error feedback, and many other common interactions. ■ Learn Haptics Learn the fundamentals of haptic design with educational content covering accessibility, best practices, and common mistakes. ■ Contact & Social Email: hapstudioapp@gmail.com X: https://x.com/HapStudioApp ■ Support Privacy Policy: https://hapstudio.design/en/privacy/ Terms of Service : https://hapstudio.design/en/terms/ EULA：https://www.apple.com/legal/internet-services/itunes/dev/stdeula/ Contact: hapstudioapp@gmail.com --- This app is primarily designed and optimized for use on iPhone. Haptic feedback is not available on iPad. To experience the app as intended, we recommend using it on an iPhone.

Trackshund offers the most elegant way to monitor your MLflow experiments—a native iOS client that transforms your training runs, metrics, and models into a delightful, intuitive experience, whether you’re on the couch, commuting, or between meetings. Simply connect Trackshund to your MLflow tracking server, and it seamlessly syncs all your experiment data to your phone. You can access your information anytime—even offline or underground, with no signal required. WATCH RUNS LIVE Watch your loss curve update in real time as your model trains. Key experiment metrics take center stage on your Home screen, updating automatically so you’re never stuck babysitting a browser tab. VISUALIZE EVERY METRIC Enjoy full, native charts for every logged metric—designed in a clear, luminous style that prioritizes clarity over decoration. COMPARE RUNS SIDE BY SIDE Use real comparison tables that highlight what changed and what stayed the same. Pin a baseline and instantly see each run’s delta—making progress clear at a glance. INTELLIGENT RUN BRIEFS Get a feed of plain-language insights for every completed run—spot new bests, re-runs, and results within the noise. With Apple Intelligence, enjoy AI-generated experiment summaries that sound like a colleague catching you up. GET ALERTED Choose exactly when your phone should notify you: when a run finishes or fails, crosses a threshold you set, changes sharply, or starts to look off. All notifications are processed securely on your device. MANAGE YOUR MODEL REGISTRY Browse registered models, versions, aliases, and tags—or register a new model directly from a run. BUILT FOR OFFLINE Everything you’ve browsed is securely cached on your device. Read, filter, and compare with zero connectivity—any edits you make are queued and sync automatically when you’re back online. The server remains the source of truth, and stale data is always clearly marked. BRING YOUR OWN SERVER Trackshund connects to any open-source MLflow tracking server—on your laptop, LAN, Tailscale network, or cloud VM. Username and password authentication are fully supported. MADE FOR ML PRACTITIONERS Designed for data scientists and researchers who live in MLflow, Trackshund brings that same reliability to your pocket—fast, native, and genuinely beautiful. It speaks MLflow’s exact vocabulary, so there’s nothing new to learn. Trackshund is an independent, unofficial client for MLflow. It is not affiliated with, endorsed by, or sponsored by MLflow, LF Projects, LLC, the Linux Foundation, or Databricks. "MLflow" is a trademark of LF Projects, LLC, used here only to describe compatibility. You bring your own MLflow server; Trackshund is simply a nicer window into it. Requires your own MLflow tracking server (open-source MLflow 3.13 or later). iOS 18 or later.