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Teslamate data beautiful and usable on mobile

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Teslamate data beautiful and usable on mobile

Hey everyone, After getting a Tesla last year, I did what many data-oriented folks do: I immediately set up Teslamate on a Raspberry Pi. For those unfamiliar, Teslamate is a fantastic open-source, self-hosted data logger for Tesla vehicles. It uses an Elixir backend to pull data from the Tesla API and stores everything in a PostgreSQL database, with Grafana for visualization. While I loved having access to every detail of my car's data, I quickly ran into a few persistent frustrations: 1. The Interface: Grafana is incredibly powerful on a desktop but almost unusable on a phone. Trying to check stats on the go meant pinching, zooming, and fighting with tiny text and complex dashboards not designed for a mobile viewport. 2. Remote Access: I wanted to access my dashboard securely when away from home. My first attempt involved setting up Nginx as a reverse proxy with LetsEncrypt for SSL. It worked, but it meant another container to manage, expiring certs to worry about, and exposing a port. I later moved to Tailscale, which was much better, but still required some manual configuration to integrate cleanly with the Docker stack. 3. Cost Tracking: Teslamate's default cost calculation is a single flat rate. My electricity plan has different peak and off-peak rates, and I also charge at work and at Superchargers. To keep my cost data accurate, I found myself SSH'ing into my Pi and manually updating rows in the `charging_processes` table in PostgreSQL, which was a real chore. I looked for a simple, mobile-first frontend for Teslamate but couldn't find one. So, I decided to build it myself. Today I'm sharing the result: https://mytesla.cc To be perfectly clear, this is not a replacement for Teslamate. It's a responsive web app that serves as a clean, mobile-friendly frontend for your existing Teslamate instance. Here’s how it addresses the problems: It’s a simple, responsive UI that provides a quick overview of your stats without the complexity of Grafana on mobile. It introduces a straightforward way to manage charging costs, allowing you to define rates based on location and time-of-use schedules. The costs are then calculated and applied automatically. To simplify secure access, the setup includes an optional, pre-configured Tailscale integration directly in the `docker-compose.yml` file, making it much easier to get a secure, end-to-end encrypted connection running without opening any ports. You can, of course, still use your own reverse proxy or Cloudflare Tunnel if you prefer. On Security and Data Privacy: This is critical. My service never sees your car data. The project consists of two parts: the web app itself (a static PWA hosted at mytesla.cc) and a new Docker container you add to your Teslamate stack. When you open the web app in your browser, it communicates directly with your self-hosted Teslamate instance over the secure channel you control (e.g., your local network or Tailscale). My server's only role is to serve the static web app to licensed users; it does not proxy, see, or store any of your personal or vehicle data. This has been a solo-developer side project for the past few months. To make it sustainable and support future development, I'm making it available for a one-time purchase of $9.90. If it doesn't work for you, there's a 7-day refund policy. I'd love to hear your thoughts and get your feedback.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
94%94% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, dock, new · Missing: mac, agents, macos
93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, nginx · Missing: https docs, excited, just released
79%79% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: host, friendly, interface · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, month, users · Missing: mobile apps, ios, entrepreneurs
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
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