Pi

PidgeIoT – open-source IoT/Embedded device management for all (free*)

Hacker News

PidgeIoT – open-source IoT/Embedded device management for all (free*)

Hi, I'm Justin! For the past ~3 years, instead of getting a real (well paying) job I've been working on PidgeIoT because, well... I'm stubborn and wanted to see if I could! PidgeIoT is a fully open source, cloud native, device management platform. Originally targeted at cellular connected devices, now aimed at everything! Currently only HTTPS and WebSockets are supported, but CoAP is planned. Bring your own SIM (if needed) and telemetry store. Its features include: device shadows, per-device keypairs (no JWTs!), telemetry ingestion with history, user-defined telemetry graphs, conditional email alerts, remote logging (on Zephyr), remote diagnostic shell (also on Zephyr via a WebSocket), OTA firmware updates (and hosting), and probably other things I've forgotten about at this point. PidgeIoT is built in Rust from front to back. The frontend is WASM (Dioxus) and the core is built around Cloudflare Workers and Durable Objects (also WASM). The backend uses a managed PostgreSQL DB (for now). The IAM is self-hosted Ory Kratos. You may be asking "Why not just use a JS library for your SPA like a sensible person?" To that I say, I hate JavaScript! Okay, okay, it's not thaaat bad; but it's not my cup of tea and WASM is cool (and the future ). Some Background: A couple years ago I was new to embedded systems and working on a project for a Regional Transit Authority (RTA) that required realtime data over a cellular connection. The system needed to receive a relatively large amount of data (around 4kB - 20kB) every 30 seconds. The data vendor “refused” to remove unnecessary information from the data or create a custom endpoint for me, and the RTA wasn’t keen on maintaining a middleware server to transform the data. Those constraints made existing IoT platforms unsuitable for the project, as they either had low data limits or charged excessively for each transmission. Other platforms required a Kubernetes cluster, an enterprise contract, or a project rewrite around their firmware. That frustrated me. I had a working system and no way to remotely manage it. So I made my own platform. A platform that hopefully makes getting into this space easier and less cost prohibitive! Currently all of the example projects are Zephyr based and include configs for native-sim plus the devices I have with me. Let me know if you have a different device/firmware and need me to whip up an example for you! I know having to signup and compile firmware are annoying barriers to test it out, but I think they're necessary for what PidgeIoT is. Plus I made these nice, Non-GMO, links just for you: - Getting started guide that requires no hardware: https://pidgeiot.com/getting-started - A live demo: https://pidgeiot.com/demo Please give me plenty of feedback; let me know what you want to see; tell me if I'm crazy for even trying this when AWS IoT exists! Is this another case of xkcd #927? * All PidgeIoT usage is free during early access; no credit card required. Pricing will come later with the express goal of not making past Justin think another platform needs to be built. GitHub Links, all repos AGPL-3.0 licensed: - pidgeiot: https://github.com/justins-engineering/pidgeiot - pigeon: https://github.com/justins-engineering/pigeon - pigeon-examples: https://github.com/justins-engineering/pigeon-examples

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
88%88% 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.
Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, existing · Missing: https docs, excited, just released
73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, email · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus, platform, host · Missing: intuitive, reviews, exclusive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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