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u/D__J

r/AIProgrammingHardware r/StrixHalo r/TrekBikes r/klippers
I Turned an Old Android Phone Into a Full Klipper Host
▲ 13 r/klippers

I Turned an Old Android Phone Into a Full Klipper Host

I’ve been working on AndroidKlipper, a project that runs Klipper, Moonraker and Mainsail directly on an Android device instead of using the phone as just a KlipperScreen/Mainsail client.

The printer MCUs connect directly to Android over USB host/OTG. AndroidKlipper bridges each MCU into a stable PTY, then runs the normal Klipper/Moonraker/Mainsail stack locally.

So far I’ve tested it on:
Retroid Pocket G2 — best-tested host; completed sustained real prints and long idle testing
Fire HD 8 — works as a host, but couldn’t charge while hosting USB with the hardware I tested
Moto G 2024 — clean install through Mainsail, normal Voron config upload, Klipper READY and successful homing

One of the biggest things I’ve learned is that USB-C + OTG + a powered hub does not necessarily mean an Android device can charge while remaining USB host. I’m documenting that per-device because it may actually matter more than CPU performance for long prints.
The goal isn’t to replace or hide normal Klipper. Your printer config stays yours, and I’m deliberately not weakening timing/safety limits to paper over Android USB problems.

Repo:
https://github.com/nozzlenaut/androidklipper

I’ve also been documenting the hardware testing on Unqualified Repairs:
https://www.youtube.com/@UnqualifiedRepairs

I’m particularly interested in people trying this on other Android hardware. Device model, USB setup, MCU type, charging-while-hosting behavior and long-print results would all be useful.

u/D__J — 7 days ago
▲ 51 r/TrekBikes

Got my first bike since I was a teen today. Didn’t even realize the paint was speckled until I got it home. Pretty obsessed with it already.

u/D__J — 2 months ago
▲ 1 r/AIProgrammingHardware

Looking for Strix Halo results for a standardized local LLM hardware comparison

I have been working on LLM Hardware Sift because I wanted a simple answer: if I replace my current PC with a Strix Halo machine, what changes when both run the exact same workload? Ideally, this becomes a resource for people to see if an upgrade is worth it to them.

Unlike LlamaBenchy, this isn’t about tuning a setup for the fastest possible result. Hardware Sift keeps the models and settings fixed so the **hardware is the variable**. It tests models from 0.6B through 32B, with an optional 72B tier for high-memory systems.

The comparison table currently includes an RTX 3060, ROG Ally, M2 MacBook Air, and Raspberry Pi 5—but no Strix Halo results yet.

It’s an early Windows alpha, and results stay local unless you choose to submit them. I’d love results or feedback from anyone with a 64 GB or 128 GB Strix Halo machine:
https://github.com/nozzlenaut/llm\\\_hardware\\\_sift

reddit.com
u/D__J — 3 months ago
▲ 0 r/StrixHalo

Looking for Strix Halo results for a standardized local LLM hardware comparison

I have been working on LLM Hardware Sift because I wanted a simple answer: if I replace my current PC with a Strix Halo machine, what changes when both run the exact same workload? Ideally, this becomes a resource for people to see if an upgrade is worth it to them.

Unlike LlamaBenchy, this isn’t about tuning a setup for the fastest possible result. Hardware Sift keeps the models and settings fixed so the hardware is the variable. It tests models from 0.6B through 32B, with an optional 72B tier for high-memory systems.

The comparison table currently includes an RTX 3060, ROG Ally, M2 MacBook Air, and Raspberry Pi 5—but no Strix Halo results yet.

It’s an early Windows alpha, and results stay local unless you choose to submit them. I’d love results or feedback from anyone with a 64 GB or 128 GB Strix Halo machine:
https://github.com/nozzlenaut/llm\_hardware\_sift

reddit.com
u/D__J — 3 months ago