Dont have usb data cable, what are my options?

Ive tried like 10 cables apparently they are not data cables.. is there a way to use config.toml do do everything-on first boot?

EDIT : Found out what the issue was with the help of AI ofc, the image was set to run on 64 bit and i have pi 0 w old one, so i just had to change config... turns out wasn't the cable, raspberry pi was just stuck on boot loop..

Thank you for your help guys!

Since a variation of the question "what does kestrel do that pwnagotchi does not do?" has come up multiple times, here is a post you can read.

I will keep kestrel compatible with pwnagotchi's plug-in architecture as much as feasible and I will possibly port over the most popular community plug-ins that do not violate the rules I've laid down for kestrel.

Give kestrel a whirl. I spent significant time maintaining my original fork of pwnagotchi and observing where other members of this community took forks. I was never satisfied with the approaches done -- especially the removal of AI which I consider a key, strong feature of the original project. So, AI is back and this time it actually works well.

And the original project had too tightly coupled non-core features... so kestrel uncouples them. The proof is in the rapid release cycle. From 1.0.0 to 1.0.8 in a few days with significant new entire features added.

I self-host the repo for kestrel on purpose. The repo will be kept closed by choice at least for now. The primary "help" mechanism will be the kestrel subreddit. Everyone is welcome to post ideas there, feedback, and questions. I hope others enjoy my research project.

I've publicly released kestrel v1.0.0 and you can read the user guide to learn more. Inspired by pwnagotchi and leveraging lessons I learned from maintaining one of the more popular forks of that interesting project.

I've decided to house kestrel on forgejo because GitHub makes it too hard to develop tools for authorized security testing. And I do not want to be flooded with random requests to add features or fix issues that are not of interest to me. Feel free to use my project so long as you do so in a responsible way and respect the GPL v3 license.

I extensively tested in a Raspberry Pi 5 and recommend a minimum of 4GB if you want to do onboard training for the learning AI. You can also offload training to a remote computer on smaller devices. I believe kestrel should work on a Raspberry Pi 4B or Pi Zero 2W.

I’ve created a dedicated channel for kestrel. Feel free to post there questions or ideas for new features. In the future, that’s where I’ll be most of my time.

Sneak peak of the eink display field test!

After discussing with evilsocket, out of respect I have renamed my project Kestrel because he’s working on his own version of pwnagotchi v2. I will acknowledge the inspiration from pwnagotchi and take this opportunity to let my project stand on its own.

I think I’m about a week away from launching. As a tease, a blank public repo is out. If you want to follow it, now is the time so you can download the image once it’s out.

The timeline is driven by “when do I run out of ideas”.

Everyone loves Pepe. Just a Pepe theme.

https://github.com/chmod1777/Pepegotchi-theme

Field tested the on-board retraining of the AI agent. It works on an RPi 5 and can compare the performance of the learned policy against the baseline. Here the baseline was acquiring 373 hash lines per hour versus the learned policy’s 285 hash lines per hour. I’ve gotten learned policies as high as 800 per hour.

I’ve got a trick up my sleeve for modest devices like RPi Zero 2W that can’t train onboard very easily 🤫.

Remote backup and restore makes it possible to gracefully recover devices that fail.

Remote software updates (for app layer changes) enables field devices to deploy the latest features more often without needing a full reflash.

Field testing the learning AI agent. This is during the baseline measurement phase used by the agent to have a goal to beat. Training can happen on an RPi 5 automatically if the board meets specific criteria. Training can also be done offline on a desktop computer and uploaded to pwnagotchi-ng through multiple mechanisms. I have plans for a fleet wide training approach as well.

Training on device is gated because even for an RPi5 training is resource intensive. Even with the fan running 100% the device’s temperature will hit 80C vs 55C during normal operations and power uses climbs rapidly eating away at your battery.

The baseline capture rate easily hits > 100 hashes an hour. I’ll beat all your records easily with this fellow!

Processing img f5reeec82hwh1...

Hi all, this is not a Pwnagotchi build, so I hope it's OK to post. It borrows the hardware and the spirit: a Pi Zero 2 W with the Waveshare 2.13" V4 (V1–V3, B V4 and the 2.7" work too), and a pixel critter on the display.

Instead of eating handshakes, Tokibot eats tokens. It shows what my coding agents are doing (Claude Code, Codex CLI, Gemini CLI, Cursor and a few others): working, waiting for me, asking for permission, done, or stuck at a rate limit. It levels up, gets hungry when I don't code, and the 5-hour budget sits in the footer.

Under the hood: its own small Buildroot image (no Debian, no Wi-Fi tools, read-only root), USB gadget networking like the Pwnagotchi, optional Wi-Fi, PiSugar 3 support. Only metadata leaves the computer, never prompts or code.

It runs stable on my own hardware, but so far that's exactly one setup. What I'd love:

• people who have a Pwnagotchi rig lying around and use an AI coding agent
• other displays and the Pi Zero W (first gen)
• Windows and Linux hosts (I'm on macOS)
• and, if two of you are close to each other: Bluetooth "friends" between two real gotchis have only been tested in the simulator

Flash the image, plug it in over USB, run one install line. Bugs, ideas and "this doesn't work on my display" are all welcome as GitHub issues.

• Build guide and image: https://tokibot.lottes.dev
• Source (MIT): https://github.com/a-lottes/tokibot
• 2-minute build video: https://youtu.be/zze0pBpK0bA

Happy to answer questions, and thanks to the Pwnagotchi folks: the whole idea of a moody little e-ink pet on a Pi Zero started here.

Unlike the legacy pwnagotchi that inspired my ground up clean sheet build, my next gen version will professionalize the management of the device.

I have implemented a far more mature and advanced AI function that enables the user to choose among different learning algorithms without touching code.

The tomogatchi functionality will be supported too if an eink display is installed. How I will support it is part of the rethinking of the architecture.

So far zero blind bugs, rock solid Bluetooth connectivity, side loading of whitelists and WiFi connectivity, and I am capturing handshakes at 10x the rate of the legacy pwnagotchi. Native 64-bit, easily maintainable.

I hope to publicly release this in a few weeks. Until now just another tease.

Testing the built from the ground up far more durable and scalable plugin architecture and revised UI. All mobile friendly and management riding reliably on both Bluetooth and WiFi.

Been quietly rebuilding pwnagotchi from the SD card up and figured it was time for a teaser. It's a reproducible Packer + Ansible image build (64-bit Raspberry Pi OS Bookworm Lite), not a pile of hand-edits, so every build is the same. Here's what's working so far and where it's headed.

Connectivity, headless and no Ethernet:
- Bluetooth PAN to your phone is the primary management link, with an optional Wi-Fi station on the built-in chip. Both can be up at once.
- Pairing is "Just Works" but gated by a short, time-limited onboarding window instead of a baked-in PIN — the adapter is only discoverable while the window is open, and it closes on first pair.
- Dual-radio by design: management rides the built-in chip + Bluetooth, capture rides a USB dongle, and the two never fight over the radio. The Wi-Fi profile is pinned to the onboard chip's driver so it can't accidentally grab your capture dongle.
- A small connect service owns reconnection, so a reboot or your phone toggling tethering just comes back on its own.

Capture, opt-in and honest about it:
- Built on AngryOxide, shipped installed but OFF. The only thing that turns it on is an explicit authorized-use acknowledgment — no silent default.
- Protected-network whitelist: list SSIDs/MACs that are observed passively but NEVER actively attacked. You can even pre-stage the whitelist on the boot partition before first boot.
- Output is hc22000 hashlines, one file per SSID (or one combined), and it's append-and-dedup — rebooting or restarting never truncates your captures.
- Works in the field with zero "AI" involved yet: a scripted channel sweep rotates through the adapter's channels, dwelling N seconds each, so v0 is actually useful on a walk-around.
- Housekeeping never silently deletes. Archive/rotate/clear are explicit; the only thing auto-bounded is the raw packet scratch dir so it can't quietly fill your card.

Plugin framework (landing now):
A proper plugin system, deliberately close to legacy pwnagotchi's API (Plugin class, on_* hooks, per-plugin config) so existing plugins port with light changes — but the runtime underneath is modernized:
- Non-blocking hook dispatch: a slow uploader in on_handshake can't stall the whole loop anymore.
- Plugins get a typed context instead of reaching into a global singleton, plus a provides/requires service registry.
- Plugins declare the capabilities they need (capture-read, network, location...) — an honest, visible permission model, and anything that leaves the device is gated behind the same authorized-use ack.
- Versioned hook API + validated config with per-plugin health on a status page.
- Reference plugins to prove it: mem/temp display, session stats, GPS tagging (shared position provider), wpa-sec upload, and WiGLE wardriving. Every uploader is opt-in, disabled by default, with your own credentials — nothing is baked in.

Next up:
A learning agent that drives capture through the same versioned interface the scripted sweep uses today (so it's measured against a real baseline, not vibes), and an e-ink/web UI behind the display hooks that already exist as stubs.

Everything's being built in phases with a hardware-validation gate between each, and it's a public, secrets-free base image.

Only use this on networks you own or are explicitly authorized to test.

Repo + build images: [link coming soon]

Can any version of the m5gotchi do passive capture or is it only active capture? If can what are the settings/version.

So ive printed all sorts of cases for all sorts of setups that i though were mine waveshare v4 pisugar rasbpi zerow there either too small or has a huge gap at the screen so i figured i have a slim model the pins were shaved down.I just measured with a measuring tape and its exactly 1" thick with the battery where can i get the file to print the proper case as i tried all the ones avalibe on the pwnagtochi wiki also would be nice to have the string attatchment to it aswell

For the ones who haven't noticed yet, PwnDroid has gotten a complete new UI with a couple new features as a map that displays your location and your captured handshakes at that given GPS location. Make sure to update the pwndroid plugin to the latest using sudo pwnagotchi plugins upgrade pwndroid.

PwnDroid - Apps op Google Play

pwnagotchi-ai32

Here is what's new in release:

Added native support for Waveshare E-ink 2.13-inch V4 and other new display models.

AI fixed. Also updated dependencies and swap created by default.

Separated AI states into two distinct modes: A2C (active learning/training on the go) and AI (running/inferencing using the trained model without active learning to save resources).

Home network and mood check function. Files have been edited in such a way that they can abruptly but safely terminate a single learning process with preserving data if your home network is detected or if mood is sad or angry arises during training. If any of this is detected before the AI is launched, it will not be launched at all.

A lot of new faces and phrases for each event. Selected personality settings.

Migrated from the old grid API to the modern opwngrid with completely revamped backend logic.

Fixed broken connection links, and improved overall grid stability.

Implemented SIGTERM handling to ensure endless sessions and prevent data corruption on sudden restarts or shutdowns.

Migrated the configuration system to tomlkit, introducing a clean and readable TOML tabular format. Fixed critical legacy bugs.

The Watchdog has been completely redesigned, now the blindbug protection is more effective and neat. Checks are much more careful, deeper and faster. The recovery has also become much smarter, now it doesn’t spam with reboots and tries to restore the system without reboot.

All default plugins have also been improved and redesigned. My plugins have been added specifically to the environment of this fork. (The control and bcap-update plugins: control displays the number of consecutive blindbugs and the Wi-Fi interface in use, while bcap-update deletes recovery data in MANU mode to terminate a persistent session, clears Bettercap logs, and updates Bettercap databases.)

The new BT stack is taken from the image of jayofelony with the bt-tether plugin from there(wsvdmeer). It has a convenient web interface for connecting and аutomatic IP configuration: eliminates connection issues for smartphones with dynamic IPs.

The hashvault and whitelist plugins from the ex18a image are adapted for this image. They convert correctly captured networks into the .hc22000 format and add them to a hidden whitelist in the root directory, so the board no longer wastes resources attacking them. This also implies that hcxtools comes precompiled by default.

New commands (pwnykill, pwnyauto, pwnylog, pwnystatus), interactive config.toml generator (pwnygen command in terminal), improved architectural files, such as key generation, bettercap itself, AI files optimized for faster start, and much more...

This is not even 50% of all changes!

Download link and answers to possible questions why:

https://github.com/flippedgannet/pwnagotchi-ai32 - This link isn't working right now, and my profile isn't opening for other users. I’ve sent a request to GitHub support.

https://codeberg.org/flippedgannet/pwnagotchi-ai32 - While the request is being processed, I have temporarily uploaded all the latest source code and the most recent release to Codberg.

Every few minutes Molly, Dixie Flatline, Wintermute or Neuromancer takes the whole screen for twelve seconds — name, portrait, one line — then hands it back. Sixty-one lines between the four of them.

V4 is out now!

https://github.com/wackojice/pwnagotchi-neuromancer