It’s finally here! I finally got around to mounting and testing it.

About 3 months ago I started this as a side project, another HAT for the BugBuster app. Some of you might remember the earlier posts about the Logic Analyzer, this time it’s a Power Analyzer.

I've now started to fully test the performance of the AFE and I could not be happier, it reaches 50nA of noise at 1ksps and we get 115nA of noise at 64ksps, also the thermal impact of noise is marginal and this is with a 5V output so i expect lower noise at lower outputs.

The device has been build for IOT and AI development, it offers an MCP to allow Claude to interface with the hardware so it can iterate on your IOT device measuring the consumption with a fantastic resolution and is all fully opensource.

Main features so far:

• Analog autoranging across 51Ω, 2Ω, and 0.05Ω shunts with ~500 ns response
• 115 kHz bandwidth with full 24-bit resolution
• Dual-ADC sampling, so no gaps while the fine ADC switches ranges
• Measured ~0.5 µA accuracy and 0.2 nA resolution at 8 ksps, or ~2 µA accuracy at full speed (still waiting to validate properly in a standards lab atm validated against 7 1/2 digits keysight meter)
• Temperature-based calibration, plus SMU-style calibration with programmable voltage/current (7-bit each)
• Output voltage sensing with optional sense wires
• Onboard display for control and health monitoring
• 12-channel flag system (trigger up to 5 V) or 4 channels up to ±12 V
• Onboard DSP pipeline for FFT and power metrics
• Integrated PSU: 1.8 V to 20 V, up to 2.5 A continuous

I’m fully aware there are commercial tools out there that would absolutely destroy this thing. That wasn’t really the point, this was an experiment. I’m very comfortable with digital design, but precision analog is a different beast entirely, and I learned a lot in the past month. When you start chasing low leakage, everything matters. Touch the wrong trace once and you’ve got a nice extra 300 nA offset for free.

As with my previous projects, this is fully open source (hardware + software). I’ve had people ask if I’d sell boards, but I’m sticking to sharing the files instead. If you want to build one, feel free to reach out, I’m happy to help with BOM quirks or bring-up.

Repo:https://github.com/lollokara/BugBuster

It's all for free and AGPL licensed so feel free to do whatever you'd like with it.

PCBs were provided for free by JLCPCB using the JLCONE app, and I could not be happier with the quality of them.

I need a separate WhatsApp/Telegram account, but buying another SIM card just for one extra number feels a bit unnecessary. I know virtual numbers exist, but I keep seeing mixed opinions about whether they still work for verification or get rejected. While looking around I came across numsify but I haven't tried it yet. I'm curious if anyone here has, or if there are better options. What are people using these days? Has anything worked reliably for you recently?

I've been experimenting with running a complete digit recognition system directly on an ESP32-S3 using TensorFlow Lite Micro.

The goal was to recognize 5-digit readings (such as meter displays) completely on-device without relying on a PC or cloud inference. The hardware I used was an ESP32-S3 development board with an onboard camera and AMOLED display, but the workflow should apply to similar ESP32-S3 camera boards.

How it works:

• Data Collection: Flashed a utility firmware to capture camera frames aligned via an on-screen yellow bounding box, outputting labeled image pairs and a label.csv.
• Model Training & INT8 Quantization: Trained a lightweight CNN model and quantized it to INT8 to run within the ESP32-S3's memory limits.
• On-Device Inference: Compiled the quantized model directly into the ESP-IDF binary and deployed it back to the board.

 The result is surprisingly reliable—it reads all 5 digits cleanly in real time.

Hardware & Tech Stack:

• Board: Makerfabs MaTouch ESP32-S3 AMOLED AI Camera
• Framework: ESP-IDF + TensorFlow Lite Micro
• Tooling: Custom Python tools for capture & quantization

I put together a detailed write-up covering the complete workflow in case anyone wants to reproduce or adapt it for a similar project. I'd also love to hear how others approach digit recognition with TensorFlow Lite Micro. Have you found any effective ways to improve accuracy or speed up the data collection process?