#cplusplus#arduino#ble_jammer#ble_spoof#ble_spoofer#cybersecurity#deauther#esp32#hack#hacktoberfest#jammer#nrf_scanner#nrf24l01#sour_apple
nRFBOX is a handheld ESP32-based tool that scans and analyzes the 2.4 GHz band (Wi‑Fi, BLE, etc.), shows signal strength and channel activity, and can run jamming, BLE jamming/spoofing, and Wi‑Fi deauthentication tests for security research and troubleshooting. It combines an ESP32, NRF24 modules, OLED display, battery management, and SD support for firmware and logging, with notes about limited range, device variability, and power limits when using multiple NRF modules. Benefit: you can use it to find crowded channels, diagnose wireless interference, and test network/device resilience in controlled, legal test environments.
https://github.com/cifertech/nRFBox
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Zeus New Pytorch Ecosystem Tool
Zeus is an open source toolkit for measuring and optimizing power consumption of deep learning workloads.
🖥Github
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Main channel: @repo_science
Coupons: @freecoupons_reposcience
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#dl
Park, Chanwook, Sourav Saha, Jiachen Guo, Hantao Zhang, Xiaoyu Xie, Miguel A. Bessa, Dong Qian, et al. 2025. “Unifying Machine Learning and Interpolation Theory via Interpolating Neural Networks.” Nature Communications 16 (1): 1–12.
https://www.nature.com/articles/s41467-025-63790-8
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A few cool ideas in this model.
Introducing Gemma 3n: The developer guide - Google Developers Blog
https://developers.googleblog.com/en/introducing-gemma-3n-developer-guide/
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There is this new lib called scale. One could compile CUDA code to use it on AMD GPU.
https://docs.scale-lang.com/manual/how-to-use/
I don't know who is more pissed off, NVidia or AMD.
#dl
This repo is really nice.
yuanchenyang/smalldiffusion: Simple and readable code for training and sampling from diffusion models
https://github.com/yuanchenyang/smalldiffusion
#dl
Google & USC benchmarked a prompt based forecasting method, and the results are amazing.
Cao D, Jia F, Arik SO, Pfister T, Zheng Y, Ye W, et al. TEMPO: Prompt-based Generative Pre-trained Transformer for time series forecasting. arXiv [cs.LG]. 2023. Available: http://arxiv.org/abs/2310.04948