#python#cybersecurity#fyp#hacking#hacking_tool#indonesia#information#information_gathering#ip_geolocation#linux#osint#osint_python#osint_tool#pentesting#phone_number#python#python_hacking#termux#termux_hacks#termux_tool
GhostTrack is a simple tool you can install on Linux or Termux to track locations, phone numbers, or social media usernames using open-source intelligence (OSINT). It offers menus for IP tracking (which can be combined with another tool called Seeker), phone number tracking, and username tracking on social media. This helps you gather information about a target’s location or identity easily. The benefit is that you can quickly find useful data for security, investigation, or personal knowledge without needing advanced skills, all through a straightforward Python-based program created by HunxByts.
https://github.com/HunxByts/GhostTrack
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El aprendizaje automático es un vasto campo con muchos conceptos clave que conocer. Nuestro curso intensivo cubre todos los componentes básicos que necesita para sumergirse en el aprendizaje automático del mundo real.
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What’s Really Going On in Machine Learning? Some Minimal Models—Stephen Wolfram Writings
https://writings.stephenwolfram.com/2024/08/whats-really-going-on-in-machine-learning-some-minimal-models/
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Meta's second version of segment anything.
https://github.com/facebookresearch/segment-anything-2
They have a nice demo:
https://sam2.metademolab.com/
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I was searching for a tool to visualize computational graphs and ran into this preprint. The hierarchical visualization idea is quite nice.
https://arxiv.org/abs/2212.10774
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Like a dictionary
Kunc, Vladim’ir, and Jivr’i Kl’ema. 2024. “Three Decades of Activations: A Comprehensive Survey of 400 Activation Functions for Neural Networks.” arXiv [Cs.LG], February. http://arxiv.org/abs/2402.09092.
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I got interested in satellite data last year and played with it a bit. It's fantastic. The spatiotemporal nature of it brings up a lot of interesting questions.
Then I saw this paper today:
Rolf, Esther, Konstantin Klemmer, Caleb Robinson, and Hannah Kerner. 2024. “Mission Critical -- Satellite Data Is a Distinct Modality in Machine Learning.” arXiv [Cs.LG], February. http://arxiv.org/abs/2402.01444.
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Jelassi S, Brandfonbrener D, Kakade SM, Malach E. Repeat after me: Transformers are better than state space models at copying. arXiv [cs.LG]. 2024. Available: http://arxiv.org/abs/2402.01032
Not surprising at all when you have direct access to a long context. But hey, look at this title.