TutorLoom
TutorLoom is an open-source, self-hosted learning management system built with Django, React, and Flutter, featuring course management, quizzes, grading, progress tracking, and multi-tenant support.
https://github.com/pacewisdomhq/TutorLoom
TutorLoom is an open-source, self-hosted learning management system built with Django, React, and Flutter, featuring course management, quizzes, grading, progress tracking, and multi-tenant support.
https://github.com/pacewisdomhq/TutorLoom
GitHub
GitHub - pacewisdomhq/TutorLoom: Self-hosted, multi-tenant learning management system (LMS) for training institutes and teams:…
Self-hosted, multi-tenant learning management system (LMS) for training institutes and teams: course builder, trainer and student portals, progress tracking. Django + React. Apache-2.0. - pacewisdo...
How to Run Turing-Complete Malware in PyTorch's Backward Pass
Elon Litman demonstrates how a malicious PyTorch loss function can report a value of zero while secretly manipulating model weights through the backward pass. By exploiting differences between floating-point arithmetic and automatic differentiation, he shows how gradient descent can execute arbitrary computations inside a neural network, creating a potential attack vector for AI training...
https://elonlit.com/scrivings/how-to-run-turing-complete-malware-in-pytorchs-backward-pass/
Elon Litman demonstrates how a malicious PyTorch loss function can report a value of zero while secretly manipulating model weights through the backward pass. By exploiting differences between floating-point arithmetic and automatic differentiation, he shows how gradient descent can execute arbitrary computations inside a neural network, creating a potential attack vector for AI training...
https://elonlit.com/scrivings/how-to-run-turing-complete-malware-in-pytorchs-backward-pass/
elonlit.com | Elon Litman
How to Run Turing-Complete Malware in PyTorch's Backward Pass
Turning PyTorch's backward pass into a Turing-complete virtual machine using a floating-point exploit.
lightseekorg / tokenspeed
TokenSpeed is a speed-of-light LLM inference engine.
https://github.com/lightseekorg/tokenspeed
TokenSpeed is a speed-of-light LLM inference engine.
https://github.com/lightseekorg/tokenspeed
GitHub
GitHub - lightseekorg/tokenspeed: TokenSpeed is a speed-of-light LLM inference engine.
TokenSpeed is a speed-of-light LLM inference engine. - lightseekorg/tokenspeed
django-lifecycle - hooks for Django models (Signals alternative)
Learn how to usedjango-lifecycleas an alternative to Django signals, with declarative hooks that run when models are created, updated, deleted, or saved. The package also supports conditional hooks that trigger only when specific fields change or transition between particular values.
https://www.youtube.com/watch?v=sNdopNFOEB8
Learn how to usedjango-lifecycleas an alternative to Django signals, with declarative hooks that run when models are created, updated, deleted, or saved. The package also supports conditional hooks that trigger only when specific fields change or transition between particular values.
https://www.youtube.com/watch?v=sNdopNFOEB8
YouTube
django-lifecycle - hooks for Django models (Signals alternative)
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🙏 Join our channel to get access to perks:
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☕️ 𝗕𝘂𝘆 𝗺𝗲 𝗮 𝗰𝗼𝗳𝗳𝗲𝗲:
To support the channel…
Decoding base64 in Python at 10 gigabytes per second
Daniel Lemire introduces fastpybase64, a Python library built on SIMD-accelerated simdutf that provides a drop-in alternative to Python's standard base64 module. In single-threaded benchmarks decoding Base64 data containing line breaks, it achieves over 13 GB/s throughput, approximately 27 times faster than the standard library and four times faster than pybase64.
https://lemire.me/blog/2026/10/17/decoding-base64-in-python-at-10-gigabytes-per-second/
Daniel Lemire introduces fastpybase64, a Python library built on SIMD-accelerated simdutf that provides a drop-in alternative to Python's standard base64 module. In single-threaded benchmarks decoding Base64 data containing line breaks, it achieves over 13 GB/s throughput, approximately 27 times faster than the standard library and four times faster than pybase64.
https://lemire.me/blog/2026/10/17/decoding-base64-in-python-at-10-gigabytes-per-second/
Daniel Lemire's blog
Decoding base64 in Python at 10 gigabytes per second
We sometimes need to convert binary data to text. For example, your emails must be stored as text files. Naïvely, you could just use a string of 0 and 1, but it would mean using one character per bit. We can do better by using 64 ASCII characters: all letters…