Fast, flexible and easy to use probabilistic modelling in Python.
- python
- machine-learning
- probabilistic-graphical-models
- pytorch
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Fast, flexible and easy to use probabilistic modelling in Python.
pySLAM is a hybrid Python/C++ Visual SLAM pipeline supporting monocular, stereo, and RGB-D cameras. It provides a broad set of modern local and global feature extractors, multiple loop-closure strategies, a volumetric reconstruction module, integrated depth-prediction models, and semantic segmentation capabilities for enhanced scene understanding.
Tool to look for several security related Android application vulnerabilities
Python Toolkit for Causal and Probabilistic Reasoning
StellarGraph - Machine Learning on Graphs
RESTler is the first stateful REST API fuzzing tool for automatically testing cloud services through their REST APIs and finding security and reliability bugs in these services.
Create randomly insecure VMs
Algorithms for explaining machine learning models
Network Automation and Programmability Abstraction Layer with Multivendor support
a generalist algorithm for cellular segmentation with human-in-the-loop capabilities
Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications. Tracing · Evals · Simulations · Datasets · Gateway · Guardrails. Self-hostable. Apache 2.0.
Chaos Engineering Toolkit & Orchestration for Developers
CVE-2026-109618/109619 — 5 RCE chains + detection rules (Suricata, Sigma, ModSecurity). PHP session.upload_progress pre-auth exploit research.
Steering-first neuron explorer for Qwen3.5-4B: push every MLP neuron, keep every rollout, browse the results
Highly available elephant herd: HA PostgreSQL cluster using Docker
The Offensive Manual Web Application Penetration Testing Framework.
A self-hosted habit tracking app without "Goals"
Interpretability and explainability of data and machine learning models
RDP monster-in-the-middle (mitm) and library for Python with the ability to watch connections live or after the fact
KQL Queries. Defender For Endpoint and Azure Sentinel Hunting and Detection Queries in KQL. Out of the box KQL queries for: Advanced Hunting, Custom Detection, Analytics Rules & Hunting Rules.
Deep probabilistic analysis of single-cell and spatial omics data
[UNMAINTAINED] A goal, task & habit tracker + personal dashboard to focus on what matters
Open decision models from 0.8B to 27B: a calibrated probability for every option in one pass, and a short thought only when unsure. Engine for vLLM, llama.cpp and MLX.
The Python ensemble sampling toolkit for affine-invariant MCMC