Mebus / cupp

Common User Passwords Profiler (CUPP)

https://github.com/Mebus/cupp

Fstache

Fast dependency-free, typed Mustache renderer for Python 3.12+

https://github.com/servletcloud/fstache

ProtoLink

Build autonomous Python agents with native Agent-to-Agent (A2A) communication.

https://github.com/nMaroulis/protolink

tempolocus

Tempolocus is a time-series activity patterns and approximate location inference

https://github.com/ail-project/tempolocus

Ban commits/transactions using AST analysis and linters

The post explains why database commits and transactions should be owned only by the DB access layer, because hidden commits and leaking DB models can silently break atomicity or lose data. It shows how to enforce those boundaries with AST tests, flake8 rules, and a narrow LLM review step to catch DB models being returned where domain models should be used.

https://www.droppedasbaby.com/posts/db-commits/

Ossie

Apache Ossie is a vendor-neutral standard for sharing semantic metadata across analytics, AI, and BI tools. Its goal is to define metrics and business meaning once, then reuse them everywhere.

https://github.com/apache/ossie

yifanfeng97 / Hyper-Extract

Hypergraph is more powerful. Transform unstructured text into structured knowledge with LLMs. Graphs, hypergraphs, and spatio-temporal extractions — with one command.

https://github.com/yifanfeng97/Hyper-Extract

Python 3.15’s Ultra-Low Overhead Interpreter Profiling Mode

The post explains how CPython 3.15’s JIT uses a low-overhead interpreter profiling mode based on swapping dispatch tables instead of running a separate profiling interpreter or adding branches to the normal interpreter. It shows how this “dual dispatch” design lets Python record execution traces for JIT compilation with much lower overhead, while raising the tradeoff between performance ...

https://fidget-spinner.github.io/posts/ultra-fast-tracing.html

WrenAI

Open-source GenBI: generative BI for AI agents.

https://github.com/Canner/WrenAI

In-House LLM Serving at Netflix

Netflix built an in-house LLM serving platform using vLLM and NVIDIA Triton, integrating self-hosted models into its existing production infrastructure through unified gRPC and OpenAI-compatible APIs. The article details production lessons around model packaging, version compatibility, zero-downtime deployments, observability, and scaling constrained decoding by moving bottlenecks from s...

https://netflixtechblog.com/in-house-llm-serving-at-netflix-a5a8e799ea2c

google / agents-cli

The CLI and skills that turn any coding assistant into an expert at creating, evaluating, and deploying AI agents on Google Cloud.

https://github.com/google/agents-cli

Reusable scenario based test framework

Write your tests once. Run them on every product variant, device or technology — without rewriting a single line.

https://github.com/balder-dev/balder

Building Agentic Workflows in Python with LangGraph

In this article, you will learn how to build a complete agentic workflow in Python with LangGraph, from a single model call to a tool-using agent with persistent conversation memory.

https://machinelearningmastery.com/building-agentic-workflows-in-python-with-langgraph/

xarray-sql

An experiment to query Xarray datasets with SQL.

https://github.com/xqlsystems/xarray-sql

venv_manager

A powerful CLI tool for managing Python virtual environments with ease.

https://github.com/jacopobonomi/venv_manager

Django: introducing django-crawl

Adam Johnson introduces django-crawl, a tool that uses Django’s test client to crawl a site and uncover broken links, exceptions, and other hidden issues without making real HTTP requests. It can run as a management command or automated test, providing a fast safety net that found seven bugs even in a project with 100% test coverage.

https://adamj.eu/tech/2026/07/22/introducing-django-crawl/

Mage

A Lightweight, Research-Friendly Multimodal Model Family.

https://github.com/microsoft/Mage

LLVMLITE in the Browser

This notebook explores how LLVM's optimization pipeline transforms a simple loop, step by step, using llvmlite running entirely in the browser via WebAssembly. We follow one small C++ function through three distinct stages — from source to raw stack-based IR (Intermediate Representation), through SSA form, to a closed-form calculation. After each stage, we inspect the control flow graph ...

https://notebook.link/@anutosh491/llvmlite