Rate limit auto-configure for Spring Cloud Netflix Zuul

A Home Assistant integration to track your load schedding schedule.

Custom Shelly Script Examples for Gen2 and Gen3 products

Request interceptor which improves the service time of cloud services by controlling GC interventions.

Python asyncio tools for web service resilience

Adaptive backpressure and load shedding for Go applications

A focused async bulkhead for Java that limits in-flight work and makes overload visible.

The objective of this project is to develop a smart energy management system that optimizes and extracts energy from various sources during load shedding situations. Load shedding refers to the deliberate shutdown of power in specific areas to prevent an overload on the electrical grid.

Lightweight fault tolerance library for Python asyncio

A lifo job queue to provide back pressure and load shedding

The Shelly Green Grid Guardian project seeks to alleviate overloads in the electrical power network through the redistribution and reduction of domestic electricity consumption.

Discord bot that monitors EcoFlow power stations in real time via MQTT. Get notified when grid power drops or returns, check battery status, and toggle AC/USB/DC outputs

Resilience Toolkit: A library for building fault-tolerant systems with Circuit Breakers and Load Shedders. Optimize reliability, handle failures gracefully, and manage high-load scenarios effectively.

Home Assistant custom integration for monitoring WAPDA electricity feeders via the Roshan Pakistan (CCMS PITC) portal — real-time feeder status, load shedding schedules, billing, net metering, and more.

Adaptive rate limiting based on PID control. A feedback-loop approach to stabilizing latency and throughput in Node.js applications.

Exploratory analysis of South Africa's economy 2000–2023 — GDP, load-shedding impact, debt, trade balance and unemployment using Python, Pandas and Matplotlib.

A low-latency, adaptive load shedding library for backend services. Protects latency-sensitive services by rejecting or degrading low-priority traffic under overload, ensuring Service Level Objectives (SLOs) are maintained.

Fail-fast admission control for async systems. Reject overload early instead of hiding it behind queues.

A Java Spring boot API to create a To-Do list app using Firestore as the DB with these patterns implemented : DTO, DAO, logging, caching, load shedding, rate limiting, time limiting, circuit breaking, retrying, Error handling

Fail-fast concurrency and token budget enforcement for LLM workloads.