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Read-only Mirror - no pull request (use https://gerrit.libreoffice.org instead)
#1 Locally hosted web application that allows you to perform various operations on PDF files
Convert PDF files into editable slides with three lines of code.
⚓ GROWI - Team collaboration software using markdown
Open Source DeepWiki: AI-Powered Wiki Generator for GitHub/Gitlab/Bitbucket Repositories. Join the discord: https://discord.gg/gMwThUMeme
Agent that generates DSL(yaml) for dify workflow
Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.
💥 A Lodash-style Go library based on Go 1.18+ Generics (map, filter, contains, find...)
Python tool for converting files and office documents to Markdown.
👋 Puncc is a python library for predictive uncertainty quantification using conformal prediction.
Designed a mechanism for on-demand resource resizing of existing containers. This repository contains deploy code and analysis code for paper "Tiny Autoscalers for Tiny Workloads: Dynamic CPU Alloc…
Forked from a technical test I did. Demonstrates the use of communication between a Celery worker and a the frontend. Not my best work, but it seems to be stared by a few people.
A scikit-learn-compatible library for estimating prediction intervals and controlling risks, based on conformal predictions.
A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.
An open-source RAG-based tool for chatting with your documents.
cluster data collected from production clusters in Alibaba for cluster management research
A modern JavaScript utility library that's 2-3 times faster and up to 97% smaller—a major upgrade to lodash.
Quantile Regression Forests compatible with scikit-learn.
nnao45 / cqr
Forked from yromano/cqrConformalized Quantile Regression
nnao45 / Deep_Learning_Prediction_Intervals
Forked from TeaPearce/Deep_Learning_Prediction_IntervalsICML paper 'High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach'