「硬地骇客 - 两个月 $12000 ARR 实践之路」是由 硬地骇客 团队编著，本书是关于 Podwise 产品历程的忠实记录：内容包含 灵感 - 构建 - 发布 - 增长 - 复盘 五个章节。如果你觉得一个人读不够过瘾，欢迎加入「硬地骇客」官方知识星球与专家们一起讨论！Podwise 的故事才刚刚开始，我们也将在星球持续分享我们的认知，成功可能无法复制，但失败一定可以借鉴。现在就点击下方链接加入吧！

A general purpose recommender metrics library for fair evaluation.

💸 Calculate the MRR, ARR, churn, LTV, ARPU, total revenue & valuation of your pay-powered Rails SaaS app.

Another database of verified startup revenues

Official codebase for the ACL 2025 Findings paper: Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval.

MRRPorn is an art project made for fun to make you smile

Structured approach in AI and ML. Fundamentals and Advanced topics. RAG, Scoring & Profiling, LangChain & LangGraph, Certified Azure AI Engineer materials.

Apache Pig UDFs for ranking (ndcg, mrr, jaccard coefficient, cosine similarity, rank-biased overlap)

Open source iOS app revenue leaderboard powered by RevenueCat. Track and compare MRR and revenue metrics from verified apps.

Korean-optimized RAG evaluation toolkit with Kiwi tokenizer, ROUGE metrics, and IR evaluation for retrieval systems (Hit@K, NDCG@K, MRR, etc.)

Program that use a direct graph and the PageRank algorithm (both simple and weighted). Use of the TF-IDF algorithm to calculate the relevance of the terms of the document collection.

GMD is a lightweight SaaS metrics dashboard that gives founders instant clarity on their business performance. Track MRR, ARR, churn, CAC, LTV, and more through automated data ingestion, Stripe integration, and CSV uploads in an all in one clean, easy-to-use interface.

Fast implementation of the MRR ranking metric

Turn your project ideas into sustainable businesses with structured missions, weekly strategy check-ins, and data-driven guidance.

Find mentors making +$10k MRR targeted to your market, stage and experience and fulfill your startup dreams. - Get equity in early-stage startups by mentoring their founders to find product-market fit.

Small demo project for evaluating RAG retrieval quality using a simple BM25-style retriever and standard metrics (Hit@k, Recall@k, MRR).

🎬 This repository focuses on building a personalized movie recommender system for ZEE5 using collaborative filtering, content-based methods, and matrix factorization, Pearson correlation, Suprise SVD techniques to deliver accurate, user-relevant recommendations & evaluated through NDCG, MRR, MAPE

SQL-based revenue metrics analysis with monthly segmentation, churn tracking, and MRR dynamics visualization. Project includes an interactive dashboard built in Tableau and fully documented SQL logic.

Design, Implementation and Evaluation of various Search Engines

A systematic growth framework for indie founders to reach $10k monthly recurring revenue through evidence-based experimentation and consistent progress.