LaunchGun is an AI-powered analytics platform that helps makers optimize their Product Hunt launches by providing real-time data-driven insights. It offers features like AI-powered launch analysis, success metrics dashboard, launch timing optimization, and competitive analysis. These tools enable users to make informed decisions, optimize launch timing, understand market trends, and benchmark their performance against top performers in their category.

MRGN is an advanced, AI-powered business intelligence platform designed to assist small and medium-sized enterprises by automating decision-making processes. The platform provides AI-driven benchmarks to compare business performance, simulate various financial scenarios, and deliver predictive insights about future risks and opportunities. This helps businesses allocate resources more effectively and make sound financial and operational decisions without needing a finance or operations degree.

Workviz transforms the way teams work by leveraging AI to analyze performance data, optimize efficiency, and foster team synergy. It integrates with existing workflows to automatically collect and analyze work logs, providing a comprehensive view of productivity. Workviz offers real-time insights, helping managers identify focus areas and drive continuous improvement. Its features also include setting benchmarks and analyzing patterns to identify top performers, thus maximizing the overall team potential.

ePH-MAPF provides an efficient pipeline for computing collision-free paths for dozens to hundreds of agents on grid-based maps. It uses prioritized heuristics, incremental search techniques, and customizable cost metrics (Manhattan, Euclidean) to balance speed and solution quality. Users can select between different heuristic functions, integrate the library into Python-based robotics systems, and benchmark performance on standard MAPF scenarios. The codebase is modular and well-documented, enabling researchers and developers to extend it for dynamic obstacles or specialized environments.

LLMs provides a unified abstraction over various open-source and hosted language models, allowing developers to load and run models through a single interface. It supports model discovery, prompt and pipeline management, batch processing, and fine-grained control over tokens, temperature, and streaming. Users can easily switch between CPU and GPU backends, integrate with local or remote model hosts, and cache responses for performance. The framework includes utilities for prompt templates, response parsing, and benchmarking model performance. By decoupling application logic from model-specific implementations, LLMs accelerates the development of NLP-powered applications such as chatbots, text generation, summarization, translation, and more, without vendor lock-in or proprietary APIs.

QueryCraft is a Python-based prompt engineering toolkit designed to streamline the development of AI agents. It enables users to define structured prompts through a modular pipeline, connect seamlessly to multiple LLM APIs, and conduct automated evaluations against custom metrics. With built-in logging of token usage and costs, developers can measure performance, compare prompt variations, and identify inefficiencies. QueryCraft also includes debugging tools to inspect model outputs, visualize workflow steps, and benchmark across different models. Its CLI and SDK interfaces allow integration into CI/CD pipelines, supporting rapid iteration and collaboration. By providing a comprehensive environment for prompt design, testing, and optimization, QueryCraft helps teams deliver more accurate, efficient, and cost-effective AI agent solutions.

RL-Agents is a research-grade reinforcement learning framework built on PyTorch that bundles popular RL algorithms across value-based, policy-based, and actor-critic methods. The library features a modular agent API, GPU acceleration, seamless integration with OpenAI Gym, and built-in logging and visualization tools. Users can configure hyperparameters, customize training loops, and benchmark performance with a few lines of code, making RL-Agents ideal for academic research, prototyping, and industrial experimentation.

Acme is a Python-based framework that simplifies the development and evaluation of reinforcement learning agents. It offers a collection of prebuilt agent implementations (e.g., DQN, PPO, SAC), environment wrappers, replay buffers, and distributed execution engines. Researchers can mix and match components to prototype new algorithms, monitor training metrics with built-in logging, and leverage scalable distributed pipelines for large-scale experiments. Acme integrates with TensorFlow and JAX, supports custom environments via OpenAI Gym interfaces, and includes utilities for checkpointing, evaluation, and hyperparameter configuration.