Popular GitHub Repositories for Layer Drop

Drag & drop UI to build your customized LLM flow

The Memory layer for AI Agents

Low-code platform for building business applications. Connect to databases, cloud storages, GraphQL, API endpoints, Airtable, Google sheets, OpenAI, etc and build apps using drag and drop application builder. Built using JavaScript/TypeScript. ?

:robot: The free, Open Source alternative to OpenAI, Claude and others. Self-hosted and local-first. Drop-in replacement for OpenAI, running on consumer-grade hardware. No GPU required. Runs gguf, transformers, diffusers and many more models architectures. Features: Generate Text, Audio, Video, Images, Voice Cloning, Distributed, P2P inference

An AI-powered task-management system you can drop into Cursor.

This repository delivers end-to-end, code-first tutorials covering every layer of production-grade GenAI agents, guiding you from spark to scale with proven patterns and reusable blueprints for real-world launches.

Supercharge Your LLM with the Fastest KV Cache Layer

End-to-end Automatic Speech Recognition for Madarian and English in Tensorflow

Keras Attention Layer (Luong and Bahdanau scores).

LiveStore is a next-generation state management framework based on reactive SQLite and built-in sync engine.

A curated set of 200 plug-and-play n8n workflows that fuse classic automation with today’s AI stack—vector DBs, embeddings, and LLMs. Import any JSON, add your creds, hit Activate, and you’re live. Built to demo, prototype, or drop straight into production.

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)

Multi-layer Recurrent Neural Networks (LSTM, RNN) for word-level language models in Python using TensorFlow.

My first Python repo with codes in Machine Learning, NLP and Deep Learning with Keras and Theano

No-code AI workflow. Drag and drop workflow nodes and use your workflow with your AI agents.

Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

An AI memory layer with short- and long-term storage, semantic clustering, and optional memory decay for context-aware applications.

Implementation of the Point Transformer layer, in Pytorch

Draw multi-layer neural network in your browser

Building Convolutional Neural Networks From Scratch using NumPy