The #1 AI Harness for Building Resumes, PDFs, Cover Letters & more, locally with 100+ LLMs support.
- text-similarity
- natural-language-processing
- word-embeddings
- python
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The #1 AI Harness for Building Resumes, PDFs, Cover Letters & more, locally with 100+ LLMs support.
☁️ Build multimodal AI applications with cloud-native stack
Low-code framework for building custom LLMs, neural networks, and other AI models
Hackable and optimized Transformers building blocks, supporting a composable construction.
Build, Manage and Deploy AI/ML Systems
A Python scikit for building and analyzing recommender systems
Build Graph Nets in Tensorflow
Lightweight library to build and train neural networks in Theano
Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds
TensorFlow Recommenders is a library for building recommender system models using TensorFlow.
TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform.
A Theano framework for building and training neural networks
🧠+🎧 Build your music algorithms and AI models with the next-gen DAW 🔥
Build fully-functioning computer vision models with PyTorch
Rebuild a reference video as an editable HyperFrames project, compare the match against the original, and export an MP4.
NNDial is an open source toolkit for building end-to-end trainable task-oriented dialogue models. It is released by Tsung-Hsien (Shawn) Wen from Cambridge Dialogue Systems Group under Apache License 2.0.
This is the official repo for paper InteractiveWorld A Verifiable Human-Centric Environment for Building Better Interactive MLLM Assistants
Kraken CI is a continuous integration and testing system.
AFI VISION BUILDER
Build and deploy a serverless data pipeline on AWS with no effort.
Friendly Environment for Neural Networks (fenn) is a simple Python framework for building ML/DL workflows and LLM agents faster, with prebuilt trainers, agent templates, logging, configuration management, and much more.
Trust metadata for AI agents — a stamp costs ~15 tokens, re-verifying costs 15,000. Agents stamp what they verify; the next agent runs 'akf check' and builds on it. pip install akf