The definitive list that answers "I want to do X in Python, which tool should I use?"
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🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Open source software that helps you create and deploy high-frequency crypto trading bots
Self-evolving agent: grows skill tree from 3.3K-line seed, achieving full system control with 6x less token consumption
Emulator for rapid prototyping of Software Defined Networks
Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.
Navigate to variable's definition with a click in JupyterLab (or with a few key strokes)
BatchFlow helps you conveniently work with random or sequential batches of your data and define data processing and machine learning workflows even for datasets that do not fit into memory.
Build autonomous AI agents that interact with blockchains — in minutes, not months.
Mock an SSH server and define all commands it supports (Python, Twisted)
trycast parses JSON-like values whose shape is defined by TypedDicts and other standard Python type hints.
HIP-3 perp DEX on Hyperliquid for Solana memecoins graduating from pump.fun. GRAD index perp from $500K, own perps for coins that hold.
The application allows you to trade on the outcomes of events, and follow the odds to garner accurate insights about the future. Users buy or sell Voting Shares, which can be redeemed for 1 unit of the stable asset if the outcome is resolved as correct, and become worthless if it’s incorrect.
Declarative data quality engine. Define checks in YAML, run anywhere.