Articles for category: AI Tools

Why Generalists Own the Future

The pace of AI development is moving extremely fast—so fast that it’s not uncommon for us to look back on what we wrote and see that the future we were describing is here. Dan Shipper‘s piece about the importance of generalists over specialists in the AI age from six months ago holds true more than ever. He reframes what it means to be a generalist—not just someone with shallow knowledge across multiple domains, but a curious, adaptable problem-solver who thrives in environments where rules are unclear and patterns aren’t obvious. In an allocation economy, the winners won’t be those who know

Introducing spaCy v3.4 · Explosion

We’re pleased to publish v3.4 of the spaCy Natural Language Processing library. spaCy v3.4 brings typing and speed improvements along with new vectors for English pipelines and new trained pipelines for Croatian. This release also includes prebuilt linux aarch64 wheels for all spaCy dependencies distributed by Explosion. Typing improvements spaCy v3.4 supports pydantic v1.9 and mypy 0.950+ through extensive updates to types in Thinc v8.1. Speed improvements For the parser, use C saxpy/sgemm provided by the Ops implementation in order to use Accelerate through thinc-apple-ops. Improved speed of vector lookups. Improved speed for Example.get_aligned_parse and Example.get_aligned. New trained pipelines Special

Total noob’s intro to Hugging Face Transformers

Welcome to “A Total Noob’s Introduction to Hugging Face Transformers,” a guide designed specifically for those looking to understand the bare basics of using open-source ML. Our goal is to demystify what Hugging Face Transformers is and how it works, not to turn you into a machine learning practitioner, but to enable better understanding of and collaboration with those who are. That being said, the best way to learn is by doing, so we’ll walk through a simple worked example of running Microsoft’s Phi-2 LLM in a notebook on a Hugging Face space. You might wonder, with the abundance of

Playwrite – Basics – DEV Community

_How to Install _ pip install pytest-playwright playwright install How to run a program If the file name starts without test*.py then just use this command. ex: file name myusecase.py $ pytest <>.py $ pytest myusecase.py Enter fullscreen mode Exit fullscreen mode If the file name starts with test<>.py then no need to specify the file name when running $ pytest Enter fullscreen mode Exit fullscreen mode If you want to see the execution in a browser. $pytest --headed --browser firefox Enter fullscreen mode Exit fullscreen mode Record the flow and copy the code $playwright codegen https://example.com Enter fullscreen mode

Introducing Holmes 4.0 · Explosion

A few weeks ago we released version 4.0 of Holmes, which we are now able to offer under a permissive MIT license. Holmes is a library in the spaCy Universe that runs on top of spaCy and enables information extraction and intelligent search, currently for English and German. Holmes goes beyond simple matching algorithms and allows you to look for a specified idea or ideas in a corpus of documents. Holmes offers two main search mechanisms. The first, structural matching, aims to find text snippets in a corpus that express a given idea exactly and is useful for extracting structured

Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval

We introduce the concept of embedding quantization and showcase their impact on retrieval speed, memory usage, disk space, and cost. We’ll discuss how embeddings can be quantized in theory and in practice, after which we introduce a demo showing a real-life retrieval scenario of 41 million Wikipedia texts. Table of Contents Why Embeddings? Embeddings are one of the most versatile tools in natural language processing, supporting a wide variety of settings and use cases. In essence, embeddings are numerical representations of more complex objects, like text, images, audio, etc. Specifically, the objects are represented as n-dimensional vectors. After transforming the

Skyren DAO: Advanced Yield Tokenization and Dynamic Airdrop Farming

Skyren DAO represents a significant leap forward in DeFi technology, combining advanced yield tokenization with dynamic airdrop farming. This post provides a technical deep dive into the innovative features and architecture of Skyren DAO. 1. Multi-Chain Architecture Skyren DAO leverages a sophisticated multi-chain architecture, primarily utilizing: Ethereum for robust security Polygon for enhanced scalability and cost-effectiveness This dual-chain approach enables: Cross-chain Interoperability Optimized Performance Cost Efficiency 2. Smart Contract Framework a) Yield Aggregator Contract pragma solidity ^0.8.0; import "@openzeppelin/contracts-upgradeable/proxy/utils/Initializable.sol"; import "@openzeppelin/contracts-upgradeable/access/OwnableUpgradeable.sol"; contract YieldAggregator is Initializable, OwnableUpgradeable { mapping(address => uint256) public userDeposits; mapping(address => uint256) public yieldAccrued; event Deposit(address indexed

Unified interface for Zero-Shot vision models in robotics

This is a guest blog post by the Pollen Robotics team. We are the creators of Reachy, an open-source humanoid robot designed for manipulation in the real world. In the context of autonomous behaviors, the essence of a robot’s usability lies in its ability to understand and interact with its environment. This understanding primarily comes from visual perception, which enables robots to identify objects, recognize people, navigate spaces, and much more. We’re excited to share the initial launch of our open-source pollen-vision library, a first step towards empowering our robots with the autonomy to grasp unknown objects. This library is