Build a decentralized application (Dapp) for a charity marketplace. Learn Solidity, Ethereum, OpenZeppelin, Next.js, TypeScript, Hardhat, EthersJs, Redux-Toolki

Uncover the three main causes leading to objective mismatch and dive into investigations and potential solutions.

How Centralized is Decentralized? Comparison of Wealth Distribution in Coins and Tokens.

Making breaking changes without breaking the system.

Query expansion, cross-encoder re-ranking, and embedding adaptors

All about Python pickle module in one place

How abandoning my training strategy in favor of Garmin’s AI launched my running to new heights

In this week’s update we are bringing you the ultimate experience on Aisekai. This update will give a chance for our users to extend…

The “Cob” in “Cobweb,” the “Hodge” in “Hodgepodge,” and more lovely old oddities that still cling to life in compounds.

You won’t wanna miss it

When we begin a project, we tend to focus on things like scalability, usability, availability, security, and others. But, as the…

Service Workers and Progressive Web Apps

Welcome to the inaugural installment of our series on Frontend Myths! Throughout this series, I’ll be tackling prevalent misconceptions…

Embark on a journey of productivity and efficiency with these game-changing hacks for Kubernetes on your local Mac. As a seasoned Platform…

Dive into the nuances of human-human interactions in dialog systems, where politeness takes center stage.

The zero trust model centers around trusting nothing and verifying everything. While it may seem like overkill at first, it has several important benefits.

Delve into the core of dialog system experimentation, exploring hyperparameters and baseline results.

In the last decade, “microservices” architectures have come into fashion for a variety of reasons.

Embark on the journey of annotating dialog datasets, tasked with identifying user dissatisfaction, new concepts, corrections, and alternative responses.

With a focus on non-native speaker annotators, the study unveils the nuances in identifying errors, emphasizing differences across datasets.

A statistical analysis of automatic filtering's effectiveness in identifying errors within different similarity ranges.