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NFT Paris 2025 brought community and innovation to life, but onboarding struggles show Web3 still has a long way to go for mass adoption.

Hosting all services in a single public cloud and relying solely on credential protection can cause server overloading and security vulnerabilities.

GraphQL is an often misunderstood framework that went from being one of the most popular development innovations to an unfairly maligned one.

Microsoft AI Red Team releases whitepaper detailing lessons from its 100 generative AI products. Security researchers have identified eight core security lessons from rigorous adversarial testing.

This guide offers a comprehensive walkthrough of response compression in ASP.NET Core. It explores what is response compression, when to use it, and how to set it up. It also analyzes the results before and after using the response compression middleware.

How are you, hacker? 🪐 What’s happening in tech today, February 20, 2025? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, we present you with these top quality stories. From Getting Started With Apples Vision Framework: A Developers Perspective to How to Obsidian Sync fo...

Traditional stock market APIs come with rate limits and high costs, so I built my own web scraper using Python. By extracting data from Yahoo Finance and CNN Money, I bypassed restrictions while maintaining flexibility. This guide covers setup, handling challenges like rate limiting and data validation, and future plans for AI-powered stock analysis.

Apex Fusion has launched its blockchain ecosystem with the AP3X token and PRIME chain, aiming to combine Bitcoin’s security with Ethereum’s programmability.

Berachain’s BERA token launch has reignited debates over fair vs. VC-backed token distributions, as insiders dumped holdings, triggering a price crash. This article explores how unfair allocations erode trust while fair launch models, like Bitcoin’s and exSat’s, foster transparency and community-driven growth.

Cognitive distortions are tricks our own brain sets for us. They can ruin our lives (and work, of course) and most importantly - how to deal with them. The most common are confirmation bias and herd instinct.

Web3 started in 2009 when Satoshi Nakamoto started the genesis block of Bitcoin. This article is a free-to-read whitepaper on the Web3 system. It will be split into two, with the latter part answering the “How can I make money from web3?” part of it.

Trust is the deciding factor for the success of your startup in the crypto industry. Users need to believe that you have their best interest in mind, and you need to show in every aspect the user has contact with. Every crypto project needs to define its niche early and make it explicit.

Katarina is a designer with 10+years of experience in the field. She is known for her bold, bright, and experimental mobile apps.

In this article, I would like to share the experience I gained over the years with unit testing in Angular.

Fast-Coresets offer competitive compression with minimal distortion, outperforming BICO and Streamkm++. Uniform sampling is fast but unreliable, breaking on outlier-heavy datasets.

Fast-Coresets are compared against uniform sampling, sensitivity sampling, and state-of-the-art methods like BICO and Streamkm++ to assess clustering efficiency.

Fast-Coresets provide reliable compression for clustering but are slower than uniform sampling. Experiments analyze when fast, inaccurate methods are viable.

By replacing log ∆ with log(log ∆), clustering computations become more efficient. A substitute dataset preserves accuracy while enabling faster processing.

Fast-kmeans++ and quadtree embeddings help compute coresets efficiently by quickly finding a rough solution. This reduces k-means complexity while maintaining accuracy.

We explore fast sampling methods for clustering, focusing on coresets that balance speed and accuracy. We discuss their role in MapReduce, database applications, and quadtree embeddings, showing how these techniques enable efficient clustering in large-scale data environments.

Clustering large datasets is slow, but compressing data first can help. We propose a near-optimal method for k-means and k-median clustering that runs almost as fast as reading the data. While simpler sampling methods exist, they risk accuracy loss, making our approach the best balance between speed and reliability.