March 2026

:::info Astounding Stories of Super-Science, October, 2022, by Astounding Stories is part of HackerNoon’s Book Blog Post series. Title: Astounding Stories of Super-Science, October, 2022 Author: Astounding Stories Release Date: October 2, 2022 [eBook #69087] Updated: November 19, 2024 Language: English THE MURDER OF ROGER ACKROYD ::: TABLE OF LINKS DR. SHEPPARD AT THE BREAKFAST TABLE WHO’S WHO IN KING’S ABBOT THE MAN WHO GREW VEGETABLE MARROWS DINNER AT FERNLY MURDER THE TUNISIAN DAGGER I LEARN MY NEIGHBOR’S PROFESSION […]

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:::info Astounding Stories of Super-Science, July, 2008, by Astounding Stories is part of HackerNoon’s Book Blog Post series. Title: Astounding Stories of Super-Science, July, 2008 Author: Astounding Stories Release Date: July 2, 2008 [eBook #215] Updated: August 4, 2024 Language: English The Call of the Wild ::: TABLE OF LINKS Into the Primitive The Law of Club and Fang The Dominant Primordial Beast Who Has Won to Mastership The Toil of Trace and Trail For the Love of a […]

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Federated learning (FL) enables a set of distributed clients to jointly train machine learning models while preserving their local data privacy, making it attractive for applications in healthcare, finance, mobility, and smart-city systems. However, FL faces several challenges, including statistical heterogeneity and uneven client participation, which can degrade convergence and model quality. In this work, we propose FedPBS, an FL algorithm that couples complementary ideas from FedBS and FedProx to address these challenges. FedPBS dynamically adapts batch sizes […]

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Training deep neural networks with noisy labels remains a significant challenge, often leading to degraded performance. Existing methods for handling label noise typically rely on either transition matrix, noise detection, or meta-learning techniques, but they often exhibit low utilization efficiency of noisy samples and incur high computational costs. In this paper, we propose a self-training label correction framework using decoupled bilevel optimization, where a classifier and neural correction function co-evolve. Leveraging a small clean dataset, our method employs […]

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OpenClaw is the current name for the project previously known as Clawdbot → Moltbot → OpenClaw. If you’ve been waiting for a “one guide that actually works” — this is it. OpenClaw is powerful for a simple reason: it’s not another chat UI. It’s a Gateway that sits on a machine you control, connects to the messaging apps you already use, and routes your messages to an agent (Pi or other providers) that can take real actions: run […]

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:::info Astounding Stories of Super-Science July, 2008, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. The Call of the Wild – Into the Primitive Astounding Stories of Super-Science July 2008: The Call of the Wild – Into the Primitive By Jack London ::: Old longings nomadic leap, n Chafing at custom’s chain; n Again from its brumal sleep n Wakens the ferine strain. Buck did not […]

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For most of the internet’s history, our online identity has never truly belonged to us. Instead, it lives inside the platforms we use every day. Social media accounts, gaming profiles, email addresses, and even work tools are all controlled by centralized companies. If one of those platforms decides to suspend an account or shut down a service, years of digital history can disappear overnight. This is one of the problems that Web3 is trying to rethink. Web3 is […]

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Physical automation integrates artificial intelligence and hardware to create autonomous machines that can perceive their surroundings, perform complex actions, and act independently. Startups can embed machine learning models into robots, sensors, drones, and vehicles to unlock new, lucrative applications. The Synergy Between AI and Physical Automation The synergy between AI and physical systems is undeniable. Advanced algorithms let machines perceive their environment and interact with their surroundings, bridging the gap between the physical and digital worlds. This intersection […]

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Many large language model applications require conditioning on long contexts. Transformers typically support this by storing a large per-layer KV-cache of past activations, which incurs substantial memory overhead. A desirable alternative is ompressive memory: read a context once, store it in a compact state, and answer many queries from that state. We study this in a context removal setting, where the model must generate an answer without access to the original context at inference time. We introduce GradMem, […]

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