~hackernoon | Bookmarks (1961)

This study compares mutation testing methods, showing LLMs generate more diverse mutations but at higher cost and slower speed, while traditional methods are faster with better usability.

This study defines cost, usability, and behavior metrics to evaluate LLM-generated Java mutations and describes experimental settings for testing their effectiveness.

This study evaluates how various LLMs generate Java code mutations through carefully designed prompts, comparing open and closed-source models for mutation testing.

The study evaluates LLMs like GPT-4 and CodeLlama for mutation testing using real Java bugs. It defines 5 research questions and leverages Defects4J and ConDefects datasets.

Traditional mutation testing is evolving—this section reviews how LLMs like GPT-4 and CodeLlama generate high-utility, cost-effective, and natural code mutations.

This study shows that GPT-4 and other LLMs can generate diverse, fault-revealing code mutations in Java, outperforming traditional mutation testing tools by 18%.

Coral Protocol co-founders dive into agent composability, interoperability, and the Internet of Agents. A must-read for AI and Web3 builders.

AI is evolving from assistant to economic agent; those who adapt early and leverage this shift will gain a significant edge in global markets.

Many-shot ICL enables quick model adaptation without fine-tuning, improving accessibility. Future work: other tasks, open models, bias reduction.

Gemini 1.5 Pro shows log-linear gains up to ~1K examples (+38% accuracy). Batching reduces costs 45x and latency 35x with minimal performance loss.

Evaluates GPT-4o vs Gemini 1.5 Pro on 10 vision datasets with many-shot ICL, using stratified sampling and standard accuracy/F1 metrics.

Many-shot multimodal ICL with thousands of examples improves LMM performance. Gemini 1.5 Pro shows log-linear gains; batching reduces costs.

InteraSSort demo using Ta-Feng grocery dataset with MNL model and GPT-3.5-turbo, showing conversational optimization with constraint handling.

This week, HackerNoon features Leobit—a .NET, AI, and web application development provider for technology companies and startups.

This article explores how principles like emergence, decomposition, and multi-agent systems (MAS) can transform AI from complex, monolithic prompts into structured, scalable, and testable architectures. Drawing from natural and software systems like ants, Conway’s Game of Life, and Apache Kafka, it argues for smarter AI system design through modularity and clear boundaries.

InteraSSort framework: prompt design → decomposition → tool execution for interactive assortment optimization with multi-turn conversation support.

Reviews AI in marketing (chatbots, personalization) and LLM tool integration frameworks (TaskMatrix, HuggingGPT, Optiguide) for assortment optimization context.

InteraSSort combines LLMs with optimization tools for interactive assortment planning, enabling store planners to optimize via natural language.

Yes, AI-assisted writing prompts is easier, but so is avoiding the gym. Sometimes you need to train your body by doing something hard. Writing by hand forces you to think for yourself.

Choosing the right tech stack is a pivotal decision that sets the trajectory of your product's future. Align tech stacks with your actual project scope: Are you building an MVP or enterprise-grade SaaS? A data-heavy AI platform or a real-time collaboration tool? In this article, we examine the tech stacks that best match common product types.

The failures of centralized cloud storage providers demonstrate the inherent risks of entrusting data to a single entity. Vendor lock-in creates financial and technical barriers, restricting users from exercising full control over their own data and preventing developers from seeking out alternatives. Traditional cloud storage models are built on artificial pricing structures, centralized infrastructure, and proprietary technology, all designed to keep users trapped. Sia...

Knowing when to pivot can be vital to staying ahead of the competition. See how Cursor AI and Heroku can be leveraged to transform an idea into a reality.

Defending against deepfake scams doesn’t necessarily mean being paranoid. Rather, it requires recognizing patterns, training staff to recognize red flags, and e