AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
The results indicate that if the OpenAPI specification is kept small and focused, LLM-based multi-agent systems are capable of generating complete functional code with business logic that aligns to the specification.
This work proposes and develops a multi-model unified platform to generate and execute code based on natural language prompts and presents practitioners feedback and insights into the use of LLMs in software development, including their strengths and weaknesses, key aspects overlooked by benchmarks and metrics.
A system that uses Large Language Models (LLMs) to automate the API-first development of RESTful microservices and assists in creating OpenAPI specification, generating server code from it, and refining the code through a feedback loop that analyzes execution logs and error messages is presented.
An LLM-based multi-agent system is indicated that an LLM-based multi-agent system is a capable solution to update components of a legacy application autonomously.
A large language models based multi-agent system enables precise task execution and inter-agent collaboration, addressing the challenges of refactoring in functional programming.
This study presents a comprehensive empirical evaluation of 20 open-source Small Language Models and reveals that several compact SLMs achieve competitive results while maintaining a balance between performance and efficiency, making them viable for deployment in resource-constrained environments.
An LLM-based multi-agent system that autonomously upgrades legacy web applications to the latest versions and maintains context across tasks and agents, improving solution quality over the base model in some cases is proposed.
This article analyzes the flow-debt tradeoffs associated with VC and identifies and explains how current model, platform, and hardware limitations contribute to these issues, and proposes countermeasures to address them, informing research and practice towards more sustainable VC approaches.
This study investigates how employees in a energy company understand AI adoption and identifies areas where AI and LLMs-based agentic workflows could assist daily activities, including reporting work, forecasting, data handling, maintenance-related tasks, and anomaly detection.
This is one of the first reviews to integrate peer-reviewed and grey literature on vibe coding under a single documented protocol and is strongest for prototyping and user-interface work and weakest for production, data-intensive, and safety-critical use, and tool visibility does not imply effectiveness.
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