Author: Tech Jacks Solutions

Implement automated smoke testing using Amazon Nova Act headless modeArtificial Intelligence This post shows how to implement automated smoke testing using Amazon Nova Act headless mode in CI/CD pipelines. We use SauceDemo, a sample ecommerce application, as our target for demonstration. We demonstrate setting up Amazon Nova Act for headless browser automation in CI/CD environments and creating smoke tests that validate key user workflows. We then show how to implement parallel execution to maximize testing efficiency, configure GitLab CI/CD for automatic test execution on every deployment, and apply best practices for maintainable and scalable test automation.

 This post shows how to implement automated smoke testing using Amazon Nova Act headless mode in CI/CD pipelines. We use SauceDemo, a sample ecommerce application, as our target for demonstration. We demonstrate setting up Amazon Nova Act for headless browser automation in CI/CD environments and creating smoke tests that validate key user workflows. We then show how to implement parallel execution to maximize testing efficiency, configure GitLab CI/CD for automatic test execution on every deployment, and apply best practices for maintainable and scalable test automation. Read More  

The Machine Learning “Advent Calendar” Day 10: DBSCAN in ExcelTowards Data Science DBSCAN shows how far we can go with a very simple idea: count how many neighbors live close to each point.
It finds clusters and marks anomalies without any probabilistic model, and it works beautifully in Excel.
But because it relies on one fixed radius, HDBSCAN is needed to make the method robust on real data.
The post The Machine Learning “Advent Calendar” Day 10: DBSCAN in Excel appeared first on Towards Data Science.

 DBSCAN shows how far we can go with a very simple idea: count how many neighbors live close to each point.
It finds clusters and marks anomalies without any probabilistic model, and it works beautifully in Excel.
But because it relies on one fixed radius, HDBSCAN is needed to make the method robust on real data.
The post The Machine Learning “Advent Calendar” Day 10: DBSCAN in Excel appeared first on Towards Data Science. Read More  

How to Maximize Agentic Memory for Continual LearningTowards Data Science Learn how to become an effective engineer with continual learning LLMs
The post How to Maximize Agentic Memory for Continual Learning appeared first on Towards Data Science.

 Learn how to become an effective engineer with continual learning LLMs
The post How to Maximize Agentic Memory for Continual Learning appeared first on Towards Data Science. Read More  

Finding Meaningful Work in the Age of Vibe CodingKDnuggets Vibe coding has devalued coding. Is there any meaningful work still left for us?

 Vibe coding has devalued coding. Is there any meaningful work still left for us? Read More  

Don’t Build an ML Portfolio Without These ProjectsTowards Data Science What recruiters are looking for in machine learning portfolios
The post Don’t Build an ML Portfolio Without These Projects appeared first on Towards Data Science.

 What recruiters are looking for in machine learning portfolios
The post Don’t Build an ML Portfolio Without These Projects appeared first on Towards Data Science. Read More  

5 Free Tools to Experiment with LLMs in Your BrowserKDnuggets Discover five free tools that let you run and test large language models directly in your browser without any setup.

 Discover five free tools that let you run and test large language models directly in your browser without any setup. Read More  

Perplexity: AI agents are taking over complex enterprise tasksAI News New adoption data from Perplexity reveals how AI agents are driving workflow efficiency gains by taking over complex enterprise tasks. For the past year, the technology sector has operated under the assumption that the next evolution of generative AI would advance beyond conversation into action. While Large Language Models (LLMs) serve as a reasoning engine,
The post Perplexity: AI agents are taking over complex enterprise tasks appeared first on AI News.

 New adoption data from Perplexity reveals how AI agents are driving workflow efficiency gains by taking over complex enterprise tasks. For the past year, the technology sector has operated under the assumption that the next evolution of generative AI would advance beyond conversation into action. While Large Language Models (LLMs) serve as a reasoning engine,
The post Perplexity: AI agents are taking over complex enterprise tasks appeared first on AI News. Read More  

Empowerment Gain and Causal Model Construction: Children and adults are sensitive to controllability and variability in their causal interventionscs.AI updates on arXiv.org arXiv:2512.08230v1 Announce Type: new
Abstract: Learning about the causal structure of the world is a fundamental problem for human cognition. Causal models and especially causal learning have proved to be difficult for large pretrained models using standard techniques of deep learning. In contrast, cognitive scientists have applied advances in our formal understanding of causation in computer science, particularly within the Causal Bayes Net formalism, to understand human causal learning. In the very different tradition of reinforcement learning, researchers have described an intrinsic reward signal called “empowerment” which maximizes mutual information between actions and their outcomes. “Empowerment” may be an important bridge between classical Bayesian causal learning and reinforcement learning and may help to characterize causal learning in humans and enable it in machines. If an agent learns an accurate causal world model, they will necessarily increase their empowerment, and increasing empowerment will lead to a more accurate causal world model. Empowerment may also explain distinctive features of childrens causal learning, as well as providing a more tractable computational account of how that learning is possible. In an empirical study, we systematically test how children and adults use cues to empowerment to infer causal relations, and design effective causal interventions.

 arXiv:2512.08230v1 Announce Type: new
Abstract: Learning about the causal structure of the world is a fundamental problem for human cognition. Causal models and especially causal learning have proved to be difficult for large pretrained models using standard techniques of deep learning. In contrast, cognitive scientists have applied advances in our formal understanding of causation in computer science, particularly within the Causal Bayes Net formalism, to understand human causal learning. In the very different tradition of reinforcement learning, researchers have described an intrinsic reward signal called “empowerment” which maximizes mutual information between actions and their outcomes. “Empowerment” may be an important bridge between classical Bayesian causal learning and reinforcement learning and may help to characterize causal learning in humans and enable it in machines. If an agent learns an accurate causal world model, they will necessarily increase their empowerment, and increasing empowerment will lead to a more accurate causal world model. Empowerment may also explain distinctive features of childrens causal learning, as well as providing a more tractable computational account of how that learning is possible. In an empirical study, we systematically test how children and adults use cues to empowerment to infer causal relations, and design effective causal interventions. Read More