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To create paragraphs in Markdown, separate lines of text with a blank line. Avoid using tabs or spaces before paragraphs to prevent unexpected formatting issues. Keep lines left-aligned for consistent rendering.

Markdown headings are created using number signs (#) corresponding to the heading level. Use alternative syntax for H1 and H2, and remember to include blank lines before and after headings for better compatibility and appearance.

This guide covers the essential Markdown syntax, including headings, lists, links, and code, while noting minor discrepancies across different Markdown processors. Ideal for mastering consistent formatting.

Alphabet’s Google Cloud offers customers computing resources to power their artificial intelligence and machine learning workloads. Google Cloud recently utilized HackerNoon for its content marketing needs, highlighting some exciting courses and features to HackerNoon readers plus an offer to utilize up to $350 in free credits to test, deploy, and explore Google Cloud for 90 days.

Ukrainian entrepreneur Volodymyr Panchenko is at the forefront of AI innovation. He founded DMarket, a global cross-game marketplace enabling gamers worldwide to trade and exchange in-game items across any gaming platform. In June 2024, Portal.ai raised [$5m in a pre-seed round] to help business owners implement its AI-driven operations.

ArkeFi Announces Development of Fractional Ownership Protection (FOP) in Strategic Collaboration with The Barker Price. ArkeFi’s unique platform allows the sale of tokenized nBAs to fractional owners at 50% of their Fair Market Value (FMV) FOP provides enhanced protection and accuracy in asset valuation.

The management of Type 1 diabetes has evolved significantly with advancements in technology, particularly in the development of artificial pancreas systems. These advancements are paving the way for more effective and convenient diabetes management, significantly improving the quality of life for diabetic individuals. This article delves into the different types of insulin delivery systems, highlighting the latest innovations.

Umami Analytics is an open-source alternative to Google Analytics. It is free (open source) and allows self hosting (no vendor lock in). It is also GDPR (General Data Protection Regulation) compliant. We are going to use Supabase (free tier plan) for database (postgres) and Vercel for hosting Umami.

Synthetic data is not a replacement for high-quality original data, especially because of the risk of model collapse. This issue was highlighted in a conversation with Jack Fitzsimons, who shared an article “AI Models Collapse When Trained on Recursively Generated Data.”

Cleaning up Google after your website has been hacked is not just about removing malicious content. It’s about restoring trust and credibility with both visitors and search engines. Identifying and removing compromised URLs can mitigate the damage caused by the hack and expedite the recovery process for your website.

Artificial Intelligence (AI) has rapidly evolved from a futuristic concept to a present-day reality. It brings numerous benefits, such as enhanced efficiency and repetitive task automation. However, its growing capabilities also pose significant risks. These include cybercriminals weaponizing AI to launch attacks.

AI image manipulation tools have come a long way in the last few years. It’s quite easy nowadays to create beautiful, stylistic portraits of people and animals. But AI models are highly unpredictable, so most tools rely on the user (or some human) to weed out bad generations and find the best one. With some clever tricks and careful tuning, you can build a...

Multipeer Connectivity allows for direct data exchange between Apple devices using Wi-Fi, Bluetooth, and Ethernet, bypassing traditional servers. The article outlines the benefits, limitations, and steps to integrate this technology into your projects.

The integration of Artificial Intelligence (AI) in diabetes management has the potential to revolutionise patient care. This technological advancement brings along various ethical challenges that need to be addressed to ensure the responsible and equitable use of AI in healthcare. This article emphasises the importance of data privacy, informed consent, algorithmic bias and transparency.

Large Language Models (LLMs) are advanced AI models trained on vast amounts of text data. They can understand, generate, and respond to human language with high accuracy. These models have the ability to process and analyse large volumes of unstructured data. LLMs hold immense potential for improving patient care, enhancing communication, and providing personalised support.

This article is the first in a series on the future of diabetes management. It explores how different AI is revolutionising the MedTech (Medical Technology) field. It focuses on the development of predictive models and artificial pancreas systems. Each article will delve into different aspects of how AI and ML are transforming diabetes care.

This article provides a comprehensive overview of diabetes, including its types, symptoms, and current management strategies. It sets the stage for subsequent discussions on how AI and machine learning can revolutionise diabetes care. Understanding the fundamentals of diabetes is crucial for appreciating the significance of innovations in its management.

This final article in the series provides a self-assessment of our project on AI-driven diabetes management. It highlights key lessons learned, and offers a comprehensive guide on how to use the findings and tools developed to improve diabetes care. We successfully developed predictive models that accurately forecast blood glucose levels.

This article explores the future directions of diabetes management technology. It showcases innovative research in AI-driven diabetes management. It presents real-world case studies to highlight the practical applications and benefits of these advancements. The integration of Artificial Intelligence (AI) in diabetes management has brought about transformative changes.

Time series data is a sequence of data points collected at regular intervals over time. By analysing temporal data, healthcare professionals can identify trends, patterns, and potential anomalies, leading to more effective and personalised diabetes care. This article explores the various time series analysis methods used in diabetes management.

Predictive modeling has become an invaluable tool in diabetes management. It enables the prediction of blood glucose levels based on dietary intake and other factors. This article explores how machine learning models are developed to predict meal behaviour. It also explains how these predictions can be used to optimise diabetesmanagement.

Carbohydrates are one of the primary sources of energy for the body. They are broken down into glucose, which is used by cells for energy. Not all carbohydrates have the same effect on blood glucose levels. Carbohydrates can be classified into three main types: fiber-rich carbs, starchy carbs, and refined sugary carbs.

Dietary recall questionnaires provide a wealth of information that can be analysed to uncover patterns and correlations. Continuous Glucose Monitoring (CGM) devices provide real-time glucose measurements throughout the day, offering insights into glucose trends and patterns. Advances in data analysis techniques have significantly enhanced our ability to process and interpret diabetes-related data.

Machine learning (ML) is transforming the landscape of diabetes management. ML offers sophisticated tools for predicting blood glucose levels and personalising treatment plans. This article explores various machine learning methods used in predicting and managing diabetes, highlighting how these technologies are enhancing the precision and effectiveness of diabetes care.