Articles for category: AI Tools

March 28, 2025

ikayaniaamirshahzad@gmail.com

The triangulation of ethical leader signals using qualitative, experimental, and data science methods

Ethical leadership is arguably-one of the most critical forms of leadership for individual stakeholders as well as society at large, as has been demonstrated by recent events, such as the COVID-19 pandemic and the social justice movement. Other events highlight the need for ethical leadership, such as corporate and political scandals as well as recognition that businesses should promote the welfare of stakeholders beyond shareholders (Freeman, 2019). Ethical leadership characterizes the extent to which leaders engage in normatively appropriate conduct and promote the wellbeing of stakeholders (Brown et al., 2005). Yet, while a great deal of conceptual (Kleshinski et al.,

March 28, 2025

ikayaniaamirshahzad@gmail.com

Ryght’s Journey to Empower Healthcare and Life Sciences with Expert Support from Hugging Face

This is a guest blog post by the Ryght team. Who is Ryght? Ryght is building an enterprise-grade generative AI platform tailored for the healthcare and life sciences sectors. Today is their official launch of Ryght Preview, now publicly available for all. Life science companies are amassing a wealth of data from diverse sources (lab data, EMR, genomics, claims, pharmacy, clinical, etc.), but analysis of that data is archaic, requiring large teams for everything from simple queries to developing useful ML models. There is huge demand for actionable knowledge to drive drug development, clinical trials, and commercial activity, and the

March 28, 2025

ikayaniaamirshahzad@gmail.com

Strata x Hadoop 2016 – How Lazada Ranks Products

Recently, I had the opportunity to share about part of my work at Lazada—ranking products in catalog and search results to improve customer experience and conversion. Conference session details available here. Here’s the deck presented. Any feedback on how we can improve our ranking framework, or how I can improve my presentation, is welcome. If you found this useful, please cite this write-up as: Yan, Ziyou. (Dec 2016). Strata x Hadoop 2016 – How Lazada Ranks Products. eugeneyan.com. https://eugeneyan.com/speaking/shared-about-my-work-in-lazada-at-strata-hadoop-singapore-2016-talk/. or @article{yan2016strata, title = {Strata x Hadoop 2016 - How Lazada Ranks Products}, author = {Yan, Ziyou}, journal = {eugeneyan.com}, year

March 28, 2025

ikayaniaamirshahzad@gmail.com

Mobile App Security: The Hidden Threat of Abandoned Libraries

Mobile apps make extensive use of third-party libraries in an attempt to speed up development and provide functionalities like network communication, data parsing, or UI components. Much as such libraries are useful, their use in older or outdated versions is a very serious mobile app security risk. Hackers would prefer to exploit weaknesses in older libraries for unauthorized access, data manipulation, or privacy invasion. A 2022 Veracode report shows that over 30% of the apps in their sample contain vulnerabilities due to outdated third-party libraries, revealing the highest severity of the issue. This highlights the need for strong cybersecurity solutions

March 28, 2025

ikayaniaamirshahzad@gmail.com

Custom Interfaces with blocks

You can create custom annotation layouts in Prodigy using the annotation widgets that Prodigy provides by using the blocks feature. This video explains how to use this feature by building a custom interface that can manually annotate and transcribe audio. Source link

March 28, 2025

ikayaniaamirshahzad@gmail.com

Welcome Llama 3 – Meta’s new open LLM

Meta’s Llama 3, the next iteration of the open-access Llama family, is now released and available at Hugging Face. It’s great to see Meta continuing its commitment to open AI, and we’re excited to fully support the launch with comprehensive integration in the Hugging Face ecosystem. Llama 3 comes in two sizes: 8B for efficient deployment and development on consumer-size GPU, and 70B for large-scale AI native applications. Both come in base and instruction-tuned variants. In addition to the 4 models, a new version of Llama Guard was fine-tuned on Llama 3 8B and is released as Llama Guard 2

March 28, 2025

ikayaniaamirshahzad@gmail.com

Diving into Nvidia Dynamo: AI Inference at Scale

Dynamo is a new open source framework from Nvidia that addresses the complex challenges of scaling AI inference operations. Introduced at the GPU Technology Conference, this framework optimizes how large language models run across multiple GPUs, balancing individual performance with system-wide throughput. CEO Jensen Huang described it as “the operating system of an AI factory,” drawing parallels to how the original dynamo sparked the industrial revolution for energy production. Why Scaling AI Models is Harder Than You Think AI models are getting enormous and are used in workflows where multiple models need to talk to each other. When deploying these

March 28, 2025

ikayaniaamirshahzad@gmail.com

Product Classification API Part 2: Data Preparation

This post is part 2 of the series on building a product classification API. The API is available for demo here. Part 1 available here; Part 3 available here. (Github repositiory) Update: API discontinued to save on cloud cost. In part 1, we focused on data acquisition and formatting the categories. Here, we’ll focus on preparing the product titles (and short description, if you want) before training our model. This is part of a series of posts on building a product classification API: Measuring data purity We’ll have products within our data that are categorized incorrectly. How do we exclude

March 28, 2025

ikayaniaamirshahzad@gmail.com

Error Handling in Go vs. C#: Trading Exceptions for Clarity

As a C# developer, you’re familiar with the convenience of exceptions. When something goes wrong, you throw an error, catch it upstream, and let the runtime unwind the stack. Go, however, takes a different path: it ditches exceptions entirely and opts for explicit error handling. Let’s break down why Go’s approach feels jarring at first—and how it can lead to more robust code. C# Exceptions: The “Fire Alarm” Approach In C#, exceptions act like fire alarms. When a problem arises (e.g., a null reference, failed I/O operation), you throw an exception, which propagates up the call stack until it’s caught:

March 28, 2025

ikayaniaamirshahzad@gmail.com

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