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As knowledge workers, the tools we use and how we integrate them into our workflow can greatly impact our productivity, minimise context switching and distractions, and improve overall work experience.
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In my previous post I discussed the benefits of using locally hosted open weights LLMs, like data privacy and cost savings. By using mostly free models and occasionally switching to GPT-4, my monthly expenses dropped from 20 USD to 0.50 USD. For mobile access, setting up a port-forward to your local LLM server is a free solution.
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As someone who regularly uses Large Language Models (LLMs) for personal use and builds apps with LLMs - the choice between self-hosted open weights LLMs and proprietary LLMs is a recurring theme. Here, I share my personal insights as to why I use locally hosted LLMs. As a disclaimer, I still use ChatGPT’s GPT-4 and GitHub Copilot for certain use cases and have not completed weaned off them yet.
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I had the pleasure of attending a keynote by Jensen Huang, NVIDIA CEO, hosted by ATxInspire Singapore. Below are a few highlights from a leader who has profoundly influenced our technological landscape:
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I recently attended H2O.ai’s Singapore GenAI Day, a fantastic event showcasing their latest contributions to GenAI. Highlights included:
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OpenAI’s recent Dev Day announcements, such as the launch of GPT-4 Turbo, custom GPTs, and the GPT Store, are going to cause a massive disruption to not just the AI landscape of applications and services but also millions of businesses around the world - by making these powerful tools more accessible.
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It’s difficult for us to stay on top of the latest AI advancements with 100s of research papers, articles, and newsletters published daily. Luckily, Kaggle has recently published their annual AI report earlier this month, which distills and summarises the latest advancements this past action-packed year.
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In this beginner-friendly post I’ll introduce the concept of anomaly detection, also known as outlier detection, focusing on the popular technique of density estimation. This introductory guide is the first post of a mini series of 3 posts, where in upcoming posts I’ll share the most cutting-edge anomaly detection methods used across industries.
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It’s been an action-packed first month in Singapore for me, settling into the new country and immersing myself in the vibrant AI community here! Here’s an overview of what I’ve been up to and my main takeaways from 24 hours of meetups and conferences on Generative AI, Machine Learning for finance, and Data Engineering.
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Thanks for stumbling upon my website. I’ve decided to finally share my thoughts and learnings online to hopefully add value to any readers out there. This space will primarily focus on data science, machine learning, AI, and my experiences living abroad in Singapore.