Apex Technologies

 Introduction Pre-training gives Large Language Models (LLMs) broad linguistic ability and general world knowledge, but post-training is the phase that actually aligns them to concrete intents, domain constraints, and the reliability requirements of production environments. At Netflix, we are exploring how LLMs can enable new member experiences across recommendation, personalization, and search, which requires adapting generic foundation models so they can better reflect our catalog and the nuances of member interaction histories. At Netflix scale, post-training quickly becomes an engineering problem as much as a modeling one: building and operating complex data pipelines, coordinating distributed state across multi-node GPU clusters, and orchestrating workflows that interleave training and inference. This blog describes the architecture and engineering philosophy of our internal Post-Training Framework, built by the AI Platform team to hide infrastructure complexity so researchers...

 Bloom Filters are great tools to make fast and cheap filtering. They also come with plenty of problems and can easily get expensive and cumbersome. We switched to user-based direct database queries, which made our filtering cheaper and easy to maintain. Here’s the full breakdown of that migration. Intro: This is a 4-part series breaking down improvements to the algorithm behind the Medium’s Daily Digest over the past year. When we started this work, the Digest was suboptimal — and since it’s a huge distribution surface, reaching millions of readers every day, we started working on incremental improvements. By the end of these projects, the digest was 10% more likely to convert users to paying members, less expensive to run, more flexible and easier to maintain and it’s now providing higher quality recommendations for all our users, including our “power readers”. https://hackmd.io/@alexaa34/rkxphCej-x https://medium.com/@alexharris59600/engineering-stories-behind-the-medium-daily-d...

 Artificial Intelligence is everywhere right now — but building something useful with it doesn’t have to be complicated. You don’t need a research team, massive datasets, or months of development. In fact, you can ship a simple AI-powered feature in just a few days if you focus on the right approach. In this blog, I’ll walk you through a practical, beginner-friendly way to build an AI feature that’s actually ready for production. 🎯 Start with a Clear Problem (Not the Technology) One of the biggest mistakes developers make is starting with “I want to use AI” instead of “I want to solve this problem.” AI works best when it solves a very specific task. Some simple but powerful use cases: Summarising long articles or documents Generating email or chat replies Creating product descriptions Answering FAQs with a chatbots 👉 Keep your first feature small and focused. You can always expand later. https://hackmd.io/@alexaa34/HJfukc1jWl https://medium.com/@alexharris59600/how-to-build-a-s...

Technical blogs are a valuable medium that lets you share knowledge, insights, and expertize with a broader audience. Additionally, the easy, casual tone of the blogs makes complex topics easy to follow. While 2/3rd of the global population is connected to the internet and consume knowledge online, there are only handful of people writing effective technical blogs.   That is why possessing the necessary skillsets can help you address the market gap to position yourself as an authoritative creator. Apart from establishing credibility, writing technical content can help you connect with like-minded individuals, and contribute to the larger tech community – not to mention, earn some decent money as well!  In this article, we will explore essential tips and strategies to help you write an effective technical blog that engages readers and delivers value.  Looking for experts to do it for you? Fill the form to get technical content samples customized for your requirements!...

 Applying recently learned knowledge is one of the best way to reinforce our learning. Turning around and teaching it to somebody else through technical blog post is excellent way to do it. Technical blogging is critical to early growth as a community. The motive of this article is not only to provide medium guidelines but also write technical blog posts that people will actually read. So, how to write such technical blog post? I have divided it into three major sections and we will discuss each one of them. Preparing raw content i.e. substance Tying up everything and packaging Publicizing i.e. making widely known Preparing raw content (Substance) Writing something that matters is challenging. We should do a lot of homework to create a quality content. Knowing the topic you’re writing about and what’s been already written about it is the first thing that’s required. After you are done with your homework, figure out what’s missing. You may feel that everything about the topic is wri...

 Dr. Magesh Kasthuri, Chief Architect & Distinguished Member of Technical Staff (Master), Wipro Limited Introduction Artificial Intelligence (AI) agents have become integral to modern enterprises, transforming how organisations automate tasks, analyse data, and interact with customers. These agents, powered by advanced algorithms and learning capabilities, are designed to perform specific functions, adapt to changing environments, and deliver intelligent outcomes. Their significance lies in the ability to enhance efficiency, reduce operational costs, and foster innovation across diverse industries. Identifying Opportunities and Use Cases The foundation of successful AI agent development begins with identifying the right opportunities. Start by analysing business processes to uncover repetitive tasks, data-heavy operations, and areas requiring decision support. Engage stakeholders to understand pain points and desired outcomes. For instance, in Banking, automating loan approvals...