It’s undeniable that AI has - for better or worse - already had a huge impact on the software industry, from its practical applications at the technology level, to the changing demand from skills and experience, to the layoffs linked to AI processes taking over. One area we haven’t really talked about in our articles yet is recruitment: it’s no secret that AI is being used to scan CVs and automatically filter candidates. Several of my developer friends have been talking about this recently, and with a slew of layoffs in tech sending a lot of IT folks on the hunt for new jobs, I thought this would be the perfect time to dive into how AI is being used to filter candidates, and what you can do to stand out and get past the filters. Forbes estimates that 65% of employers will use AI tools to reject candidates in 2025. That’s a staggeringly high number, but for folks who work in hiring or are currently job hunting it’s not a surprising one. The article offers a further breakdown of how employers plan to use–or are already using–AI in their hiring process.

Learn how to prompt Replit Assistant, an advanced AI-coding assistant, to create your first Slack bot with Bolt, a framework for building Slack apps with Python (or JavaScript). Part I covers forking a Slack bot project template, the anatomy of a basic Slack bot, personalization of Replit Assistant, and prompting techniques for Replit Assistant.

Learn how to create your first Discord bot with Replit Agent, an advanced AI-coding agent, and discord.js, a Node.js-based Discord API library. This step-by-step guide teaches you how to go from an initial idea to a 24/7 Discord bot in minutes, regardless of skill level.

Welcome! This is the sixth and final lesson on how to build fullstack apps with Bolt and Supabase If you’re just joining, you’re in luck, ‘cause we already have tons of content for you to enjoy while turning your app from just an idea to deployed web application in just an afternoon. Before you dive into this lesson, here’s where you can find Part 1 , Part 2 , Part 3 , Part 4 , and Part 5 if you want to get up to speed (which you probably do, otherwise, what exactly are you going to deploy? 🤔)

In the last article we covered statistical metrics like Perplexity, BLEU, ROUGE and more, as well as some of the statistical concepts that underpin them, their strengths (accuracy, reliability) and weaknesses (no subjective focus, use of reference texts. Between human evaluation (manual testing) and statistical measures we get a mix of high-value qualitative assessment on a small part of the test surface, and a rigorous but limited view on a wider area. That still leaves a lot of middle ground uncovered! That’s why there’s been a push the last few years to get coverage for the space between - something that has a level of subjectivity and nuance but that also scales up. This is where LLM-as-a-Judge comes in. In our manual testing for LLMs article I compared this to a kind of ouroboros where AI validates AI - and rightly so, that isn’t necessarily a bad thing. LLMs are able to do some things better than humans and LLM-as-a-Judge plays to those strengths - but it does not replace the need for human oversight and statistical assessment. There’s also metrics that combine LLM-as-a-Judge with statistical metrics - but we’ll talk more about that later.

Everyone knows chatgpt, but how do modern large language models fully work? The fundamentals start at the transformer. This workshop is a workshop to dymstify the transformer and be able to run through concept to code on how the transformer work. This workshops combines concept at an intutive level, to code, to math all with the intent at providing an end to end understanding at the fundamentals of large language models.

<!-- Your template is like this (markdown): --> Learn the strategies senior engineers use to configure their projects to run securely, anywhere with environment variables. <!-- ( the summary is short - one or two lines ) -->

Large Language Models are reshaping industries, yet integrating them into real-time streaming UIs presents unique challenges. In this course we will learn how to seamlessly integrate LLM APIs into applications and build AI-powered streaming text and chat UIs with TypeScript, React, and Python. Step-by-step, we will build a full-stack AI application with quality code and very flexible implementation. The LLM application in this course includes: - Completion Use-Case (english to emojis) - Chat - Retrieval Augmented Generation use-case - AI Agent Use-Cases (code execution, data-Analyste agent) This app can be used as a starting point in most projects, saving a huge amount of time, and its flexibilty allows new tools to be added as needed. At the end of this course, you will have mastered end-to-end implementation of a flexible and high-quality LLM application. This course will also equip you with the knowledge and skills necessary to create sophisticated LLM solutions of your own.

This course will teach you how to fetch in a performant way for React without relying on a third-party library or the useEffect hook. We will build a custom data fetching library from scratch that will prefetch, cache, and address all the common issues with fetching data.

Learn Pro Patterns for Hooks, Testing, Redux, SSR, and GraphQL

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