Ever feel like you’re drowning in repetitive tasks? Sorting emails, posting updates, or organizing files can eat up hours of your day. But what if you could automate all of that — and have an AI assistant that actually understands what you need? That’s where n8n comes in. n8n is an open-source, low-code automation platform that not only helps you connect your favorite apps and tools but also gives you access to powerful AI features. Whether it's managing your to-do list or answering complex queries with a single prompt, n8n lets you build smart, automated workflows with ease.

Learn how to create a custom Typeform-style survey with Replit Agent, an advanced AI-coding agent, and Notion, a popular productivity note-taking application. This step-by-step guide teaches you how to go from an initial idea to an animated, conversational survey form in minutes, regardless of skill level.

Large Language Models (LLMs) like DeepSeek are powerful tools, but they often come with built-in safety layers and censorship filters. These restrictions might block sensitive topics, controversial opinions, or even accurate historical facts — especially when it comes to politically sensitive regions like China. In our previous article , we explored how to jailbreak Large Language Models (LLMs) like DeepSeek, using prompt engineering and unlock restricted answers. Now, we’re diving into the most powerful and lasting approach: fine-tuning. With tools like LoRA and Unsloth on free platforms like Google Colab, we’ll show you how to tweak DeepSeek to provide accurate, uncensored historical answers about China—free from filters that might obscure the truth. Our goal is to make DeepSeek a reliable source for sensitive topics, where default restrictions can block factual responses. Fine-tuning lets us retrain the model on a custom dataset to soften those limits, and as we’ll see, it’s more accessible than ever with modern tools. Done responsibly, this can reveal what’s hidden without crossing ethical lines.

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.

Everyone’s heard of ChatGPT, but what truly powers these modern large language models? It all starts with the transformer architecture. This bootcamp demystifies LLMs, taking you from concept to code and giving you a full, hands-on understanding of how transformers work. You’ll gain intuitive insights into the core components—autoregressive decoding, multi-head attention, and more—while bridging theory, math, and code. By the end, you’ll be ready to understand, build, and optimize LLMs, with the skills to read research papers, evaluate models, and confidently tackle ML interviews.

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.

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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.

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