Say goodbye to manual topic labeling headaches! Dive into Alex Davis's latest article on Advanced Topic Modeling with LLMs using BERTopic, where you'll discover how to generate reproducible, human-readable topic names automatically.

Gen Z data pros, futureproof your résumé in 3 moves. 🚀 Learn from Marina Tosic’s latest article how to recession-proof your skill stack, ride the AI hiring wave instead of fearing it, and convert downtime into portfolio gains that compound career ROI.

Why do LLMs struggle with plot holes or similar documents? Tobias Schnabel explains how working memory overload causes performance to regress to random guessing on tasks like variable tracking, even for state-of-the-art models.

Struggling to score GPT outputs when “good” is in the eye of the beholder? Learn how @EvidentlyAI CEO & Co-Founder @elenasamuylova turns LLMs into reliable judges. Her latest article walks you through prompt design, calibration loops, and trust metrics.

If you're deploying AI across teams, you won't want to miss this FREE webinar. Join our sister site @TheNewStack with @RedHat and @Intel TOMORROW on July 22 for practical steps to make AI work for your business + more. Register now 👉

RT @roadmapsh: Take the guesswork out of your tech education. Our new AI Tutor chat makes it easy to select the right roadmap for your goal….

💡Did you know? The future of LLM training is in your hands. Zsombor Varnagy-Toth makes a powerful case for work data as the single most valuable resource for advancing Gen AI, discussing its profound implications for businesses and knowledge workers .

Stop losing information with oversimplified indices. @theDrewDag explores how POSET representations offer a more faithful way to represent complex data, ensuring methodological coherence and preserving original information in your scoring systems.

Ready to elevate your ML practice?? Mena Wang introduces MLarena, an open-source Python toolkit designed to balance automation with expert insight. Get practical examples for building, diagnosing, and optimizing your models.

Shor's algorithm: a quantum computing cornerstone. Benjamin Assel shares a comprehensive guide to its implementation and analyzes actual quantum runs, offering a clear view of where we stand in its journey from theory to breaking encryption.

Can you finally grasp the mechanics behind @ChatGPTapp's learning process? Vyacheslav Efimov demystifies RLHF, breaking down this sophisticated training method into intuitive components that both beginners and practitioners can understand.

LLMs, but make it software. Mariya Mansurova’s practical guide to @databricks DSPy explores how to move from brittle prompting to declarative AI programming. You’ll learn how to build smarter agents, use MLflow for tracing, and optimize prompts automatically.

Alessandra Alpino shares 3 powerful workflows that you can apply to your personal life or business today.

Is the AI-2027 Jobocalypse scenario becoming reality?? Marina Tosic unpacks a forecast predicting superhuman AI by 2027, and how current LLM capabilities are already impacting entry-level jobs and the future of mentorship.

Prompt Learning vs. traditional Prompt Optimization. what's the difference? @aparnadhinak highlights how PL's English feedback and instruction management offer unprecedented control over prompt tuning, solving problems unsolvable by score-based methods.

Struggling to visualize geographical data for impactful decisions? Learn how Lee Vaughan uses grid-based heatmaps to reveal tornado activity patterns with sharper boundaries than traditional kernel density methods.

Learn 3 easy steps for gaining an intelligent picture for any project by using the skill of context engineering. By Kory Becker.

Is your RAG model giving contradictory answers? 🤔 Subha Ganapathi explores Simpson's Paradox in RAG systems, showing how lurking variables (like publication date) can flip sentiment.

When do custom models beat Foundation Models? Vincent Vandenbussche's guide provides a visual decision framework to help data scientists navigate this crucial choice based on teams, resources, and business needs.

Your models are only as good as the tools they can access. Mariya Mansurova explains how MCP can automate your personal analytics workflows and boost agent performance with the right context.