LangGraph 201: Adding Human Oversight to Your Deep Research Agent in @TDataScience https://t.co/ecnv4kFo6y

How can you tell if a client in your Federated Learning network is malicious? Salman Toor's debut TDS article found that even robust defenses like Multi-KRUM can be tricked. https://t.co/005PKwVgpG

That 5-point difference in your dashboard might be meaningless. @mena_wang's new article reveals how a simple bar chart can hide three distinct business realities and lead to misinterpretations. https://t.co/T7XHbBW1qR

Why do task-based evaluations matter more than benchmarks for a production system? Mark Derdzinski's new article explores the why behind a fundamental shift in AI development. https://t.co/cogC2n4qwJ

Struggling to balance ROI with sustainability goals in your budget planning? @Samir_Saci_'s new article breaks down how a LangGraph agent, connected to a @FastAPI microservice, can use linear programming to find an optimal CAPEX portfolio. https://t.co/mHzayDf9M9

Looking to expand your knowledge of Transformer positional embeddings? Sathya Krishnan Suresh takes us on a detailed walkthrough of APE, RoPE, and ALiBi and their practical applications.

Your path to a career in data engineering just got a lot clearer! ✨ Our new free roadmap is packed with resources, guides, and quizzes. It's designed to help you go from beginner to professional, with a structured path to success. https://t.co/kI9dl5JkUX

If your LLM workflow requires up-to-date web content but you'd like to avoid building a RAG pipeline, @taupirho suggests URL context grounding might just be the tool you need.

Books, courses, online resources, and more: Egor Howell has put together a comprehensive roadmap for becoming a self-taught machine learning engineer.

Not sure how to choose the right LLM for your project? @EivindKjos outlines a streamlined process for building your own, purpose-built benchmark.

"These techniques demonstrate how the right combination of models and extraction strategies can turn long, complex documents into structured insights that are accurate, traceable, and ready for practical use." Kenneth Leung explores the possibilities of working with LangExtract

"Our job is to remind decision-makers and other stakeholders that these numbers and models we work on are simply reflections, not reality itself." @polmarin_ reflects on the kinds of truth data science can reveal.

What's the best way to ensure an LLM judge aligns with human judgment? @EivindKjos explains why a blind comparison with a human evaluator is crucial for building a reliable and trustworthy LLM-as-a-Judge system. https://t.co/Ov63A7A4G4

Why is Data Mesh so hard to get right? Corné Potgieter explores the challenges of building a decentralized data architecture, from the lack of clear consensus on definitions to the difficulty of navigating existing IT policies. https://t.co/uN8hFejGcH

How do you manage stakeholder expectations for a probabilistic AI project? Ivo Bernardo provides a guide for the B2B space, sharing tips on communicating AI's probabilistic nature and avoiding upfront promises. https://t.co/q6mfwa9NVs

Master matrix multiplication once and for all! @rohitpandey576 teaches you the "why" behind the operation, giving you the conceptual tools to understand its role in a composition of linear maps and in a change of basis. https://t.co/rlveAJ1lZo

How do you build a custom voice assistant that runs entirely on your local machine? Benjamin Lee shows you how using LangGraph, @Ollama, and a custom MCP server to build a powerful and free personal AI. https://t.co/JMRP6HrHXf

How do you apply LLMs to the age-old problem of anomaly detection? Shuai Guo breaks down 7 emerging patterns, including LLM-based representation learning and multi-agent systems, and their use cases. https://t.co/xdIYxDKkHQ

Your code handles relative dates, but testing them is an absolute headache. @taupirho reveals how Freezegun turns time-dependent tests into simple, robust, and deterministic ones. https://t.co/UE2euHwPxs

Struggling to automate your exploratory data analysis?? 😫 Sarah Schürch's new article shows how to build a CSV sanity-check agent with @LangChainAI that automatically inspects data for you. https://t.co/yCuKPaMsrZ