AI

In this tutorial, we build an advanced Reflex web application entirely in Python that runs seamlessly inside Colab. We design the app to demonstrate how Reflex enables full-stack development with no JavaScript, just reactive Python code. We create a complete notes-management dashboard featuring two pages, real-time database interactions, filtering, sorting, analytics, and user personalization. We…

How do you build reliable AI agents that plug into your existing Go services without bolting on a separate language stack? Google has just released Agent Development Kit for Go. Go developers can now build AI agents with the same framework that already supports Python and Java, while keeping everything inside a familiar Go toolchain…

Even strong ‘long-context’ AI models fail badly when they must track objects and counts over long, messy video streams, so the next competitive edge will come from models that predict what comes next and selectively remember only surprising, important events, not from just buying more compute and bigger context windows. A team of researchers from…

Large language models are now limited less by training and more by how fast and cheaply we can serve tokens under real traffic. That comes down to three implementation details: how the runtime batches requests, how it overlaps prefill and decode, and how it stores and reuses the KV cache. Different engines make different tradeoffs…

In this tutorial, we build an advanced multi-agent pipeline that interprets integrated omics data, including transcriptomics, proteomics, and metabolomics, to uncover key biological insights. We begin by generating coherent synthetic datasets that mimic realistic biological trends and then move step by step through agents designed for statistical analysis, network inference, pathway enrichment, and drug repurposing….

How do we design AI systems that can plan, reason, and act over long sequences of decisions without constant human guidance? Moonshot AI has released Kimi K2 Thinking, an open source thinking agent model that exposes the full reasoning stream of the Kimi K2 Mixture of Experts architecture. It targets workloads that need deep reasoning,…

In this tutorial, we build a Wet-Lab Protocol Planner & Validator that acts as an intelligent agent for experimental design and execution. We design the system using Python and integrate Salesforce’s CodeGen-350M-mono model for natural language reasoning. We structure the pipeline into modular components: ProtocolParser for extracting structured data, such as steps, durations, and temperatures,…

How do you turn a vague business style question over messy folders of CSV, JSON and text into reliable Python code without a human analyst in the loop? Google researchers introduce DS STAR (Data Science Agent via Iterative Planning and Verification), a multi agent framework that turns open ended data science questions into executable Python…

Most LLM agents are tuned to maximize task success. They resolve GitHub issues or answer deep research queries, but they do not reason carefully about when to ask the user questions or how to respect different interaction preferences. How can we design LLM agents that know when to ask better questions and adapt their behavior…

How do you build a single model that can learn physical skills from chaotic real world robot data without relying on simulation? Generalist AI has unveiled GEN-θ, a family of embodied foundation models trained directly on high fidelity raw physical interaction data instead of internet video or simulation. The system is built to establish scaling…