Introducing GIST: The next stage in smart sampling
Algorithms & Theory
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How a Haystack-Powered Multi-Agent System Detects Incidents, Investigates Metrics and Logs, and Produces Production-Grade Incident Reviews End-to-End
In this tutorial, we design this implementation to demonstrate how Haystack enables building advanced, agentic AI systems that go far beyond toy examples while remaining fully runnable. We focus on a cohesive, end-to-end setup that highlights orchestration, stateful decision-making, tool execution, and structured control flow, demonstrating how complex agent behavior can be cleanly expressed. We…
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NVIDIA Revolutionizes Climate Tech with ‘Earth-2’: The World’s First Fully Open Accelerated AI Weather Stack
For decades, predicting the weather has been the exclusive domain of massive government supercomputers running complex physics-based equations. NVIDIA has shattered that barrier with the release of the Earth-2 family of open models and tools for AI weather and climate prediction accessible to virtually anyone, from tech startups to national meteorological agencies. In a move…
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What is Clawdbot? How a Local First Agent Stack Turns Chats into Real Automations
Clawdbot is an open source personal AI assistant that you run on your own hardware. It connects large language models from providers such as Anthropic and OpenAI to real tools such as messaging apps, files, shell, browser and smart home devices, while keeping the orchestration layer under your control. The interesting part is not that…
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StepFun AI Introduce Step-DeepResearch: A Cost-Effective Deep Research Agent Model Built Around Atomic Capabilities
StepFun has introduced Step-DeepResearch, a 32B parameter end to end deep research agent that aims to turn web search into actual research workflows with long horizon reasoning, tool use and structured reporting. The model is built on Qwen2.5 32B-Base and is trained to act as a single agent that plans, explores sources, verifies evidence and…
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A Coding Implementation to Automating LLM Quality Assurance with DeepEval, Custom Retrievers, and LLM-as-a-Judge Metrics
We initiate this tutorial by configuring a high-performance evaluation environment, specifically focused on integrating the DeepEval framework to bring unit-testing rigor to our LLM applications. By bridging the gap between raw retrieval and final generation, we implement a system that treats model outputs as testable code and uses LLM-as-a-judge metrics to quantify performance. We move…
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Reimagining UI/UX education for AI and neuroinclusion
UI/UX education and real-world practice no longer move in step. While current work in interaction design wrestles with cognitive load, agentic systems, and neuroadaptive feedback loops, many programs still circle around layout, typography, and polished screens. The gap is not only about outdated syllabi. It sits in the systems that run schools, the assumptions baked…
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How Machine Learning and Semantic Embeddings Reorder CVE Vulnerabilities Beyond Raw CVSS Scores
In this tutorial, we build an AI-assisted vulnerability scanner that goes beyond static CVSS scoring and instead learns to prioritize vulnerabilities using semantic understanding and machine learning. We treat vulnerability descriptions as rich linguistic artifacts, embed them using modern sentence transformers, and combine these representations with structural metadata to produce a data-driven priority score. Also,…
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GitHub Releases Copilot-SDK to Embed Its Agentic Runtime in Any App
GitHub has opened up the internal agent runtime that powers GitHub Copilot CLI and exposed it as a programmable SDK. The GitHub Copilot-SDK, now in technical preview, lets you embed the same agentic execution loop into any application so the agent can plan, invoke tools, edit files, and run commands as part of your own…
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How an AI Agent Chooses What to Do Under Tokens, Latency, and Tool-Call Budget Constraints?
In this tutorial, we build a cost-aware planning agent that deliberately balances output quality against real-world constraints such as token usage, latency, and tool-call budgets. We design the agent to generate multiple candidate actions, estimate their expected costs and benefits, and then select an execution plan that maximizes value while staying within strict budgets. With…
