The future of AI products lies in 'always-on' agents that orchestrate themselves. We explore the shift from completions to multi-agent systems, demonstrating how agents can run complex, end-to-end businesses.
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Learn how to build a business dashboard using AI agents (Codex/Claude Code) on the Hugging Face Hub. The process covers data exploration, defining KPIs, drafting the dashboard, and deploying the final product to a private Hugging Face Space for team collaboration.
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Druid AI presents a framework to evaluate AI agents, arguing that simple metrics like containment rate are misleading. They propose a multi-pillar scorecard focusing on governance, risk, and business outcomes.
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Learn how to build a continuous 'train-to-deploy' AI loop using open models. Discover FireAttention, custom CUDA kernels, and advanced training methods to match closed-source model quality at scale.
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The future of autonomous agents lies in a neuro-symbolic approach, combining VLM adaptability with deterministic coding agents (Simulan). This infrastructure is designed for scalable cloud deployment, enabling automation in legacy enterprise systems.
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Discover how to improve browser agents without changing model weights using the AutoBrowse loop. This system pairs a Doer agent with a Teacher agent to generate reusable 'skill' files, dramatically increasing reliability and reducing operational costs on complex websites.
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Modular addresses the AI software mess by introducing Mojo, a Pythonic systems language, and MAX, a unified inference framework. This stack enables portable, high-performance deployment across mixed, multi-node hardware.
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Learn about AutoResearch: an agent-driven loop that automates code proposals and evaluations to optimize ML models. We demonstrate Mini Parameter Golf, a scaled-down challenge for inspecting complex optimization processes.
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The presentation details how to adapt interactive CLIs for AI agents, moving beyond simple flags. Techniques include using tmux sessions, IPC side channels for structured JSON, and context injection to manage complex, contextual workflows.
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Perception Agents enable AI to understand visual screens, moving beyond APIs. The workflow uses Visual Annotation (deterministic JSON output) and Visual Verification (Gherkin files) to close the design feedback loop, accelerating development.
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The workshop details the shift from traditional software security to AI-native defenses, covering topics like Prompt Injection, AI Bomb inventory, and integrating security hooks into CI/CD pipelines for agentic workflows.
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Learn how to build reproducible AI agent evaluation pipelines using WolfBench. Discover why single scores are insufficient and how to track consistency, cost, and capability across six critical pipeline layers.
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Learn how AI agents leak secrets through logs and tool calls. Discover advanced patterns like split-key custody, narrow agent scope, and confidential compute to build truly private AI systems.
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Learn how NVIDIA Data Designer builds synthetic data pipelines to teach AI agents complex search and reasoning skills. The framework uses DAGs, seed data, and tool calls to create scalable, diverse training records.
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Learn how to build quality gates into agentic coding workflows. The process involves codifying standards, planning, local verification, and automated PR review using tools like Qodo/Codto.
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AI News & Strategy Daily | Nate B Jones
AI is increasing the value of human skills like Taste, Agency, Focus, Resourcefulness, and Follow-through. Learn how to leverage these skills to turn shelved projects into real impact.
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The talk details building self-learning loops for AI agents, moving observability from human dashboards to programmatic interfaces. Key tools include Arise Signal and Arise Skills, enabling agents to detect, investigate, and self-correct production failures.
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Learn how to build robust AI agents by mastering hybrid search. This workshop covers combining semantic (vector) and lexical (BM25) search, using ESQL, and implementing advanced reranking techniques in Elasticsearch.
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The Data Context Layer provides a graph-based solution to give AI agents semantic understanding of data and code. By computing lineage, grain, and dependencies, it moves agents beyond ambiguous text search and unreliable SQL.
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Learn how to build a private, cost-effective AI workflow using an Obsidian vault and Superlinked's open-source inference engine. We cover self-hosting, model routing, and secure document processing in a controlled VPC.
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