Reducto is an agentic document platform that turns messy, unstructured documents (scans, handwriting, redacted PDFs) into structured data for AI. It uses a rasterization-based approach and a multi-stage pipeline (Parse, Split, Extract, Edit, Classify).
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Learn how AI agents use real-time web data from sources like Amazon and Reddit to build product intelligence recommendations. We cover the shift from brittle scraping pipelines to scalable, resilient data collection using Bright Data's MCP.
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Jesse Hall explains that for voice agents, latency is a budget, but the goal is human-like conversation. He details why component leaderboards fail and how to build a robust, real-time pipeline.
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To escape AI lock-in, modern AI deployments must be modular. An AI gateway centralizes identity, manages budgets, and decouples the LLM, data connectors (MCP), and interfaces, ensuring maximum choice and ROI.
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For complex AI agents, traditional input-output testing fails. The solution is 'simulation': replaying real historical production traces against changes to detect side effects and ensure the agent's behavior remains consistent.
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MiniMax M3 combines agentic coding, 1M context, and native multimodality. The model uses MSA sparse attention, achieving 9x faster pre-fill and 15x faster decode vs. full attention.
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AI governance must move from prompt instructions to the infrastructure layer. Learn how a Gateway blocks data exfiltration, enforces rate limits, and controls costs in agentic workflows.
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Learn how a Context Engine moves beyond vector search to handle structural, relational, and temporal data queries, significantly improving AI agent reliability and cutting token burn.
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Discover how to build ultra-fast AI agents and MCP servers using WebAssembly, Spin, and Akamai Functions. Learn how to achieve global distribution with zero cold starts.
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Learn the optimal freelance model: combining long-term contracts, project work, and maintenance retainers. Focus on AI automation for SMBs and building proprietary IP.
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Learn how to scale custom AI agents from local development to autonomous CI pipelines. Discover agentic workflows, marketplaces, and cross-repo automation using GitHub Copilot.
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AI agents pose a 'lethal trifecta' threat: private data access, untrusted content exposure, and external communication. Secure adoption requires three guardrails: CDEs, a Model Proxy, and an Agent Firewall.
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This deep dive into GRPO covers advanced RL techniques like clipped policy ratios and KL divergence to stabilize reasoning model training. Learn how to interpret metrics like advantage and entropy for robust model development.
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The panel discusses why AlphaFold's breakthrough was only the beginning. Progress requires moving beyond scaling compute and data, focusing instead on finding the right biological scaling laws and building holistic models of living systems.
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A 4B model trained with RL on financial data beat a 235B model on complex tasks. Specialization and simple rewards proved more effective than raw scale for enterprise AI.
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Building a production AI agent requires more than connecting APIs. Learn why a Context Graph—combining synced data, live lookups, skills, and memory—is essential for semantic querying and reliable business logic.
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Gabriel Martinez argues that AI accelerates 'slop'—work lacking judgment. He stresses that generation is cheap, but evaluation is expensive, advocating for diagrams and conventions over massive PRs.
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Learn how progressive IR lowering transforms PyTorch layers into efficient GPU kernels. We explore the compiler stack: PyTorch $\rightarrow$ Tensor AR $\rightarrow$ Loop IR $\rightarrow$ Tile IR $\rightarrow$ CUDA, detailing fusion, normalization, and tiling for maximum performance.
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Learn how Gemini AI was used to build a 3D web controller, enabling real-time operation of a physical robot arm from 1,700 miles away by transmitting joint coordinates.
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MasterClass details its multi-agent AI system, NCX, and the advanced observability stack built on CoreWeave. Learn how tracing, MCP servers, and an AgentOps loop enable self-improvement in complex AI applications.
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