InfoQ Homepage Programming Content on InfoQ
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Python, Numba, and Algorithm Design: Building Efficient Models in Financial Services
Chad Schuster explains how using Numba JIT compilation and GPUs speeds up compute-intensive Python models, driving 10x cloud cost reductions while navigating architecture trade-offs.
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Can Claude Fix Itself? Using LLMs for Incident Response
Anthropic's Alex Palcuie shares how LLMs transform incident response, highlighting where Claude excels at log analysis and why automated AI SREs still can't replace human judgment.
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Prompt to Prod: Engineering an Autonomous SDLC at Scale
Andrew Swerdlow shares how Roblox transitions from AI autocomplete to fully autonomous software development with "Prompt to Prod," covering safety guardrails, infrastructure, and metrics.
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SafeChat: Building AI-Powered Safety Systems at Scale in a Real-Time Marketplace
Bruna Pereira shares how DoorDash built a scalable AI moderation platform. Learn how combining cheap classifiers with LLM scoring reduced incidents and cut latency in real-time chat.
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Enchant Your AI and APIs with eBPF Magic 🪄
Dan Finneran explains how to use eBPF and AI gateways in Kubernetes to transparently observe, modify, and control unowned AI agent API calls without altering source code.
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From Fab To Token - The State Of The Market
Jordan Nanos explains how hardware constraints, data center scale, and chip co-design shape modern AI performance and tokenomics from silicon to inference.
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From Thousands to One: Building LLM-Powered Selection Systems
Jendrik Jördening explains how to build reliable LLM architecture by enforcing strict schemas, separating AI text reasoning from deterministic code, and applying automated output validation.
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From Models to Agents: Building Context-Aware Consumer AI at Scale at DoorDash
Sudeep Das explains how DoorDash transforms e-commerce recommendations using LLM-driven semantic consumer memory, hierarchical semantic IDs, grounded agentic search, and multi-tiered LLM ranking.
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The Right 300 Tokens Beat 100k Noisy Ones: The Architecture of Context Engineering
Baruch Sadogursky and Patrick Debois explain context engineering antipatterns in coding agents, demonstrating how skills, right-tool retrieval, and external memory optimize developer workflows.
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Adopting Memory-Safety and Fine-Grained Compartmentalisation with CHERI
David Chisnall explains how CHERI architecture unifies hardware capabilities and pointer metadata to deliver memory safety and fine-grained, efficient software compartmentalization.
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Producing the World's Cheapest Tokens: A How-to Guide
Meryem Arik explains how to cut AI inference costs by up to 90% by optimizing batch sizes, hardware selection, and request scheduling for high-volume, non-real-time workloads.
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Leveraging Adversary Emulation for GenAI Red Teaming
Kennedy Torkura explains how to apply GenAI red teaming to secure AWS Bedrock models and knowledge bases, leveraging MITRE ATLAS to discover cloud supply chain vulnerabilities.
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