Browse entries 1369–1392 of 1597. Return to the first page to search and filter the complete collection.
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AI Engineer session on Building Production-Ready RAG Applications: Jerry Liu. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Pragmatic AI with TypeChat: Daniel Rosenwasser. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on The Hidden Life of Embeddings: Linus Lee. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on The Future of Work: Toran Bruce Richards, Silen Naihin et al. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Using AI to Build an Infinite Game: Jeff Schomay. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on GPT Web App Generator - 10,000 apps created in a month: Matija Sosic. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on The AI Pivot: With Chris White of Prefect & Bryan Bischof of Hex. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on See, Hear, Speak, Draw: Logan Kilpatrick & Simón Fishman. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Domain adaptation and fine-tuning for domain-specific LLMs: Abi Aryan. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Building Context-Aware Reasoning Applications with LangChain and LangSmith: Harrison Chase. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Announcing the AI Engineer Network: Benjamin Dunphy. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on The Intelligent Interface: Sam Whitmore & Jason Yuan of New Computer. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Building AI For All: Amjad Masad & Michele Catasta. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on The 1,000x AI Engineer: Swyx. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Building Reactive AI Apps: Matt Welsh. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on The AI Evolution: Mario Rodriguez, GitHub. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Pydantic is all you need: Jason Liu. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on AI Engineering 201: The Rest of the Owl. It adds practical context for how teams are building and operating AI systems in production.
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TurboQuant reached twenty million people in March, and the memory stock index dipped because everyone assumed the KV cache had just halved. Philip Kiely had published Inference Engineering weeks earlier and watched a technique he had not covered go viral. So his team did the math.
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Byung-Gon Chun's team invented continuous batching, now standard across the industry, and the work that followed inspired one of the most widely used open source serving frameworks. So when he says agents have changed the economics of inference, he knows the tooling from the inside.
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Once in roughly a thousand prompts, the model returned gibberish. No crash, no warning, and high confidence, which made it an engineering problem, not a quality one. It happened only in vLLM, only under load, and only with Jamba, AI21's hybrid of attention and Mamba layers.
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An RMS norm layer does almost none of the arithmetic in a transformer, yet a single decode step can launch it around 33 times, and a GPU is fast at math and slow at everything else: starting work, moving data, waiting. That gap is what FlashNorm attacks.
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On an agentic request, somewhere between 80 and 90 percent of the input is identical to the request before it, and prefill is the most expensive thing an inference stack does.
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The routing weights inside OpenAI's inference load balancer used to come out of a feedback loop. Engines reported signals, a controller smoothed them into a score, compared it to the fleet average, and nudged each weight up or down.