Browse entries 961–984 of 1034. Return to the first page to search and filter the complete collection.
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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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Chat and coding assistants still hand you walls of text when a button, a chart, or a small interactive view would say it faster.
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You invoke a tool and expect an answer, but real work takes time, and over that time connections drop, networks blip, and processes crash.
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You cannot tell great engineers what to do, and you increasingly cannot tell what an agent did either, so Vaibhav Gupta's answer is to fight slop with slop.
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Reinforcement learning has been easy to sell where the answer is checkable, like math or code, and Will Brown's talk is about everything else. Most valuable tasks have no clean verifier, so Prime Intellect's work is on how you build reward signal when there is no ground truth waiting.
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In this conversation, Olive Song, who leads reinforcement learning at MiniMax, opens up the stack behind the company's open weight models and the infrastructure that serves them. Her starting point is a belief in open source: put the weights out, let builders optimize on them, and share the capability widely.
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Mahesh Sathiamoorthy's pitch is to stand in the researcher's shoes: the hard part of post-training is not the algorithm but the data and the environments that feed it.
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Thais Castello Branco's starting point is that AI is still badly behind on the subjective work, the writing and design where quality is real but hard to pin down, and that ending the slop means building data and reinforcement environments for taste.
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To train an agent that can run production software, you need training data that looks like production, and that is what Joseph Wang's team at Emulated builds.
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A card gets declined and no one, including the customer, can say exactly why. That gray zone is where Divakar Kumar points his agents. In a payments and fraud system, a rule based engine and an ML model already score most transactions cleanly; the hard cases are the ambiguous ones that neither can resolve.
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Humanity compressed the road from the enlightenment to the moon landing into a few hundred years, and Richard Socher's wager is that automating research compresses it again.
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The slowest part of shipping a production finance agent is not the model or the GPUs, it is you, the developer in the loop. Ramana Siddanth Emani's point is that the same agent harnesses you use to build products can automate your own developer loop.
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Since skills were open sourced, Yogendra Miraje's team at FactSet stopped thinking about shipping features and started thinking about shipping skills.
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Nubank serves 135 million customers, so an AI agent that mishandles a support conversation fails at scale. The talk opens with the result: five agents in production, higher customer satisfaction, and roughly 20 times faster shipping.