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State of the Claw — Peter Steinberger
AI Engineer session on State of the Claw, presented by Peter Steinberger. It adds practical context for how teams are building and operating AI systems in production.
Research archive, page 25
Browse entries 577–600 of 1025. Return to the first page to search and filter the complete collection.
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Contact Center Voice AI: Low-Latency Intelligence Extraction from Messy Audio Streams — Dippu Singh
AI Engineer session on Contact Center Voice AI: Low-Latency Intelligence Extraction from Messy Audio Streams, presented by Dippu Singh. It adds practical context for how teams are building and operating AI systems in production.
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AI Didn’t Kill the Web, It Moved in! — Olivier Leplus (AWS) & Yohan Lasorsa (Microsoft)
AI Engineer session on AI Didn’t Kill the Web, It Moved in!, presented by Olivier Leplus (AWS) & Yohan Lasorsa (Microsoft). It adds practical context for how teams are building and operating AI systems in production.
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Running LLMs locally: Practical LLM Performance on DGX Spark — Mozhgan Kabiri chimeh, NVIDIA
AI Engineer session on Running LLMs locally: Practical LLM Performance on DGX Spark, presented by Mozhgan Kabiri chimeh, NVIDIA. It adds practical context for how teams are building and operating AI systems in production.
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Cognitive Exhaust Fumes, or: Read-Only AI Is Underrated — Šimon Podhajský, Head of AI, Waypoint
AI Engineer session on Cognitive Exhaust Fumes, or: Read-Only AI Is Underrated, presented by Šimon Podhajský, Head of AI, Waypoint. It adds practical context for how teams are building and operating AI systems in production.
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Measuring AGI: Interactive Reasoning Benchmarks for ARC-AGI-3 — Greg Kamradt, ARC Prize Foundation
AI Engineer session on Measuring AGI: Interactive Reasoning Benchmarks for ARC-AGI-3, presented by Greg Kamradt, ARC Prize Foundation. It adds practical context for how teams are building and operating AI systems in production.
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Thinking Deeper in Gemini — Jack Rae, Google DeepMind
AI Engineer session on Thinking Deeper in Gemini, presented by Jack Rae, Google DeepMind. It adds practical context for how teams are building and operating AI systems in production.
![[Evals Workshop] Mastering AI Evaluation: From Playground to Production video thumbnail](https://i.ytimg.com/vi/9iN-cPnp7xg/hqdefault.jpg)
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[Evals Workshop] Mastering AI Evaluation: From Playground to Production
AI Engineer session on [Evals Workshop] Mastering AI Evaluation: From Playground to Production. It adds practical context for how teams are building and operating AI systems in production.
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Netflix's Big Bet: One model to rule recommendations: Yesu Feng, Netflix
AI Engineer session on Netflix's Big Bet: One model to rule recommendations: Yesu Feng, Netflix. It adds practical context for how teams are building and operating AI systems in production.
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The Bitter Layout or: How I Learned to Love the Model Picker — Maximillian Piras, Yutori
AI Engineer session on The Bitter Layout or: How I Learned to Love the Model Picker, presented by Maximillian Piras, Yutori. It adds practical context for how teams are building and operating AI systems in production.
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How fast are LLM inference engines anyway? — Charles Frye, Modal
AI Engineer session on How fast are LLM inference engines anyway?, presented by Charles Frye, Modal. It adds practical context for how teams are building and operating AI systems in production.
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Production software keeps breaking and it will only get worse — Anish Agarwal, Traversal.ai
AI Engineer session on Production software keeps breaking and it will only get worse, presented by Anish Agarwal, Traversal.ai. It adds practical context for how teams are building and operating AI systems in production.
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RL for Autonomous Coding — Aakanksha Chowdhery, Reflection.ai
AI Engineer session on RL for Autonomous Coding, presented by Aakanksha Chowdhery, Reflection.ai. It adds practical context for how teams are building and operating AI systems in production.
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Benchmarks Are Memes: How What We Measure Shapes AI — and Us - Alex Duffy, Every.to
AI Engineer session on Benchmarks Are Memes: How What We Measure Shapes AI, presented by and Us - Alex Duffy, Every.to. It adds practical context for how teams are building and operating AI systems in production.
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When Vectors Break Down: Graph-Based RAG for Dense Enterprise Knowledge - Sam Julien, Writer
AI Engineer session on When Vectors Break Down: Graph-Based RAG for Dense Enterprise Knowledge - Sam Julien, Writer. It adds practical context for how teams are building and operating AI systems in production.
![Vector Search Benchmark[eting] - Philipp Krenn, Elastic video thumbnail](https://i.ytimg.com/vi/YrUBFXa1KUY/hqdefault.jpg)
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Vector Search Benchmark[eting] - Philipp Krenn, Elastic
AI Engineer session on Vector Search Benchmark[eting] - Philipp Krenn, Elastic. It adds practical context for how teams are building and operating AI systems in production.
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Using OSS models to build AI apps with millions of users — Hassan El Mghari
AI Engineer session on Using OSS models to build AI apps with millions of users, presented by Hassan El Mghari. It adds practical context for how teams are building and operating AI systems in production.
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Optimizing inference for voice models in production - Philip Kiely, Baseten
AI Engineer session on Optimizing inference for voice models in production - Philip Kiely, Baseten. It adds practical context for how teams are building and operating AI systems in production.
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OpenThoughts: Data Recipes for Reasoning Models — Ryan Marten, Bespoke Labs
AI Engineer session on OpenThoughts: Data Recipes for Reasoning Models, presented by Ryan Marten, Bespoke Labs. It adds practical context for how teams are building and operating AI systems in production.
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Engineering Better Evals: Scalable LLM Evaluation Pipelines That Work — Dat Ngo, Aman Khan, Arize
AI Engineer session on Engineering Better Evals: Scalable LLM Evaluation Pipelines That Work, presented by Dat Ngo, Aman Khan, Arize. It adds practical context for how teams are building and operating AI systems in production.
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A year of Gemini progress + what comes next — Logan Kilpatrick, Google DeepMind
AI Engineer session on A year of Gemini progress + what comes next, presented by Logan Kilpatrick, Google DeepMind. It adds practical context for how teams are building and operating AI systems in production.
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What every AI engineer needs to know about GPUs — Charles Frye, Modal
AI Engineer session on What every AI engineer needs to know about GPUs, presented by Charles Frye, Modal. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineering with the Google Gemini 2.5 Model Family - Philipp Schmid, Google DeepMind
AI Engineer session on AI Engineering with the Google Gemini 2.5 Model Family - Philipp Schmid, Google DeepMind. It adds practical context for how teams are building and operating AI systems in production.
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open-rag-eval: RAG Evaluation without "golden" answers — Ofer Mendelevitch, Vectara
AI Engineer session on open-rag-eval: RAG Evaluation without "golden" answers, presented by Ofer Mendelevitch, Vectara. It adds practical context for how teams are building and operating AI systems in production.