Browse entries 961–984 of 1597. Return to the first page to search and filter the complete collection.
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AI Engineer session on Decoding Mistral AI's Large Language Models: Devendra Chaplot. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Frontier Feud: Anthropic, Google DeepMind, Meta FAIR, Thinking Machines, presented by Barr Yaron, Amplify. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Making Open Models 10x faster and better for Modern Application Innovation: Dmytro (Dima) Dzhulgakov. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Navigating RAG Optimization with an Evaluation Driven Compass: Atita Arora and Deanna Emery. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on How Codeium Breaks Through the Ceiling for Retrieval: Kevin Hou. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on The Model Isn’t Wrong, presented by You’re Just Bad at Prompting. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on From model weights to API endpoint with TensorRT LLM: Philip Kiely and Pankaj Gupta. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on BotDojo Launch: Enhancing AI Assistants with Evaluations and Synthetic Data. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Moondream: how does a tiny vision model slap so hard?, presented by Vikhyat Korrapati. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Fixing bugs in Gemma, Llama, & Phi 3: Daniel Han. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on State Space Models for Realtime Multimodal Intelligence: Karan Goel. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Unveiling the latest Gemma model advancements: Kathleen Kenealy. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on The Adversarial Path to the Personal Assistant: Sumit Agarwal. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Best Practices for Evaluating Large Language Model Applications with llmeval: Niklas Nielsen. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Breaking AI's 1-GHz Barrier: Sunny Madra (Groq). It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on System Design for Next-Gen Frontier Models, presented by Dylan Patel, SemiAnalysis. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Accelerate your AI journey with Azure AI model catalog: Sharmila Chokalingam. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Lessons from the Trenches: Building LLM Evals That Work IRL: Aparna Dhinkaran. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Evaluating Domain Specific LLMs for Real World Finance, presented by Waseem Alshikh, Writer. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Productionizing GenAI Models, presented by Lessons from the world's best AI teams: Lukas Biewald. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on WTF do people use Open Models for??. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Fine tune 20 Llama Models in 5 Minutes: Santosh Radha. It adds practical context for how teams are building and operating AI systems in production.
METR’s March 2024 study compares model versions and agent scaffolds on 195 tasks across twelve families. Later post-training versions of the same base models improved measured autonomy, while the additional scaffold gains studied were small and not statistically significant. The task selection, limited enhancement methods and lack of model-weight access constrain generalization.
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An AI skill is a piece of code you hand a model to extend what it can do, and once engineers start sharing skills with each other, each one becomes a supply chain risk, more so inside a regulated bank.