Browse entries 673–696 of 1123. Return to the first page to search and filter the complete collection.
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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.
Two security teams have shown, in separate research published this week, that OpenClaw, the popular self-hosted AI agent, can be driven to run attacker-controlled code or hand over sensitive data through ordinary-looking inputs.
Artificial intelligence platforms may be just as susceptible to social engineering as human beings, but they are proving remarkably good at finding security vulnerabilities in human-made computer code.
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GPT 5.5 full analysis, plus DeepSeek V4 paper highlights, comparisons with Mythos, a vibe-coded game w/ GPT Image 2, and 50 data-points you wouldn’t get from just reading the headlines.
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AI Engineer session on The Age of the Agent: Flo Crivello. It adds practical context for how teams are building and operating AI systems in production.
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Every leaderboard you have seen was built by asking a model to do one task, wiping its memory, and asking it another. Parth Asawa's objection is that this quietly assumes learning across instances does not count.
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A Carnegie Mellon study sorted GitHub projects by whether an AI tool wrote the code, and found the productivity gain ran out after about three months while the static analysis warnings and the added complexity stayed.
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Wisedocs processes medical claims that arrive as PDFs over 10,000 pages long, some of them larger than video files, through a pipeline of ML models spread across ten repositories nobody enjoyed touching.
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The Claude Certified Architect exam hands you six production scenarios and picks four at random, and Frank Coyle walks through them backwards, leading with the anti pattern in each one.
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Wow. Mathematical breakthroughs that would be called genius if done by humans. A secret message-board w/ AI agent swarms leaving notes read by future versions. Hassabis leaves CEO position, or was pushed out?