good point, adaptability's the name of the game for both humans and lls; intelligence isn't just about facts, it's about how you handle chaos.
LLMS
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Claude 4.1 Update: Game-Changer for Coding and Agentic Tasks
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Is the Claude 4.1 update a game-changer or a small step? 🤔
— Louis-François Bouchard 🎥🤖 (@Whats_AI) 19 août 2025
Let’s break it down 👇
What’s new
• Big boost in agentic tasks, coding, and reasoning → 74.5% on SWE-bench
• Stronger at multi-file editing & precision (nice for coding!)
• Opus 4.1’s coding focus feels like a… pic.twitter.com/aXAMJEu3jCIs the Claude 4.1 update a game-changer or a small step? Let’s break it down What’s new • Big boost in agentic tasks, coding, and reasoning → 74.5% on SWE-bench • Stronger at multi-file editing & precision (nice for coding!) • Opus 4.1’s coding focus feels like a
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SFT Notebook Available in Model Card for Fine-tuning
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Yes! you can use our SFT notebook available in the model card
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Chain-of-Thought prompting compared to modular code
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The analogy to modular code hit hard. Imagine building an entire app in one function. That’s what we’re doing with CoT.
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Treating AI Prompts as Structured Products
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The real unlock is treating prompts as products, versioned, structured, and use case specific.
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The Role of Tool Use and APIs in AI Agent Value
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Tool use is where agents stop being interesting and start being valuable.
APIs are their superpowers. -
The importance of context engineering for AI agents
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Context engineering might quietly be the most important concept here. If your agent has bad context, it doesn’t matter how smart the model is.
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OdysseyBench for Evaluating AI Agent Capabilities
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OdysseyBench doesn’t just evaluate agents. It interrogates them. If your agent passes OdysseyBench, it's not just good it's real-world ready. Otherwise? You're still benchmarking illusions.