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New DeepSeek just did something crazy...
Matthew Berman· 2025-10-22 17:15
Deepseek just did it again. They just dropped a new paper and model DeepSseek OCR. OCR is basically image recognition.But why is that a big deal. Image recognition has been around forever, right. Well, they discovered something completely novel that has the potential to make language models, textbased models so much more powerful.Let me show you. This is the new paper from Deep Seek. Now, like I said, image recognition has been around for a long time.It's nothing special. We've seen it. It's been done a mil ...
12-Factor Agents: Patterns of reliable LLM applications — Dex Horthy, HumanLayer
AI Engineer· 2025-07-03 20:50
Core Principles of Agent Building - The industry emphasizes rethinking agent development from first principles, applying established software engineering practices to build reliable agents [11] - The industry highlights the importance of owning the control flow in agent design, allowing for flexibility in managing execution and business states [24][25] - The industry suggests that agents should be stateless, with state management handled externally to provide greater flexibility and control [47][49] Key Factors for Reliable Agents - The industry recognizes the ability of LLMs to convert natural language into JSON as a fundamental capability for building effective agents [13] - The industry suggests that direct tool use by agents can be harmful, advocating for a more structured approach using JSON and deterministic code [14][16] - The industry emphasizes the need to own and optimize prompts and context windows to ensure the quality and reliability of agent outputs [30][33] Practical Applications and Considerations - The industry promotes the use of small, focused "micro agents" within deterministic workflows to improve manageability and reliability [40] - The industry encourages integrating agents with various communication channels (email, Slack, Discord, SMS) to meet users where they are [39] - The industry advises focusing on the "hard AI parts" of agent development, such as prompt engineering and flow optimization, rather than relying on frameworks to abstract away complexity [52]