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Field notes from Stable Panther
The Convergence of AI, DevOps, and Compliance
The intersection of artificial intelligence, DevOps practices, and regulatory compliance is creating new challenges and opportunities for organizations. As AI systems become more integrated into business operations, the traditional boundaries between development, operations, and compliance are blurring. The New Reality Modern AI systems don’t exist in isolation. They’re embedded in production environments, processing real user data, and making decisions that affect business outcomes. This integration brings new responsibilities that span technical, operational, and regulatory domains.
Read more…How to Think Like an AI System Architect
AI system architecture is fundamentally different from traditional software architecture. While traditional systems focus on data flow and business logic, AI systems must balance model performance, data requirements, computational resources, and real-world constraints. Thinking like an AI system architect requires a unique mindset that combines technical expertise with business acumen and user empathy. The AI Architect’s Mindset Systems Thinking AI architects must think in terms of complex, interconnected systems rather than isolated components:
Read more…RAG Architecture Deep Dive
Retrieval-Augmented Generation (RAG) has become the de facto standard for building production LLM applications. But implementing RAG effectively requires understanding the full architecture, from data ingestion to response generation. This deep dive explores the components, patterns, and best practices for building robust RAG systems. What is RAG Architecture? RAG combines the power of large language models with external knowledge retrieval to generate more accurate, contextual, and up-to-date responses. Unlike traditional LLMs that rely solely on their training data, RAG systems can access and incorporate real-time information from external sources.
Read more…ISO, NIST, SOC 2: A Comprehensive Compliance Guide
Organizations today face a complex landscape of security and compliance requirements. Understanding the differences between ISO 27001, NIST frameworks, and SOC 2 is crucial for building robust security programs that meet both regulatory requirements and business needs. Understanding the Compliance Landscape ISO 27001: Information Security Management ISO 27001 is an international standard for information security management systems (ISMS). It provides a framework for establishing, implementing, maintaining, and continually improving information security management.
Read more…PyTorch Debugging
PyTorch is great for research, but debugging PyTorch models in production is a different beast. Here’s what I’ve learned from debugging models that actually matter. The Debugging Reality Gradient issues are everywhere. Vanishing gradients, exploding gradients, gradient clipping that’s too aggressive or too lenient. Most model problems start with gradients that aren’t behaving properly. Memory leaks in training loops. PyTorch’s automatic differentiation is powerful, but it can eat memory if you’re not careful.
Read more…OCR History: From Mechanical Readers to AI-Powered Recognition
Optical Character Recognition (OCR) has evolved from mechanical reading machines to sophisticated AI-powered systems that can understand context, handle multiple languages, and even recognize handwriting. This journey through OCR history reveals how technological advances have transformed document processing and information extraction. The Early Days: Mechanical and Optical Systems 1870s-1920s: The Birth of Reading Machines The concept of automated text recognition began with mechanical devices designed to assist the visually impaired. The first significant development was the Optophone, invented by Edmund Fournier d’Albe in 1913.
Read more…Agentic AI: Beyond LLM Wrappers
The current landscape of AI agents is dominated by simple wrappers around large language models. While these approaches can be effective for basic tasks, true agentic AI requires a more sophisticated architecture that goes far beyond just prompting an LLM and hoping for the best. The Current State: LLM Wrappers Most “AI agents” today are essentially sophisticated prompt engineering around large language models. They follow a simple pattern: Receive a user request Format it as a prompt for an LLM Parse the LLM response Execute some action based on the response Return results to the user This approach has significant limitations:
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