Tagged "best-practices"
25 articles tagged best-practices, 17 February 2026 to 26 July 2026. Newest first.
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Anthropic Secures Its AI-Native Software Development Lifecycle
Anthropic publishes security practices for AI-integrated development workflows, offering insights into safe deployment patterns for LLM-assisted coding and infrastructure.
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AI Coding Agents Should Optimize for Less Owned Code
An analysis of how AI coding agents should be designed to minimize technical debt and proprietary code ownership, offering principles for sustainable local LLM-based code generation.
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'AI Code Is Insane Trash' – David Gerard on Code Generation Quality
A critical perspective on AI-generated code quality raises important questions about deploying LLMs for code synthesis tasks. This discussion highlights the need for careful evaluation and guardrails when using local LLMs for software development.
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What Every AI Builder Learns the Hard Way
A video compilation of hard-won lessons from experienced AI practitioners deploying models in production, covering practical challenges and solutions.
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Beyond Setup: Production Practices for Local LLM Deployment
A practical guide exploring what comes after initial local LLM setup, covering production considerations like monitoring, optimization, and operational best practices for sustained on-device inference.
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Ask HN: How do you provide your AI agents with access to credentials/secrets?
Community discussion on secure credential management patterns for local AI agents, covering practical solutions for handling API keys, database credentials, and other secrets safely within agent systems.
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Switching AI Tools Mid-Sprint Cost Us a Day (and What We Learned)
A case study documenting the operational costs and lessons learned from switching between AI tools during active development. The piece offers practical insights for teams deploying local versus cloud-based LLM solutions.
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Why Tool Calling is More Important Than Model Size for Local LLMs
A critical perspective on local LLM deployment emphasizes that even the largest models are ineffective without proper tool-calling capabilities. Understanding function calling implementation becomes essential for practical local inference applications.
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Ask HN: What is the AI setup for an experienced dev starting on a new project?
A community discussion on Hacker News where experienced developers share their practical AI tooling preferences and workflows, offering real-world insights for setting up local LLM development environments.
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Supply Chain DLP: Stop Leaked .env Files, Credentials, SSH Keys, and API Tokens
A security-focused tool and framework for preventing credential leaks in development and deployment pipelines, critical for teams running local LLMs with sensitive infrastructure.
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Good LLM Development and Usage Patterns
A practical guide outlining recommended patterns for developing and deploying LLMs in production environments, covering best practices for local and self-hosted inference.
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How to Run LLM Locally Without Falling for the Hype
Practical guide addressing common misconceptions and providing actionable steps for deploying large language models on local hardware. Emphasises realistic expectations and cost-benefit analysis.
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5 Things I Wish Someone Had Told Me Before I Tried Self-Hosting a Local LLM
A practical guide sharing key lessons learned from self-hosting local LLMs, covering pitfalls and best practices that can accelerate the learning curve for practitioners new to on-device inference. The article distills common mistakes and recommendations from real-world deployment experience.
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Self-Hosted LLMs in Production: Real-World Limits and Practical Lessons
Deep dive into the operational challenges and workarounds for deploying LLMs in production environments, drawing on practical experience with self-hosted systems.
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What Type of AI Usage? Deployment Patterns and Implementation Considerations
A framework for categorizing different AI implementation patterns, helping developers choose appropriate architectures for local versus cloud deployment.
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Why the Same LLM Gives Different Answers in Different Environments
An analysis of how environmental factors and context affect LLM behavior and output consistency across different deployment scenarios. Critical insights for practitioners deploying models locally.
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My AI Workflow: Practical Guide to Using AI Without Skill Atrophy
Marc G shares detailed insights on integrating AI tools into professional workflows while maintaining technical skills. The article provides practical patterns for responsible local and cloud model usage.
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On-Device AI Inference Emerges as New Security Blind Spot for CISOs
Security research identifies critical gaps in organizational understanding of on-device AI inference risks and safeguards. This analysis highlights essential security considerations for enterprises deploying local language models.
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LLM Wiki v2: Extended Knowledge Base for LLM Practitioners
An expanded version of Karpathy's foundational LLM wiki providing comprehensive reference material for understanding and deploying language models locally.
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RAG Deployment Lessons from Regulated Industries
Practical insights from deploying RAG-powered local AI assistants in highly regulated sectors including construction, aged care, and mining operations.
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Why Responsible AI Is the Bedrock of AI-Powered Applications
An exploration of responsible AI principles and their critical importance in building trustworthy, reliable AI-powered applications. Essential reading for practitioners deploying LLMs in production environments.
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What AI Augmentation Means for Technical Leaders
Birgitta Boeckeler discusses practical implications of AI augmentation for engineering teams, covering deployment strategies, tool selection, and organizational considerations for AI-augmented workflows.
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You're Using Your Local LLM Wrong If You're Prompting It Like a Cloud LLM
A practical guide highlighting how local LLM prompting strategies differ from cloud-based models, offering insights into optimizing inference for self-hosted deployments. This addresses a critical gap where many practitioners apply cloud LLM techniques to local models without accounting for architectural differences.
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We Audited the Security of 7 Open-Source AI Agents – Here Is What We Found
A comprehensive security audit of popular open-source AI agents reveals vulnerabilities and best practices for securing locally-deployed agentic systems, critical for production deployments.
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Self-Hosted AI: A Complete Roadmap for Beginners
KDnuggets publishes a comprehensive guide for deploying and running AI models locally, covering essential concepts, tools, and best practices for self-hosted inference. This resource serves as a practical entry point for developers new to local LLM deployment.