Author: Calder Rivenhall
Enterprise Adoption, Governance, and Risk Management for Vibe Coding
Enterprise vibe coding accelerates development but introduces new risks. Learn how to govern AI-generated code, enforce compliance, and manage security without slowing innovation.
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Infrastructure Requirements for Serving Large Language Models in Production
Serving large language models in production requires specialized hardware, dynamic scaling, and smart cost optimization. Learn the real infrastructure needs-VRAM, GPUs, quantization, and hybrid cloud strategies-that make LLMs work at scale.
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Quantization-Aware Training for LLMs: How to Keep Accuracy While Shrinking Model Size
Quantization-aware training lets you shrink large language models to 4-bit without losing accuracy. Learn how it works, why it beats traditional methods, and how to use it in 2026.
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Multilingual Performance of Large Language Models: How Transfer Learning Bridges Language Gaps
Multilingual large language models use transfer learning to understand multiple languages, but performance drops sharply for low-resource languages. Learn why, how new techniques like CSCL are helping, and what it means for global AI equity.
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Memory Planning to Avoid OOM in Large Language Model Inference
Learn how memory planning techniques like CAMELoT and Dynamic Memory Sparsification reduce OOM errors in LLM inference without sacrificing accuracy, enabling larger models to run on standard hardware.
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Privacy and Security Risks of Distilled Large Language Models - What You Must Know
Distilled LLMs are faster and cheaper but inherit the same privacy risks as their larger models. Learn how model compression creates hidden security flaws - and what you must do to protect your data.
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Open Source in the Vibe Coding Era: How Community Models Are Shaping AI-Powered Development
Open-source AI models are reshaping software development through community-driven fine-tuning, offering customization and control that closed-source models can't match-especially in privacy-sensitive and legacy code environments.
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Knowledge Management with Generative AI: Answer Engines Over Enterprise Documents
Generative AI is transforming enterprise knowledge management by turning document repositories into intelligent answer engines that deliver accurate, sourced responses to natural language questions - cutting search time by up to 75% and accelerating onboarding by 50%.
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Security Risks in LLM Agents: Injection, Escalation, and Isolation
LLM agents can act autonomously, making them powerful but vulnerable to prompt injection, privilege escalation, and isolation failures. Learn how these attacks work and how to protect your systems before it's too late.
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LLM Evaluation Gates Before Switching from API to Self-Hosted
Before switching from an LLM API to self-hosted, organizations must pass strict performance, cost, and security gates. Learn the key thresholds, real-world failure rates, and the 7-step evaluation process that separates success from costly mistakes.
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Constrained Decoding for LLMs: How JSON, Regex, and Schema Control Improve Output Reliability
Learn how constrained decoding ensures LLMs generate valid JSON, regex, and schema-compliant outputs-without manual fixes. See when it helps, when it hurts, and how to use it right.
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Latency and Cost in Multimodal Generative AI: How to Budget Across Text, Images, and Video
Multimodal AI can boost accuracy but skyrockets costs and latency. Learn how to budget across text, images, and video by optimizing token use, choosing the right hardware, and avoiding common overspending traps.
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