Production AI Engineering,
Not Just Theory
CyberInsist is a technical AI engineering blog focused on what actually works in production — LLM fine-tuning, RAG pipelines, inference optimization, and the engineering decisions that matter at scale.
Our Mission
The AI space is flooded with surface-level content — listicles, hype pieces, and rewritten documentation. CyberInsist exists to be different.
Every article is written for ML engineers who build real systems. We focus on the technical depth that practitioners need: benchmark comparisons with actual numbers, code that runs in production, and architectural decisions explained with context.
Whether you're choosing between DeepSpeed ZeRO-3 and PyTorch FSDP for distributed training, deciding how to structure your RAG pipeline, or optimizing LLM inference latency — we aim to give you the information you need to make informed decisions.
About the Author
Gulshan Sharma
AI/ML Engineer & Full-Stack Developer
Gulshan is an AI/ML engineer who builds production machine learning systems. His work spans LLM fine-tuning, RAG architectures, inference optimization, and full-stack AI application development.
At CyberInsist, he shares the technical insights and practical lessons learned from building AI systems that serve real users at scale.
Editorial Standards
Technical Accuracy
Every claim is backed by documentation, benchmarks, or verifiable code examples. We link to primary sources and official documentation.
Code-First Approach
Tutorials include runnable code examples. We show what works in practice, not just theory from papers.
Regular Updates
AI moves fast. We update articles when tools, frameworks, or best practices change. Every article shows its last updated date.
Practical Focus
We write for engineers building real systems. No hype, no clickbait — just the information you need to make better technical decisions.
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