Progressive skill loading achieves 98% token reduction in LLM workflows through modular context architecture—lessons from building production systems

Vision-Language-Action models transform AI from code into physical robots, with practical implications for security, safety, and homelab automation

AI is evolving from tools into cognitive infrastructure that shapes how billions think, yet we understand little about its long-term effects

Claude-Flow orchestrates AI agent swarms for development—84.8% SWE-Bench solve rate with neural learning. Here's my experience building with it

The ongoing saga of turning my standards repo into an MCP server for Claude. Spoiler: It's working mostly, and I've only rewritten it three times so far

How I built a standards repository that transforms Claude CLI into a context-aware development powerhouse with 90% token reduction and workflow automation

Complete guide to fine-tuning open-source LLMs on homelab hardware using QLoRA, covering dataset prep, training optimization, and evaluation

Lessons from running LLMs and AI experiments at home while keeping data secure, covering model isolation, network segmentation, and privacy controls

Foundation models that understand text, images, and audio together—architecture, capabilities, and applications beyond single-modality systems

From 2K to 2M tokens—how expanding context windows transform LLMs from chatbots to reasoning engines, with practical implications for applications

Can LLMs detect smart contract vulnerabilities? Testing GPT-4 and Claude against known exploits with surprising results and security implications

Training effective AI models with limited compute—techniques like quantization, pruning, distillation, and efficient architectures for resource constraints

How AI and edge computing create responsive, private systems that process data locally, revolutionizing autonomous vehicles and smart manufacturing

AI revolutionizes both attack and defense in cybersecurity—from automated threat detection to AI-powered exploits. Exploring the evolving battleground

How nature's 3.8 billion years of R&D inspires robot design—from gecko feet to swarm intelligence, exploring biomimetic principles in modern robotics

Training AI agents to learn from physical interaction with the world, combining vision, language, and action for robots that adapt to real environments

Effective prompt engineering techniques for LLMs—few-shot learning, chain-of-thought, system prompts, and strategies for reliable outputs

Ethical implications of LLMs—bias, misinformation, privacy, and accountability. Exploring responsible AI development and deployment frameworks

High-performance computing brings supercomputer capabilities to research and industry—parallel processing, distributed systems, and optimization strategies

The moment I realized how LLMs confidently hallucinate facts was when I understood why RAG isn't optional - it's essential for trustworthy AI systems

Reading 'Attention is All You Need' felt like discovering a secret that would reshape everything I thought I knew about natural language processing - and it did

Running both Llama and GPT-4 in my homelab taught me the real trade-offs between open-source and proprietary LLMs beyond hype and marketing

AI-generated deepfakes threaten truth itself. Exploring detection techniques, authentication methods, and the arms race between creation and detection