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8 posts on this thread of the journal.
Vectrel's Technical archive collects engineering guidance on model selection, MCP adoption, RAG versus fine-tuning trade-offs, open source versus proprietary stacks, and broader AI architecture patterns. Each post is grounded in systems we have deployed, with explicit constraints, benchmarks, and failure modes rather than vendor marketing or speculative trend coverage.
Z.ai's GLM-5.1 topped SWE-Bench Pro and can code autonomously for 8 hours straight. Here is what this open-source AI breakthrough means for your team.
An unbiased comparison of Claude, GPT, Gemini, and DeepSeek for business use cases. Compare capability, cost, privacy, and best fit for your needs.
Prompt engineering, RAG, and fine-tuning are the three main ways to customize AI behavior. Here is when to use each, what they cost, and how to decide.
Multi-agent AI systems use specialized agents working together to tackle complex tasks that single AI tools cannot. Here is how they work and when to use them.
GPT-4, Claude, Gemini, open-source models -- the landscape is crowded. Here is a framework for choosing the right AI model based on your actual use case, not marketing hype.
MCP is the emerging standard for connecting AI models to your tools and data. Here is what it means for businesses building AI systems and why it matters.
Open-source AI models like Llama 3 and Mistral can outperform paid alternatives for specific use cases. Learn when self-hosting saves money and when it does not.
DeepSeek R1 disrupted AI pricing in early 2025 with comparable performance at a fraction of the cost. Here is what it means for your business and how to benefit.
