A 27B AI Model Now Runs on an iPhone: What Model Compression Means for Business
PrismML's Bonsai 27B runs a 27-billion-parameter model on an iPhone. Here is what AI model compression changes for business cost, privacy, and strategy.
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Technical
13 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.
Self-Verifying AI Agents: Why 2026 Is the Year AI Started Checking Its Own Work
AI agents are learning to check their own work. Here is how self-verification and Plan-Execute-Verify architectures make agentic automation reliable in 2026.
DiffusionGemma: What Google's Parallel Text Model Means for Your AI Costs
Google's DiffusionGemma generates text in parallel blocks, not one token at a time. Here is what this open-weight, self-hostable model means for businesses.
Local AI Comes to the Laptop: What NVIDIA's RTX Spark Means for Business
NVIDIA's RTX Spark runs 120B-parameter models on a laptop. Here is what on-device AI changes for business cost, privacy, and architecture decisions.
Beyond the Transformer: What Subquadratic AI Models Mean for Your Inference Costs
New subquadratic AI architectures scale linearly instead of quadratically. Here is what that shift means for enterprise inference costs and strategy.
An Open-Source AI Can Now Code for 8 Hours Straight: What GLM-5.1 Means for Your Engineering Team
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.
Claude, GPT, Gemini, and DeepSeek: An Honest Comparison for Business Use Cases
An unbiased comparison of Claude, GPT, Gemini, and DeepSeek for business use cases. Compare capability, cost, privacy, and best fit for your needs.
Fine-Tuning vs. RAG vs. Prompt Engineering: Choosing the Right Approach
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 Systems Explained: How AI Teams Outperform Single AI Tools
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.
Choosing the Right AI Model for Your Business: A Practical Guide
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.
What Is the Model Context Protocol (MCP) and Why Should Your Business Care?
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: When Free Beats Paid
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.
What the DeepSeek Effect Means for Your AI Budget
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.
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