Alignment

Training a model on a large corpus to learn general language patterns before task-specific adaptation.

Design a personalized posture correction and ergonomic optimization protocol to reduce pain, improve alignment, and enhance daily function and performance.

Optimize 3D models for additive manufacturing considering orientation, supports, infill, and material properties.

Official Firecrawl MCP Server - Adds powerful web scraping and search to Cursor, Claude and any other LLM clients.

Fine-tuning is the process of further training a pre-trained model on a smaller, task-specific dataset so it becomes better at a particular job. The base model already knows grammar, facts, and reasoning from pre-training; fine-tuning teaches it the style, format, or domain you care about. Common reasons to fine-tune include matching a brand voice, classifying support tickets, extracting structured fields from documents, and improving performance on low-resource languages. You typically need hundreds to thousands of high-quality examples. Each example pairs an input with the desired output, and the model's weights are updated to reduce the error on those examples. Fine-tuning is not always the right first step. Prompt engineering, retrieval augmentation, and few-shot examples are faster and cheaper to iterate. Fine-tuning becomes worthwhile when the behavior you want is hard to describe in a prompt, must be consistent at scale, or needs to run without sending long examples every request. Techniques like LoRA and QLoRA make fine-tuning feasible on consumer hardware by updating only a small subset of weights.