Working on a knife steel comparison database. It lets you visually compare steels across key performance axes — edge retention, corrosion resistance, toughness, and ease of sharpening — using interactive radar charts.
You can filter by use case (EDC favorites, budget-friendly, high edge retention, etc.) and get a quick read on tradeoffs between steels like S35VN, MagnaCut, M390, and others.
Text I built a scraping project to solve a niche domain problem: figuring out which chef knife steels are actually "good" vs. just marketing hype, based on r/chefknives archives.
The core technical challenge was Entity Resolution. I didn't want to burn thousands of tokens feeding raw threads to an LLM just to identify common terms like "Wüsthof" or "VG-10."
My solution was a 4-step "Inverse Masking" pipeline:
Local Fuzzy Match: Fuse.js scans text against a local catalog of ~500 brands/steels. This catches 80% of entities for zero cost.
Masking: I replace found matches in the text (e.g., "[ENTITY_FOUND]") to hide them.
LLM Discovery: I send the remaining text to an LLM (via OpenRouter). Because the "loud" common entities are masked, the model is much better at spotting obscure artisan makers or slang that the fuzzy matcher missed.
Sentiment: The LLM assigns context-aware scores (-1.0 to 1.0) to distinguish between "I want to buy X" (Neutral) and "X chipped immediately" (Negative).
The Findings (from 542 threads):
MagnaCut is the technical winner (28:1 positive ratio).
Ginsan (Silver 3) is the practical kitchen favorite. It beats premium powdered steels simply because users almost never complain about chipping.
VG-10 is the most controversial. Highest volume, but statistically the highest ratio of "micro-chipping" complaints.
Stack is Node.js and MongoDB. Full charts and breakdown are in the post.
I'm curious if others are using this "Fuzzy First -> LLM Second" pattern for NER tasks to save context window, or if I should just move to vector embeddings for the initial lookup?
that is correct, edge retention, ease of sharpening and toughness usually come at a trade off to one another.
A harder blade is more brittle (less tough) and keeps its edge longer... but is also more difficult to sharpen once it gets dull.... generally speaking.
You can filter by use case (EDC favorites, budget-friendly, high edge retention, etc.) and get a quick read on tradeoffs between steels like S35VN, MagnaCut, M390, and others.
Built it because I kept seeing the same "which steel is best" debates in knife forums with no good way to actually compare data side by side. Site: https://new.knife.day/blog/knife-steel-comparisons/all https://new.knife.day/steels