Not a bad idea but harder to identify unless the trade is on something like polymarket where info is available on public ledger

It wasn’t as simple as just mapping brands… for one I don’t have a complete dictionary of all the brands that could be mention.

Other problems are:

Brand names that match common English words, brand names that are also item models used by another brand… etc

the fact that i used AI to assist with creating this doesn’t detract from the value of the research

Maybe it doesn't detract from the value of the research, but you wanted to convey that research to readers like OP and me, otherwise you wouldn't have written it/had an LLM generate it, and posted to your blog.

I found the prose full of jargon that just wasn't comprehensible, like "sales motion", "perfect shape", "shelf ranked", "opaque majority". I can figure out what some of these mean by context, but they did make your article a tough read. I suspect that a lot of the jargon is LLMese, I see tons of unfamiliar jargon and impenetrable sentence structure in LLM-generated PRs at work. I can usually work out what the LLMese means, but it's definitely harder to comprehend than human-written prose.

Now there is a valid critique , thank you

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.

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

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?

https://new.knife.day/blog/reddit-steel-sentiment-analysis

cool, how did you create it? whats the architecture like ?

Thanks very much! Architecture - is truly recidivistic - LAMP, cron jobs, o3-mini, bootstrap. It works, its fast because its not complicated, and b/c I'm doing things like updating hourly vs. real time.

New Knife Day: (https://new.knife.day/blog/knife-steel-comparisons/all) My goal is to build the most complete wiki and social network for knife collectors, makers and consumers researching a new purchase

will add, thanks

yes I plan to add these