📰 Dev.to · benzsevern

Cross-database ER across OSV, GHSA, PyPA, RustSec, Go vulndb — 869k records, 608k canonical vulns, and one structural blind spot.

Running entity resolution across 10 public blockchain attribution datasets surfaces cross-jurisdictional sanctions and universal infrastructure patterns.

The OSS ER Bargain: What Entity Resolution Actually Costs You Benchmarking dedupe vs...

An AI agent can write SQL, draft an email, and refactor a repo. Ask it to deduplicate a 50,000-row...

Download a government CSV, load it into pandas, and you'll find "MEMORIAL HOSPITAL" listed twelve...

We ran four Python entity resolution libraries on the same three datasets — Febrl, DBLP-ACM, and 10K real voter records. Here's where each shines.

We benchmarked GoldenMatch on Amazon's BPID dataset — 10,000 adversarial PII pairs. With DOB parsing and Vertex AI embeddings, we hit 0.750 F1 — matching Ditto

We ran GoldenMatch on 401,125 bulldozer auction records from Kaggle. Iterative LLM calibration learned the optimal match threshold from just 200 pairs (~$0.01).

Enable LLM boost across GoldenCheck, GoldenFlow, and GoldenMatch to catch what fuzzy matching misses — with real costs under $0.10.

Add a production-ready data quality pipeline to your Python backend in 5 minutes. One pip install, one function call, zero config.

We ran the full Golden Suite pipeline on 208,505 real NC voter registration records. 61 quality findings, 197K addresses cleaned, 10,718 duplicate clusters foun

The same 10 data quality issues show up in every dataset. Here's what they look like and how to fix each in one line.

Privacy-preserving record linkage with bloom filters. 92% accuracy. Zero raw data exchanged.

How GoldenMatch auto-detects columns, picks scoring algorithms, and hits 97% F1 with zero configuration.

You have a CSV with duplicate records. Maybe it's customer data exported from two CRMs, a product...