Major_Grooves
Joined 17 years ago
Karma
842
Submissions
334
Member since
Feb 20
Building: tilores.io
View on Hacker NewsRecent submissions
Comment · 3 months ago
meanwhile you practically have to pay people to take away current used Ikea pieces.
The market for old DDR (East Germany) furniture here in Berlin is a bit crazy. Old cheap poor quality furniture (all MDF) selling for loads because it looks good on Instagram.
I know a guy that goes to house clearance sales in random places in East Germany, then brings pieces back to sell to hipsters in Berlin. Like me.
Comment · 4 months ago
I'm the founder of Tilores, the entity-resolution tool used here - so full disclosure, this is my company's product. This wasn't a paid engagement or a case study. It started because my wife is from Venezuela, and she saw people on social media pointing out that the missing-persons lists had huge numbers of duplicates.
On the data: these are public citizen-lead efforts to crowd-source the names of the missing - hosted on websites and spreadsheets. There is no official verification process behind the individual entries, which is part of why the duplicate problem existed in the first place.
An issue we have now realised is "bad actors" trying to access the data...
Happy to answer anything - methodology, false positives, data handling, whatever.
Comment · 4 months ago
I am only slightly miffed that you did not mention your other co-worker who, before the co-worker you did mention, but an entire two-sided marketplace using LLMs (lovable). https://minnnis.com/
Comment · 1 year ago
Please make it work with Lovable for vibe coders like myself.
Comment · 2 years ago
Do you end up with lots of duplicates when you are scraping? If you also scrape IG, YouTube and LinkedIn, would you link them all to the same influencer?
That might be quite an interesting identity resolution challenge (disclosure: I build identity resolution tech).
I would not mind taking a look. Always interested to see how others are handling such data.
Comment · 2 years ago
Hello HN! We (Steven, Hendrik and Stefan) built a real-time identity resolution system that can handle hundreds of millions of customer records, and recently launched a LangChain integration to use it as a RAG source for LLMs.
We built this while working at a European credit bureau, where we needed to deduplicate and match millions of monthly record updates from various sources. Traditional approaches using graph databases and Spark couldn't handle the scale, so we built our own solution using AWS Serverless.
Each identity is stored as an individual graph structure, using rules-based and ML matching. Performance: <300ms ingest (tested to 5,000/sec), <150ms search regardless of graph size. Several fintech companies use it for fraud detection, KYC, and customer 360.
Unlike vector databases which can blur similar entities together, IdentityRAG maintains distinct customer identities while pulling data from multiple systems - even when customer details differ across databases.
You can try it out with our sample chatbot in the Github repo (linked above). Free to sign up, we charge based on number of unified customer records (it is free for playing and testing). We would love to hear your comments and questions.
There is also a demo video in the repo and you can find more details about us here: https://tilores.io/