tbrannan

4 ms·

1. 1.▲
   by tbrannan 8mo ago

   Great minds think alike!
2. 2.▲
   by tbrannan 8mo ago

   because it is, but its still true lol
3. 3.▲
   by tbrannan 8mo ago

   Thanks!! means a lot coming from you. Best of luck at Supabase.
4. 4.▲
   by tbrannan 9mo ago

   Really appreciate the input. I'll make sure to give you early access once we implement this, I'll keep you posted.
5. 5.▲
   by tbrannan 9mo ago

   Noted, we've been focused on Postgres first but SQL Server keeps coming up. Appreciate the feedback.
6. 6.▲
   by tbrannan 9mo ago

   I haven’t come across anything else like this. It’s genuinely impressive.
7. 7.▲
   by tbrannan 9mo ago

   This is useful. What if you ran a CLI locally that extracts just the statistical profile from prod cardinality, relationship ratios, etc. and uploaded that? We'd never touch your database, you just hand us the metrics and we match the
8. 8.▲
   by tbrannan 9mo ago

   Not yet, but you're the second person in this thread to call out distribution control as a gap. It's on our radar now. Thanks for the feedback.
9. 9.▲
   by tbrannan 9mo ago

   That is a really good point one-to-many relationships blow up fast. The trunk table idea is interesting, would simplify how people reason about limits. Appreciate the feedback, genuinely helpful!
10. 10.▲
    by tbrannan 9mo ago

    Appreciate the Snaplet comparison, they were doing good work. You're right that realistic looking strings are the easy part. We're focused on relational integrity first (FKs, constraints, realistic cardinality), but business domai
11. 11.▲
    by tbrannan 9mo ago

    Different focus, ShadowTraffic is config-driven and optimized for streaming/Kafka workloads. We're schema-driven: point us at your DDL and we generate relational test data automatically. Less config, more just give me test data th
12. 12.▲
    by tbrannan 9mo ago

    Thanks for the feedback! Honestly, we're still dialing in the tiers, what row limits would feel reasonable to you for your use case? Always helpful to hear what people actually need.
13. 13.▲
    by tbrannan 9mo ago

    Thanks! At 1M rows, I think a few things matter: Streaming: Can't hold it all in memory. Generate in chunks, write, release, repeat. Format choice: Parquet with row groups is fast and compresses well. SQL needs batched inserts (~1000&#
14. 14.▲
    by tbrannan 9mo ago

    Thanks! To clarify, the core engine isn't AI. It's deterministic pattern matching, so it runs in milliseconds with no token costs. There's an optional "Story Mode" that uses AI for narrative-coherent data (like &quo
15. 15.▲
    Show HN: DDL to Data – Generate realistic test data from SQL schemas

    55 points by tbrannan 9mo ago|32 comments
16. 16.▲
    by tbrannan 9mo ago

    I recently learned about the hooks and skills feature. This is cool