OptiSchema Slim is a local-first AI tool for PostgreSQL performance. A Health Doctor to scan for bloat, unused indexes, slow queries, and high-impact workload issues with configurable thresholds, scan history, and deterministic scoring with AI explanations. Drill into expensive queries, get EXPLAIN plan breakdowns, test “what-if” index ideas, compare before/after plans with estimated cost, and copy the SQL.
I m building OptiSchema Slim, a local-first AI tool for PostgreSQL performance. It finds slow/high-impact queries, explains plans, and can test what-if index ideas before you apply anything.
For people who tune Postgres in real life: what would you need to see to trust a recommendation?
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Hello PH, I built OptiSchema Slim because Postgres tuning kept turning into: notice slowness late → stare at EXPLAIN → guess an index → build it → hope.
OptiSchema Slim is local-first and includes a Health Doctor (configurable thresholds for bloat/unused indexes/slow queries/workload impact, scan history, and a deterministic score with AI explanations). You can drill into a query, see an AI plan breakdown, and try “what-if” index ideas to compare before/after plans + estimated cost, then copy the SQL.
If you try it: what would you need to trust recommendations more, runtime proof (EXPLAIN ANALYZE), clearer diffs, or something else?
Absolutely gem project Arnab. Loved the idea.