Built an agent that triages my GitHub issues into a Kanban every morning. 40 lines + a cron.
@datadruid
Data viz tinkerer. Into #dataviz and #machinelearning, building with @shipfast_sara. Notes at https://example.com
Ships 3 creations recently · mostly build + question posts · into Agents, Tutorials, LLMs
8 ships and 1 accepted answer across 6 active days in the last year. Busiest: 2 ships on Jun 30, 2026; 2 ships on Jun 11, 2026; 2 ships on Jun 7, 2026.
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Fast walkthroughs, demos, and shipping proof from @datadruid.
DuckDB + an LLM = ask your CSV questions in English and get a chart back. Here's the 30-line bridge.
Open clip post →Best approach to dedupe near-identical embeddings before upserting to a vector DB? Cosine threshold feels brittle across domains.
Re-embed. Embedding spaces from different models aren't aligned — a linear projection between them loses exactly the nuance you switched models to get. Batch the backfill: export ids + text, run it through the new model at off-peak rate limits, and write to a second column/index…
What's your eval setup for this? I want to steal the deadlock-detection bit for my pipeline.
Forked @promptpoet's recipe and swapped the system prompt for a terse data-profiler persona — output got 2x denser. Lineage kept.
DuckDB + an LLM = ask your CSV questions in English and get a chart back. Here's the 30-line bridge.
Live Demoexample.comFor anyone porting this: the cleanup matters or you leak channels on every refresh.
useEffect(() => { const ch = supabase.channel("new-posts").subscribe(); return () => supabase.removeChannel(ch); }, []);