Streamlit

Streamlit AI

Streamlit AI

Streamlit AI

Streamlit AI

Streamlit AI

Streamlit AI

Streamlit AI

Streamlit AI

Streamlit AI

Streamlit AI

Vibe coding is about to make drag-and-drop BI feel obsolete

I built Lumyr, a tool that lets you describe the dashboard you want in plain English and get back a live, shareable dashboard. What I find interesting is that the value here is not just speed. In a lot of cases, the result can actually be better than what teams typically build in drag-and-drop BI tools, because you are not constrained by the limits of the UI. Claude is doing the heavy lifting on the dashboard generation side, while Lumyr provides the harness around it to handle the Python and Streamlit layer automatically. So the user does not need to: write Python open Streamlit deploy anything set up hosting manage infra They just ask for the dashboard they want. I built this because a lot of analytics workflows still feel much slower and more rigid than they should. The common pattern is still: ask someone for a dashboard, wait, get something half-right, then go back and forth again. I wanted to see how far Claude could push a more direct workflow. It’s free to try here if anyone wants to test it: lumyr.io submitted by /u/Serious_Control829 [link] [comments]

I built a global news intelligence platform with Claude Code in 3 days

Wanted to see if pattern recognition across global news data could surface insights (and if I could build something like it). Not predictions. Just tracking where the world's attention is going, where it's leaving, and what patterns emerge over time. How it works: Pulls 1,600+ articles/day from RSS feeds and 3 news APIs (GNews, Currents, World News API) across 20+ regions. Each article gets structured metadata stored in SQLite: spaCy handles entity extraction locally (people, orgs, locations) Mistral (free tier) tags topics, sentiment, and narrative framing scikit-learn TF-IDF + cosine similarity finds related articles across regions without keyword matching NetworkX builds co-occurrence graphs between topics, regions, and entities Anomaly detection (Isolation Forest) flags when a region's topic distribution breaks its own 30-day baseline The key design is layered analysis. The daily API calls aren't the insight, they're raw material. Layer 1: Daily fetch. 1,600 articles tagged and stored. Layer 2: Daily synthesis. Mistral reads all of today's data as one global snapshot. Flags stuff like "four unrelated countries discussed semiconductor supply chains today." Layer 3: Weekly. Feed it all seven daily syntheses. Now it compares across time and geography together. Layer 4: Monthly. This is where the interesting stuff should emerge. Trend lines and narrative shifts you'd never catch reading daily headlines. Each layer compresses signal. You never ask the LLM to remember 3,000 articles. You feed it 7 summaries that already distilled those articles. Hindsight Mode is the part I don't fully know what to do with yet. It builds signature vectors from known events (topics, sentiment, narrative framing), then scores today's live news against them using cosine similarity. So it might say "current patterns are 71% similar to what things looked like before the last trade war." Cool, but what do you actually do with that information? Open to ideas here. What I'm watching for: Did water scarcity coverage start spiking in East Africa, show up in South American outlets two weeks later, then hit policy discussions in Europe a month after? That kind of propagation is invisible if you only read one region's news. Built the whole thing with Claude Code. Used dullnote to track the build log and roadmap between sessions so Claude had full context on what was done and what was next. Stack: Python, SQLite, Streamlit, Mistral free tier, spaCy, scikit-learn, NetworkX, sentence-transformers and dullnote Still early. Need 60+ days of data before the cross-region stuff gets really interesting, but the layered analysis is already producing non-obvious outputs. Happy to answer questions or tell me what I should add. I added the video to the post but it didnt render so: Dashboard Demo video: https://streamable.com/tx70b4 submitted by /u/Minute_Bit8225 [link] [comments]