@akshay_pachaar: Claude Code is now scary good at full-stack! I asked it to build a real-time weather intelligence dashboard with an int…

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Summary

Claude Code builds a real-time weather intelligence dashboard with an interactive 3D globe, forecasting, and anomaly detection using Tiger Cloud and TimescaleDB for backend and data management.

Claude Code is now scary good at full-stack! I asked it to build a real-time weather intelligence dashboard with an interactive 3D globe, a forecasting layer that predicts weather 3 days ahead, and an anomaly detector that flags cities whose weather is behaving abnormally. It came back with a spinning globe that has a day/night cycle using NASA satellite imagery, city lights on the dark side, weather icons that switch between sun and moon based on local time, and a time travel slider that scrubs through 10 days of data. And when a city's weather breaks from its own normal, it pulses red (abnormally hot) or blue (abnormally cold) right on the globe, updating live and reflecting the anomaly state at any point you drag the slider to. Claude Code built the whole thing in a single session, including the backend, database, data pipeline, and frontend. For the database, I needed something fast for time-series workloads since the app ingests hourly weather readings across many cities and serves time-range queries on every slider interaction. I used Tiger Cloud by @TigerDatabase, which gives you managed TimescaleDB on the Postgres you already know. Claude Code connected to it through the Tiger CLI MCP server and set up the entire backend directly: - Provisioned the database service - Created hypertables for time-partitioned weather storage - Set up continuous aggregates for pre-computed rollups - Built the data ingestion pipeline and the full NextJS + ThreeJS frontend The time travel slider queries thousands of rows on every position change. On a regular Postgres table, this would require manual partitioning and index tuning to stay fast as data grows. TimescaleDB partitions the data by timestamp automatically, so each query only hits the relevant time chunk. Continuous aggregates serve the trend charts, the forecast layer, and the anomaly baselines from pre-computed rollups instead of rescanning raw data on every request. The video below shows the final build in action, and I worked with the Tiger Data team to put this together. Tiger CLI is open-source (Apache 2.0) and works with Claude Code, Cursor, Codex, Gemini CLI, and VS Code. To try this yourself: → Sign up for Tiger Cloud (I have shared the link in the replies). It gives you $1,000 free credits (no card needed) → Install Tiger CLI: curl -fsSL https(:)//cli(.)tigerdata(.)com | sh → Run tiger mcp install claude-code → Give Claude Code a prompt and let it build sign-up here: https://fandf.co/4gFoRHn My co-founder also wrote a detailed article on this. The article is quoted below.
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Claude Code is now scary good at full-stack!

I asked it to build a real-time weather intelligence dashboard with an interactive 3D globe, a forecasting layer that predicts weather 3 days ahead, and an anomaly detector that flags cities whose weather is behaving abnormally.

It came back with a spinning globe that has a day/night cycle using NASA satellite imagery, city lights on the dark side, weather icons that switch between sun and moon based on local time, and a time travel slider that scrubs through 10 days of data.

And when a city’s weather breaks from its own normal, it pulses red (abnormally hot) or blue (abnormally cold) right on the globe, updating live and reflecting the anomaly state at any point you drag the slider to.

Claude Code built the whole thing in a single session, including the backend, database, data pipeline, and frontend.

For the database, I needed something fast for time-series workloads since the app ingests hourly weather readings across many cities and serves time-range queries on every slider interaction.

I used Tiger Cloud by @TigerDatabase, which gives you managed TimescaleDB on the Postgres you already know.

Claude Code connected to it through the Tiger CLI MCP server and set up the entire backend directly:

  • Provisioned the database service
  • Created hypertables for time-partitioned weather storage
  • Set up continuous aggregates for pre-computed rollups
  • Built the data ingestion pipeline and the full NextJS + ThreeJS frontend

The time travel slider queries thousands of rows on every position change.

On a regular Postgres table, this would require manual partitioning and index tuning to stay fast as data grows.

TimescaleDB partitions the data by timestamp automatically, so each query only hits the relevant time chunk.

Continuous aggregates serve the trend charts, the forecast layer, and the anomaly baselines from pre-computed rollups instead of rescanning raw data on every request.

The video below shows the final build in action, and I worked with the Tiger Data team to put this together.

Tiger CLI is open-source (Apache 2.0) and works with Claude Code, Cursor, Codex, Gemini CLI, and VS Code.

To try this yourself:

→ Sign up for Tiger Cloud (I have shared the link in the replies). It gives you $1,000 free credits (no card needed)

→ Install Tiger CLI: curl -fsSL https(:)//cli(.)tigerdata(.)com | sh

→ Run tiger mcp install claude-code

→ Give Claude Code a prompt and let it build

sign-up here: https://fandf.co/4gFoRHn

My co-founder also wrote a detailed article on this.

The article is quoted below.


Postgres for time-series workloads at any scale | Tiger Data

Source: https://www.tigerdata.com/go/kol?utm_source=X&utm_medium=affiliate&utm_campaign=fnf-july&utm_content=apachaar-1kcredits-tsdb TigerData logo

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Two Extensions. Complete Search.

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CREATE INDEX ON documents USING bm25\(content\) WITH \(text\_config='english'\); SELECT \* FROM documents ORDER BY content <@\> 'database system' LIMIT 10;

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