The Claude Code analytics space is really interesting to me right now as well, this is cool.
I'm coming at it from more of the data infrastructure side (e.g. send all of your logs and metrics to a cheap Iceberg catalog in the cloud so you have a central place to query[1]) but also check out https://github.com/tobilg/ai-observer -- duckdb popping up everything to make this interesting and easy.
edit: forgot to mention StackQL [3], which is somewhat similar but takes a SQL-first vs python-first approach. The IaC space is about to get a lot more interesting.
Anthropic ships an official plugin to create linters for you based on your Claude Code history or instructions, it’s great. You can vibe code your lint rules per repo.
This is part of a bigger consolidation trend, AI hype or not: which general-purpose data vendor gets to store and query all of your observability and business data?
Snowflake acquired Observe last week, AWS made it easy in December to put logs from Cloudwatch in their managed iceberg catalog, and Azure is doing a bunch of interesting stuff with Fabric.
The line between your data lake/analytics vendor and observability vendor is getting blurry.
This is an emerging pattern that’s surprisingly powerful: thick clients that embed wasm query engines (pglite, duckdb) and do powerful analytics (with or without AI agents writing the queries).
Below are two examples using duckdb under the hood for similar purposes. Like the author, excited for this type of architecture making semi-advanced analytics more attainable if you’re not a data engineer.
Agree with the author, will add: duckdb is an extremely compelling choice if you’re a developer and want to embed analytics in your app (which can also run in a web browser with wasm!)
Think this opens up a lot of interesting possibilities like more powerful analytics notebooks like marimo (https://marimo.io/) … and that’s just one example of many.
We recently created a survey website for the community survey results for Node-RED making it completely dynamic and segment-able. Creates lots of value and allows everyone to look at the data through their own lens. It uses DuckDB with WASM under the hood. Awesome technologies.
I'd really love a minimalist version, I'm not sure how small it's feasible for them to shrink it. As long as it doesn't get bigger and devices keep getting faster, I suppose?
I'm coming at it from more of the data infrastructure side (e.g. send all of your logs and metrics to a cheap Iceberg catalog in the cloud so you have a central place to query[1]) but also check out https://github.com/tobilg/ai-observer -- duckdb popping up everything to make this interesting and easy.
[1] https://github.com/smithclay/otlp2pipeline