Recently started some agentic features for paid version, and this lead to a side project https://eatmydata.ai - a question-to-sql-to-dashboard builder, where data doesn't get exposed to AI (with bundled in-browser SQLite vector search, NER and many other features).

The latter is open-sourced under MIT: https://github.com/eatmydata-org/eatmydata

We have low-cardinality data and yes this is safe to share and required to build an actual query.

Then we have high-cardinality and possibly PII - there’s absolutely no reason to share that data, there’s nothing for LLM to analyse there. Also semantic index (vector search) will find relevant records much faster and more accurately that any chain-of-thoughts just with an LLM-authored search fn call.

Further there are continuous numerical values and there’s not much LLM needs to see in there either. We can say, for example, if you look at data distributions when building your analysis, it can drive your analysis logic, but another point of view here is taht it creates unnecessary bias instead.

For example, there is no way neither in Claude nor in ChatGPT to run your own WASM or JS or whatever AI produces directly in user's browser context as a tool/skill - there is no call site for that. The only option is remote server-side.

My whole idea was that AI can perfectly write SQL and dashboard code knowing only the shape of your data and not it's contents. With direct upload to vendor now we're forced to share the contents.

For example, MIT-licensed sqlite vector search extension.

Overall, I have a orchestrator - sql coder - js coder - dashboards, all without backend, running locally in the browser. It's mostly tested on small analysis and question answering with Gemini Flash Lite, and the overall target was speed from question to answer, including data sharing and waiting.

https://eatmydata.ai/

https://github.com/eatmydata-org/eatmydata

For the "show me how many people are from Dallas" in AdventureWorks, it really depends on the model.

With `gemini-3.1-flash-lite` for all agents it produces this sql from the first try for $0.0033: ``` SELECT COUNT(DISTINCT ca.CustomerID) as customer_count FROM CustomerAddress ca JOIN ( SELECT a.AddressID FROM vector_search('Address', 'City', 'Dallas', 20) vs JOIN Address a ON a.rowid = vs.rowid WHERE a.City = 'Dallas' ) addr ON ca.AddressID = addr.AddressID ```

and then formats output in Markdown.

I'll bake some default model in later, and more examples. And I guess it needs some default token quota.

You ask a question, agents produce multiple SQL queries to in-browser sqlite, never seeing results, and write dashboard configuration code. The data you analyze will be indexed with a local semantic index (embeddings generation + sqlite vector search fully local).

Next, sandboxed QuickJS runs this code to produce rich dashboards directly in your browser, no backend attached. This is a fully frontend app (except OpenRouter or other remote LLM).

All data sent to LLM's is heavily sanitized and obfuscated at several points. The remote LLM never sees the contents of data it analyzes. Why does it exist - I started this is a testbed for my local-first AI projects, agentic workflows and contextual data analysis experiments.

It grew into a tool I use daily for quick and dirty data analytics when I don't want to waste time debugging SQL or building charts for simple data questions, when I literally need an answer under 10s.

I also don't like the idea of sharing random data in Claude/ChatGPT chat, neither uploading any work-related datasets to them. Plus they both often choke on tiny 100k rows data.

Fully open-sourced under MIT https://github.com/eatmydata-org/eatmydata, run it yourself it's a static web app.

What's in the box:

- SQLite OPFS adapted from wa-sqlite, data queried only locally;

- TurboQuant semantic indexing extension for sqlite (MIT-licensed);

- Quantized PII detection and embedding generation models straight in browser;

- NER and embeddings inference engines in zero-dependency C and wasm-simd128 optimizations (1.7x faster and 38x lighter binary compare to onnxruntime);

- QuickJS sandbox for AI-generated code;

- Orchestrator <-> SQL Planner <-> Coder agent loop that build SQL and dashboards from user query;

- Apache ECharts for dashboards;

- Fork of xslx Community edition to support styles (missing in OSS version upstream).

Hope it'll be useful to anyone who is interested in local-first stuff.

Comments URL: https://news.ycombinator.com/item?id=48472867

Points: 8

# Comments: 2

Multi-threaded WebAssembly in action for route optimization, all bundled with geocoding, OSM maps and routing, and provided world-wide.

We're adding driver's PWA, saving and sharing of route optimization projects, editing of optimized routes, sharing it with others either for execution or for approval, integrations and AI-assisted data imports, auth flows, support prompts, sales automation and all that boring stuff.

It's been approx. an year since we are up and running, and we helped 100+ businesses in the US and world-wide to understand the value and savings of automated route planning, and prepared tens thousands of optimized routes for companies operating from 1 to 50 vehicles. All this keeping our operational expenses low and flat, thanks to our local-first route optimization engine working in the browser, and reliance on OpenStreetMap.

Let's start it with index of whole Spain, 2.4gb download, 4gb on disk: https://gist.github.com/dkourilov/e243270684b5973f1fac005c78...

I'd say it's pretty usable to run a EU-sized country or several US states on any commodity PC. For embedded devices, it really depends what is the device. On Raspberry PI it should be fine for batch geocoding, realtime (typeahead) will definitely be lagging.

You can balance traffic to external networks or clouds with it too.

DJ_Dave live events are the best illustration for all of it. If you love electronic music, ever touched any generative art, and know basic coding this is for you.

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