dennis16384

If you give it a real solver to drive and business constraints to operate under, it will do pretty good job (which is to map your business rules to constraints and balanced costs, develop deterministic tests, probabilistic tests, acceptance gates, all boring stuff).

Why this kind of marketing is even allowed?

It's been great even since gemini-3.1-flash-lite, which I heavily use in both complex vertical domain tools calling, plus JS code writing for eval-style dynamic tools. At least in my applications, cost x quality x speed there are simply no alternatives.

It is the core of ClickHouse for example, for many years. Is it secure enough in your opinion?

I had a similar success with Model2Vec static embedder and NER inference (both GGUF, compiled for WASM), ported to plain C from ONNX Runtime.

Wasm size from 30Mb to 300kb and 1.5x speedup. It's definitely worth it for performance or distribution size.

People still complain about 95mb download in 2026. It's literally 2 minutes of 1080p youtube watching.

Why bring server software to edge devices? Because its fucking awesome!

clickhouse?

"With experience and support from" is a nice landing trick!

How do you extract and relate to each other the facts from the documents that require comprehension and not simple similarity matching using common embeddings models?

This is the kind of content we all deserved in 2026, and this is still why I ask during interviews to explain how cookies are represented in HTTP protocol.

Hope they'll move eventually to consumer AI devices

Why would an LLM want to look into the contents, what for?

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.

Totally. One thing that all major AI vendors are not doing currently is merging server AI with edge devices.

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.

Fast response. I can upload Excel/csv and iterate under 10 seconds from question to result. Doing same thing in Claude with 10x less data takes 5 minutes.