Just to clarify the background a bit. This project wasn’t planned as a big standalone release at first. On January 16, Ollama added support for an Anthropic-compatible API, and I was curious how far this could be pushed in practice. I decided to try plugging local Ollama models directly into a Claude Code-style workflow and see if it would actually work end to end.

Here is the release note from Ollama that made this possible: https://ollama.com/blog/claude

Technically, what I do is pretty straightforward:

- Detect which local models are available in Ollama.

- When internet access is unavailable, the client automatically switches to Ollama-backed local models instead of remote ones.

- From the user’s perspective, it is the same Claude Code flow, just backed by local inference.

In practice, the best-performing model so far has been qwen3-coder:30b. I also tested glm-4.7-flash, which was released very recently, but it struggles with reliably following tool-calling instructions, so it is not usable for this workflow yet.

Built an auditable AI (Bible) translation pipeline: Hebrew/Greek source packets -> verse JSON with notes rolling up to chapters, books, and testaments. Final texts compiled with metrics (TTR, n-grams).

This is the first full-text example as far as I know (Gen Z bible doesn't count).

There are hallucinations and issues, but the overall quality surprised me.

LLMs have a lot of promise translating and rendering 'accessible' more ancient texts.

The technology has a lot of benefit for the faithful, that I think is only beginning to be explored.

jSciPy is an open-source Java signal processing and scientific computing library inspired by SciPy.

It focuses on FFT, filters, PSD, STFT, DCT and Android compatibility, aiming to fill the gap for DSP-heavy workloads on JVM and Android.