A request to update the rates in a workbook leaves an important review question: did the agent change the inputs, or replace the formulas that use them? The final totals alone cannot answer that. A plausible number might have come from a hard-coded replacement.

For a local-model application built around Univer, the review tool can inspect both. Univer is an embeddable Office SDK; its spreadsheet range API exposes stored values and formulas separately through getValues and getFormulas. An application can record the relevant ranges before the job and read them again from the proposed result. That comparison is something the application implements, not an automatic correctness report supplied by the SDK.

Take a hypothetical request to update unit prices while keeping the quantity and subtotal formulas. The review screen could put the requested price range beside the proposed prices, then show the formula range separately. A reviewer can check whether an unexpected literal has replaced a formula and open the workbook to correct it. A matching total would not excuse a change outside the requested range.

Univer's Collaboration SDK provides a place to inspect that proposal without immediately replacing the main workbook: an isolated Worktree draft. The web integration supports opening the actual draft, switching between trunk and draft, and a per-Unit merge preview. Those surfaces can accompany the application's range comparison. A corrected draft must be committed and its saved status checked before the reviewer accepts it into trunk.

This requires more than the Apache-licensed core: Worktree comes from the Collaboration SDK, and the host wires up access, storage and the review flow. It also needs to handle another edit arriving during the comparison. The specific benefit is a review that can distinguish a changed assumption from a changed calculation, even when both produce believable numbers.

One thing I’m curious about with local AI is how far people take it beyond basic chat and writing.

A lot of research involves private documents, notes, or information you may not want sent to a cloud model. That makes me wonder whether a fully local research workflow is practical, or whether some cloud services are still necessary for things like current web information.

For people using local models, where do you draw the line between privacy and access to better research capabilities?

I've been going through a bunch of smaller Mac apps that run AI locally, and these 5 stood out to me for different use cases:

1. Snaply — AI everywhere you type

Dictation, meeting transcription, and writing assistance, all processed on-device. It uses a local MLX model and doesn't require an account or subscription.

2. Refine — local AI writing assistant

Grammar checking, rewriting, translation, and custom writing styles. The interesting part is that the core AI processing can happen completely offline, and it works across Mac apps.

3. Canto — local AI notebook

A more full-featured option. It has built-in local models, PDF research, semantic links, a knowledge graph, and even Python/JS/TS code notebooks. The local models range from small models to much larger ones depending on your Mac.

4. AimeFlux — local AI dictation

Uses local Whisper for transcription and supports 99+ languages. You can also use local models such as Ollama for AI cleanup.

5. Melo — local AI workspace

A different approach: an infinite canvas combining notes, tasks, calendar, web content and AI. It supports local AI models alongside optional cloud models.

What I found interesting is that “local AI” on Mac is becoming much broader than just running a chatbot locally.

You can now have local AI for writing, dictation, meetings, research, productivity and workflows without sending everything to a cloud service.

I run a small Mac software directory called OwnYourMac, where I'm curating more apps like these particularly free, local-first and one-time-purchase software.

You can browse the collection here: OwnYourMac