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I want everything local – Building my offline AI workspace

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Re: I want everything local – Building my offline AI workspace

#261

I’m trying to do something similar but hyper fine tune a model of choice for my specific local data source. For example, use existing code models to answer dquestions with code examples based on my private source files and documentation. I tried doing it with using Huggingface and Unsloth but keep getting OOM errors. Have anyone done this that runs locally against your own data?

I was just writing up a plan this morning. I use local models a fair amount, especially on trips.

My plan is to build a $150 AI bot, host it in my bedroom, give it access to all my writing, and let the world access it.

Re: I want everything local – Building my offline AI workspace

#262

This is fantastic work. The focus on a local, sandboxed execution layer is a huge piece of the puzzle for a private AI workspace. The `coderunner` tool looks incredibly useful. A complementary challenge is the knowledge layer: making the AI aware of your personal data (emails, notes, files) via RAG. As soon as you try this on a large scale, storage becomes a massive bottleneck. A vector database for years of emails c…

> A vector database for years of emails can easily exceed 50GB. In 2025 I would consider this a relatively meager requirement.

Take whatever you're indexing and make it 16-20x and that’s a good approximation of what the vector db’s total size is going to be.

Re: I want everything local – Building my offline AI workspace

#263
post #55

> Even with help from the "world's best" LLMs, things didn't go quite as smoothly as we had expected. They hallucinated steps, missed platform-specific quirks, and often left us worse off. This shows how little native app training data is even available. People rarely write blog posts about designing native apps, long winded medium tutorials don't exist, heck even the number of open source projects for native desktop…

You left out the next lines, which add some important context: > Then we tried wrapping a NextJS app inside Electron. It took us longer than we'd like to admit. As of this writing, it looks like there's just no (clean) way to do it. > So, we gave up on the Mac app. They weren't writing a fully native app. They started with a NextJS web app and then tried to put it inside Electron, a cross-platform toolkit. All the tr…

I thought that was odd too. There are lots of ChatGPT clones implemented as native MacOS apps.

The main advancement in TFA is using the new Container Swift API for local tool use. That functionality would probably be a welcome contribution to any of these:

https://github.com/Renset/macai

https://github.com/huggingface/chat-macOS

https://github.com/SidhuK/WardenApp

https://github.com/psugihara/FreeChat

Re: I want everything local – Building my offline AI workspace

#264
post #212

Half-OT: Anything useful that runs reasonably fast on a regular Intel CPU/GPU?

For Intel CPUs, Phi-2 (2.7B) and TinyLlama (1.1B) run reasonably well using llama.cpp with 4-bit quantization. GGUF models with INT4 quantization typically need ~2GB RAM per billion parameters, so even older machines can handle smaller models.

Re: I want everything local – Building my offline AI workspace

#265

Earlier quoted context omitted.

A lot of us just don't want to be web developers. I mostly write IEC 61131 code, with sprinkles of BASIC (yuck), C, Perl, and Lisp. I've used JavaScript and quite frankly, you can keep it.

Does anyone else think javascript bad? Wow brave!

Having personal preferences is brave? I've got tons of those! Maybe I'll go start some bar fights.

Re: I want everything local – Building my offline AI workspace

#266

Earlier quoted context omitted.

You left out the next lines, which add some important context: > Then we tried wrapping a NextJS app inside Electron. It took us longer than we'd like to admit. As of this writing, it looks like there's just no (clean) way to do it. > So, we gave up on the Mac app. They weren't writing a fully native app. They started with a NextJS web app and then tried to put it inside Electron, a cross-platform toolkit. All the tr…

I thought that was odd too. There are lots of ChatGPT clones implemented as native MacOS apps. The main advancement in TFA is using the new Container Swift API for local tool use. That functionality would probably be a welcome contribution to any of these: https://github.com/Renset/macai https://github.com/huggingface/chat-macOS https://github.com/SidhuK/WardenApp https://github.com/psugihara/FreeChat

I think they started with what they knew (web app development) and then wanted to wrap it into a standalone app later.

Re: I want everything local – Building my offline AI workspace

#267

Earlier quoted context omitted.

Yeah, that's a fair point at first glance. 50GB might not sound like a huge burden for a modern SSD. However, the 50GB figure was just a starting point for emails. A true "local Jarvis," would need to index everything: all your code repositories, documents, notes, and chat histories. That raw data can easily be hundreds of gigabytes. For a 200GB text corpus, a traditional vector index can swell to >500GB. At that poi…

You already need very high end hardware to run useful local LLMs, I don't know if a 200gb vector database will be the dealbreaker in that scenario. But I wonder how small you could get it with compression and quantization on top

The mid-spec 2025 iPhone can run “useful local LLMs” yet has 256GB of total storage.

(Sure, this is a spec distortion due to Apple’s market-segmentation tactics, but due to the sheer install-base, it’s still a configuration you might want to take into consideration when talking about the potential deployment-targets for this sort of local-first tech.)

Re: I want everything local – Building my offline AI workspace

#268

Earlier quoted context omitted.

> A vector database for years of emails can easily exceed 50GB. In 2025 I would consider this a relatively meager requirement.

Yeah, that's a fair point at first glance. 50GB might not sound like a huge burden for a modern SSD. However, the 50GB figure was just a starting point for emails. A true "local Jarvis," would need to index everything: all your code repositories, documents, notes, and chat histories. That raw data can easily be hundreds of gigabytes. For a 200GB text corpus, a traditional vector index can swell to >500GB. At that poi…

Question: would it be possible to invert the problem? I.e., rather than decreasing the size of the RAG — use the RAG to compress everything other than the RAG index itself.

E.g., design a filesystem so that the RAG index is part of / managed internally within the metadata of the filesystem itself; and then, for each FS inode data-extent, give it two polymorphic on-disk representations:

1. extents hold raw data; rag-vectors are derivatives and updated after extent is updated (as today)

2. rag-vectors are canonical; extents hold residuals from a predictive-coding model that took the rag-vectors as input and tried to regenerate the raw data of the extent. When extent is read [or partially overwritten], use predictive-coding model to generate data from vectors and then repair it with residue (as in modern video-codec p-frame generation.)

———

Of course, even if this did work (in the sense of providing a meaningful decrease in storage use), this storage model would only really be practical for document files that are read entirely on open and atomically overwritten/updated (think Word and Excel docs, PDFs, PSDs, etc), not for files meant to be streamed.

But, luckily, the types of files this technique are amenable to are exactly the same types of files that a “user’s documents” RAG would have any hope of indexing in the first place!

Re: I want everything local – Building my offline AI workspace

#269
It would be nice to have something more modest like a local offline foreign language translator.

Basically I'd like to be able to have an emacs "M-x translate-french-to-english" function. This should be easier than a full chat app but doesn't exist as far as I know.

Re: I want everything local – Building my offline AI workspace

#270
post #152

Earlier quoted context omitted.

Because the browser is gross and you can reclaim lot of performance and security when you don't need to use it.

Sure but you're also constrained to only one platform. It's like the C++ vs Python argument in ML, yes writing everything in low level high speed highly optimized native code would be perfect, but ain't (almost) nobody got fucking time or skill for that.

"Lack of skill" is a real problem I've seen grow over the past decade.

No matter the company I'm with or in conversations with others at other places, there just hasn't been a solid intake of junior programmers / sysadmins / network engineers / etc.

Which sucks, because now there's very few junior staff to teach, which makes backfills harder.

Any junior positions that do seem to happen are just a money funnel to offshoring and the results are /mostly/ less than stellar and ultimately aren't setup to solve the knowledge transfer problem in a meaningful, long-term way.

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