Spite_By_Design_LLC

Iirc shreddit lets you download all your posts before deleting them

I personally use RedReader (signed out) and "share to" Linkora to save useful threads.

https://f-droid.org/packages/org.quantumbadger.redreader/

https://f-droid.org/packages/com.sakethh.linkora/

That works for now. If redreader stops working... 🤷 bye Felicia. I don't visit Reddit much anyway.

I've replaced "sitting at PC, nothing to read" with neal.fun, Atlas Obscura, Smithsonian Smart News and Lemmy 🤣

PS: IIRC, you can download a good chunk of reddit as an archive to search against.

https://github.com/ArthurHeitmann/arctic_shift

https://github.com/19-84/redd-archiver

I think the full pushshift reddit archive (to 2024?) was 4TB, but you can just grab smaller useful subsections. So it's less of a workaround and more a "tube is out of the toothpaste, suck shit Spez".

Actually, redd-archive (which is up to end of 2025) has an MCP, so you could point your local LLM at it and get it to find the info you need, all off line.

Last I checked, it even let you grab snapshots of particular subreddits of your choice, so you're not grabbing multiple terabytes. 2026 snapshots aren't out yet IIRC

Yes, very much so.

Take a look at my comment here.

PS: you might also be interested in this

https://en.wikipedia.org/wiki/Apertus_(LLM)

That should be well within reach of a local LLM.

If you've got a discrete GPU with sufficient VRAM, something like Ornith running on PolyStrata could be worth a shot. Try a Q3 size.

https://huggingface.co/bartowski/Ornith-1.5-35B-A3B-GGUF

https://github.com/VecSzn/PolyStrata

Hell, Tiny Ling 3 might be able to do it. Try a Q6.

https://huggingface.co/bartowski/Ling-3.0-tiny-GGUF

Another good option is Qwen3.5-9B. I'd try a Q4 or Q5.

Whichever model you choose, I'd also suggest a few MCP servers to give it access to reliable information rather than rely entirely on its internal knowledge.

• Microsoft Learn MCP: Official PowerShell, Windows and network documentation. Will help with your scripting stuff.

https://learn.microsoft.com/en-us/training/support/mcp

• Exa MCP: Web search for current technical information, Home Assistant documentation and code examples.

https://github.com/exa-labs/exa-mcp-server

• Filesystem MCP: Allows the model to examine your existing scripts and configuration files rather than guess what you've got.

https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem

If you're actually running Home Assistant, its official MCP server is worth a look too.

https://www.home-assistant.io/integrations/mcp_server/

I'd start with Microsoft Learn and Exa, then add the others as needed. You'll need an MCP-capable client (which pretty much all of them are now - eg: llama.cpp web ui, OpenWebUI, Light Local Chat etc), and I'd keep file access read-only initially.

I'd personally use McProxy instead of adding MCPs singularly, but I fear I'm already over loading you with information.

EDIT: The PolyStrata recommendation assumes you have a discrete GPU with sufficient VRAM (unclear if you do?). If not, disregard Ornith and try Tiny Ling 3 Q6 or Qwen3.5-9B with ik_llama.cpp instead.

https://github.com/ikawrakow/ik_llama.cpp

Either way, both smaller models should be capable of the tasks you've described, particularly with access to the right documentation. Just verify any network or firewall changes before applying them.

Yes, quite a few, but it depends on what you're trying to do with them. Any ideas?

They are the way to go, but hardware pricing makes it cost-prohibitive to match frontier capabilities, which makes decomp and recomp difficult. Or rather, they require more skill and take longer, introducing some barriers against the current floodgates being flung open.

We all know the eye-watering prices GPUs and RAM cost now, to say nothing of Unified Memory devices like the Strix. Most people just don't have a spare $10K to throw at an inference box that can run (say) a 300B model at interactive (not batch) speeds.

Coming at this from another angle tho, I note that according to Steam stats, the most popular consumer GPUs are in the 8GB and 16GB range...which is smack bang in the VRAM range of what a good MoE (say, Qwen 3.6 35B-A3B) runs on (for 8GB) or arguably the best local dense coder (Qwen 3.8 27B) can live at. Which leaves us with a capable assistant and a capable coder.

I think there's probably room for LLM improvement in the 8–16GB range too...and given that most consumer PCs and laptops live there, I'm sanguine the open-weight devs might make it happen.

But I also suspect the gatekeeping of high-end capacities is intentional.

We're probably not going to have the ability to vibe code Mario into Elden Ring again...not without significantly more skill. Dunno.