The scraping/bandwidth abuse problem can easily be worked around.

But there still are actual good reasons to not host a public forge.

For example, as long as pull requests are allowed (which is required for actual contributors), anyone can abuse the PR feature to fork your repository, then start pushing random shit into their fork (since the fork is an actual separate git repository).

Bad actors can do it on github all they want, it’s not my storage, not my server used to host potentially illegal content.

Self-hosting public services where you are the only authenticated user and sole publisher of content is easy (using your public forge as a mirror with account creation disabled is fine), hosting other’s people content is another can of worms. Think twice before you do that.

The blurb is my own submission, since it was not so evident how the article was related to self-hosting. I am not the author of the blog post. I am a maintainer of awesome-selfhosted.

• Aggregation of multiple engines
• Per-engine weight control
• Good UX
• Filtering of bad domains from the search results
• More generally, very customizable

Fair enough.

I decided against web/network-based password managers for my personal needs since the additional attack surface is a concern. A Keepass database file synced across machines strikes a good balance for me (requires password + keyfile to open). It’s also simple to backup and protect.

So yeah, for you use case, I’d recommend Aegis Authenticator.

No, I’m not interested in a password manager, thank you

Ok. But since you already use a password manager (right?), why not use its built-in TOTP management. Why do you need yet-another-separate app?

If I really had to, I’d recommend Aegis.

But I’ll still recommend using a password manager (I use KeepassXC on desktop and KeepassDX on Android).

• Small 4B models like gemma3 will run on anything (I have it running on a 2020 laptop with integrated graphics). Don’t expect superintelligence, but it works for basic classification tasks, writing/reviewing/fixing small scripts and basic chat, writing, etc
• I use https://github.com/ggml-org/llama.cpp in server mode pointing to a directory of GGUF model files downloaded from huggingface. I access it it from the built-in web interface or API (wrote a small assistant script)
• To load larger models you need more RAM (preferably fast VRAM/GPU but DDR5 on the motherboard will work - it will be noticeably slower). My gaming rig with 16GB AMD 9070 runs 20-30B models at decent speeds. You can grab quantized (lower precision, lower output quality) versions of those larger models if the full-size/unquantized models don’t fit. Check out https://whatmodelscanirun.com/
• For image generation I found https://github.com/vladmandic/sdnext which works extremely well and fast wth Z-Image Turbo, FLUX.1-schnell, Stable Diffusion XL and a few other models

As for the prices… well the rig I bought for ~1500€ in september is now up to ~2200€ (once-in-a-decade investment). It’s not a beast but it works, the primary use case was general computing and gaming, I’m glad it works for local AI, but costs for a dedicated, performant AI rig are ridiculously high right now. It’s not economically competitive yet against commercial LLM services for complex tasks, but that’s not the point. Check https://old.reddit.com/r/LocalLLaMA/ (yeah reddit I know). 10k€ of hardware to run ~200-300B models, not counting electricity bills