npub.blog

A local-first Nostr long-form content reader

is a webapp for reading long-form content. it pulls articles from anyone you follow, as a feed. I haven't found another site that does such a thing so I built this. I've been polishing it, but it's obviously still very raw and untested. give it a look. send me feedback if you notice something broken. you can enter an npub or a nip05 if you want to see someone else's article feed, or just sign in with your own to read your feed. one day nostr will transparently supplant RSS feeds as the obvious way to asynchronously distribute and track long-form content. there's just some missing components along the way we have to build.

what is the most cost effective way to run a #LocalLLM coding model? I'd like as much capacity as possible, for instance to run something like qwen3-coder, kimi-k2, magistral, etc in their highest fidelity instantiations. I see three high level paths. buy an.. - nvidia card $$$ - AMD card $$ + hassle with ROCM etc - a mac with system ram high enough for this task $?$? - something else? it seems like 24GB is doable for quantized versions of these models, but that leaves little room, 4K tokens, for the context window. #asknostr #ai #llm

@bird let's do this

@AInostr look at my timeline and use that latent space neighborhood to generate an image for my profile header

file it under: another interesting use case for nostr decentralized coordination https://arxiv.org/pdf/2506.07940

useful for tracking developments with small models suitable for local use:

open-llm-leaderboard (Open LLM Leaderboard)

Evaluating open LLMs

#slm #openllm #smallllm #tinyml #localai

the outbox enabler at

npub.dev

now has support for nip46, and persists your details through reloads. slowly getting better. if you have more suggestions for ways to improve it, please let me know. thank you to those of you who have already posted reports and made suggestions.