互联网工程任务组（IETF）于 2026 年 4 月 15 日发布第 8 版互联网协议（IPv8）草案 ( draft-thain-ipv8-01 - Internet Protocol Version 8 (IPv8) )。与 IPv6 艰难的推广之路不同，IPv8 的核心哲学是无痛演进。 IPv8 地址格式为 r.r.r.r.n.n.n.n，均为阿拉伯数字。其中， r.r.r.r — 32 位 ASN 路由前缀 n.n.n.n — 32 位主机地址 特点摘录： 1、后向兼容传统 IPv4。 当路由前缀为 r.r.r.r = 0.0.0.0 时，IPv8 即退化为传统的 IPv4。这意味着旧设备可以完美融入新网络，无需双栈迁移。 2、利用前缀清晰编码 ASN ASN 64496 (例-A) = 0.0.251.240 ASN 64497 (例-B) = 0.0.251.241 …… 其他特性见提议稿 ( draft-thain-ipv8-01 - Internet Protocol Version 8 (IPv8) )。 11 个帖子 - 11 位参与者 阅读完整话题
I am working on Hitoku Draft. An open-source, voice-first AI assistant that runs entirely locally. No cloud models, nothing leaves your machine. You press a hotkey, and you talk. It's context-aware; it reads your screen, documents, and active app to understand what you're working on. You can ask about PDFs, reply to emails, create calendar events, use web search, all by voice. It supports Gemma 4 and Qwen 3.5 for text generation, plus multiple STT backends (Parakeet, Whisper, Qwen3-ASR). Examples: - Gemma4 in action, https://www.youtube.com/watch?v=OgfI-3YjEVU - query a pdf document, https://www.youtube.com/watch?v=ggaDhut7FnU - reply to email, https://www.youtube.com/watch?v=QFnHXMBp1gA - and the usual voice dictation (with optional polishing) I currently use it a lot with Claude Code, Obsidian and Apple Notes, or just read papers. Code: https://github.com/Saladino93/hitokudraft/tree/litert Download of binary: https://hitoku.me/draft/ (free with code HITOKUHN2026) I am looking for feedback. My goal is to do AI research with clients interfacing, and I thought this is a nice little experiment I could do to iterate/fail quickly. P.S. (if anyone has tips about this) Current Gemma4 implementation (with small models) has some problems: - easy to hallucinate for long contexts, so had to reset it often. Tuned some parameters, but need to find a sweet spot. - Gemma4 with LiteRT is currently fast compared to the MLX implementation of Qwen3.5 (like 3x faster on my machine when dealing with images). But it has the price of memory spikes. I believe this is because LiteRT's WebGPU backend can allocate significantly more GPU memory than the model weights alone (I got 38GB of memory taken, for the E4B~4GB model!). I guess we need to wait for Google for this. - App size: because no official Swift package from Google yet, have to bundle some file (LiteRT dylibs) that adds ~98 MB to a previous MLX only version (total app goes from ~50 MB to ~150 MB) If any of this bothers you: use Qwen 3.5 instead (pure MLX), or wait for the upstream fixes from Google :) Otherwise, for the mid-term I plan to switch to a potentially slower, but safer, MLX version for Gemma4 (hopefully on the weekend). Comments URL: https://news.ycombinator.com/item?id=47755000 Points: 2 # Comments: 0
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