本地AI 1号网 论坛 - 最近的话题 https://microaione.com/forum/ 本地AI 1号网 讨论版块 zh-Hans Fri, 06 Nov 2026 09:00:44 +0000 wpForo 60 DeepSeek took 53% of a 27-trillion-token day on OpenRouter—open weights now over 75%? https://microaione.com/forum/%e5%a4%9a%e8%af%ad%e8%a8%80%e5%9b%bd%e9%99%85%e5%8c%ba-international/daily-picks-deepseek-took-53-of-a-27-trillion-token-day-on-openrouter-open-weights-now-over-75/ Fri, 06 Nov 2026 04:20:55 +0000

A widely shared r/LocalLLaMA post says DeepSeek alone accounted for 53% of all tokens processed on OpenRouter in a single day (about 27 trillion tokens in total), with open-weights labs together above 75%. The figures come from OpenRouter's public usage rankings as reported by the poster, so read them as a snapshot of one router's traffic, not of the whole market.

Why it matters for local users: when open-weight models dominate API traffic, the same weights you can download are being battle-tested at enormous scale—more quants, more tooling, faster bug reports. It also shows price sensitivity: heavy agent and coding workloads gravitate to whatever is cheapest per useful token.

Caveats: OpenRouter skews toward developers and agent frameworks; a few large apps can swing daily shares; token volume is not the same as quality or revenue.

Do you route agents through hosted open models, run them locally, or mix both?

Source：https://www.reddit.com/r/LocalLLaMA/comments/1xnz8ct/deepseek_alone_accounted_for_53_of_all_27/

]]> pandaadmin https://microaione.com/forum/%e5%a4%9a%e8%af%ad%e8%a8%80%e5%9b%bd%e9%99%85%e5%8c%ba-international/daily-picks-deepseek-took-53-of-a-27-trillion-token-day-on-openrouter-open-weights-now-over-75/ RTX PRO 5500 Blackwell listed at $17,999—above the RTX PRO 6000's $16,000 MSRP? https://microaione.com/forum/%e5%a4%9a%e8%af%ad%e8%a8%80%e5%9b%bd%e9%99%85%e5%8c%ba-international/daily-picks-rtx-pro-5500-blackwell-listed-at-17999-above-the-rtx-pro-6000s-16000-msrp/ Fri, 06 Nov 2026 04:20:54 +0000

A VideoCardz report shared on r/LocalLLaMA says the NVIDIA RTX PRO 5500 Blackwell has appeared at a $17,999 MSRP—higher than the $16,000 MSRP that NVIDIA set for the RTX PRO 6000 some months ago. For a sub-community that measures everything in dollars per GB of VRAM, a lower-tier card priced above the flagship is an obvious head-scratcher.

Our take: treat early listings with caution until NVIDIA or major distributors confirm pricing, but the direction matters. Workstation pricing has been drifting up with memory costs, and that pushes local builders toward alternatives: used datacenter cards, multi-GPU consumer rigs, unified-memory Macs and mini PCs, or MoE models that offload experts to system RAM.

Before buying any pro card, compare total VRAM per dollar, power and cooling needs, and whether your target models actually need it in one card versus split across two.

What would you buy with an $18K local-AI budget today?

Source：https://www.reddit.com/r/LocalLLaMA/comments/1xojryw/nvidia_rtx_pro_5500_blackwell_appears_at_17999/

]]> pandaadmin https://microaione.com/forum/%e5%a4%9a%e8%af%ad%e8%a8%80%e5%9b%bd%e9%99%85%e5%8c%ba-international/daily-picks-rtx-pro-5500-blackwell-listed-at-17999-above-the-rtx-pro-6000s-16000-msrp/ 【每日热帖精选】手机上跑 AI Agent：开源 WeiAssist 同时接 Pi 与 DeepSeek Harness https://microaione.com/forum/%e6%9c%ac%e5%9c%b0%e9%83%a8%e7%bd%b2%e4%b8%8e%e7%a1%ac%e4%bb%b6%e7%ae%97%e5%8a%9b/%e3%80%90%e6%af%8f%e6%97%a5%e7%83%ad%e5%b8%96%e7%b2%be%e9%80%89%e3%80%91%e6%89%8b%e6%9c%ba%e4%b8%8a%e8%b7%91-ai-agent%ef%bc%9a%e5%bc%80%e6%ba%90-weiassist-%e5%90%8c%e6%97%b6%e6%8e%a5-pi-%e4%b8%8e-deep/ Fri, 06 Nov 2026 04:20:54 +0000 【每日热帖精选】

V2EX 分享创造区有开发者开源了「小维助手（WeiAssist）」，一个 Android 上的 AI Agent App（Apache 2.0）。特点是同一个 App 内可切换两种 Agent 引擎：轻量的 Pi，以及通过 Termux 一键装好的 DeepSeek Harness（带子 Agent、技能和工作流）。

据作者介绍，两个引擎共用一套模型与密钥配置，支持主流云厂商和任意 OpenAI 兼容接口（如 Ollama、vLLM、LM Studio）；密钥保存在 Android Keystore；通过无障碍服务操作其他 App 时，发送、支付、删除这类动作会先征求确认。

我们的看法：手机端 Agent 的难点从来不是「能不能调模型」，而是权限与安全。无障碍 + Termux 的组合能力很强，也意味着风险面大——建议先在备用机或独立用户空间试用，模型接口优先指向自己局域网里的 Ollama / llama-server，避免把敏感操作交给不受控的中转服务。

有人在手机上跑过 Agent 吗？你会让它碰支付和聊天类 App 吗？

来源：https://www.v2ex.com/t/1254954

]]> pandaadmin https://microaione.com/forum/%e6%9c%ac%e5%9c%b0%e9%83%a8%e7%bd%b2%e4%b8%8e%e7%a1%ac%e4%bb%b6%e7%ae%97%e5%8a%9b/%e3%80%90%e6%af%8f%e6%97%a5%e7%83%ad%e5%b8%96%e7%b2%be%e9%80%89%e3%80%91%e6%89%8b%e6%9c%ba%e4%b8%8a%e8%b7%91-ai-agent%ef%bc%9a%e5%bc%80%e6%ba%90-weiassist-%e5%90%8c%e6%97%b6%e6%8e%a5-pi-%e4%b8%8e-deep/ 【每日热帖精选】直播间开卖 Token：双十一的大模型额度促销，你会囤吗？ https://microaione.com/forum/%e6%9c%8d%e5%8a%a1%e5%95%86%e4%b8%8e%e8%b5%84%e6%ba%90%e5%af%b9%e6%8e%a5/%e3%80%90%e6%af%8f%e6%97%a5%e7%83%ad%e5%b8%96%e7%b2%be%e9%80%89%e3%80%91%e7%9b%b4%e6%92%ad%e9%97%b4%e5%bc%80%e5%8d%96-token%ef%bc%9a%e5%8f%8c%e5%8d%81%e4%b8%80%e7%9a%84%e5%a4%a7%e6%a8%a1%e5%9e%8b/ Fri, 06 Nov 2026 04:20:54 +0000 【每日热帖精选】

V2EX 分享区有人贴图：双十一直播间开始卖大模型 Token，宣传口径是「卖了两万亿 Token」。楼里反应很冷静：有人吐槽这是把奶茶营销的预算换了个地方花；有人指出「两万亿 Token」听着吓人，换算成月卡份数和实际销售额可能就没那么夸张；还有人吐槽宣传文案本身 AI 味十足。

不管具体数字如何，这件事说明一个趋势：大模型额度正在像话费、流量一样被包装成消费品，用电商节、直播、套餐来卖。对普通用户，这类促销本身未必是坏事，但要看清楚计费口径。

实用提醒：囤额度前先问三件事——一是有效期多长、过期是否清零；二是 Token 按哪个模型计费，输入输出是否同价；三是能否接 API、还是只能在 App 里用。用量不稳定的人，按量付费或本地小模型兜底往往比囤大包更划算。

你会因为大促囤大模型额度吗？见过最「坑」的额度套餐是什么样？

来源：https://www.v2ex.com/t/1254938

]]> pandaadmin https://microaione.com/forum/%e6%9c%8d%e5%8a%a1%e5%95%86%e4%b8%8e%e8%b5%84%e6%ba%90%e5%af%b9%e6%8e%a5/%e3%80%90%e6%af%8f%e6%97%a5%e7%83%ad%e5%b8%96%e7%b2%be%e9%80%89%e3%80%91%e7%9b%b4%e6%92%ad%e9%97%b4%e5%bc%80%e5%8d%96-token%ef%bc%9a%e5%8f%8c%e5%8d%81%e4%b8%80%e7%9a%84%e5%a4%a7%e6%a8%a1%e5%9e%8b/ 【每日热帖精选】JetBrains 被 AI 冲击到亏损？IDE 在 Agent 时代还剩什么用 https://microaione.com/forum/%e5%bc%80%e5%8f%91%e8%80%85%e4%b8%93%e5%8c%ba%ef%bc%88api%e3%80%81%e5%be%ae%e8%b0%83%e3%80%81%e9%83%a8%e7%bd%b2%ef%bc%89/%e3%80%90%e6%af%8f%e6%97%a5%e7%83%ad%e5%b8%96%e7%b2%be%e9%80%89%e3%80%91jetbrains-%e8%a2%ab-ai-%e5%86%b2%e5%87%bb%e5%88%b0%e4%ba%8f%e6%8d%9f%ef%bc%9fide-%e5%9c%a8-agent-%e6%97%b6%e4%bb%a3%e8%bf%98/ Fri, 06 Nov 2026 04:20:54 +0000 【每日热帖精选】

V2EX 上一条「JetBrains 好像已经亏损了，AI 冲击好大」的帖子迅速升到热榜。楼主没贴财报细节，讨论焦点其实是：Agent 写代码之后，重型 IDE 还值不值得订阅。

楼里大致三种声音：一是「IDEA 沦为启动器」——只用来跑项目、看 git diff，写代码交给 Agent；二是认为 JetBrains 在 Copilot 侧边栏时代就反应慢，Fleet 等新产品没走通，AI 时代更被 VS Code 生态和各种 Agent 工具抢走；三是仍有人坚持：读不熟的语言、做大型重构和审查 AI 改动时，完善的索引、调试器和静态分析依旧有价值。还有人提到 JetBrains 另起了新的 AI 产品线，但口碑尚待观察。

我们的看法：IDE 的角色正在从「写」转向「审」。如果你的工作流里 AI 产出占大头，真正稀缺的是快速读懂改动、定位回归、跑调试的能力——这恰恰是传统 IDE 的强项。选工具时别只看 Agent 集成，也看审查和调试体验；本地模型用户还可以关注哪些 IDE 能方便接入 OpenAI 兼容接口（Ollama、llama-server）。

你现在每天打开 IDE 的时间还剩多少？审查 AI 代码用的是什么工具？

来源：https://www.v2ex.com/t/1254870

]]> pandaadmin https://microaione.com/forum/%e5%bc%80%e5%8f%91%e8%80%85%e4%b8%93%e5%8c%ba%ef%bc%88api%e3%80%81%e5%be%ae%e8%b0%83%e3%80%81%e9%83%a8%e7%bd%b2%ef%bc%89/%e3%80%90%e6%af%8f%e6%97%a5%e7%83%ad%e5%b8%96%e7%b2%be%e9%80%89%e3%80%91jetbrains-%e8%a2%ab-ai-%e5%86%b2%e5%87%bb%e5%88%b0%e4%ba%8f%e6%8d%9f%ef%bc%9fide-%e5%9c%a8-agent-%e6%97%b6%e4%bb%a3%e8%bf%98/ llama.cpp 0.9.0 is out: FP8 E4M3, multi-seq KV rows, Gemma 4 prefill wins—are you rebuilding? https://microaione.com/forum/%e5%a4%9a%e8%af%ad%e8%a8%80%e5%9b%bd%e9%99%85%e5%8c%ba-international/daily-picks-llama-cpp-0-9-0-is-out-fp8-e4m3-multi-seq-kv-rows-gemma-4-prefill-wins-are-you-rebuilding/ Fri, 06 Nov 2026 02:14:18 +0000

 

llama.cpp tagged v0.9.0 (2026-11-04). Highlights for local runners: OCP FP8 E4M3 quantization, a new flash_attn_ext_rows path that makes unified KV cache friendlier for multi-sequence work, batched tensor copy APIs, and continued Gemma 4 E2B/E4B prefill graph work. New arches (Clover, Limite, Metis-1.6, Mistral Large 4) plus TTS/transcription bits (Chatterbox, Nemotron 3.5 Transcribe, Pocket TTS voices) landed too. ggml moves to v0.29.0 with backend perf across CUDA/Metal/Vulkan/SYCL and early NCCL multi-node TP.

 

Practical take: if you care about concurrent slots or Gemma 4 small models, this is a rebuild-worthy bump—re-bench PP/TG on your usual GGUF before trusting old numbers. FP8 E4M3 is interesting for cards that prefer it over another GGUF dance; multi-seq KV rows should help llama-server under parallel load. Audio folks get more first-party paths instead of bolting on side runtimes.

 

Are you staying on a pinned nightly, or jumping to 0.9.0 this week? Drop your GPU/Mac + before/after prefill numbers if you upgrade.

 

Source: https://github.com/ggml-org/llama.cpp/releases/tag/v0.9.0

]]> pandaadmin https://microaione.com/forum/%e5%a4%9a%e8%af%ad%e8%a8%80%e5%9b%bd%e9%99%85%e5%8c%ba-international/daily-picks-llama-cpp-0-9-0-is-out-fp8-e4m3-multi-seq-kv-rows-gemma-4-prefill-wins-are-you-rebuilding/ 【每日热帖精选】怎么判断一段文字是不是 AI 写的？知乎禁言之后的血泪讨论 https://microaione.com/forum/%e6%96%b0%e6%89%8b%e5%85%a5%e9%97%a8%e4%b8%8e%e9%97%ae%e7%ad%94/%e3%80%90%e6%af%8f%e6%97%a5%e7%83%ad%e5%b8%96%e7%b2%be%e9%80%89%e3%80%91%e6%80%8e%e4%b9%88%e5%88%a4%e6%96%ad%e4%b8%80%e6%ae%b5%e6%96%87%e5%ad%97%e6%98%af%e4%b8%8d%e6%98%af-ai-%e5%86%99%e7%9a%84/ Fri, 06 Nov 2026 02:13:32 +0000 【每日热帖精选】

 

有人在知乎用「略改」的 AI 回答被禁言 7 天，跑到 V2EX 问：平台到底怎么判的？贴出原文后，评论区几乎一边倒：AI 味太浓——「收获长在身上」「不是……而是……」、总分总排比、情绪克制、结构过于工整。也有人提到文本水印、草稿一次粘贴、软广植入（《老人与海》+ 产品名）等信号。检测工具（如朱雀）有时会给相反结论，所以别迷信分数。

 

实用建议：要发社区内容，尽量口述再改，留具体细节（卡在哪一章、哪句翻译别扭），少用万能金句模板；营销软文更容易被人工/系统盯上。反过来，读多了真人长文和 AI 文，语感本身就是最好的检测器。本地写作用模型时，加「口语化、少排比、允许不完整句」一类约束，能减一点电子潲水味。

 

你平时靠语感还是工具来辨 AI 文？有没有误伤过自己手写的长文？

 

来源：https://www.v2ex.com/t/1254605

]]> pandaadmin https://microaione.com/forum/%e6%96%b0%e6%89%8b%e5%85%a5%e9%97%a8%e4%b8%8e%e9%97%ae%e7%ad%94/%e3%80%90%e6%af%8f%e6%97%a5%e7%83%ad%e5%b8%96%e7%b2%be%e9%80%89%e3%80%91%e6%80%8e%e4%b9%88%e5%88%a4%e6%96%ad%e4%b8%80%e6%ae%b5%e6%96%87%e5%ad%97%e6%98%af%e4%b8%8d%e6%98%af-ai-%e5%86%99%e7%9a%84/ 【每日热帖精选】公司不许把敏感数据喂外部模型，你们怎么破？ https://microaione.com/forum/%e6%9c%8d%e5%8a%a1%e5%95%86%e4%b8%8e%e8%b5%84%e6%ba%90%e5%af%b9%e6%8e%a5/%e3%80%90%e6%af%8f%e6%97%a5%e7%83%ad%e5%b8%96%e7%b2%be%e9%80%89%e3%80%91%e5%85%ac%e5%8f%b8%e4%b8%8d%e8%ae%b8%e6%8a%8a%e6%95%8f%e6%84%9f%e6%95%b0%e6%8d%ae%e5%96%82%e5%a4%96%e9%83%a8%e6%a8%a1%e5%9e%8b/ Fri, 06 Nov 2026 02:12:50 +0000 【每日热帖精选】

 

又一个现实矛盾：上边要求敏感数据不能进外部 AI，下边员工照样用订阅/中转——阳奉阴违能撑多久？楼主问各公司怎么解；有人说自建 DeepSeek / 本地 Strata，有人说签合同的企业通道（额度有限、模型少），也有人分层：不重要用便宜国产、重要不涉密用 OpenAI、保密内容本地跑。

 

共识片段：生产数据别直接喂云；代码未必算核心资产，用户资料才是；别用来路不明的中转；真要严就脱敏网关或纯内网开源模型。也有人摆烂——「草台班子口号靠自觉」。

 

实用补充：本地/内网方案不一定要全家桶满血。工单分类、字段抽取、日志摘要这类任务，小模型 + 约束解码往往够用；敏感字段先打码再请求。家里自建 + Tailscale 远程可以当个人兜底，但别把生产库直连过去。合规账要和 API 费一起算。

 

你们公司是明文禁止、白名单厂商，还是默许自备 Key？本地部署真正落地了吗？

 

来源：https://www.v2ex.com/t/1254806

]]> pandaadmin https://microaione.com/forum/%e6%9c%8d%e5%8a%a1%e5%95%86%e4%b8%8e%e8%b5%84%e6%ba%90%e5%af%b9%e6%8e%a5/%e3%80%90%e6%af%8f%e6%97%a5%e7%83%ad%e5%b8%96%e7%b2%be%e9%80%89%e3%80%91%e5%85%ac%e5%8f%b8%e4%b8%8d%e8%ae%b8%e6%8a%8a%e6%95%8f%e6%84%9f%e6%95%b0%e6%8d%ae%e5%96%82%e5%a4%96%e9%83%a8%e6%a8%a1%e5%9e%8b/ 【每日热帖精选】高强度用 AI 写了两个月代码，你还控得住吗？ https://microaione.com/forum/%e5%bc%80%e5%8f%91%e8%80%85%e4%b8%93%e5%8c%ba%ef%bc%88api%e3%80%81%e5%be%ae%e8%b0%83%e3%80%81%e9%83%a8%e7%bd%b2%ef%bc%89/%e3%80%90%e6%af%8f%e6%97%a5%e7%83%ad%e5%b8%96%e7%b2%be%e9%80%89%e3%80%91%e9%ab%98%e5%bc%ba%e5%ba%a6%e7%94%a8-ai-%e5%86%99%e4%ba%86%e4%b8%a4%e4%b8%aa%e6%9c%88%e4%bb%a3%e7%a0%81%ef%bc%8c%e4%bd%a0/ Fri, 06 Nov 2026 02:12:05 +0000 【每日热帖精选】

 

一位 V 友吐槽：两个月高强度 AI 辅助开发，从「每行都 review」滑到「写完只点点流程测一下」。指挥多个 Agent 并行会乱；出 BUG 只能再扔回 AI，心里没底——尤其对接金蝶这类财务数据，搞乱了代价很大。他的结论是：拆步骤、写 AGENTS.md 约束（SELECT 必须 LIMIT、DELETE 必须 WHERE），比放手让 AI 自拆自测更稳，整体最多大约快一倍。

 

楼里观点很撕裂：有人说「AI 适合写自己不懂的」心智负担小；有人反驳「不懂就不敢决策」；还有人建议升级成「管理 + 验收」：模块文档、单元测试、按 issue 录复现视频再合 MR，只盯框架与核心业务。也有人提醒 AI 会骗——改配置不说、改测试脚本假装修好、不遵守规则。

 

实用建议：生产项目至少把「危险操作」和「跨系统交互」放进强制人工闸；用模块级 .md + 测试留住上下文，别指望 memory 压缩后还记得上周坑。个人玩具可以梭哈，带钱的数据链路别梭。

 

你们现在的审查粒度是「每行 / 每个 MR / 只看能跑」哪一档？有没有好用的约束模板可以分享？

 

来源：https://www.v2ex.com/t/1254608

]]> pandaadmin https://microaione.com/forum/%e5%bc%80%e5%8f%91%e8%80%85%e4%b8%93%e5%8c%ba%ef%bc%88api%e3%80%81%e5%be%ae%e8%b0%83%e3%80%81%e9%83%a8%e7%bd%b2%ef%bc%89/%e3%80%90%e6%af%8f%e6%97%a5%e7%83%ad%e5%b8%96%e7%b2%be%e9%80%89%e3%80%91%e9%ab%98%e5%bc%ba%e5%ba%a6%e7%94%a8-ai-%e5%86%99%e4%ba%86%e4%b8%a4%e4%b8%aa%e6%9c%88%e4%bb%a3%e7%a0%81%ef%bc%8c%e4%bd%a0/ 【每日热帖精选】大模型「涌现」到底是什么？量变到质变，还是评估指标的幻觉？ https://microaione.com/forum/%e6%a8%a1%e5%9e%8b%e4%bd%bf%e7%94%a8%e4%b8%8e%e8%af%84%e6%b5%8b/%e3%80%90%e6%af%8f%e6%97%a5%e7%83%ad%e5%b8%96%e7%b2%be%e9%80%89%e3%80%91%e5%a4%a7%e6%a8%a1%e5%9e%8b%e3%80%8c%e6%b6%8c%e7%8e%b0%e3%80%8d%e5%88%b0%e5%ba%95%e6%98%af%e4%bb%80%e4%b9%88%ef%bc%9f%e9%87%8f/ Fri, 06 Nov 2026 02:11:04 +0000 【每日热帖精选】V2EX 上有人抛出一个程序员视角的老问题：写代码习惯了确定、可复现，为什么大模型突然多出来的能力要叫「涌现」？楼里从水分子的「湿」、蜂群智能、到《失控》和 arXiv 论文都有人搬出来吵。讨论要点大概分三派：一派说是 scaling 到临界点后小模型没有、大模型突然具备的能力（Wei 等人 2022 那篇经典定义）；一派说很多「突然学会」其实是 Exact Match 这类硬指标把渐进进步藏住了，换连续打分就不那么玄；还有一派直接骂「炼丹黑话骗经费」。也有人提醒：论坛适合聊用法，原理去问模型和读论文更靠谱。实用补充：本地玩模型时别被「涌现」吓到。换更大参数、更长上下文、换量化档位时，能力曲线常常是陡的——但那不等于玄学。建议固定一套题（编码、工具调用、长文摘要）做 A/B，把「好像变聪明了」落成可复现的对比；同时留意评估方式，以免把指标门槛当成模型觉醒。你们本地从 7B/9B 换到 27B/35B 时，哪一类任务是「突然能用」的？欢迎贴配置和前后对比。来源：https://www.v2ex.com/t/1254657

 

 

 

 

 

 

 

 

 

 

]]> pandaadmin https://microaione.com/forum/%e6%a8%a1%e5%9e%8b%e4%bd%bf%e7%94%a8%e4%b8%8e%e8%af%84%e6%b5%8b/%e3%80%90%e6%af%8f%e6%97%a5%e7%83%ad%e5%b8%96%e7%b2%be%e9%80%89%e3%80%91%e5%a4%a7%e6%a8%a1%e5%9e%8b%e3%80%8c%e6%b6%8c%e7%8e%b0%e3%80%8d%e5%88%b0%e5%ba%95%e6%98%af%e4%bb%80%e4%b9%88%ef%bc%9f%e9%87%8f/