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8月16日周日
8月15日周六
  1. MarkTechPost64

    微调工具调用大语言模型:基于 XYZ-Aquila-SFT 与 Qwen3 的完整指南

    本教程实现了一个端到端的监督微调流水线,使用 XYZ-Aquila-SFT 数据集、Hugging Face Transformers、PyTorch 和 PEFT,对 Qwen3-0.6B 模型进行 LoRA 微调。流程涵盖数据流式解析、工具调用提取、ChatML 格式转换及助手专用损失掩码,并在训练前后评估工具调用预测性能。最终导出转换后的数据集和语料统计,供进一步实验使用。

8月14日周五
  1. 公众号:蚂蚁百灵62

    蚂蚁百灵与 ASystem 团队打通单机 Agentic RL 后训练闭环

    蚂蚁百灵与 ASystem 团队合作,用 Ling-3.0-tiny 和 AReno 在 DGX Spark 上跑通单机 Agentic RL 后训练闭环。以井字棋为最小验证任务,用 GSPO 算法训练 400 步后,rollout/rewards_mean 从约 -0.5 升至 0.4,response_len 降至约 850 tokens,工具调用与动作选择趋于稳定。

    推荐理由:真正可复用的不是井字棋,而是把可复现错误、可验证反馈、本地训练和同环境复测串成的轻量后训练闭环,适合为本地小模型稳定工具调用和格式约束。

  2. 公众号:数字生命卡兹克69

    从0到1速通DeepSeek Harness:一切皆插件的Agent基建

    DeepSeek发布Agent产品DeepSeek Harness,核心理念“一切皆插件”,基于名为Cordis的内核,支持在Agent运行中随时更换插件并保持状态不崩,官方预设100多个一方插件。产品提供标准、PTC、极简、创造四种模式,其中创造模式可让Agent现场创造并挂载新插件实现自改造。目前为开发者预览版,GitHub已获3.7万Star。

    推荐理由:文章把Harness定位为插件化基建而非单一Agent,Cordis内核的时间与空间可组合性解释了Agent如何在运行中插拔能力,这比模板本身更值得理解。

  3. Claude:Blog55

    Anthropic 如何在 Slack 中用 Claude Tag 部署自助式数据分析智能体

    Anthropic 数据团队将 Claude Tag(公开测试版)作为 Slack 中数据分析智能体的基础,让非分析师也能用受治理的语义层提问并获得答案。团队分享了五项关键经验:将技能文件视为持续刷新的内容、为智能体配备预测/留存/漏斗等分析技能、接入内部知识索引而非仅连接数仓,并强调权限、数据新鲜度与可观测性的设计。

  4. Elon Musk41

    𝕏 再次开源 For You 推荐算法,Elon Musk 称正主动寻求批评以提升推荐公平性与质量。Grok 通读全部 370,523 行代码后总结:算法按预测用户行为加权计分,复制链接分享 +20、回复 +5、点赞仅 +0.5,举报 −234、屏蔽 −58.8;同作者第二篇帖子权重 ×0.625,未关注者帖子 ×0.75。

    引用Dogan Ural@doganuraldesign

    𝕏 just open-sourced the For You algorithm again! Grok Bot read all 370,523 lines of the code. Here’s the summary: Grok does not give your post a “quality” score. For every reader, it guesses what that person will do to your post. Then it multiplies each guess by a weight and adds them up. That total is the value of the post. Highest total goes highest in For You. These are the production weights (last synced 12 Aug 2026). WHAT THE ALGORITHM PAYS FOR If Grok thinks a reader will: • copy the link and share it: +20 • reply, on an original post, and you follow each other: +20 (5 + 15 extra) • quote: +5 • reply (anyone): +5 • share via DM: +5 • follow you from the post: +4 • hit the share button: +2 • repost: +1 • like: +0.5 • click the post: +0.4 • open a link: +0.2 • expand a photo, open a video, or watch a “quality” video: +0.05 each • click a quoted post: +0.05 • keep reading (dwell time): +0.004 • “dwell” as yes/no: +0 • click your profile: +0 A like is 0.5. Copying the link is 20. That is 40 likes. A normal reply is 10 likes. A follow is 8 likes. Clicking your profile is worth nothing. WHAT KILLS A POST If Grok thinks a reader will: • report it: −234 • mute you: −58.8 • hit “not interested”: −43.2 • block you: −31.2 • not dwell: −0.02 A report is about 468 likes in the other direction. A mute hurts more than a block. “Not interested” also hurts more than a block. AFTER THE SCORE, THREE MORE HAIRCUTS 1) Same-author penalty. Your 2nd post in that person’s feed is multiplied by 0.625. 3rd by ~0.44. It never goes below 0.25. Flooding one timeline is coded against. 2) The 25% tax. If they do not follow you: ×0.75. Same tax on replies and reposts even when they DO follow you. Originals from people they follow keep full weight. Replies and reposts are treated as weaker on purpose. 3) Similar-post shuffle. After scoring, a reranker (θ = 0.65) spreads similar posts apart. You can lose a few spots for looking like the post above you. HOW YOUR POST EVEN GETS IN THE ROOM For You is rebuilt every time someone opens it. About 35 posts make the feed. • They follow you: Thunder (a live store of recent posts from accounts they follow). Up to 1,200 candidates. • They don’t: Phoenix retrieval (Grok finds “nearby” posts) + SimClusters (Twitter’s 2020 interest clusters, still on). Up to 1,000 and 800. Then Grok scores all of them together. It does not care which door you came through, except for that ×0.75 tax. Your own posts never appear in your For You. Nothing older than 48 hours gets in. There is no “best of last week.” Small-account bump: under 1,000 followers, under 1,000 impressions, post under 24 hours old. One original can get lifted to position 15 or 16. Not the top. You still have to already be in the top 85% of the pile. Replies and reposts from accounts the reader does not follow are dropped before scoring. You cannot reply-guy your way into a stranger’s For You. FILTERS THAT CAN STILL KILL A HIGH SCORE Ranking and visibility are different machines. A post can score well and then get deleted from the feed. Followers (in-network) is milder: blocks, mutes, suspensions. NSFW often sits behind a warning instead of disappearing. Recommendations (out-of-network) are much harsher. Spam, “Do Not Amplify,” NSFW, compromised accounts, impersonation: followers can still see it, For You recommendations will drop it. Video only gets the tiny “quality view” credit if it is at least 10 seconds. THE LITTLE-KNOWN PARTS (this is the stuff people miss) • Scrolling is not a vote. Yes/no dwell is 0. Time spent barely moves the number (0.004). • Mute damages you more than block (−58.8 vs −31.2). • Copy-link is the single biggest positive. The share button is only +2. A repost is only +1. • The mutual-follow boost is originals only, not your replies. They shipped a bigger version in July and cut it after World Cup complaints (extra reply weight 20 → 15). • The Following tab is not this algorithm. Following is newest-first. • Grok, Gork, and an internal products account are hardcoded out of the in-network store. • Scores are cached for 3 hours because posts inside the model cannot “see” each other. That is why they can rank first and filter later. • Ads, Who to Follow (around slot 7), and prompts are mixed in after ranking. Grok does not score those. • The real Grok ranking model is a 2560-dimension, 8-layer transformer looking at 1,022 items of the reader’s history. They did not ship those model weights. The numbers above are the formula sitting on top of the predictions. • SimClusters is still a 2020 model (20M users, 145k clusters). Old Twitter code, still in the path. • Inferred gender and IP/geo are on as features for the model. Installed apps too. • Who to Follow shows about every 30 hours. Ads try to sit next to “safe” posts, with a minimum gap of 3 organic posts. WHAT TO DO • Write originals people want to reply to, quote, copy, and send to a friend. • Make the post itself worth following you for. • Talk with people who follow you back. A reply on your original from a mutual is 20, not 5. • Stay inside 48 hours. Recycle by posting something new. Old posts do not come back. • If you are small, post originals in the first 24 hours. Don’t expect slot 1. A bump around 15–16 is the actual gift. • Space your posts. The second one in the same feed is already worth 37.5% less. • If you post video, make it at least 10 seconds. WHAT NOT TO DO • Do not farm likes. They are almost decorative. • Do not farm profile visits. Weight is zero. • Do not farm “time on post.” Yes/no dwell is zero. • Do not use replies and reposts as a growth hack for strangers. Those are filtered out. Even for followers they get a 25% haircut. • Do not dump several posts in a row. • Do not bait reports, mutes, or “not interested.” One predicted report can erase a pile of predicted likes. • Do not lean on NSFW or spammy tricks for reach. • Recommendations will drop you even if your followers can still see it. • Do not wait a week and expect For You to revive the post.

  5. OpenAI:官网动态80

    GPT-5.6 构建者指南:如何以更低成本实现前沿智能体性能

    GPT-5.6 模型家族以更低成本实现前沿级智能体性能,并新增推理持久化、原生多智能体编排和程序化工具调用等 API 能力。在 ARC-AGI-3 上,启用保留推理和压缩后,Sol 得分从 13.3% 跃升至 38.3%,且输出 token 减少约 6 倍。Luna 在 BrowseComp 上以 84.04% 的得分追平 GPT-5.5(84.36%),成本从 $33.27 降至 $1.33。

    推荐理由:最实际的改变在 API 原语,让同一模型在 ARC-AGI-3 上用约六分之一输出 token 拿到近三倍分数,影响开发者对推理成本和上下文架构的判断。

  6. 蚂蚁百灵:Developer Blog60

    Ling-3.0-tiny × ASystem AReno 实践:在 DGX Spark 上打通单机 Agentic RL 闭环

    蚂蚁百灵开发者博客介绍用 AReno 在 DGX Spark 上对 Ling-3.0-tiny 做井字棋 Agentic RL 后训练,验证一条基线评测、反馈定义、后训练、同环境复测的轻量任务适配路径。

    推荐理由:原文给出完整的单机 Agentic RL 后训练路径和数据与 Reward 设计细节,开发者可按教程复现并迁移到自己的任务。

8月13日周四
  1. 蚂蚁 inclusionAI:GitHub 新仓库46

    inclusionAI 发布 Ling Cookbook:Ling 系列模型部署与开发指南仓库

    inclusionAI 在 GitHub 开源 Ling Cookbook,为 Ling 系列大语言模型提供部署与开发指南,目前仍在开发中。仓库包含已验证的硬件部署方案,如 Ling-3.0-flash 在单台 NVIDIA DGX Spark 上通过 SGLang(MXFP4/INT4)、vLLM(INT4)及 llama.cpp(GGUF)等框架的推理示例,并附有各方案的量化配置与部署文档。

  2. 公众号:卡尔的AI沃茨60

    GoodCase.ai 开源:卡尔公开三年实测 AI 提示词案例库

    博主卡尔开源了维护三年的 AI 提示语案例网站 GoodCase.ai,收录上千条带完整提示语、模型版本和生成效果的 AI 视频、AI 前端及 Agent 案例。他用同一批提示语在 Seedance 2.0 Fast、MiniMax H3、Wan 3.0 三个模型上交叉验证,并烧掉十万积分完成首轮横测,将跨模型可复用的案例抽象成 Skill,供用户直接调用。

  3. OpenRouter:Announcements61

    OpenRouter 工具调用指南:一次编写循环,切换模型字符串即可跨模型运行

    OpenRouter 发布工具调用指南,展示如何用同一套代码在 Claude、GPT 和开源权重模型间切换,仅需更改模型字符串。指南涵盖定义工具、发送请求、读取 tool_calls 响应、执行函数并返回结果的完整循环,支持 OpenAI 兼容的 JSON schema,并提供 cURL、Python 和 JavaScript/TypeScript 示例。

  4. GitHub Blog67

    AutoGPT 如何用 AGENTS.md 和技能门控管理 AI 生成的拉取请求

    AutoGPT 维护者发现,AI 智能体不会主动阅读文档,因此将指令放在 AGENTS.md 和技能文件中,并置于代码目录旁。他们通过强制 PR 模板、测试计划、CI 覆盖率门槛和 CLA 签名等门控机制,将智能体提交的 PR 从“不可用”转变为“可用但不符合路线图”。其中 CLA 签名因需浏览器和 OAuth 流程,被用作区分人类与智能体的“人类探测器”。

    推荐理由:AutoGPT 的经验指出发现机制比文档完善度关键,agent 只读当前目录层级的指令,所以把 AGENTS.md 放在代码旁比继续写 wiki 更有效。

  5. MarkTechPost60

    AllenAI 教程:用 SFT、DPO、RLVR、GRPO 与验证器评估构建 Tulu 3 后训练流程

    本教程基于 AllenAI 的 Open Instruct 框架,构建了一个端到端的后训练流程,涵盖监督微调、直接偏好优化和基于 GRPO 的可验证奖励强化学习三个阶段。该流程将原多 GPU 的 Tulu 3 栈适配至 16 GB 运行时,用轻量级 Hugging Face 和 PyTorch 实现替换 vLLM、Ray、DeepSpeed 等分布式组件,并利用确定性验证器评估数学答案。

8月12日周三
  1. 公众号:卡尔的AI沃茨72

    跨会话传消息后,Codex 和 Claude 如何重构 vibe coding 工作流

    Codex 和 Claude 新增跨 Session 传消息功能后,新对话可从之前所有对话中选择可用消息,省去交接文档和 git 备份。作者据此重构工作流:主对话守住目标与决策,分支对话独立探索新想法,完成后由主对话读取完整记录。Codex 通过复制会话 ID 精确寻址,Claude Code 则用内置 session management 搜索对话历史,但桌面端只能搜索当前可访问的会话记录。

    推荐理由:将跨会话消息转化为多分支探索的工作流,并对比了Codex精确寻址与Claude搜索式检索的实现差异和踩坑记录,可直接复用其指令来管理复杂的探索任务。

  2. MiniMax (official)54

    MiniMax 官方转发演示:用 DeepSeek V4 Flash 0731 编写 Three.js 场景,再交由 H3 生成视频,48 分钟、单次生成仅花费 1.97 美元,即可完成一镜到底的游戏射击场景。该工作流比反复重roll视频模型便宜得多,被视为更经济高效的 Seedance 2.5 替代方案。

    引用GMI Cloud@gmi_cloud

    you don't need Seedance 2.5, here's cheaper and more efficient workflow we made this entire game shooting scene in one shot for $1.97 with DeepSeek V4 Flash 0731 + MiniMax H3 we use DeepSeek to code the raw Three.js scene first: exact camera movement, character motion, timing, everything. keep tweaking it there for basically pennies. then feed the final footage into H3 and let it cook the realism 48 mins, $1.97 total, one H3 generation way cheaper than rerolling a video model 10 times

  3. 公众号:数字生命卡兹克70

    零基础用户半天上手AI的12步实操流程

    文章给出一套零基础用户半天上手AI的12步实操流程:准备内存不低于16G的电脑,订阅ChatGPT并安装Codex或使用WorkBuddy,用语音输入法以【背景、痛点、需求】框架向AI交代任务,经苏格拉底提问澄清需求后投喂文件让AI直接完成,最后沉淀为可复用Skill。文中建议Codex选GPT-5.6 Sol最高模型,WorkBuddy选Kimi K3。

    推荐理由:将语音输入、苏格拉底提问与沉淀 Skill 串联成一套从零到交付的闭环,直接缓解了新手不敢开始的心理卡点。

  4. LlamaIndex:产品、工程与评测45

    LlamaIndex 发布 OCR 自动化生产级落地指南

    文章探讨从基础文本提取到生产级文档处理流水线的差距,指出传统 OCR 在复杂场景下准确率下降的问题。对比了规则型、API 型和基于 LLM 编排的 Agentic 解析方案(如 LlamaParse),强调输入质量(分辨率、倾斜度)、置信度阈值设定及直通率(STP)是决定生产环境准确性的关键因素。

8月11日周二
  1. Google Developers Blog62

    Google 发布在 Raspberry Pi 5 上用 LiteRT 与 Gemma 部署边缘 AI 的完整指南

    Google 发布教程,讲解如何用 LiteRT 和 Gemma 在 Raspberry Pi 5 上实现完全离线的实时边缘 AI,并演示其驱动 Reachy Mini 机器人。

    推荐理由:原文给出 LiteRT-LM 在树莓派 5 上运行 Gemma 的具体性能数字与 CPU/GPU 分工架构,还附完整部署命令,方法可直接迁移到自己的边缘部署。

  2. MarkTechPost72

    用 ComfyUI API 实现 MiniMax-H3 多模态视频与音频生成流水线

    本教程演示如何以 ComfyUI 为无头推理后端,构建端到端的 MiniMax-H3 视频生成工作流。通过 Python 直接构建执行图,支持文生视频、首尾帧条件生成和参考图像条件生成,并自动根据 GPU 显存选择 quality、balanced、squeeze 三种权重配置。流水线涵盖模型自动下载、节点模式校验、音视频联合解码与进度监控,无需图形界面即可复现实验。

    推荐理由:用 Python 编程替代 ComfyUI 图形界面,在 Colab 上搭建 MiniMax-H3 视频生成管道,并自动适配不同 GPU 显存方案,教程提供的全流程代码可直接复用。