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Every published item, in order · 29223 items
Nov 13
PapersBuildersSelected00:02
Sebastian Raschka on why optimizing chain-of-thought hurts monitorability

The finding that RL training alone, not process supervision, drives CoT controllability redirects where safety teams should focus their monitoring effort. Researchers building reasoning models and eval teams designing oversight need to track both no-CoT capability and controllability as training scales up. The pretraining-perplexity link gives labs a concrete metric to watch when weighing next pretraining runs against safety budgets.

Official accountSebastian Raschka@rasbtSource
Nov 12
PapersBuildersSelected23:50
SoMem Lets Agents Self-Organize Memory as Linked Documents

The deeper implication is that memory schema design may be an unnecessary constraint now that frontier models can organize knowledge themselves, suggesting teams should test simpler file-based memory before investing in vector-store pipelines. Builder teams and agent developers evaluating long-term memory architectures should care most, since the approach needs only file read/write capability their stacks already have. It also transfers across models, so memory written by one model can be consumed by another in production.

Event
elvis@omarsar0Source
ProductsBuildersMulti-source23:26
Chinese contentElevenLabs 推出 Partner Program:550 家认证伙伴覆盖 44 国助力语音智能体落地

如果你在做语音智能体集成业务,可以直接申请认证成为伙伴接项目。想上线语音客服、外呼的企业也多了 550 家服务商可选,不用自己从零搭。比起 OpenAI、Vapi 这类只给 API 的做法,ElevenLabs 把实施服务也打包进来了。

Event
Official accountElevenLabs@elevenlabsioSource
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IndustryUS vs ChinaMulti-source21:30
Chinese contentGary Marcus 质疑 Anthropic:Opus 5.5 训练成本超 100 亿美元值不值

Anthropic 说 Opus 5.5 花了超过 100 亿美元,Gary Marcus 直接开炮:模型几个月就过时,边际提升根本不值这个钱。做模型选型或者关注前沿实验室烧钱路线的人,这篇吐槽很值得看看成本逻辑。

Event
Gary Marcus@GaryMarcusSource
How-toBuildersMulti-sourceSelected21:20
Home-Repo Pattern: Centralised Agent Steering Across Multiple Repos

The practical shift is from per-repo agent configuration to a single control plane, which means global rule changes land instantly across every project an agent touches. Teams juggling several related codebases — especially small startups without platform engineers — can adopt it by copying the article's prompt set and running the grill-me Q&A to tune it. The start-repo setup also pairs naturally with skills-based agents like Claude Code or Cursor, since both already read markdown steering files.

Event
Viking@vikingmuteSource
Chinese content用 AI 算多站点路线:调基础 Routes API 而非车队排班接口

让 AI 写路线规划的代码时,它可能顺手给你换成企业级车队排班接口,免费 Key 一调就报错。这篇提醒你在提示词里写死基础 Routes API 的名字,能少走弯路,做物流或配送类应用的开发者可以直接记下来。

小互@imxiaohuSource
ProductsBuildersMulti-source20:39
Chinese contentGoogle AI Studio 支持免绑卡调用 Google 地图 API,单接口每天免费 100 次

对做过地图小工具的人都懂绑卡开权限有多烦。现在 AI Studio 里说句话就能跑真地图,每天 100 次免费够原型验证了,跑通后绑卡一键转正式。个人开发者做门店查找、路线规划这类 demo 的首选入口,比直接上 Google Cloud 后台省事太多。

Event
小互@imxiaohuSource
ModelsBuildersSelected20:35
AlphaGenome 2 podcast explores rare disease genetic diagnosis

Beyond the model itself, the episode shows how AI variant interpretation is moving from research demos toward clinical diagnostic workflows for rare diseases. Genomicists, computational biologists and rare disease patient foundations can use it to understand what the model does and where it falls short, straight from its creators. Compared with academic preprints, the conversational format makes the transformer-based architecture accessible to non-specialists.

Official accountGoogle DeepMind@GoogleDeepMindSource
IndustryMulti-source20:32
Chinese contentJensen Huang 深陷二次绯闻风波,职位引质疑

四条消息拼出行业全貌:NVIDIA CEO 深陷绯闻,Claude Projects 会自己记笔记了,ChatGPT 开放商家入驻,Google 的 AlphaGenome 能把疾病追到细胞。关心算力供应链的、做协作工具选型的、做生命科学的都能找到相关的一条。

Event
The Rundown AI@therundownaiSource
ProductsBuildersMulti-source19:14
Chinese contentEnconvo:把会议转录、截图和快捷键聚合到一起的 all-in-one AI 助手

如果你喜欢 Raycast 那套快捷键工作流,可以试试 Enconvo,它把会议实时转录、截图和一堆快捷方式塞进一个 AI 助手里,按快捷键随时呼出。对经常要在应用之间搬运上下文的人来说省事不少。

Event
Yangyi@YangyixxxxSource
Chinese content2026硅谷AIE大会判断:agent 放开跑,先崩的是模型外面的一切

Steve Yegge 一个人用 agent 每天烧 50 亿 token,4 个月清掉 60 年积压,这条数据本身就够震撼。更值得看的是他把 agent 落地时按崩溃顺序排出的四层:成本、人力、回滚、基础设施。在做 agent 工作流或打算放开的团队,都能对号入座。

AI Will@FinanceYF5Source
IndustryBuildersMulti-sourceSelected18:38
Lauren Tan says agents shipped 3,000 PRs with 0.5% rollback rate

The takeaway is that human review scales poorly with agent output, so the review layer moves into test suites and deterministic gates rather than people. Teams running coding agents in production can borrow the three-part loop: convert each failure into a check, trust receipts over narratives, and tier agent permissions by track record. Engineering leads comparing this against traditional code-review-heavy workflows should weigh how their rollback rates stack up against the reported 0.5%.

Event
AI Will@FinanceYF5Source
Chinese content百度推出 Token Exchange 模型交易所,上线一月日均调用量超 5000 万

百度把 Token 中转站包装成了交易所,加了一层模型指纹比对、抽查监控和公开信用记录。上线一个月已有 100 多个模型、日均 5000 万次调用。做模型选型的团队可以去看看定价和成功率数据,但要留意虚标问题是不是真能被它管住。

shao__meng@shao__mengSource
Chinese content企业AI落地路线:从培训切入到微信数据打通的8步路径

这篇把企业AI落地顺序讲得很实在:先培训建信任,再用OpenCLI逆向SaaS给Agent配工具,最后靠微信和SCRM打通数据。对比那些上来就砍生产环节人力的做法,这套路径落地阻力小得多,适合在企业里推动AI的团队照着排优先级。

Yangyi@YangyixxxxSource
ModelsBuildersMulti-source17:02
Chinese contentFable 5.2 与 Opus 5.5 按指定三角形数在 Blender 重建猫咪 3D 模型

有人拿同一张猫照测试 Fable 5.2 和 Opus 5.5 在 Blender 里按 100/200/500/1000 个三角形精确建模,数量全部对上,还不靠图生 3D 模型。想看前沿模型执行严格约束任务的水平,这个直观测试比跑分有意思多了,做 3D 工具链的人可以参考。

Event
AI Will@FinanceYF5Source
How-toBuildersMulti-source16:43
Chinese content用户用一行反向提示词测试 Fable5.2 视频生成能力

有个日本博主拿 Fable5.2 玩了个邪门测试:让它用最强实力做「最烂」视频,结果成片质量还是没崩。比起官方 demo,这种用户单行提示词实测更能看出真实水平,做视频生成的可以拿同样玩法去测自己手上的模型。

Event
AI Will@FinanceYF5Source

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