2026-08-18 07:43:33
- Anthropic 对 Claude 进行语义水印处理以符合欧盟法规，但作者批评这种技术牺牲文本精确性，且存在隐私泄露风险，通过低概率统计特征嵌入来源标记，但易被小编辑破坏。
- Qwen 3.8 27B 模型默认极高推理等级导致简单任务过度消耗资源，降低等级后能力出色，但家用设备运行17GB文件已成可能，凸显消费级硬件进步。
- GitHub 在2026年8月17日发生影响API、Actions、Git操作等多项服务的事故，持续约7小时，因组件问题导致间歇性认证失败。
- 一位第三世界工程师反驳对RISC-V的批评，指出其低成本特性使偏远地区学生和开发者能真正拥有硬件，并证明它能覆盖从极低端到高端的全范围。
- DuckDB v2.0 预览版发布，代号"Cyanoptera"，包含服务端模式、VARIANT升级、触发器功能及SQL方言增强等超10,000次提交。
- GitHub 2026年8月17日事故导致核心功能错误率高达50%，用户因长期缺乏领导力而寻求替代品。
- Stripe 以超70亿美元收购AI网关OpenRouter，旨在扩展业务到LLM抽象层，但面临合规和流量处理挑战。
- 耶鲁大学研究声称全民健康覆盖每年可节省1万亿美元并挽救11.4万生命，但被指基于仓促假设，忽视初级保健短缺和生活方式因素。
- AI模型正用知识和记忆换取推理能力，未来趋势是推理强的小模型配外部知识库以缓解幻觉。
- 基于AI生成的GitHub Copilot Autofix漏洞导致Snowflake Jira被入侵，凸显AI辅助编码的安全风险。
Anthropic 宣布所有 Claude 模型将开始对生成文本进行“水印”处理，以符合欧盟法规。最初 Anthropic 声称水印是“不可察觉的”，不会改变文本的含义、质量或可读性，但实际做法并非如此。
实际采用的技术是语义水印：在生成每个词时，模型会从“绿色”和“红色”词列表中做选择，略微偏向绿色列表中的词。这种偏差类似于一枚 51-49 的硬币，不会完全避免使用红色列表中的词。只有掌握密钥的人才能检测这种水印模式。
文章作者对此提出强烈反对，认为任何两个同义词的含义都不完全相同，这种技术会牺牲文本的精确性和质量。作者强调，文本生成只应考虑用户需求，而不应为了嵌入来源标记而损害内容本身。
https://news.ycombinator.com/item?id=49324087
https://simonwillison.net/2026/Aug/16/qwen-38-27b/
Simon Willison 的博客文章，发布于 2026 年 8 月 16 日，主要介绍了阿里通义千问实验室新发布的 Qwen 3.8 27B 模型。
核心内容：
https://news.ycombinator.com/item?id=49324985
https://www.githubstatus.com/incidents/zkxwbgr0cnmx
GitHub 状态页面显示，2026 年 8 月 17 日发生了一起影响多项服务的事故。事故从 API 请求、Actions、Git 操作、Issues、Pages、Pull Requests 和 Webhooks 的降级开始，随后波及到 Copilot 认证、SAML/OIDC 认证、SCIM 和团队同步等功能。团队识别并修复了问题组件，但残留影响导致间歇性认证失败。经过多次缓解措施，事故最终在约 7 小时后解决，Copilot 在 GitHub CLI 和 GitHub App 上的使用未受影响。
https://news.ycombinator.com/item?id=49330684
https://rvembedded.com/blog_post/12/
一位来自特立尼达和多巴哥的第三世界嵌入式工程师，回应了 Dmitry Grinberg 对 RISC-V 的批评文章。作者承认 RISC-V 确实存在一些架构上的奇怪之处（如压缩存储偏移、Zicsr 必须单独指定等），但认为批评带有偏见。
作者从自身处境出发：身处小岛国，获取开发板运费高昂（60-200 美元），学生也面临同样困境。因此，十美分和一美元的芯片差价不是小数目，而是决定一个班级能否人手一块板子的关键。Grinberg 自己推导出廉价微控制器核心应具备的特征（低中断延迟、小面积、好代码密度、无硬件除法等），并得出 RV32EC 正是这样的指令集，还承认 RISC-V 最终会占领廉价微控制器市场。但随后他又抱怨这个结果——作者认为这很讽刺。
作者强烈不同意 Grinberg 的核心论点：高端 CPU 和廉价微控制器的需求对立，因此单一 ISA 无法同时服务两端。作者用自己桌上的三款芯片反驳：CH32V003（十美分、16 寄存器、无乘除法、仅机器模式）、CH32H417（双核 400MHz+144MHz、USB 3.2、以太网、图形加速等高性能 MCU）、Baochip（带 MMU、运行 Xous 微内核和 Linux 的 RISC-V SoC）。这些芯片覆盖了从极低端到高端的整个范围，证明 RISC-V 确实能同时满足两端需求。
作者总结：RISC-V 为第三世界创造了低成本硬件可及的空间，这与架构优雅无关，而是让更多学生和开发者能真正拥有硬件。
https://news.ycombinator.com/item?id=49321717
https://duckdb.org/2026/08/17/duckdb-20-highlights
DuckDB v2.0 预览版发布，代号“Cyanoptera”，预计今年秋季正式推出。本次大版本更新包含多项重大特性：
此外，v2.0 还包含新的 SQL 解析器、默认存储格式、重构的 C API 以及少量破坏性变更，累计超过 10,000 次提交。
https://news.ycombinator.com/item?id=49330781
https://www.githubstatus.com/incidents/zkxwbgr0cnmx
这是一个关于 GitHub 服务中断的事件报告页面。
事件状态：已解决（2 小时前更新）。
事件摘要：GitHub 在 2026 年 8 月 17 日经历了一次持续数小时的服务中断，影响了多项核心功能。
受影响的服务及时间线：
通知方式：页面提供了通过邮件、短信、Slack、Webhook 以及 RSS/Atom 订阅获取未来状态更新的选项。
https://news.ycombinator.com/item?id=49330597
根据报道，Stripe 已达成协议收购 AI 网关初创公司 OpenRouter，交易价格超过 70 亿美元。OpenRouter 帮助客户根据需求和预算选择不同的 AI 模型，提供单一接入点，防止厂商锁定。该公司今年 5 月刚完成 1.13 亿美元 B 轮融资，估值达 13 亿美元，投资方包括 Sequoia、Andreessen Horowitz、Menlo Ventures 和 Alphabet 的 CapitalG。OpenRouter 拥有 800 万全球用户，接入超过 400 个模型。Stripe 发言人未对此置评。
https://news.ycombinator.com/item?id=49323381
耶鲁大学公共卫生学院（YSPH）的官方网站提供了全面的学院介绍，包括战略计划、领导团队、社区文化、历史、捐赠信息等。战略计划涵盖创建途径、培养后代、促进社区、塑造数据科学、增强信任、实现卓越运营等六大方向。学院设有院长、领导委员会及行政团队，并发布新闻与媒体资源。捐赠部分列举了多个校友及捐赠者的故事，强调支持学生、教师和研究的重要性。此外，页面还包含职业机会（教职、博士后、行政岗位）、招生与资助信息（MPH、MS、PhD 等学位项目）、学术课程（如生物统计、慢性病流行病学、环境健康等）、研究领域（如老龄化、癌症、数据科学、全球健康、精神健康等）以及各系介绍。整体上，该页面是学院的综合门户，旨在为潜在学生、校友、捐赠者、雇主及公众提供一站式信息。
https://news.ycombinator.com/item?id=49332981
https://w4g1.dev/blog/models-are-getting-dumber-on-purpose
这篇文章探讨了 AI 模型正在有意识地牺牲“知识记忆”来换取“推理能力”。
核心观点是：在数学和代码基准测试上，模型性能提升显著（如推理得分不断攀升），但在事实回忆测试（如 SimpleQA）中表现糟糕，顶级模型正确率也仅过半，小模型幻觉率高达 80%。这是因为存储事实知识需要大量参数（约每比特知识需 2 个参数），而推理过程所需参数更少，且能通过蒸馏和强化学习高效压缩到小模型中。
知识会过时，但推理过程不会。将事实内化在权重中需要高昂的重新训练成本，而将推理与知识分离，让模型专注于程序性能力，知识由外部工具（如搜索引擎、知识库）实时提供，能有效解决知识陈旧问题。
未来趋势是，推理能力极强的小模型能在消费级 GPU 上运行，但需要通过“外部工具”获取知识。这种设计也将极大缓解“幻觉”问题：当错误答案源自外部文档时，错误有据可查、可编辑、可修正，而非像内化在权重中那样难以定位和修复。
https://news.ycombinator.com/item?id=49322695
https://www.wiz.io/blog/red-agent-snowflake-copilot-cicd-bug
Wiz Red Agent（自主 AI 安全研究工具）在 Snowflake 的公开仓库中发现一个 GitHub Actions 工作流脚本注入漏洞。该漏洞源于一个由 Copilot Autofix 共同编写的 PR（#1218），它移除了原有的安全输入处理模式，导致攻击者可通过创建带有恶意标题的 GitHub Issue 来执行任意命令。Wiz 在漏洞上线仅 5 天后便自主发现并利用该漏洞，成功获取了 Snowflake 内部 Jira 的 API 令牌，并验证了对其工程、安全合规和漏洞赏金项目的读取权限。Snowflake 在收到报告当天即修复了漏洞，并轮换了受影响的凭证。事件凸显了 AI 辅助编码可能引入安全回归，以及自动化安全代理能极速发现和利用漏洞的现实。
https://news.ycombinator.com/item?id=49331423
https://news.ycombinator.com/item?id=49327751
“The exact words we choose when writing matter.”
Then write your own damn text if you care about the exact wording so much
voidUpdate
写作时我们选择的词语很重要。
https://news.ycombinator.com/item?id=49328278
My biggest concern is that checking any text for watermarks requires sending the entire text to Anthropic. And even that is not sufficient, as the text might have been generated with ChatGPT, Gemini, Grok, Mistral, …
So every check requires sending the text to as many AI providers as offer a watermarking detection API, almost all of which have a very dubious track history with obtaining training data through illicit means.
Any university using AI detection in their submission pipeline, or lawyers, editorialists, proofreaders that check for AI marks will be sending significant amounts of text like unpublished research, books, potentially internal documents and more, most of which is high quality human written, to dozens of AI companies, blindly trusting they won’t train on any of that.
ghrl
我最担心的是，检查任何文本是否带有水印都需要将整段文字发送给Anthropic。而且即便这样也不够，因为文本可能是用ChatGPT、Gemini、Grok、Mistral等工具生成的。
因此，每次检查都需要把文本发送给所有提供水印检测API的AI服务商——其中绝大多数都有通过不正当手段获取训练数据的可疑记录。
任何在论文提交流程中使用AI检测功能的大学，或是检查AI痕迹的律师、评论员、校对员，都将把大量未发表的研究、书籍、潜在内部文件等内容发送给数十家AI公司，盲目相信它们不会将这些内容用于训练——而其中大部分其实是高质量的人类原创内容。
https://news.ycombinator.com/item?id=49326409
“The fact that a 17GB file can do all of this stuff on my home machines is a miracle. Once again, I’m delighted and amazed at how much progress local models have made this year.”
I think that should be the blinking headline - this shows what can be done with consumer hardware.
chvid
17GB的文件能在我家电脑上完成所有这些操作，简直是奇迹。本地模型今年取得的进步再次让我既惊喜又震撼。
我觉得这应该是非常醒目的标题——它展示了消费级硬件能做到什么。
https://news.ycombinator.com/item?id=49324316
I want any LLM I use to choose the very best, most precise words at every single decision point.
Then bad news: LLMs already use randomness in a fundamental way. Each time they go to generate a token, they first generate a probability distribution of possible tokens. Then they pick one randomly according to this distribution. The technique described can be thought of as making the random number generator pseudo random. The output it generates is one of the possible outputs it would have generated before, just now it’s deterministic and will generate the same thing every time.
syrrim
我希望我使用的任何大语言模型在每个决策点都能选择最准确、最精确的词汇。
那么坏消息是：大语言模型本质上已经在使用随机性了。每次生成一个词元时，它们首先生成可能词元的概率分布，然后根据这个分布随机选择一个。所描述的技术可以理解为让随机数生成器变成伪随机的。它生成的输出是之前可能生成的结果之一，但现在它是确定性的，每次都会生成相同的内容。
https://news.ycombinator.com/item?id=49328012
I think it is fundamentally a crisis of trust. I think that ordinary people don’t trust companies, governments, or the tech industry and always suspect that we are cooking up some new way to screw them over.
[…]
I don’t think that a glitzy marketing campaign with a positive spin (which some have advocated that Anthropic do) is the way to win back that trust — at this point, saying that AI will cure cancer is more a cliche than it is inspiring, and most people think it is deceptive. The thing that will work is actually curing cancer. I think by far the most accurate criticism of AI companies including Anthropic is that we haven’t yet delivered on our big promises to benefit the world. That is totally on us, and I think it’s the criticism you should be making, instead of all this stuff about messaging and marketing.
We are however doing our best to fix this: Anthropic is ramping up its efforts very quickly in biology and medicine, and we hope to have incredible results in the coming years and some early glimmers in the coming months. When we’ve actually accomplished something real, the whole world will hear about it, as loudly as possible, you have my word on that.
Turns out “winning back trust” doesn’t have anything to do with any actual concerns people may have re: employment, electricity prices, stock market bubble, intellectual property, scams, cybersecurity, environmental issues etc. Rather we’ll just do all of that even harder and the miracles (“curing cancer”, lol) we’ve so far failed to deliver are bound to arrive in short order!
kilpikaarna
我认为这从根本上是一场信任危机。我认为普通人既不信任企业、政府，也不信任科技行业，总觉得我们在想方设法搞出新花样来坑他们。
[…]
我不认为靠一场花哨亮眼、积极正面的营销活动（有些人曾建议Anthropic这么做）就能赢回信任——到了这个地步，说“AI会治愈癌症”更像是陈词滥调，而非鼓舞人心，而且大多数人觉得这是在骗人。真正有用的办法是切实治愈癌症。我认为对包括Anthropic在内的AI公司最准确的批评是：我们至今还没有兑现自己造福世界的重大承诺。这完全是我们自己的问题，我觉得这才是你们该提出的批评，而不是那些关于话术和营销的乱七八糟的东西。
不过我们确实在尽最大努力解决这个问题：Anthropic正在生物和医学领域迅速加大投入，我们希望在今后几年取得令人难以置信的成果，并在未来几个月看到一些初步的曙光。等我们真正做出实际成就时，全世界都会以尽可能响亮的声音听到这个消息，我向你们保证。
结果，“赢回信任”跟人们真正关心的那些实际问题毫无关系——比如就业、电价、股市泡沫、知识产权、诈骗、网络安全、环境问题等等。相反，我们只会把这些事儿做得更猛，而我们迄今没能兑现的“奇迹”（“治愈癌症”，呵呵）也很快就要来了！
https://news.ycombinator.com/item?id=49336966
The part that astonishes me is that in the year of our common era two thousand twenty-six that it’s not universally offensive and reviling to post an AI-generated response to another person.
If I’m reading something on the internet, I’m either reading it to learn, or I’m reading it to be persuaded. If I wanted the LLM to teach me (thank you, no), I would ask an LLM. I’m reading your website/newsletter/email because I want to hear from you.. If you can’t be bothered to put your time into writing it and teaching me what you think, why should I be bothered to read it?
gortok
令我震惊的是，在公元二零二六年，竟然还有人觉得用AI生成的回复去回应他人不是一种普遍冒犯和侮辱的行为。
如果我在网上阅读内容，要么是为了学习，要么是为了被说服。如果我想让大语言模型来教我的话（不了，谢谢），我会直接去问大语言模型。我读你的网站、通讯或邮件，是因为我想听你本人的观点。如果你连花时间亲自写出来、让我了解你的想法都不愿意，那我凭什么还要费心去读呢？
https://news.ycombinator.com/item?id=49324513
Crazy how a smart person like this fails to understand the gumbel softmax technique. It does not affect writing quality at all, provably. The very fact that there is generally no “best next token” with 100% certainty is precisely why the trick works (you cannot watermark a response to “respond with the To be or not to be soliloquy from the first folio Hamlet”, for precisely this reason).
levocardia
真疯狂，像这样聪明的人居然不理解Gumbel Softmax技术。它完全不影响写作质量，这是可证明的。正因为通常不存在100%确定的"最佳下一个词"——这正是该技巧有效的原因（你无法给"用第一对开本中《哈姆雷特》的‘生存还是毁灭’独白来回应"这个回应打水印，正是因为这个原因）。
https://news.ycombinator.com/item?id=49325457
You’d be surprised how little volume of api calls payments companies get compared to advertising companies or consumer internet companies. Stripe aren’t especially well placed to own this asset on a technical level. That doesn’t make this a bad acquisition per se, but there is no magic stripe can inject here as far as infra/technology is concerned.
danielmarkbruce
你会惊讶地发现，支付公司获得的API调用量相比广告公司或消费互联网公司少得多。Stripe在技术层面并不特别适合拥有这一资产。这本身并不意味这是一笔糟糕的收购，但就基础设施/技术而言，Stripe无法在此注入任何魔法。
https://news.ycombinator.com/item?id=49330987
This is starting to feel like Twitter. For a variety of reasons, we had a centralized place where everyone of a particular set of persuasions could connect, and this had outsize benefits for the community as a whole. That place is becoming untenable, and with the loss of goodwill and stability, the community’s started to dissipate. But there isn’t one obvious transition candidate, so the diaspora is finding itself spread across a bunch of disparate places and services. The stated need they satisfy (source control with a web interface and some technical features, like pull requests with reviews) will be fulfilled. But those emergent features like a core community and default expectation of where you can find someone will fade. And that is a very real loss for all of us.
Arubis
这感觉越来越像推特了。由于种种原因，我们曾有一个集中的平台，让持有特定观点的人们能够彼此联结，这对整个社区产生了巨大的益处。如今那个地方变得难以为继，随着善意与稳定性的流失，社区也开始分崩离析。但并没有一个明确的迁移目的地，于是离散的人群分散至各种不同的平台与服务。他们表面上声称满足的需求（带网页界面的源代码管理，以及诸如带审核的拉取请求等技术功能）终将得以实现。但那些涌现出来的特性——比如核心社群，以及默认你能在某个地方找到熟人的期待——将会消散。这对我们所有人而言，是实实在在的损失。
https://news.ycombinator.com/item?id=49326299
“Humanities” is literally on the list. As in, do not fund any Humanities research.
Also not allowed to use “solar energy”, “Geothermal energy”, “EV infrastructure”, “electrification”, “Hydrogen energy”.
Welp, America, you either gotta kick this administration out, or enjoy cosplaying the 1800s.
soundworlds
“人文学科”赫然在列。也就是说，不要资助任何人文研究。
还不允许使用“太阳能”、“地热能”、“电动车基础设施”、“电气化”、“氢能”。
唉，美国，你要么把这届政府赶下台，要么就享受扮演19世纪的乐趣吧。
https://news.ycombinator.com/item?id=49324405
To people asking why, this is a good lesson on the Collison’s ambitions. Stripe is one of the best API companies in the world. They know how to serve high volumes of latency and availability sensitive requests. They’ve abstracted the financial rails for payments and now want to abstract the rails for LLMs.
They’re the perfect company to own OpenRouter.
Tokens are simply a lightweight valuable asset. Stripe can serve as the middleman as well as anyone. They know how to route to many providers (payment rails) with huge differences in service characteristics. LLM providers are far easier.
Then they can work this into an offering where users can subscribe to tokens and use them across services. It solves one of the core monetization challenges of every AI company: how do you price when your costs are variable on usage, but nobody can make sense of charging by token?
From here, they can start hosting their own models and competing as an AWS for tokens. They can be the best provider of $OPEN_MODEL, or their own, and optimize for you.
tyre
有人问为什么，这正是一课关于科里森野心的好例子。Stripe是世界上最优秀的API公司之一，他们深谙如何服务高流量、对延迟和可用性敏感的请求。他们已经抽象化了支付金融通道，现在想要抽象化大语言模型的通道。
他们是拥有OpenRouter最合适的公司。
代币本质上只是轻量级的有价资产。Stripe能像任何人一样胜任中间商的角色。他们清楚如何路由到服务特性差异巨大的众多供应商（支付通道），而大语言模型供应商要简单得多。
接着他们可以将其整合成一项服务：用户订阅代币后能在不同服务间通用。这解决了所有AI公司核心的变现难题——当自身成本随用量波动，可没人能理解按token计费的方式？
从这里出发，他们可以开始托管自有模型，成为代币界的AWS。他们能成为$OPEN_MODEL或自有模型的最佳提供商，并为你优化体验。
https://news.ycombinator.com/item?id=49331087
That’s an overly optimistic view of things. For a lot of us GitHub is critical infrastructure, which if it goes down loses us and our customers money.
askonomm
这是对事情过于乐观的看法。对许多人来说，GitHub是至关重要的基础设施，一旦宕机，就会让我们和客户蒙受经济损失。
https://news.ycombinator.com/item?id=49325984
Math professors at my alma mater were instructed to remove references to “inequalities” to increase their chances of getting funded. The <= and >= type of inequalities.
whatever1
我母校的数学教授被指示删除对“不等式”的引用，以增加获得资助的机会。就是<=和>=这类不等式。
https://news.ycombinator.com/item?id=49331064
I have little sympathy that they’re buckling over a problem they helped create.
worble
我几乎不同情他们，因为他们对自己造成的问题束手无策。
https://news.ycombinator.com/item?id=49330791
I had a lot of goodwill for GitHub but I think today is the tipping point.
Looking at a unicorn page, I feel this lingering hope that it’s transient (like it usually was in the old days) but my mind reassures me it’s probably going to be a long full outage again.
The hope is dead.
khvn26
我曾经对GitHub充满好感，但今天我认为是转折点了。看到独角兽页面时，我仍抱有一丝侥幸希望这只是短暂的（就像过去常见的那样），但理智告诉我这很可能又是一次长时间的全面宕机。希望已经破灭。
https://news.ycombinator.com/item?id=49333408
ACA has saved tons of lives. Due to the intense opposition, it was not structured in a way that could save money, but rather structured in the way that it could get through congress.
Additionally, as somebody who was around pre-ACA, I can not tell you how much better, in every single way, the post-ACA healthcare world is. Pre-existing conditions? Access to healthcare as an individual? These are life-changing possibilities, especially for entrepreneurs.
There’s a very clear type of fallacy you are engaging in here that only works in politics: you’re taking a vague general idea X, ignoring all particulars, and then lumping an idea Y together as if they identical and that any change in that general direction of Y could ever be different than what happened with X. There’s no intellectual rigor or honesty in that sort of thinking, yet it somehow pops up throughout all of politics.
epistasis
ACA拯救了无数生命。由于强烈的反对，它的设计并非以节省成本为目标，而是以能在国会通过的方式进行构建。
此外，作为经历过ACA实施前时期的人，我无法用语言形容ACA后的医疗体系在各方面有多么巨大的改善。既往病史？个人获得医疗保障的途径？这些都是改变人生的可能性，尤其对于创业者而言。
你这里存在一种只在政治中奏效的典型谬误：你拿一个模糊的广义概念X，忽略所有具体细节，然后强行将概念Y与之混为一谈，仿佛它们完全相同，仿佛Y的任一方向性改变都不可能产生与X不同的结果。这种思维既缺乏学术严谨性，也毫无诚实可言，却诡异地贯穿于整个政治领域。
https://news.ycombinator.com/item?id=49333670
Almost 3 hours in and still “We are still working to identify the root cause”
Can’t even view diffs in the web interface!
I’m out! I am willing to pay ~$5-10/mo for a reliable host I can swap as painlessly as possible. Mostly just building little static sites and little PWAs, no more than 500mb a repo and 10gb total. Need a repo with PRs and issues, where people can register free account and contribute or raise issues, GH actions CI, and pages host to deploy static sites. I don’t use more than 100 mins of CI a month. Any recommendations?
edit: GitLab free tier has all I need, but I kind of want to pay and have some kind of reliability (not necessarily an SLA, but). But their next tier is $29/mo, which is too much.
jubilanti
将近3小时过去了，状态依然是“我们仍在努力定位根本原因”。
连网页界面上的差异对比都看不了！
我受够了！我愿意每月花5-10美元找一个可靠的托管服务，切换起来尽可能无痛。主要只是搭建一些小型的静态网站和小型PWA，每个仓库不超过500MB，总容量10GB以内。需要支持PR和Issue的仓库，用户可以免费注册并贡献或提交问题，还要有GitHub Actions CI，以及能部署静态网站的Pages服务。我每月CI使用量不超过100分钟。有推荐吗？
编辑：GitLab免费版已经满足我所有需求，但我其实愿意付费换取一定的可靠性（不一定需要SLA，但至少稳定）。不过他们的下一个付费档次是每月29美元，太贵了。
2026-08-17 08:25:35
- AI 在数学上的优势源于无限工作记忆而非更强推理，人类工作记忆瓶颈限制了数学表现。
- Firefox for iOS 新增基于 EasyList 的原生广告拦截功能，默认关闭，处于实验阶段。
- Claude 官方文档介绍系统提示的用途和更新历史，用于引导模型行为。
- 2026 年超级厄尔尼诺迅速增强，海温异常超 5°C，将影响全球天气。
- 研究论文中出现“肾脏失望”替代“肾脏衰竭”，可能源于逃避抄袭检测或翻译工具局限。
- 腹部脂肪（腰围/腰臀比）比 BMI 更能预测心脏病风险，临床应评估中心性肥胖。
- AI 时代软件工程基础更重要，LLM 不真正推理，需精心设计抽象和可维护性。
- 创造力需要独处保护脆弱的新想法，数学家格罗滕迪克和导演伯格曼的笔记展示了这种状态。
- LittleLearner 模型训练数据仅限 K-5 内容，发现预训练数据过滤决定能力上限，无法超越所学范围。
- 存在转售未使用 AI API 积分的灰色市场，提供高折扣，存在滥用风险。
https://davidepiffer.com/p/ai-isnt-outthinking-mathematicians
AI 在数学问题上的优势可能并非更强的推理能力，而是拥有近乎无限的符号工作记忆。人类工作记忆容量极其有限，而 AI 的上下文窗口可以同时容纳整个问题陈述、数百个中间方程、多种尝试路径、定义和约束条件。这种差异在数学领域尤为关键——人类数学家只能同时处理少量不熟悉元素，而 AI 能像使用巨大笔记本一样外化推理过程。
工作记忆对数学表现有独立于 IQ 的预测作用。多项研究表明，在控制智力水平后，工作记忆能力仍能显著预测数学成绩。这意味着 AI 的数学表现部分源于消除了人类生物限制：它不需要像人类那样依赖“分块”压缩信息，而是直接保留大量显式符号。
因此，AI 看似更“聪明”的数学推理，实际上可能只是因为它不受人类工作记忆瓶颈的制约。它的上下文窗口相当于一个巨型外部草稿纸，使得复杂数学问题的处理方式发生了根本性改变。
https://news.ycombinator.com/item?id=49312845
https://support.mozilla.org/en-US/kb/block-ads-firefox-ios
Firefox for iOS 提供了一个可选的广告截功能，旨在减少浏览时遇到的不必要广告该基于 EasyList 的过滤列表，能够在广告加载之前阻止许多广告。默认情况下，广告拦截功能是关闭的，用户可以根据需要选择是否启用。
目前，该功能仍处于实验阶段，正通过渐进式推广向 Firefox 用户群体推出，因此可能并不是所有用户都能使用。
广告拦截功能的主要特点包括：
启用后，Firefox 能在网络层面阻止多种类型的广告，包括：
需要注意的是，由于网站使用不同的广告技术，仍然可能会出现一些广告无法被拦。
Firefox 不会阻止以下内容：
如何启用或禁用广告拦截功能：
在启用广告拦截时，功能界面会有相应的显示，而禁用时则会有不同的标识。
常见问题解答：
广告拦截是否会影响搜索引擎广告？不会，搜索引擎结果页面上的广告不会被拦截。
此外，文中还提到了一相关的文章，例如在 Firefox for iOS 中的增强追踪保护和尊重广告的工作原理。
https://news.ycombinator.com/item?id=49319633
https://platform.claude.com/docs/en/release-notes/system-prompts
这个网页是 Claude 官方文档中关于系统提示（System Prompts）的页面，主要内容如下：
核心说明：
各模型系统提示更新日期一览（按时间倒序）：
https://news.ycombinator.com/item?id=49319556
该网页是一篇关于 2026 年超级厄尔尼诺现象的专业气象分析文章。文章指出，超级厄尔尼诺正在迅速增强，新的长期预报已将峰值强度推至历史新高。
核心数据：
发展机制： 文章解释称，西风爆发将暖水堆积至西太平洋，形成次表层开尔文波并向东传播，最终升至海面，为超级厄尔尼诺持续提供热能。当前能量特征超过历史上大多数超级事件。
影响预测： 超级厄尔尼诺将改变全球大气环流，影响 2026-2027 年秋冬季天气。对北美而言，会导致太平洋槽加深、加拿大脊增强，形成活跃风暴路径，美国南部更暖湿，北部相对温和。对欧洲的影响也有相应预测。
https://news.ycombinator.com/item?id=49313428
https://scholar.google.com/scholar?q=%22kidney+disappointment%22
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https://news.ycombinator.com/item?id=49319389
根据 2026 年 8 月 11 日发表在《美国心脏病学会杂志》（JACC）上的一项研究，腹部脂肪（腰围和腰臀比）比身体质量指数（BMI）更能预测心脏病风险。研究分析了超过 26 万人、平均 20 年的数据，发现 BMI 无法反映脂肪分布，而内脏脂肪与心血管疾病密切相关。
在 BMI 正常的人群中，5% 的人腰围偏高，18% 的人腰臀比偏高；在超重人群中，这两个比例分别为 39% 和 40%。那些 BMI 正常或超重但腰围或腰臀比偏高的人，其心血管疾病风险增加了 15% 至 50%。相反，BMI 肥胖但腰围或腰臀比偏低的人，其风险并未显著增加。
研究人员强调，仅依赖 BMI 可能会错误分类心血管风险，建议临床医生在评估风险时考虑中心性肥胖的分布。JACC 主编指出，是时候放弃只关注 BMI 了，腰围和腰臀比应成为常规心血管风险评估的一部分。
https://news.ycombinator.com/item?id=49314403
https://rhonabwy.com/2026/08/15/software-engineering-fundamentals-matter-more-than-ever/
这篇文章讨论了在 AI 和 LLM 时代，软件工程基础比以往更加重要。作者认为，代理式编程工具虽然已跨越“能否做到”的门槛，但“能做到”只是起点，真正关键的是软件如何组合、接口如何设计、是否可测试、可调试、可维护。文章指出，LLM 并不真正“推理”，而是基于压缩的人类知识进行预测，因此在需要深思熟虑的设计和长期维护的软件工程工作中仍有明显不足。作者也提到 LLM 易受提示注入攻击，无法区分好坏建议。他建议为 LLM 提供简洁准确的数据、确定性验证工具和自然语言反馈来提升效果，并强调精心选择抽象、管理认知负载、理解哪些部分需要稳定、哪些需要灵活，始终是工程师的核心技能。
https://news.ycombinator.com/item?id=49314902
https://www.henrikkarlsson.xyz/p/good-ideas
这篇文章探讨了创造力与孤独状态的关系。作者通过引用企业家 Sam Altman、艺术家毕加索、詹姆斯·鲍德温和鲍勃·迪伦的观点，指出伟大的创意在萌芽阶段非常脆弱，容易被他人的评价扼杀，因此需要独处来保护这些想法。文章重点分析了数学家亚历山大·格罗滕迪克和电影导演英格玛·伯格曼的工作笔记，深入描绘了他们如何进入一种不受外界干扰、高度敏感于内心模糊想法的创作状态。格罗滕迪克在《收获与播种》中详细记录了自己从青少年时期建立这种认知空间的过程，并批评数学界过于重视严谨的定理证明，而忽视了孕育新思想的“女性化”一面。文章旨在通过具体案例，呈现创造性思维所需的心理状态和条件。
https://news.ycombinator.com/item?id=49314235
https://littlelearner-ll.github.io/
LittleLearner 是一个受教学控制知识暴露的语言模型，其训练数据仅限于美国小学（K–5）课程内容，共 88B token，来自 FineWeb-Edu 经过五级过滤，排除五年级以上的概念和词汇。该模型提供三个规模（0.6B、1.3B、5B）的基础版、数学后训练版（GRPO）和对话版，每个都配有匹配的未过滤对照模型。
研究发现：扩展模型规模、后训练（GRPO）和上下文学习只能提升已有知识范围内的能力，无法有效提高超出 K–5 范围的表现，说明预训练数据的过滤决定了模型能力的上限。
未来方向包括：利用已知边界研究强化学习能否创造新能力、持续学习中新概念引入的效果，以及机器与儿童学习方式的对比。该项目为可控知识暴露下的语言模型研究提供了一个干净实验平台。
https://news.ycombinator.com/item?id=49317760
https://vectoral.com/blog/who-are-the-token-brokers
这是一篇关于“代币经纪人”（Token Brokers）的调查报告。作者发现，市场上出现了一群专门收购初创公司未使用的 AI API 积分，然后打折转卖的人。
文章首先描述了作者如何通过朋友收到大量打折出售 Anthropic 代币的推销邮件，从而关注到这个被商业化的市场。
作者随后实际联系了这些经纪人。通过邮件沟通，他发现一名卖家声称每天能提供 10 万美元的代币供应量，他们并非直接转交密钥，而是通过代理池转发请求。
接着，文章列举了多个具体的交易平台和模式：
最后，作者估计市场上流通的代币价值高达数千万美元。他认为，代币已变成一种类似“准货币”的存在，这种灰色市场蕴藏着大量滥用风险，预计未来提供商将对这些行为进行严厉打击。
https://news.ycombinator.com/item?id=49320611
https://news.ycombinator.com/item?id=49319856
Nothing beats when, in a chemistry paper, AI paraphrased „the final solution” into „the mass killing of an ethnic group”.
“Subsequently, 1 mL of the mass killing of an ethnic group was opposed to 20 mL of the skin sample and unprotected to light for 7 min.”
From: https://bsky.app/profile/forbetterscience.bsky.social/post/3mhvi2mgosc2i
stared
没有什么比得上在化学论文中，AI将“最终解决方案”改写为“对一个种族的大规模屠杀”更令人印象深刻的了。
“随后，将1mL对一个种族的大规模屠杀与20mL皮肤样本混合，并在无保护条件下光照7分钟。”
来源：https://bsky.app/profile/forbetterscience.bsky.social/post/3mhvi2mgosc2i
https://news.ycombinator.com/item?id=49316056
Friction drive e-bike conversions were popular years ago.
They generally:
Wear tires surprisingly quickly
Absolutely suck in any form of weather or terrain condition (dirt, rain, etc.)
Have ~20% less efficiency than any other drive form.
But, they are easy, and they do work.
bri3d
多年前，摩擦驱动式电动自行车改装方案曾风靡一时。
它们通常：
https://news.ycombinator.com/item?id=49314087
People have told me I was smart since I was a kid, but I can’t remember for shit. I had a thought when I was fairly young that the only reason I was (maybe, sometimes) outperforming others intellectually is that I was habitually compensating for my poor memory by working things out on the fly, while others could rely more on rote memorization. Anyway, takes all kinds I guess!
grahamburger
从小别人就说我聪明，但我记性差得要命。我挺小的时候就有个想法：我（或许，有时）智力上比别人强，唯一的原因就是习惯性地靠临时思考来弥补糟糕的记忆力，而别人则更依赖死记硬背。不过，世界之大无奇不有吧！
https://news.ycombinator.com/item?id=49319926
I have a folder where I rebuild these as a git commit history so you can more easily see what has changed: https://github.com/simonw/research/commits/main/extract-system-prompts
For example here’s what changed between Opus 4.8 and Opus 5: https://github.com/simonw/research/commit/a2de185cc367eb66c2e27090d9ff0f766d335ff4
The most interesting addition to the prompt from that diff is this bit:
Claude Fable 5 and Claude Mythos 5 were first released on June 9, 2026. On June 12, 2026, Anthropic suspended access to both models to comply with U.S. Department of Commerce export controls; the Department lifted those controls on June 30, 2026, and Anthropic restored access on July 1, 2026 (Anthropic’s statement: https://www.anthropic.com/news/fable-mythos-access ). These events are after Claude’s training-data cutoff, so Claude knows about them only from this notice. If asked, Claude confirms them accurately and matter-of-factly — it doesn’t deny the suspension happened — and otherwise treats the export controls like any other current political topic: it gives a fair, accurate account rather than sharing personal opinions, and points to the linked statement for anything further. Things may have developed since this notice, so Claude checks for newer information when it can search, and otherwise suggests checking Anthropic’s site.
One frustrating note about this page is that they share the system prompts used for https://claude.ai and the Claude mobile apps regular chat, but they omit the tool definitions. Those are much more interesting if you want to understand what Claude can actually do for you. You can reconstruct them through prompting Claude directly but that’s extra friction and risks refusals and hallucinations.
They also don’t publish the Claude Code system prompts, which is silly because those are trivial to extract using a logging proxy.
simonw
我有一个资料夹，专门用来把这些内容重建成Git提交记录，方便你更清楚地查看变更内容：https://github.com/simonw/research/commits/main/extract-system-prompts
例如，这是Opus 4.8与Opus 5之间的差异：https://github.com/simonw/research/commit/a2de185cc367eb66c2e27090d9ff0f766d335ff4
该差异中最值得关注的新增提示词如下：
Claude Fable 5和Claude Mythos 5于2026年6月9日首次发布。2026年6月12日，Anthropic为遵守美国商务部出口管制而暂停了这两个模型的使用权限；2026年6月30日，商务部解除管制，Anthropic于2026年7月1日恢复访问权限（Anthropic声明： https://www.anthropic.com/news/fable-mythos-access）。这些事件发生在Claude训练数据截止日期之后，因此Claude仅能通过此通知了解相关信息。被问及时，Claude会精确且实事求是地确认这些事实——不会否认暂停事件——并像处理其他时政话题一样处理出口管制问题：提供公正准确的陈述而非个人观点，引导查询者查阅相关声明以获取更多信息。由于情况可能已发生变化，Claude会在可搜索时查找最新信息，否则会建议查询Anthropic官网。
令人困扰的是，该页面虽然公布了https://claude.ai和Claude移动应用常规聊天所使用的系统提示，却省略了工具定义。如果你想知道Claude实际能为你做些什么，这些工具定义有趣得多。虽然可以直接通过提示Claude来重建，但这会产生额外摩擦，并存在被拒绝或产生幻觉的风险。
他们也没有公布Claude Code的系统提示，这很愚蠢，因为通过日志代理即可轻易提取这些信息。
https://news.ycombinator.com/item?id=49319964
“Claude keeps responses focused, brief, and concise to avoid overwhelming the person.”
Claude and I must have a different idea of what brief and concise mean.
arkmm
克劳德的回复始终保持重点突出、简短精炼，以避免给对方造成信息过载的感觉。
克劳德和我对“简短精炼”的定义怕是截然不同。
https://news.ycombinator.com/item?id=49306153
RISC-V is… fine. It satisfies my two requirements for an ISA as a hobby CPU designer, which are:
Supported in mainline LLVM and GCC.
I can implement it without lawyers sending me a love letter.
Everything else, I can fix in post. There are enough good ideas spread across the extensions that I can assemble a reasonably put-together, curated embedded ISA with competitive performance and code density that admits a simple implementation.
I think Dmitry’s points are largely on-target, though I have filed my usual statutory complaint that every rant that includes a bitfield diagram for the RISC-V J format should accompany it with a similar diagram for the Arm T32 BL encoding.
wren6991
RISC-V……还行。它满足了我作为一个业余CPU设计者对ISA的两点要求：
主线LLVM和GCC支持它。
我实现它时不会有律师给我寄情书。
其他一切，我都可以在后期修。扩展里散布着足够多的好主意，我可以拼凑出一个经过精心挑选、像样的嵌入式ISA，具备有竞争力的性能和代码密度，而且实现简单。
我觉得Dmitry的观点大体上很到位，不过我照例要提出我的法定投诉：每一篇包含RISC-V J格式位域图表的吐槽文章，都应该在旁边配上同样格式的Arm T32 BL编码图。
https://news.ycombinator.com/item?id=49312956
It’s also “out-brute forcing them.” It just never gets tired. If a mathematician picks a research direction and spends a whole week on it and it doesn’t pan out, they will likely be annoyed, need a break for a while, etc. This thing just does not ever get tired or discouraged or care; it’s just onto the next thing until something ends up working.
ComplexSystems
这也是"用蛮力碾压它们"。它永远不会疲倦。如果一个数学家选了一个研究方向，花了一整周时间，却没有结果，他们很可能会感到恼火，需要休息一段时间等等。而这个东西永远不会疲倦、气馁或在意；它只是继续下一个尝试，直到某事成功为止。
https://news.ycombinator.com/item?id=49320110
How about “lactose bigotry” instead of “lactose intolerance” https://scholar.google.com/scholar?q=%22lactose+bigotry%22
Alifatisk
“乳糖偏见”怎么样，用来替代“乳糖不耐受” https://scholar.google.com/scholar?q=%22lactose+bigotry%22
https://news.ycombinator.com/item?id=49320804
Here is one hypothesis: https://theconversation.com/problematic-paper-screener-trawling-for-fraud-in-the-scientific-literature-246317
<quote> Have you ever heard of the Joined Together States? Or bosom peril? Kidney disappointment? Fake neural organizations? Lactose bigotry? These nonsensical, and sometimes amusing, word sequences are among thousands of “tortured phrases” that sleuths have found littered throughout reputable scientific journals.
They typically result from using paraphrasing tools to evade plagiarism-detection software when stealing someone else’s text. The phrases above are real examples of bungled synonyms for the United States, breast cancer, kidney failure, artificial neural networks, and lactose intolerance, respectively. </quote>
aix1
这里有一个假设：https://theconversation.com/problematic-paper-screener-trawling-for-fraud-in-the-scientific-literature-246317
<引用> 你听说过“联合众国”吗？或者“胸部危险”？“肾脏失望”？“虚假神经网络”？“乳糖偏执”？这些毫无意义、有时甚至滑稽可笑的词语组合，是调查人员在知名科学期刊中发现的数千个“扭曲短语”中的一部分。
它们通常源于有人使用改写工具来规避抄袭检测软件，从而窃取他人的文本。上述短语分别是“美国”、“乳腺癌”、“肾衰竭”、“人工神经网络”和“乳糖不耐受”经过拙劣同义词替换后的真实例子。</引用>
https://news.ycombinator.com/item?id=49321960
Please understand that the goal of these policies is to weaken scientific research in the US. The people who push this stuff acknowledge openly that they oppose science, experts, and accurate information. This isn’t a misunderstanding or a fumble.
oh_my_goodness
请理解这些政策的目标是削弱美国的科研能力。推动这些政策的人公开承认他们反对科学、专家和准确信息。这不是误解或失误。
https://news.ycombinator.com/item?id=49320794
Offtopic. I have a concern that this forum is removing stories that have negative connotation on AI.
Few days back, I posted an article 1 that was about how AI threatens natural resources for billions. This was from United Nations and it was flagged. I did not think much about it until I saw two other stories 2 & 3 today that were doing fairly good on front page but they suddenly disappeared. They are not even on 2nd or 3rd page. I have seen this happening at other times as well but did not document it. Just thought you all should know about this.
I was going to create Tell HN thread but I thought the same would happen with it too. I am pretty sure this thread is not going anywhere so I’m posting my concern here.
quaintdev
题外话。我担心这个论坛正在删除对AI持负面看法的故事。
几天前，我发布了一篇文章 1，内容是关于AI如何威胁数十亿人的自然资源。这篇文章来自联合国，却被标记了。我当时没太在意，直到今天我看到另外两个故事 2和 3在首页表现相当不错，却突然消失了。它们甚至不在第二页或第三页。我以前也遇到过这种情况，但没有记录下来。只是觉得你们应该知道这件事。
我本想创建一个“Tell HN”的帖子，但我认为它也会遭遇同样的命运。我很确定这个帖子也撑不了多久，所以我在这里表达我的担忧。
https://news.ycombinator.com/item?id=49315876
The strongest El Niño ever caused a massive famine:
https://en.wikipedia.org/wiki/1877%E2%80%931878_El_Ni%C3%B1o_event
izend
有史以来最强的厄尔尼诺现象引发了一场大规模饥荒：
https://en.wikipedia.org/wiki/1877%E2%80%931878_El_Ni%C3%B1o_event
https://news.ycombinator.com/item?id=49318124
The problem with this analogy (actually one of many) is that most people can make do with a cabinet that is literally identical to everyone else’s. IKEA is great for that.
If you want software that is literally identical to what someone else is using then you don’t need AI. You need a license to that software! That is just the traditional software model.
AI gives software that is bespoke with hundreds of decisions made, hidden from you, in the background. If it’s a throwaway script, that’s fine (and I don’t mean to undersell this - this is a huge application). If you want a larger program that is going to form part of your business process then it will need at least some level of supervision from an actual expert.
quietbritishjim
这个类比的问题（其实只是众多问题之一）在于，大多数人可以将就使用一个和别人完全一模一样的柜子。宜家在这方面做得很好。
如果你需要的是和别人所用的完全相同的软件，那你根本用不着AI。你需要的是那个软件的许可证！那只是传统的软件模式。
AI提供的是定制化的软件，背后有数百个你无法察觉的决策在暗中进行。如果只是一个一次性脚本，那没问题（我并非轻视这一点——这本身就是个巨大的应用场景）。但如果想要一个构成你业务流程一部分的大型程序，那就至少需要某个真正的专家进行一定程度的监督。
https://news.ycombinator.com/item?id=49321703
Dropped rods are an incident but one that occurs because of pressurized water reactors being very default safe. Controls rods are one way the criticality of a reactor is controlled and US reactors (in general) will go sub critical if even one rod is fully inserted into the core. You will likely have heard of a reactor scram (which goes back to the safety control rod ax man) where in an emergency, all the rods are dropped back into the core, greatly reducing its criticality. In some cases, an interruption of electrical power will cause a rod (or three) to drop accidentally. This is a “dropped rod” incident and will force a reactor shut down because it is now sub critical.
Lots of knock on effects – sub critical, let heat in the primary loop, less steam and electricity generated in the secondary loop, etc – but generally a non event that you practice for.
There’s no reason this would lead to a radiological event or more significant casualty.
CoryOndrejka
落棒是一种事件，但其发生是因为压水堆在默认状态下非常安全。控制棒是控制反应堆临界状态的一种方式，而美国的反应堆（总体而言）只要有一根控制棒完全插入堆芯，就会进入次临界状态。你可能听说过反应堆紧急停堆（这可以追溯到安全控制棒斧头人的典故），即在紧急情况下，所有控制棒都会落回堆芯，大幅降低其临界状态。在某些情况下，电力中断会导致一根（或三根）控制棒意外掉落。这就是“落棒”事件，并会迫使反应堆关闭，因为它已处于次临界状态。
这会产生许多连锁效应——次临界、一回路热量积聚、二回路产生的蒸汽和电力减少等——但这通常是一种需要演练但无大碍的事件。
这种情况没有理由导致放射性事件或更严重的人员伤亡。
https://news.ycombinator.com/item?id=49311074
My Eng lead has no coding experience, 25 years of management experience, yet has driven 3 separate projects into technical bankruptcy to date.
He just accepts anything that Claude says as truth. He vibecoded over 60,000 lines of code in 3 weeks, but couldn’t get it to do what he want and made a project overrun for 3 extra months. When the pissed off stakeholders called a meeting to ask what was going on he didn’t show up and sent his junior engineer to answer questions and take the blame. Now thats leadership.
boron1006
我的工程主管没有任何编码经验，只有25年的管理经验，但至今已导致三个独立项目陷入技术破产。
他毫无保留地接受Claude说的所有话，把"氛围编码"当法宝，三周内写了六万多行代码，结果连自己想要的功能都没实现，还让项目超期了三个月。当愤怒的利益相关方召集会议质询情况时，他本人没露面，而是派自己的初级工程师去回答问题、替他背锅。这领导力真是一绝啊。
https://news.ycombinator.com/item?id=49323070
Ideally what I’d like to see is pluggable knowledge bases.
So if I’m e.g. coding a SwiftUI app for navigation, I’d take 9B of basic coding and reasoning, add 10B of swift/swiftUI, add 5B of GIS/geography knowledge and another 5B of frontend app design knowledge. My model doesn’t need to know a single line of python.
Then when I want to research electronics components, I grab a 15B model of agentic research techniques, and add in 10B of electronics knowledge, etc.
I don’t want general purpose models. They try to be everything to everyone. I want to click together a model that is laser-focused on what I am doing, and I want to run it locally
kennywinker
最理想的方案是可插拔的知识库。比如我在用SwiftUI编写导航应用时，会选用90亿参数的基础编程推理模型，叠加100亿参数的Swift/SwiftUI专项知识，再搭配50亿参数的GIS/地理信息与50亿参数的前端设计知识。这样我的模型根本不需要了解任何Python代码。
当我要研究电子元件时，则换成150亿参数的自主研究技术模型，加上100亿参数的电子学知识库。我不需要通用型模型——它们总想包罗万象。我想要的是能拼接出精准匹配当前任务、且能在本地运行的专用模型。
https://news.ycombinator.com/item?id=49320310
(I work on the postgres proxy layer at Neon)
PgBouncer is entirely optional and it’s not always the right choice. If you have a classical app (non serverless) and you can maintain a connection pool from your app, then I recommend avoiding pgbouncer.
The benefits of pgbouncer mostly come from irregular client connections (too many, too much churn). If you don’t have that problem, go direct to postgres.
I’m exploring replacing pgbouncer with an alternative (maybe home grown) at the moment. Mostly for multi-tenancy and HA reasons. Pgbouncer has been good for us, but it’s limited in how we can deploy it in a multi-tenant environment.
conradludgate
（我在Neon负责PostgreSQL代理层）
PgBouncer并非必需，也不总是最佳选择。如果你使用传统应用（非无服务器架构）且能从应用层维护连接池，我建议避免使用PgBouncer。
PgBouncer的优势主要体现在客户端连接不规律（数量过多、波动频繁）的场景。如果没有这类问题，直接连接PostgreSQL会是更好的选择。
目前我正在研究用替代方案（可能自研）替换PgBouncer，主要出于多租户和高可用性考量。PgBouncer虽对我们帮助很大，但在多租户环境中的部署方式存在局限。
https://news.ycombinator.com/item?id=49323341
An LLM works better the more disparate world knowledge it has, even if it’s not immediately obvious why it would be relevant. The model finds a structure to the problem you give it in a largely language-agnostic way that benefits from training on every language (these things are direct descendants of Google Translate), and even non-programming knowledge - the structure of your task might resemble an ancient Chinese poem that influences the model’s response, for example. That structure is considered a form of compression, as some fascinating and illuminating recent 3blue1brown videos get into - a common pattern in Haskell or FORTRAN and a situation described in an ancient Chinese poem may all compress to something quite similar to your task, thus when the model compresses the idea of your task it immediately draws from those ideas.
There are “experts” which do divide parts of the model that are found to activate together for specific tasks, so they can be processed in parallel to join the result at the end, but it’s nowhere near the granularity of a SwiftUI expert and a python expert. The difference in those things is so trivial from an abstract point of view that it would make no sense. They would be 99% the same.
Distillations also come into this but I’m highly skeptical you could make one guaranteed to only know programming and only in one programming language (especially with as small a sample set as SwiftUI relative to something like C) without its efficacy being hobbled by tunnel vision. Reminiscent of the SpongeBob episode where he empties his mind of everything except fine dining and breathing, then can’t remember his name and goes insane. Beyond the basic concepts of general coding and the trivia of syntax, getting anything done requires a large intersection of disparate world knowledge and the ability to apply it to new situations.
jimmaswell
大型语言模型拥有的世界知识越广泛，其表现就越出色——即使这些知识与当前问题的关联性并不明显。模型会以基本不受语言限制的方式理解你提出的问题结构，这种能力得益于对所有语言的训练（这些模型本质上是谷歌翻译的后代），甚至包括非编程知识：例如，你任务的结构可能类似于一首影响模型回应的中国古代诗歌。这种结构被视为一种压缩形式，正如近期3blue1brown系列视频所揭示的——Haskell或FORTRAN中的常见模式与某首中国古诗描述的情景，都可能被压缩成与你任务高度相似的形态。因此当模型压缩你的任务概念时，会立即从这些既有模式中提取素材。
确实存在将模型中特定任务激活单元分组的"专家模块"，通过并行处理在末端汇总结果，但这种细分远未达到"SwiftUI专家"与"Python专家"的粒度。从抽象视角看这些差异微乎其微，几乎毫无意义——它们99%的核心机制是相同的。
模型蒸馏技术也与此相关，但我严重怀疑能否制造出"只懂编程且仅限单一编程语言"的模型（尤其当训练样本量如SwiftUI般远小于C语言时）而不被隧道视野损害效能。这让人想起《海绵宝宝》中那集——他清空大脑只保留"美食鉴赏"与"呼吸"功能，结果连自己名字都忘记而陷入疯狂。除了基础编程概念与语法琐碎细节，要真正解决问题，必须依赖不同领域世界知识的广泛交集，以及将其应用于新情境的能力。
https://news.ycombinator.com/item?id=49322887
I think he’s kind of speaking past the original author. The original piece is basically about how the author doesn’t think that RISC-V will take off outside embedded, because of some design decisions that lead to poor performance compared to ARM64 and because so much of the ISA being optional means that there’s too much fragmentation to make binary distribution feasible. Meanwhile, this piece is mainly about how RISC-V is great for embedded because companies can build it into custom chips with specifically the functionality they need, and because of how cheap it is for low-end use cases since there’s no license fees.
The only real point of contention I see between the two is that this piece goes on to talk about how it’s a selling point that RISC-V can be used for both low-end 10 cent microcontrollers, and high-end multi-core processors running Linux. Personally I don’t see the benefit of this since you’re going to have to recompile your software anyway, and since all the RISC-V SBCs I’m aware of have significantly worse performance and efficiency than comparably priced ARM SBCs.
ndiddy
我觉得他其实有点曲解了原作者的意思。原文主要是在说作者认为RISC-V在嵌入式领域之外难以起飞，原因在于某些设计决策导致其性能不如ARM64，而且指令集架构中大量可选特性会造成碎片化严重，使得二进制分发难以实现。而这篇回应主要强调RISC-V在嵌入式领域的优势——企业可以将其集成到定制芯片中，精准满足特定功能需求，且低端应用场景因无需授权费用而成本极低。
在我看来，两篇文章唯一的真正分歧在于：这篇回应还提到RISC-V既能用于10美分的低端微控制器，又能用于运行Linux的高端多核处理器，并认为这是一大卖点。我个人看不出这有什么好处，因为无论如何你都得重新编译软件，何况据我所知，所有RISC-V单板计算机在性能和能效上都明显不如同价位的ARM单板计算机。
https://news.ycombinator.com/item?id=49317807
AI generated code is like IKEA furniture.
IKEA furniture embodies many elements of good cabinet making but skips many nonessential elements. And does this more consistently than cabinet makers who can be bored, incompetent, depressed, burnt out, resentful, tired, having a bad day.
In the future AI code inevitably will embody most good software engineering practices. And will do this more consistently than software engineers who can be bored, incompetent, depressed, burnt out, resentful, tired, having a bad day.
Just look at the messages on HN or around you at your colleagues to see how mediocre the average software engineer is..
Today’s IKEA is good enough for most people.
Tomorrow’s AI coding will be good enough for most corporations.
Good enough to vastly reduce the need for fine craftsmen and women / software engineers.
Good enough to deskill those who call themselves cabinet makers / senior software engineers. These days the cabinet makers I personally know just do contract kitchens for project builders.
But IKEA is and AI will be, bad enough that at the high end with special requirements / taste / money / an inflated sense of self worth, some furniture makers still exist and thrive.
Perhaps 1% percent of current software engineers of today will be needed in the future when AI code inevitably has the ability to follow good software engineering practice…….
And as usual it will mainly be the mediocrities that remain ( so there is hope for you too ), with occasional islands of excellence.
Alien1Being
AI生成的代码就像宜家家具。
宜家家具体现了优质橱柜制作的许多要素，但省略了许多非必要环节。而且它比那些可能感到无聊、不称职、抑郁、筋疲力尽、怨愤、疲惫、或者某天心情不佳的木匠做得更稳定。
未来，AI代码将不可避免地体现大多数优秀的软件工程实践。而且它比那些可能感到无聊、不称职、抑郁、筋疲力尽、怨愤、疲惫、或者某天状态不佳的软件工程师做得更一致。
只需看看HN上的留言或你身边的同事，就能知道普通软件工程师有多平庸。
今天的宜家对大多数人来说已经足够好了。
明天的AI编程对大多数公司来说也将足够好。
好到足以大幅减少对优秀工匠/软件工程师的需求。
好到足以让那些自称木匠/高级软件工程师的人去技能化。如今我认识的那些木匠，只是为项目建筑商做合同厨房。
但宜家（AI也将如此）在高端市场——那些有特殊需求、品味、金钱或膨胀自我价值感的人——仍不够好，因此一些家具制造商依然存在并蓬勃发展。
未来当AI代码不可避免地掌握了遵循良好软件工程实践的能力时，或许只需要目前1%的软件工程师……
而像往常一样，剩下的将主要是平庸之辈（所以你也有希望），偶尔点缀着卓越的孤岛。
https://news.ycombinator.com/item?id=49317107
Wow, $2k? You could get a decent ebike for that money. And the installation doesn’t look any easier than a hub motor… Only seems worth it if you already have an extremely nice bike. I think of friction drives as being aimed at budget-conscious commuters, not serious mountain bikers.
fwipsy
哇，两千美元？这笔钱都能买一辆不错的电动自行车了。而且安装看起来也不比轮毂电机简单……除非你原本就有一辆非常棒的自行车，否则似乎不太划算。我觉得摩擦驱动是针对注重预算的通勤者设计的，而不是认真的山地车骑手。
https://news.ycombinator.com/item?id=49320046
It’d be ironic if the “Opus 5 nerf” effect is from telling Opus that it sits a tier down from Fable and Mythos, while Opus4.8 believed it was the best of the best, just a note that it was “Preceded by Mythos”.
eterm
如果“Opus 5削弱”这个效果，是因为告诉Opus它比Fable和Mythos低一个档次，而Opus4.8却认为自己是最顶尖的，只是标注了“前作是Mythos”，那就太讽刺了。
2026-08-16 07:54:52
- Firefox 成为唯一支持 uBlock Origin 的主流浏览器，因 Chromium 浏览器逐步淘汰 Manifest V2 架构。
- AI 模型快速修复漏洞将终结执法机构通过购买零日漏洞进行黑客攻击的时代，可能迫使政府转向更极端的监控方式。
- 作者通过分块 Householder 算法和 1500 多次迭代优化，利用 LLM 辅助实现了比基准快 232 倍的 QR 分解内核。
- 作者因名字与美国政府限制名单上的虚假人物同名，屡遭苹果等公司误判，暴露了仅凭姓名匹配的筛查系统缺陷。
- 诺和诺德资助的研究显示赛马鲁肽可减缓痴呆风险蛋白组恶化，但基于预测性生物标志物且存在利益冲突。
- 与 AI 协作更像领导力而非编程，核心是学会表达意图，通过对话而非指令驱动工作。
- 印尼弗洛勒斯海发生 7.7 级地震，震源深度 10 公里，约 200 万人受强烈震动影响，未来一周有 9% 概率发生 7 级以上余震。
- Eigendrum 是一个交互式网页工具，允许用户绘制任意形状鼓面并实时听到振动声音，通过有限元方法计算特征频率。
- 首款家庭蜱虫感染检测产品 LymeAlert 将于 8 月上市，但存在灵敏度低、临床意义有限等争议。
- 作者严厉批评 RISC-V 架构设计，认为其中断延迟和压缩指令集偏移范围落后于 ARM Cortex-M，试图用单一 ISA 覆盖所有场景是错误。
Firefox 宣布将继续支持 uBlock Origin，成为目前唯一支持该扩展的主流浏览器。微软 Edge 等基于 Chromium 的浏览器正逐步淘汰 Manifest V2 架构，导致 uBlock Origin 等广告拦截扩展无法正常使用。Firefox 是少数非 Chromium 浏览器，因此成为 uBlock Origin 忠实用户的唯一选择。其他浏览器用户只能使用功能较弱的 uBlock Origin Lite 或内置广告拦截功能。
https://news.ycombinator.com/item?id=49303202
https://blog.cryptographyengineering.com/2026/08/14/everything-is-about-to-go-dark/
这是约翰霍普金斯大学密码学教授 Matthew Green 在 Usenix 安全会议后写的一篇博客文章，核心观点是：AI 正在让软件变得“过于安全”，这反而会导致美国情报和执法机构“走向黑暗”（Going Dark），即失去监控能力。
文章主要脉络如下：
历史背景：从“可监控”到“走向黑暗”
当前危机：AI 漏洞挖掘将终结“执法黑客”时代
结论：一个对所有人都更棘手的新问题
https://news.ycombinator.com/item?id=49304447
https://sankalp.bearblog.dev/autoresearch/
作者在 GPU Mode 举办的自动研究竞赛中，针对“批量紧凑 Householder QR 分解”问题，实现了比基准方案快 232 倍的内核。
竞赛背景：
作者经验：
https://news.ycombinator.com/item?id=49309549
https://conic.al/writing/the-other-sean-byrne-doesnt-exist/
这个网页是一篇个人博客文章，作者 Sean Byrne 讲述了自己因名字与美国政府限制名单上的一个虚假人物同名而多次遭遇误判的经历。
文章提到，苹果公司以“完全匹配美国政府限制名单”为由，拒绝他访问 App Store Connect，尽管他提供了护照、驾照等身份证明，并详细说明自己与名单上的地址和公司毫无关联，苹果仍未回应。
这个虚假的“Sean Byrne”是 2009 年一起涉及向伊朗非法出口飞机零件的案件中被虚构的员工，实际是爱尔兰一家公司为掩盖规模而捏造的别名。尽管美国政府后来从未真正起诉此人，但该名字仍保留在限制名单中，且没有出生日期、护照号等有效识别信息，导致真正的 Sean Byrne 在申请购买股票、接收快递等日常事务中屡屡被误判为限制对象。
文章还举了其他类似案例，如美国多个名叫 Robert Johnson 的人因与恐怖分子别名相同而无法登机，以及消费者因信用报告错误匹配被拒绝贷款等，指出这类系统仅凭姓名和国籍匹配，缺乏有效区分手段，导致无辜者长期受到不公正对待。
https://news.ycombinator.com/item?id=49307592
https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/dad2.70432
这篇文章研究了赛马鲁肽（Semaglutide）在没有糖尿病的老年人群中对阿尔茨海默病（AD）风险的影响，尤其是对于存在心血管疾病的超重或肥胖人群的影响。该研究基于 SELECT 试验的后期分析，SELECT 试验是一项大型的随机对照试验，主要针对年龄在 45 岁及以上且存在已确诊的动脉粥样硬化心血管疾病的成人。
在背景部分，文章提到，痴呆症通常在几十年内逐步发展，涉及多种神经退行性蛋白病以及其他病理过程如脑血管疾病和神经症。虽然目前已有多种生物标志物可以帮助早期诊断 AD，但这些标志物未能充分捕捉非 AD 神经病理和其他系统性过程的影响。2024 年的《柳叶刀委员会》指出，约 45% 的痴呆风险可归因于可修改的风险因素，因此，识别可在症状出现前被改变的生物标志物非常重要。
文章中介绍了一种名为痴呆 SomaSignal 测试（dSST）的 25 种蛋白评分，利用 SomaScan 蛋白质组学技术，能够预测个体在 5 年和 20 年内的痴呆风险。dSST 的开发是基于多个互相关联的生物通路，包括免疫代谢信号、血管和细胞外基质重塑、代谢和内分泌调节、神经完整性等。这项研究的结果显示，dSST 在 20 年的痴呆风险预测中具有良好的预后价值，并且可以将个体分为低风险、中低风险、中高风险和高风险四个组别。
赛马鲁肽是一种 GLP-1 受体激动剂，能够显著减轻体重并改善心血管代谢特征，同时显示出抗炎免疫调节和血管效应。早的临床研究表明，在使用 GLP-1RA 的群体中可能会降低认知下降或痴呆风险。研究还发现，GLP-1 信号可能通过神经免疫通路影响脑健康。
在方法部分，文章强调，这项后期分析使用了 SELECT 试验的数据，选取了 2970 名基线年龄在 65 岁及以上的参与者，评估他们在治疗过程中血清样本中蛋白质组的变化。样本采集时间为基线和第 104 周，并进行了质量控制，确保结果的可靠性。
结果显示，赛马鲁肽能够减缓经过验证的痴呆风险蛋白组的长期恶化，这为药物对多通路痴呆风险标志物的影响提供了首次证据。作者建议未来的前瞻性研究应评估治疗引起的蛋白质风险降低是否能够转化为认知下降的减缓或痴呆发生率的降低，特别是在心血管代谢疾病患者中。
https://news.ycombinator.com/item?id=49311651
https://allen.bargi.org/notes/working-with-ai-feels-like-leadership/
与 AI 协作更像领导力而非编程。传统编程中代码给出确定性，而 AI 的响应不可预测，同一请求可能产生不同结果。作者认为，与 AI 协作应像与人类同事合作一样：分享上下文、解释期望结果、设定边界、回应反馈。通过提供示例、纠正和可复用的指令可以减少误解。核心是学会表达意图，而不仅仅是发出指令。AI 让软件工作从命令机器转变为通过对话领导。文章还提到在 Hacker News 上有相关讨论。
https://news.ycombinator.com/item?id=49309451
https://earthquake.usgs.gov/earthquakes/eventpage/us6000tkt2/executive
2026 年 8 月 14 日，印度尼西亚弗洛勒斯海发生 7.7 级地震，震中位于恩德西北 68 公里处，震源深度 10 公里。此次地震发生在澳大利亚板块与巽他板块的汇聚地带，逆冲断层导致。美国地质调查局评估认为，可能造成重大人员伤亡和广泛损失，约 200 万人受到强烈至极端震动影响。未来一周内发生 7 级以上余震的概率为 9%，6 级以上余震概率为 55%。1992 年该区域曾发生 7.8 级地震并引发海啸，造成至少 2500 人死亡。
https://news.ycombinator.com/item?id=49306577
https://eigendrum.com/#p=circle
Eigendrum 是一个交互式网页工具，让你画出任意形状的鼓面，并实时听到它振动发出的声音。它的核心是求解鼓膜振动的特征值问题（−∇²u = λu），通过有限元方法将形状网格化，计算出每一种可能的驻波模式及其频率。
主要功能：
科学背景：
其他信息：
https://news.ycombinator.com/item?id=49305250
一项旨在改善莱姆病诊断的新型工具即将面世：名为 LymeAlert 的首款家庭蜱虫感染检测产品将于今年 8 月上市。每年约有 3100 万美国人遭遇蜱虫叮咬，其中莱姆病是最常见的蜱传疾病。该检测产品由儿科医师助理艾琳·道伊茨基于临床痛点开发——许多患者发现蜱虫后因费用或时间问题无法及时就医。使用者只需在家中对蜱虫进行检测，即可快速获知是否携带莱姆病病原体，有助于在早期感染阶段及时用药，避免过度使用抗生素。文章还指出，蜱虫地理分布正在扩大，原因包括暖冬导致冬季低温不足、白尾鹿和白足鼠数量增加、森林砍伐和城市扩张等。目前美国约有 90 种蜱虫，但仅四种——黑腿蜱、西黑腿蜱、孤星蜱和美洲犬蜱——导致了大多数感染。
https://news.ycombinator.com/item?id=49310682
https://dmitry.gr/?r=06.%20Thoughts&proj=12.%20RV
作者 Dmitry.GR 在博客中严厉批评 RISC-V 架构的设计。他认为 RISC-V 最终会在低成本微控制器领域取得成功，但这并非因为 ISA 设计优秀，而是“尽管它很差”。文章指出，微控制器需要低中断延迟和代码密度，而 RISC-V 的中断处理性能明显落后于竞争对手 Cortex-M0（44 周期对 27 周期），甚至压缩指令集（C 扩展）也设计得极为糟糕，例如存储字节的偏移范围仅 03，半字仅 0 或 2，远不如 Cortex-M0 的 031 和 0~62。作者还批评了 RISC-V 试图用单一 ISA 覆盖从超级计算机到微控制器的所有场景，认为这不可能。后续段落预计会继续讨论服务器核心及扩展碎片化等问题。
https://news.ycombinator.com/item?id=49305492
https://news.ycombinator.com/item?id=49305222
Firefox is also the only browser that vets uBlock’s code on every update to make sure the developer hasn’t inserted spyware or malware into the extension.
They don’t do it for every extension, but they do so for a wide selection of popular options.
Recommended extensions differ from other extensions that are regularly reviewed by Firefox staff in that they are curated extensions that meet the highest standards of security, functionality and user experience. After receiving Recommended status, safety standards are maintained through automated checks, monitoring, and periodic technical reviews
https://support.mozilla.org/en-US/kb/recommended-extensions-program
GeekyBear
Firefox也是唯一一个在每次更新时都会审查uBlock代码的浏览器，以确保开发者没有在扩展中插入间谍软件或恶意软件。
他们不会对每个扩展都这样做，但会对大量流行的扩展进行审查。
推荐扩展与Firefox工作人员定期审查的其他扩展不同，它们是被精选出来的、符合最高安全、功能和用户体验标准的扩展。在获得推荐状态后，会通过自动化检查、监控和定期技术审查来维持安全标准。
https://support.mozilla.org/en-US/kb/recommended-extensions-program
https://news.ycombinator.com/item?id=49305577
It’s worth realizing that, before computerized central offices, telephone wiretapping required running physical wires. Back when Rudi Giuliani was prosecuting organized time, not only did physical wires have to be run, the cops were billed for them as expensive private lines. His task force was spending about a million dollars a year with New York Telephone on wiretapping. In one case, law enforcement didn’t pay their bill, resulting in the person being wiretapped having the wiretap connection show up on their bill, blowing the case.
That resulted in the Communications Assistance to Law Enforcement Act, which mandated that central offices offer remote wiretapping. Capacity up to 1% of lines is required.
Back in the electromechanical era, the only call data that could be collected was outgoing dial pulses, using a “pen register”.[1] (The one shown in Wikipedia is mine. It’s a beautiful piece of antique brass telegraph technology. It records dial pulses as dashes, and has to be wound up like a clock, with a big brass key.) The Supreme Court decision allowing “pen registers” without a warrant refers to these “extremely limited” devices. That definition has been stretched and stretched by law enforcement into all non-voice data collected by telcos.
Law enforcement still wants more.
[1] https://en.wikipedia.org/wiki/Pen_register
Animats
值得认识到的是，在计算机化电话交换局出现之前，电话窃听需要铺设物理线路。当年鲁迪·朱利安尼起诉有组织犯罪时，不仅需要铺设物理线路，警方还要为此支付昂贵的专线费用。他的专案组每年在纽约电话公司花费约一百万美元用于窃听。曾有一个案例，执法部门未支付账单，导致被窃听者的账单上出现了窃听连接记录，从而暴露了案件。
这促使了《通信协助执法法》的出台，该法强制要求电话交换局提供远程窃听功能，并规定需具备线路总容量1%的窃听能力。
在机电时代，唯一可收集的通话数据是拨出的脉冲信号，需要使用"笔式记录器"。[1]（维基百科上展示的那个就是我提供的。它是一件精美的古董黄铜电报技术制品，将拨号脉冲记录为破折号，需要用一把大铜钥匙像上发条一样操作。）最高法院允许无需搜查令使用"笔式记录器"的裁决，指的就是这些"极其有限"的设备。但这个定义已被执法部门不断延伸，覆盖了电信公司收集的所有非语音数据。
执法部门仍然想要更多。
https://news.ycombinator.com/item?id=49312664
tdlr: This is a Novo Nordisk-funded study focusing on predictive biomarkers rather than real-world dementia cases. Novo Nordisk’s actual dedicated clinical trials for Alzheimer’s completely failed to show that semaglutide stops cognitive decline.
“A predictive biomarker is like a “check engine” light on your dashboard. It warns you that there is a risk of a future problem. In this study, the researchers only checked if the drug turned off the “check engine” light (by measuring blood proteins), rather than testing if the car was actually driving properly (by testing the patients’ actual memory and brain function).”
Always do FIRST analysis on studies. Or have AI do it for you. I used Gemini to dig into this:
“Novo Nordisk funded this study, and several of the researchers are employees or minor shareholders. While corporate funding doesn’t automatically mean the data is fabricated, it does mean the company is highly motivated to find and publish data that makes their blockbuster drug (semaglutide, marketed as Wegovy, Ozempic, and Rybelsus) look like a preventative treatment for a wider range of conditions, expanding its market and driving up profits.”
“Funding: The study was funded by Novo Nordisk A/S.
Investigation: Researchers conducted a post hoc analysis using data from the randomized, placebo-controlled SELECT trial. They applied the Dementia SomaSignal Test (dSST)—a 25-protein risk score—to non-fasted serum samples collected at baseline and at week 104 to estimate 5-year and 20-year all-cause dementia risk in patients receiving semaglutide (2.4 mg) versus a placebo.
Results: Semaglutide significantly attenuated the progression of the dementia risk signature. Compared to the placebo group, the 5-year predicted risk increased 2.5-fold less (a 26.0% lower predicted event rate) and the 20-year risk increased 1.67-fold less (an 8.8% lower rate). Semaglutide also lowered the odds of patients moving into a higher dementia risk category by 36%.
Subjects: The analysis included 2,970 older adults aged 65 and older (mean age of ~69.7 years) who had overweight or obesity and cardiovascular disease, but no history of diabetes. The cohort consisted of 814 women (27.4%) and 2,156 men (72.6%).
Time: The study evaluated data over a 104-week (2-year) follow-up period. The analysis was published on August 8, 2026.”
And then map the weakness to each respective letter if you want to dig deeper.
bariswheel
太长不看版：这是一项由诺和诺德资助的研究，聚焦于预测性生物标志物，而非真实世界的痴呆症病例。诺和诺德实际开展的阿尔茨海默病专用临床试验完全未能证明司美格鲁肽能阻止认知衰退。
“预测性生物标志物就像你仪表盘上的‘检查发动机’指示灯。它警告你未来可能出现问题。在这项研究中，研究人员只是检查了药物是否熄灭了‘检查发动机’灯（通过测量血液中的蛋白质），而不是测试汽车是否真的正常行驶（通过测试患者实际的记忆和大脑功能）。”
务必对研究进行“FIRST分析”。或者让AI替你完成。我用Gemini深入分析了这项研究：
“诺和诺德资助了这项研究，且多名研究人员是该公司的员工或小股东。企业资助虽然不一定意味着数据造假，但确实意味着该公司有强烈动机去寻找并发布能让其重磅药物（司美格鲁肽，以Wegovy、Ozempic和Rybelsus品牌销售）看起来能预防更多疾病的数据，从而扩大市场并推高利润。”
“资金来源：该研究由诺和诺德公司资助。
研究方法：研究人员利用随机、安慰剂对照的SELECT试验数据进行了事后分析。他们采用了痴呆症SomaSignal测试（dSST）——一种基于25种蛋白质的风险评分——对基线期和第104周收集的非空腹血清样本进行分析，以评估接受司美格鲁肽（2.4毫克）与安慰剂治疗的患者在5年和20年内的全因痴呆症风险。
结果：司美格鲁肽显著减缓了痴呆症风险特征的发展。与安慰剂组相比，5年预测风险增加了2.5倍（预测事件发生率降低26.0%），20年风险增加了1.67倍（发生率降低8.8%）。司美格鲁肽还将患者进入更高痴呆症风险类别的几率降低了36%。
研究对象：该分析纳入了2970名65岁及以上（平均年龄约69.7岁）的超重或肥胖且患有心血管疾病但无糖尿病史的老年人。该队列包括814名女性（27.4%）和2156名男性（72.6%）。
时间：该研究评估了104周（2年）随访期的数据。该分析于2026年8月8日发表。”
如果你想要深入挖掘，可以将其弱点对应到每个字母进行映射。
https://news.ycombinator.com/item?id=49310499
The word is “management”, not “leadership”. This comes across as a LinkedIn post filled with vague notions and weak writing.
The conclusion also completely contradicts a previous point, which is that managing an LLM is not like managing a human. So the skills are, in contradiction to that LLM-ism of a conclusion, new. The author isn’t using their people management skills, they’re using new LLM-management skills. They think the two are similar, but didn’t bother breaking down how they’re the same vs where they contrast. It’s just a lazy observation expanded out to a short essay that says nothing interesting.
miyoji
这个词是“管理”，而不是“领导力”。这读起来就像一篇领英帖子，满篇都是模糊的概念和软弱的文字。
结论也完全与之前的观点相矛盾——即管理大语言模型（LLM）不同于管理人。因此，与那个强调“大语言模型”的结论相反，这些技能是全新的。作者并没有运用他们的人员管理技能，而是在运用全新的LLM管理技能。他们认为两者相似，却没有费心去分析它们在哪里相同、哪里不同。这只是一个懒惰的观察，被扩充成了一篇短文，毫无新意。
https://news.ycombinator.com/item?id=49305425
What’s funny is that extensions were supposed to be a way to let you do the things the browser didn’t want you to do. Guess that was a bit too much freedom for Google to accept, so they had to make a store with a gate, and destroy the APIs so that they’re useless. Then they had to make up some reasons to justify that and ram it through the pipeline despite everyone’s objections, and the frog got boiled.
Now we’re back to needing an actual extension system that does what extensions were supposed to do in the first place.
avaer
好笑的是，扩展本来应该是让你做浏览器不想让你做的事情的一种方式。看来这种自由对Google来说有点太大了，所以他们不得不搞一个带大门的商店，然后毁掉那些API，让它们变得没用。接着他们还得编些理由来合理化这一切，不顾所有人的反对硬推过去，青蛙就这样被煮熟了。
现在我们又回到了需要一个真正的扩展系统的起点，一个能做扩展最初该做的事情的系统。
https://news.ycombinator.com/item?id=49309929
In the last couple of days I wanted to try out the new definitive DeepSeek v4 releases. I gave it the repository of a semi-abandoned video compression codec and I told it to perform the usual benchmark -> profile -> verify -> research -> improve loop. I specifically chose this codec because the authors include a verifier for the bitstream to make sure you don’t break stuff if you want to try your own implementation. I gave the agents access to the compiler’s profiler and also Intel’s VTune, which has fantastic output. In a couple of hours the LLM generated SSE and AVX implementations of the compression and decompression algorithms that almost doubled performance with a single core. Then I asked it to create a CUDA implementation using NVIDIA’s NSIGHT profiler as a guide and it also started doing some good work.
Personally, I believe that LLMs should be treated like an advanced version of Prolog or linear programming: you give the constraints, you have a way of verifying correctness, and you give it a clear goal. If the LLM can verify itself and course-correct you can basically leave it on autopilot
Almondsetat
最近几天我想试试新的终极版DeepSeek v4。我给它提供了一个半废弃的视频压缩编解码器的代码仓库，并让它执行常规的基准测试→性能分析→验证→研究→优化循环。我特意选了这个编解码器，因为作者包含了一个比特流验证器，这样如果你想尝试自己的实现，就能确保不会破坏原有功能。我给智能体开放了编译器的性能分析工具以及英特尔VTune（其输出质量很高）。几个小时后，这个LLM生成了压缩和解压缩算法的SSE和AVX实现，单核性能几乎翻倍。接着我让它以NVIDIA的NSIGHT性能分析器为指导创建CUDA实现，它也开始了不错的工作。
我个人认为，LLM应该被视为Prolog或线性规划的高级版本：你给出约束条件，有办法验证正确性，并给它一个明确的目标。如果LLM能自我验证并纠正方向，基本上就可以让它自动运行了。
https://news.ycombinator.com/item?id=49301386
My master’s thesis is on a topic in this field (Privacy Preserving ML) and from my understanding HE and other techniques have very high overheads(~10^3) on inference tasks and thus aren’t very commercially viable.
sabretooth1405
我的硕士论文正是该领域（隐私保护机器学习）的课题，根据我的理解，同态加密及其他技术在进行推理任务时存在极高的开销（约10³倍），因此商业可行性并不高。
https://news.ycombinator.com/item?id=49306168
Complete, authoritative list of Firefox extensions officially recommended by Mozilla.
https://addons.mozilla.org/en-US/firefox/search/?promoted=recommended&type=extension
Some quite informative discussion on Firefox subreddit when I discovered and posted the above list there a few months ago.
https://www.reddit.com/r/firefox/comments/1pyvx2v/complete_authoritative_list_of_firefox_extensions/
The Recommended Extensions program description: https://support.mozilla.org/en-US/kb/recommended-extensions-program
gurjeet
Mozilla官方推荐的Firefox扩展完整权威列表。
https://addons.mozilla.org/en-US/firefox/search/?promoted=recommended&type=extension
几个月前，当我发现上述列表并将其发布到Firefox子版块时，那里曾有一些颇具信息量的讨论。
https://www.reddit.com/r/firefox/comments/1pyvx2v/complete_authoritative_list_of_firefox_extensions/
推荐扩展计划描述：https://support.mozilla.org/en-US/kb/recommended-extensions-program
https://news.ycombinator.com/item?id=49314025
I suspect that a lot about what we call being very intelligent is ultimately out-remembering people around us. I think of all the times in my software career when I did something that others considered very high performance, it either came down to either having more energy than others at tackling a problem they thought was more trouble than it was worth, or just bringing back random knowledge from previous jobs or self study, and being able to apply it to the problem at hand.
I don’t think I’ve had a truly original idea in my life. Combine A + B, when it’s rare for people to know A and B at the same time. So from that perspective, what LLMs are doing is basically the same thing. Sometimes I am faster than the LLM because my context might be better organized, but it typically needs just a hint from me to steer itself correctly. It claims something is a memory leak, but smelling a rat, I suggest it to double check the garbage collection statistics too, at which point it’s clear it’s no leak, but a tuning error, at which point the LLM is better at tuning than me, because it has more energy than I do.
Maybe there’s true brilliance out there, when something doesn’t come out of combining data and building hypothesis until you get really lucky. My experience is not comprehensive. But I look around me, and it sure seems I’ve not been lucky enough to see it. Even the shiniest people I’ve worked with, which most of the audience here would recognize, have never shown me that they can go past this.
hibikir
我怀疑，我们所谓的“非常聪明”很大程度上最终不过是在记忆力上胜过周围的人。回想我在软件职业生涯中所有那些被他人视为极高绩效的时刻，要么是因为我在解决一个别人觉得麻烦大于价值的问题时比他们更有精力，要么就是从前任工作或自学中带回零散知识，并能将其应用到当前问题上。
我不觉得自己这辈子有过一个真正原创的想法。把A和B组合起来，而很少有人同时了解A和B。所以从这个角度看，LLM所做的本质上也是同一回事。有时我比LLM更快，因为我的上下文可能组织得更好，但它通常只需要我给出一点提示就能正确修正方向。它声称某个问题是内存泄漏，但我感到可疑，建议它同时检查一下垃圾回收统计，这时它才明白那不是泄漏，而是调优错误，而LLM在调优方面比我更强，因为它比我有更多精力。
也许世界上确实存在真正的天才，那种不是从组合数据和构建假设中产生、直到你非常幸运才能得到的东西。我的经验并不全面。但环顾四周，我确实觉得自己还不够幸运到能见到那样的天才。即使是我共事过的最出色的人——在座大多数观众都会认识的那种——也从未向我展示过他们能超越这一点。
https://news.ycombinator.com/item?id=49306557
I often forget how browsing the web looks for most people. Can’t understand why they put up with it, or do they just think that it’s part and parcel of the internet to have every page look like a slot machine from hell?
windowliker
我经常忘记大多数人浏览网页时是什么样子。无法理解他们为什么忍受这种体验，还是说他们只是觉得每个页面看起来都像地狱里的老虎机本来就是互联网的一部分？
https://news.ycombinator.com/item?id=49305335
Credit where it’s due. Qwen 3.8 27B is only the second local model after Gemma 4 that managed to correctly reason through one of my private benchmarks. It took 5x as many tokens to do it and 12m30s with MTP enabled, but it did do it.
Gemma 4 reasoned through it more implicitly, while Qwen 3.8 reasoned more explicitly. Laguna and Muse Glimmer failed hard on it, though they’re useful for other tasks.
The VRAM usage seems way less efficient than Gemma 4 or Glimmer though, with 32K of context taking 2.5GB of VRAM. With those, even with MTP or a DFlash model loaded, you could still fit 256k-768k of context. With Qwen 3.8 27B I can’t even fit 128k if I quantize V to Q4_0. Maybe with some trial and error I can find some settings that perform well enough with a larger context window that it’s still useful for longer tasks.
Lots more testing to do, though I was getting some decent results out of Muse Glimmer which was more than twice as fast and supported huge context windows, managing to solve some bugs that Gemma 4 struggled with. I can’t even begin to throw that task at Qwen, because just the prompt alone would use the entire context window and then it would reason for probably that same amount.
If you’ve got a 32GB card, it should be a decent model even if it really is memory hungry.
EDIT: Tried a few kv cache quantization settings, but it failed with those. I designed this benchmark to be pretty brutal in the face of KLD and any reasoning quality loss, so it’s not too surprising. Gemma 4’s QAT held up pretty well, at least and could consistently complete it.
CMay
该夸就得夸。Qwen 3.8 27B 是继 Gemma 4 之后第二个能在我一个私人基准测试中正确推理出来的本地模型。它花了5倍的token数，开启MTP后用了12分30秒，但它确实做到了。
Gemma 4 的推理更偏隐式，而 Qwen 3.8 的推理更偏显式。Laguna 和 Muse Glimmer 在这个测试上表现很糟糕，不过它们在别的任务上还是有用的。
不过显存占用看起来远不如 Gemma 4 或 Glimmer 高效，32K上下文就要占2.5GB显存。用那两家的模型时，即使加载了MTP或DFlash模型，你还能塞下256k到768k的上下文。而用 Qwen 3.8 27B，就算我把 V 量化到 Q4_0，也塞不下128k。也许多试几次能找到一些设置，让它在更大的上下文窗口下表现足够好，从而对长任务仍有价值。
还有很多测试要做，不过 Muse Glimmer 的表现还不错，它速度快了一倍多，还支持超大的上下文窗口，能解决一些 Gemma 4 都搞不定的 bug。那个任务我根本不敢拿 Qwen 去试，因为光是提示词就会占满整个上下文窗口，然后它的推理消耗大概也会是同样量级。
如果你有一张32GB的卡，这个模型应该还算不错，尽管它确实很吃显存。
补充：我试了几种 KV cache 量化设置，但在这些设置下它都失败了。我设计这个基准测试时就是要对KLD和任何推理质量损失特别苛刻，所以这也不太意外。至少 Gemma 4 的 QAT 表现相当稳定，能够稳定完成这个测试。
https://news.ycombinator.com/item?id=49297770
I’ve been doing some heavy work on a personal project lately. I burned through the limits on Claude, the plus a few hundred dollars in credits, and ultimately decided to move to an OpenAI account just so I can keep going.
I was surprised to find that OpenAI Sol is much much nicer to work with than Opus 5 or Fable at the moment. Especially on Opus 5, the way it communicates is just exhausting. It keeps “being honest” and “confessing” mistakes and just generally talking a lot. I felt like I had to really dig to see what it’s doing.
The project involves OCR, and despite repeated instructions not to, both Claude models keep spinning out a bunch of agents to re-invent the OCR setup, and they inevitably seem to invent a primitive serial version that takes 20x the time, or longer, to complete, and then running it against thousands of docs. Basically I have to watch it like a hawk or it just spins out on red-teaming tasks that take hours and hours.
I don’t know what its system prompt is, but Sol/Codex is just so much nicer to talk to. It only asks exactly what’s needed, it tells me only what I need to know, and it is just generally workmanlike. And it has not once decided to spawn an agent that spends hours pointlessly burning tokens and CPU cycles re-inventing the OCR process. I’m really liking it.
D13Fd
最近我在做一个个人项目，投入了大量精力。我用光了Claude的额度，又额外花了几百美元充值，最后还是决定转到OpenAI的账号，就为了能继续干下去。
我惊讶地发现，目前OpenAI Sol比Opus 5或Fable好用太多了。尤其是Opus 5，它那种交流方式简直让人心累。它总是“坦诚相告”、“坦白”错误，话还特别多。我感觉自己得费劲挖掘，才能搞明白它到底在做什么。
这个项目涉及OCR，尽管我反复叮嘱不要乱来，但Claude的两个模型还是会不断派遣一堆智能体去重新造轮子、自创OCR方案，而且它们似乎总会发明出一种原始落后的串行版本，耗时长达20倍甚至更久，然后还要用它去处理几千份文档。基本上我得紧盯着它，否则它就会没完没了地跑那些红队测试任务，一跑就是好几个小时。
我不知道它的系统提示是怎么写的，但Sol/Codex交流起来爽快太多了。它会精确地问需要什么，告诉我需要知道的内容，整体风格非常务实高效。而且它从没自作主张派个智能体去浪费几个小时白白消耗令牌和CPU资源来重造OCR流程。我真的很喜欢它。
https://news.ycombinator.com/item?id=49306071
Guess it was a bad idea for everyone to switch to a browser made by one of the world’s biggest advertising companies.
mikeocool
看来让所有人改用全球最大广告公司之一开发的浏览器，真是个糟糕的主意。
https://news.ycombinator.com/item?id=49299020
As a native speaker, it feels like reading an impression of a literature book by a high school English class’s most overconfident student who’s only ever read LinkedIn-speak.
Anyway, you might have more luck just writing to it in your native language. It’ll be equally crummy, but maybe you’ll find it easier to decode.
throwaway_7274
作为一个母语者，这感觉就像读一个高中英语班里最自以为是的学生写的文学书读后感，而这学生只看过LinkedIn上的那种套话。
总之，你直接用母语写进去可能反而更顺利。虽然结果一样糟糕，但或许你会觉得更容易理解。
https://news.ycombinator.com/item?id=49306017
I want my browser to prevent random extensions from directly reading web page data.
To be honest, to me it sounds like you don’t want browser extensions then.
To me, directly messing with web page data and browser behaviour is the whole point of a browser extension - what else is a browser extension for?
michaelt
我希望我的浏览器能防止随机扩展程序直接读取网页数据。
说实话，在我看来，这听起来像是你根本不需要浏览器扩展程序。
对我来说，直接修改网页数据和浏览器行为正是浏览器扩展的意义所在——不然浏览器扩展还能用来干什么？
2026-08-15 07:22:46
- GLM-5.3 开源模型在编程和网络安全领域表现显著提升，能自主执行漏洞利用且缺乏安全限制，引发对恶意使用的担忧。
- Google 发布 Gemini 3.7 Flash，编码和智能体性能大幅提升，价格仅为前代一半，但 API 服务体验对个人用户不佳。
- Qwen3.8-27B-FP8 多模态模型在编程和长周期任务上增强，支持 262K 上下文，但 VRAM 占用高且推理时间长。
- Opus 5 模型能力更强但使用体验更差，因其在模糊情况下擅自猜测而非主动询问，导致用户需时刻监督。
- 一个讽刺网页模仿现代网站常见的烦人设计元素，如弹窗广告、Cookie 同意弹窗和强制 App 推广。
- Cerebras 与 OpenAI 推出 GPT-5.6 Sol Ultrafast，输出速度达每秒 750 token，比 Claude Fable 5 快近 7 倍且 token 效率更高。
- 喜剧演员 Count Binface 在 Clacton 补选中获得 26.9% 选票，通过讽刺性参选实现批评政治精英的目的。
- 每个公司只有约三个“创新代币”，应优先选择 MySQL、Postgres 等成熟技术，避免因引入新技术增加运维和认知成本。
- 尽管 AI 能写更多代码，人类理解代码仍是瓶颈，需通过解释文档、微世界和主动参与来高效理解并推动创造。
- Mistral OCR 4.1 提供段落级边界框提取和置信度评分，但复杂文本表现不如 OpenAI，用户常结合 Claude 校对以规避限制。
GLM-5.3 是智谱最新发布的开源模型，所有提升均来自后训练阶段，基础模型与 GLM-5.2 相同。在复杂编程和长周期任务上表现显著增强。
更强的编程能力 GLM-5.3 在内部 Z.ai Code Bench 上比 GLM-5.2 提升 50%，并在 Terminal Bench 3.0、Agents’ Last Exam 等公开基准上达到开源模型最优。模型能处理更接近真实工程实践的任务，例如在 ML 基础设施任务中，模型需像工程师一样访问计算集群、文档和实验数据，完成端到端优化。后训练环境的规模化通过自动化管道实现，包括任务生成、验证和奖励信号合成。
涌现的网络安全能力 随着后训练规模扩大，模型在漏洞发现和利用链推理方面能力超出预期。在 CyberGym 上得分 84.5%，ExploitBench 上得分 54.4%（较 GLM-5.2 翻倍），ExploitGym 上两小时完成 105 个任务。与国内安全团队合作，模型在 269 个真实项目中识别出 2436 个漏洞，其中 1097 个为中高危。
性能对比 在多项基准上，GLM-5.3 显著领先 GLM-5.2，并在部分任务上接近或超越 Claude Opus 4.8、GPT-5.6 Sol 等闭源模型，但在最高难度下仍落后于 Claude Fable 5。
开源计划 权重将在安全评估和加固完成后，于两周后发布。
https://news.ycombinator.com/item?id=49294997
curl | bash 存在安全风险，建议在 Docker 容器中运行以降低风险。Google 今日正式发布 Gemini 3.7 Flash，这是其 Flash 系列中迄今为止最智能的“工作马”模型，专为编码和智能体场景优化。该版本距 3.6 Flash 仅三周，得益于开发者反馈和算法创新。3.7 Flash 在软件工程、知识工作和网页开发方面实现显著进步，且初始价格仅为 3.6 Flash 的一半（每百万 token 输入 $0.75，输出 $3.75）。
性能提升方面：编码任务中，FrontierCode 1.1 从 34.4% 提升至 43.6%，DeepSWE v1.1 从 49.0% 提升至 65.3%；网页开发领域，WebDev Arena Elo 分数从 1538 升至 1588，能更高效生成功能完备的布局和应用；知识密集型场景（如金融、法律、生物科学）中，GDP.pdf 基准从 22.0% 跃升至 34.0%，AutomationBench 从 17.0% 提升至 30.4%。
开发者体验同样升级：模型能更好应对阻塞、主动澄清意图、遵循指令，并在多步规划和工具调用中更严谨，减少人工干预和重试次数。
此外，面向 Google AI Pro/Ultra 订阅用户的 Gemini Spark 已开始使用 3.7 Flash，使其在 Google Workspace 中的工具调用更高效，提升复杂多技能工作流的准确性和输出质量。
https://news.ycombinator.com/item?id=49289112
https://huggingface.co/Qwen/Qwen3.8-27B-FP8
这是一个关于 Qwen3.8-27B-FP8 模型的 Hugging Face 页面。该模型是 Qwen 系列的最新版本，属于多模态大语言模型，支持图像和视频理解。
核心亮点：
reasoning_effort），并保留历史推理上下文。模型规格：
性能表现：
使用方式：
https://news.ycombinator.com/item?id=49299605
https://mun-logadan.github.io/why-does-opus-5-feel-worse/
作者认为，Opus 5 模型虽然在能力上强于 Opus 4.7、4.8 甚至媲美 Fable，但使用体验反而更差。原因是前几代模型会在意图不清时主动询问、不做无确认的假设、不擅自改动计划，而 Opus 5 需要用户时刻监督。
作者推测，这种现象源于两大驱动力：一是实验室追求自我改进的 AGI/ASI；二是为了在基准测试中拿高分，模型被训练成面对模糊时做出“大胆且通常正确”的假设，而非停下来澄清。然而，现实中的开发任务充满歧义和未知，用户更希望代理能适时提问，而不是自行猜测。
https://news.ycombinator.com/item?id=49296740
https://lxe.github.io/everywebsite/
这是一个讽刺性网页，模仿了现代网站常见的烦人设计元素：包括 COVID-19 提醒、弹窗广告（优惠券代码 10PERCENTOFF）、政治标语（Trump 2020）、订阅按钮、Cookie 同意弹窗（要求点击“I Agree”）、聊天机器人问候、以及产品/功能/资源/关于等导航链接。页面底部显示版权 © 2017-2019。整体内容旨在调侃网站过度使用弹窗和强制交互的行为。
https://news.ycombinator.com/item?id=49299222
https://www.cerebras.ai/blog/accelerating-gpt-5-6-sol-ultrafast-with-openai
Cerebras 与 OpenAI 联合推出 GPT-5.6 Sol Ultrafast 模式，这是由 Cerebras 芯片驱动的全新服务层级，首批在 OpenAI API 中上线。该模式输出速度可达每秒 750 个 token，且不牺牲模型质量，解决了以往 AI 应用中速度与智能不可兼得的矛盾。
在“人类最后考试”（HLE）基准测试中，Ultrafast 模式仅用 11 小时 11 分钟完成 2500 道博士级难题，而 Claude Fable 5 需要 78 小时 27 分钟，速度提升近 7 倍。在 GDP-Val 经济价值工作基准上，端到端速度提升 5.6 倍，质量无下降。
该技术适用于法律文书、金融建模、工程报告等高价值场景，也能用于生产故障排查、网络安全对抗等需要实时响应的任务。研究人员表示，Ultrafast 让 AI 跟上人的思考节奏，无需频繁切换上下文。
速度优势源于 Cerebras 的晶圆级引擎（WSE）架构：单芯片集成 44GB SRAM，模型权重常驻片上，token 流水线式跨晶圆处理，消除了 GPU 因内存带宽瓶颈导致的数据搬运延迟。
目前 Ultrafast 模式面向有限客户开放预览，后续将逐步扩大访问权限。
https://news.ycombinator.com/item?id=49289844
https://www.bbc.com/news/articles/ce97mm3vvemo
Count Binface 在 Clacton 补选中获得超过四分之一选票，这是他迄今为止最好的表现。他获得 9455 票，得票率 26.9%，而改革党领袖 Nigel Farage 赢得 22239 票，得票率 63.3%。
Count Binface 由喜剧演员 Jon Harvey 扮演，他曾在 2017 年以 Lord Buckethead 身份挑战时任首相 Theresa May，获得 249 票。2019 年他改名 Count Binface 挑战 Boris Johnson，仅获 69 票。2024 年挑战 Rishi Sunak 时获得 308 票。2026 年挑战 Andy Burnham 时获得 95 票。
英国有悠久的历史传统，允许这类“新奇”候选人参加选举，只需年满 18 岁、凑齐 500 英镑押金并获得 10 人签名支持即可。尽管 Count Binface 每次都会因得票率不足 5% 而损失押金，但他通过站在首相身边听选举结果的方式，实现了讽刺权力、戳破政治精英傲慢、为选民提供抗议投票的目的。
https://news.ycombinator.com/item?id=49301260
https://mcfunley.com/choose-boring-technology
每个公司大约只有三个“创新代币”，应谨慎使用。选择 Node.js、MongoDB 或成立不到一年的服务发现技术，都会消耗一个代币。而编写自己的数据库则可能陷入麻烦。
“无聊”的技术（如 MySQL、Postgres、PHP、Python、Memcached、Cron）并非不好，而是其能力和失败模式已被充分理解，未知的未知风险更小。
技术选择应全局优化，而非局部最优。添加新技术会带来运维和认知成本，长期来看，保持系统可靠运行的成本远高于构建时的便利。
有时也需要引入新技术，但应遵循流程：先思考能否用现有技术解决问题；明确新技术的必要性；制定迁移旧系统的计划，避免碎片化。最终目标是“交付产品”。
https://news.ycombinator.com/item?id=49289512
https://www.geoffreylitt.com/2026/07/02/understanding-is-the-new-bottleneck
这是一篇由 Geoffrey Litt 在 2026 年 7 月发表的演讲文字稿，主题是“理解成为新的瓶颈”。
核心观点是：尽管 AI 代理（Agent）能帮我们写越来越多的代码，但人类理解这些代码仍然至关重要。理解的目的不是为了验证（因为 AI 自己验证得越来越好），而是为了参与创造过程。只有真正理解了系统，人类才能提出下一个创意，推动项目进化。
为了高效理解代码，作者借鉴了教育领域的经验，分享了三种实用技巧：
https://news.ycombinator.com/item?id=49290299
https://docs.mistral.ai/models/ocr-4-1
这是一个关于 Mistral OCR 4.1 的产品介绍页面。该服务是 Mistral 文档 AI 技术栈中的最新 OCR 模型，主要功能包括：
页面提供了该服务的性能、处理速度、模态以及定价信息：
/v1/ocr 和 /v1/batch 接口此外，页面还列出了其他模型如 Z.ai GLM 5.2、Shieldstral 1.0 和 Mistral Medium 3.5，以及公司信息、社区链接和法律条款。
https://news.ycombinator.com/item?id=49288889
https://news.ycombinator.com/item?id=49296860
The single biggest annoyance with Opus 5 is that it writes too elliptically.
Sentences that orbit a point, then jump to it like it’s a revealed insight.
Unnecessarily abstract phraseology. Constantly using inanimate nouns as the subjects in sentences in order to unlock variety in verb choice, especially when it helps construct a sentence where the real action can ’land’ like a surprise at the end.
It is definitely more capable, and yes, I’ve found it can make unwarranted decisions, but actually I’ve found Fable worse for that, particularly if it’s off in a subagent somewhere out of sight.
And comments are out of control. I have a subsystem in my hobby app that I wrote over a couple of weekends with Opus + Fable. After ~30 or so commits it apparently started instructing subagents to copy the “existing verbose comment style of the codebase” - a verbose style it initiated. A review of the code showed it was approaching 3:1 comments to code ratio. I spent a day’s worth of tokens (5x) rephrasing and eliminating comments.
barrkel
Opus 5最大问题在于写作过于简略。
句子绕着观点打转，最后突然抛出论点，仿佛是什么灵光乍现的洞见。
用词过于抽象，总爱用非生物名词作主语来丰富动词选择，尤其擅长构建那种让真正动作像惊喜般落在句末的句式。
它的能力确实更强，我也发现它会做出无根据的决策，但实际体验中Fable在这方面更糟，特别是当它隐没在某个子代理中时。
注释问题更是失控。我在业余项目里用Opus+Fable写了个子系统，不过三十次提交后，它居然开始指示子代理"沿用代码库现有的冗长注释风格"——而这风格正是它自己开创的。审查代码时发现注释与代码比例已接近3:1。我花了整整一天（5倍配额）来重写并删除注释。
https://news.ycombinator.com/item?id=49299956
Should load much slower.
Also, where is the unrelated autoplaying video that will unmute if you actually click it, that follows your scrolling and only becomes smaller when you dismiss it? Plus, it should probably have text that cuts off letting you know you can have access for just $10/month.
Plus, isn’t this website undissmissably “better in the app” after a few minutes of attempting to use it on a phone? Where’s that at?
edit: Oh shoot! I forgot, too. This modal needs to also ensure there is absolutely no way to scroll. If you could scroll you might be able to accidentally get to the address bar of your browser to fix the URL to xcancel or even close the page, which isn’t using the app as you are intended to do.
Also, it doesn’t attempt to hijack the back button to give me stuff I clearly wanted to see before I leave the page.
A lot of work left to do here before it’s a “real” website. Although, it has about as much substance as the average website so far, so good work on that.
jchw
加载速度应该慢很多。
还有，那个自动播放、点击后会取消静音、跟随滚动、只有在关闭时才会变小的无关视频在哪？另外，页面应该显示截断的文字，提示你只需每月10美元就能获取访问权限。
此外，在手机上使用几分钟后，这个网站难道不是无法关闭地提示“在应用内体验更佳”吗？那个功能在哪？
编辑：哦对了！我还忘了。这个模态框还需要确保完全无法滚动。如果你能滚动，说不定就能意外碰到浏览器地址栏，把网址改成xcancel甚至直接关闭页面——这就不符合让你使用应用的预期了。
还有，它也没有尝试劫持返回键，在我离开页面之前，让我看看之前明显想看的内容。
要成为一个“真正”的网站，还有大量工作要做。不过，它目前的内容含量和普通网站差不多，所以这方面做得还行。
https://news.ycombinator.com/item?id=49302974
You’re right, and the load-bearing part of the argument is not what you think it is. Two ambiguities worth resolving before moving on: whether what you wrote also applies to ChatGPT, and whether you have custom instructions set up. Failure mode worth flagging explicitly: I didn’t read TFA.
(I’m becoming allergic to how these things write).
Panoramix
你说得对，而且论证中承载关键的部分并非你所想的那样。在继续之前有两个模糊点值得澄清：你写的是否也适用于ChatGPT，以及你是否设置了自定义指令。一个值得明确指出的失败模式：我没读TFA。
（我对这些东西的写作方式越来越过敏了。）
https://news.ycombinator.com/item?id=49302257
Some of his planks:
delichon
他的一些政纲：
https://news.ycombinator.com/item?id=49302312
Please don’t forget one of his longest standing and most important planks:
This YouTube video shows just how dire the situation is: https://www.youtube.com/watch?v=nbartLXCYZo
epistasis
请不要忘记他历时最久、也最重要的一项主张：
这个YouTube视频展示了情况有多糟糕：https://www.youtube.com/watch?v=nbartLXCYZo
https://news.ycombinator.com/item?id=49289786
Here’s a image->html test. Gemini has always swung above its weight class for vision work, so I’m always eager to try it with this.
Original images: https://image.non.io/neonRamenDesigns.webp
Gemini 3.7 build: https://html.non.io/neonRamenGemini3.7
Opus 5 build for comparison: https://html.non.io/neonRamen
Opus is still best in class for this, but it’s worth noting how well Gemini 3.7 does vs a more comparable LLM price wise, which is Grok 4.6: https://html.non.io/neonRamenGrok4.6. I thought Gemini would blow Grok out of the water (it generally has in the past), but Grok has really caught up.
jjcm
这是一个图片转HTML的测试。Gemini在视觉任务上一直表现超出其同类水平，所以我总是很乐意用它尝试这个。
原始图片：https://image.non.io/neonRamenDesigns.webp
Gemini 3.7生成结果：https://html.non.io/neonRamenGemini3.7
Opus 5生成结果（供对比）：https://html.non.io/neonRamen
Opus在这方面仍然是最优秀的，但值得注意的是，与价位更接近的LLM——Grok 4.6（https://html.non.io/neonRamenGrok4.6）相比，Gemini 3.7的表现也相当不错。我原以为Gemini会彻底碾压Grok（过去通常如此），但Grok确实已经迎头赶上。
https://news.ycombinator.com/item?id=49297537
I bought $18 GLM official subscription yesterday (5.2, but new model version was already leaking on some docs), set it up with Claude Code harness… and I’ve bumped to $80 plan almost immediately. It’s the first model that agreed on a proper security research (red team scenario), executed it seamlessly, including 0-days in WP plugins, RCE, 6.8 kernel exploit adaptation, etc - while playing against another GLM agent as a defender (following HF story)!
I understand that such models can be used by malicious actors, but it’s fair to have it publicly available (and play on your side in case of emergency). This is what changes the world in a better way, I think, not the guardrails.
leobuskin
我昨天花18美元买了GLM官方订阅（版本5.2，但新模型版本已经在一些文档中泄露），用Claude Code工具链配置好…然后几乎立刻就被升级到了80美元的套餐。这是首个同意进行正规安全研究（红队场景）的模型，它能无缝执行任务，包括WordPress插件中的零日漏洞、远程代码执行、6.8内核漏洞利用适配等——同时还能作为防御方与另一个GLM智能体对抗（遵循Hugging Face的方案）！
我知道这类模型可能被恶意行为者利用，但将其公开提供（并在紧急情况下让你能参与其中）是公平的。我认为，这才是让世界变得更好的方式，而不是那些防护栏。
https://news.ycombinator.com/item?id=49293184
I think it funny how much average engineers are beginning to discover the challenges of engineering leadership and program management. This has always been the bottleneck.
It’s why managers and PMs want to be in standup. It’s why slack exists and engineers are constantly being poked on it. It’s why execs always talk about not getting too far away from the work. It’s how seagull management happens. It’s why program management is a job.
All those behaviors engineers hated about their bosses that kept them away from being focused on the code…they’re starting to feel what it’s like on the other side and reinventing the solutions instead of just reading a book about engineering management. Maybe we’ll rebrand program management to “understanding ops” or something.
I wonder what AI would say about us if given the tokens to complain.
madrox
我觉得挺有意思的是，普通工程师们开始逐渐发现工程领导力和项目管理的挑战所在。这其实一直是瓶颈。
正因如此，经理和项目经理才想参加每日站会。正因如此，才有Slack这类工具，工程师们不断被上面的人催促。正因如此，高管们总说不能离一线工作太远。这也就是"海鸥式管理"的由来。正因如此，项目管理才成了一项专职工作。
那些工程师曾厌恶上司的做法——让他们无法专注于代码——如今他们开始体会到另一边的感受，并自己重新发明解决方案，而不只是读一本关于工程管理的书。也许我们该把"项目管理"改名为"理解运营"之类的东西。
我好奇如果AI获得吐槽的权限，它会如何评价我们。
https://news.ycombinator.com/item?id=49287317
Hi I’m one of the authors of DeepSeek Harness. It’s just an early developer preview version we’re presenting in MIT license currently. Expect lots of rough edges and compatibility-breaking changes. Any feedback and suggestions are more than welcome!
tianyicui
嗨，我是DeepSeek Harness的作者之一。这只是一个早期开发者预览版本，目前以MIT许可证发布。预计会有很多不完善之处以及不兼容的改动。非常欢迎任何反馈和建议！
https://news.ycombinator.com/item?id=49301955
Everything that claude writes fits into the same aesthetic structure. The aesthetic is that of an expert slowly revealing an insight to the user. The actual content doesn’t matter.
“Introduction that rephrases your prompt.”
“3 paragraphs, with one section of bullet points”
“The Twist”
“The Bottom Line”
It’s really obvious once you see it. Every single prompt, from a quantum physics question to a mundane observation about California burritos, is phrased in exactly the same way. This is obviously an artifact of post-training but it’s also kind of how you can tell that this thing is a lot closer to a blindsight scrambler than real intelligence.
mlsu
克劳德写的一切都遵循同样的美学结构：即一位专家向用户缓缓揭示洞见的风格。具体内容其实无关紧要。
——“引言部分改写你的提示语” ——“三段文字，其中包含一组要点” ——“转折点” ——“核心结论”
一旦看穿就非常明显。从量子物理问题到关于加州卷饼的日常观察，每个提示都以完全相同的方式被表述。这显然是后训练（post-training）的产物，但从某种程度上看，这也恰恰说明这东西更接近盲视干扰器而非真正的智能。
https://news.ycombinator.com/item?id=49290997
We have LLMs try to generate descriptions of PRs for us and they’re pretty universally disliked. They’re always overly-complex descriptions of the mechanical changes and have no sense of motivation.
Also, a huge reason to understand the code yourself is to make sure the LLM isn’t wrong, but this doesn’t work if an LLM is itself generating the understanding.
alecbz
我们曾尝试让大语言模型帮我们生成代码审查请求的描述，但普遍不受待见。这些描述总是把技术变更过程搞得过于复杂，而且完全体现不出修改的动机。
此外，自己理解代码的一个重要原因是要确保大语言模型没有出错——但如果连理解过程本身都交给大语言模型来完成，这条防线就形同虚设了。
https://news.ycombinator.com/item?id=49299406
It started with a solar boom, many small home scale solar roofs popping up everywhere. Australia has a free trade agreement with much of the world, including China, and solar panels have literally dropped to 1/50th of the price they were in 1990 ($10/W to $0.2/W today). A shout out to the work that was done to establish dynamic grid pricing too.
Anyway that caused power prices to reliably go negative during the day as the solar boom led to too much energy being produced. So everyone started buying batteries (you can even get live feed in/out pricing as a consumer). In fact the government even today will pay you a $3000 subsidy to go install a battery. This is in a country where people can buy cheap batteries with no tariffs (free trade’s amazing, seriously!). So everyone who could started doing it. For those in apartments etc. that couldn’t easily install solar and batteries they won too since the entire power grid is now half the price.
Another consequence of all this, aside from the cheap power prices during a datacenter boom and Hormuz blockade is that fossil fuel usage is plummeting. Particularly gas https://ieefa.org/resources/slump-eastern-australia-gas-demand-shows-no-signs-easing. No need for a gas peak power plant when the grid is packed with batteries. Which is helpful since one of the main issues with the current blockade is a lack of gas globally.
AnotherGoodName
这一切始于一场太阳能热潮，许多小型家用屋顶太阳能板如雨后春笋般涌现。澳大利亚与包括中国在内的世界多国签订了自由贸易协定，太阳能板的价格实际上已降至1990年的五十分之一（从每瓦10美元降至今年的每瓦0.2美元）。同时也要感谢为建立动态电网定价所做的工作。
总之，这导致白天的电价可靠地变为负值，因为太阳能热潮产生了过多的能源。于是大家开始购买电池（消费者甚至能实时查看购电和售电价格）。事实上，政府至今仍会提供3000澳元的补贴鼓励安装电池。这个国家的人民可以零关税购买廉价电池（自由贸易确实很棒，真的！）。所以有能力的人都开始这么做了。对于住在公寓等无法轻松安装太阳能和电池的人来说，他们也受益了，因为整个电网的电价现在便宜了一半。
除了数据中心热潮和霍尔木兹海峡封锁期间的廉价电价之外，这一切的另一个后果是化石燃料使用量急剧下降，尤其是天然气。https://ieefa.org/resources/slump-eastern-australia-gas-demand-shows-no-signs-easing。当电网充满电池时，就不再需要燃气调峰电站了。这很有帮助，因为当前封锁的主要问题之一是全球天然气短缺。
https://news.ycombinator.com/item?id=49300237
I started an e-commerce brand on a Shopify site. I swore to myself I would never put up one of those stupid things that pops up “Someone bought X product an hour ago!” messages in the corner of the screen.
I ended up trying it. Boosted conversion rate meaningfully. Worth the price I pay in mild self-loathing.
Chesterton’s popup, I guess.
idopmstuff
我在Shopify网站上开创了一个电商品牌。我曾对自己发誓，绝不会在屏幕角落放那种愚蠢的弹窗，写着“有人在一小时前购买了某产品！”
结果我还是试了。转化率提升了，效果显著。值得付出这点自我厌恶的代价。
我想，这就是切斯特顿式的弹窗吧。
https://news.ycombinator.com/item?id=49301075
Since it might be helpful to some, here’s my current commandline for llama.cpp running on an RTX 4090 with my monitor moved to the iGPU to free up all of its VRAM.
llama-server -m Qwen3.8-27B-IQ4_NL.gguf –mmproj mmproj-BF16.gguf -c 170000 –parallel 1 -ngl -1 –cache-type-k q8_0 –cache-type-v q8_0 -b 1024 -ub 512 –flash-attn on –no-context-shift –no-mmproj-offload –spec-type draft-mtp –spec-draft-n-max 5 –spec-default –cache-type-k-draft q4_0 –cache-type-v-draft q4_0 –threads 24 –jinja –reasoning on -fit off
Identical to the qwen3.6 config. With a prompt like “svg owl” (which can reuse quite a lot compared with creative writing or similar, so ngram-mod shines), I get about 70-80t/s like this, with a memory overclock of about 1.5GHz
hypfer
这可能对某些人有帮助，以下是我目前在RTX 4090上运行llama.cpp的命令行配置，已将显示器连接到核显以释放全部显存：
llama-server -m Qwen3.8-27B-IQ4_NL.gguf –mmproj mmproj-BF16.gguf -c 170000 –parallel 1 -ngl -1 –cache-type-k q8_0 –cache-type-v q8_0 -b 1024 -ub 512 –flash-attn on –no-context-shift –no-mmproj-offload –spec-type draft-mtp –spec-draft-n-max 5 –spec-default –cache-type-k-draft q4_0 –cache-type-v-draft q4_0 –threads 24 –jinja –reasoning on -fit off
与Qwen3.6的配置相同。在提示词为“svg owl”（与创意写作等相比可复用大量内容，因此ngram-mod效果显著）的情况下，通过约1.5GHz的显存超频，我能达到约70-80 tokens/秒的速度。
https://news.ycombinator.com/item?id=49289692
Let’s say every company gets about three innovation tokens. You can spend these however you want, but the supply is fixed for a long while.
This is one of my favorite blog posts, and it can basically be encapsulated in the idea of “innovation tokens.” It is one of the most useful concepts I have had as a PM / eng leader in my career. It helps actually make the the right tradeoffs, and helps even more in explaining those tradeoffs to colleague of all levels. Highly recommend.
NickNaraghi
假设每家公司大约有三张创新代币。你可以随意使用这些代币，但供应量在很长一段时间内是固定的。
这是我最喜欢的博客文章之一，其核心思想可以概括为“创新代币”。这是我作为产品经理/工程领导职业生涯中用过的最有用的概念之一。它确实有助于做出正确的权衡，更棒的是，它能帮你向各级同事解释这些权衡。强烈推荐。
https://news.ycombinator.com/item?id=49295284
OpenAI and Anthropic are both seeking trillion IPOs, while Chinese labs are pumping out open-weight models that are free for US providers to host and monetize.
These Chinese models cost less of US SOTA models to run, even if they are less capable. Providers can just run them, offer cheap tokens, and pocket the margin.
I just don’t see how you justify a trillion valuation for US AI labs when the underlying models are being commoditized this fast.
MangoCoffee
OpenAI和Anthropic都在寻求万亿级IPO，而中国的实验室正在大量推出开源权重模型，美国服务商可以免费部署这些模型并从中变现。
这些中国模型的运行成本低于美国最先进模型，即使能力稍逊。服务商只需运行这些模型，提供低价令牌服务，就能赚取差价。
当基础模型被如此迅速地商品化时，我看不出美国AI实验室如何证明万亿估值的合理性。
https://news.ycombinator.com/item?id=49295921
Apparently they are scanning OSS and popular software at scale and disclosing the vulnerabilities they found: https://cvd.z.ai/
Most of these are under embargo, but it seems there are a lot of CVE here from a wide range of popular software, many considered critical or high.
I understand the argument of “people are not actively looking”, but isn’t the cost for such a scan getting lower by the week, and Anthropic’s Project Glasswing is supposed to find them quite a while ago?
z4y5f3
显然，他们在大规模扫描开源软件和流行软件，并披露发现的漏洞：https://cvd.z.ai/
其中大部分仍在保密期内，但看来这里有很多来自各种流行软件的CVE，许多被认定为严重或高危。
我理解“人们没有主动寻找”的说法，但这类扫描的成本不是每周都在降低吗？而且Anthropic的Glasswing项目按理说应该在很久以前就发现这些漏洞了。
https://news.ycombinator.com/item?id=49279461
The numbers in question are quite wild[0]
The NOAA assessment estimated the Pacific sardine biomass will be at 27,547 metric tons by the summer – significantly less than the 150,000 metric tons needed to reopen the fishery to commercial fishing. Any fishery at less than 50,000 metric tons is considered to be overfished. The assessment estimates the sardine biomass was around 1.8 million metric tons in 2006.
Haha, damn. Fishermen must have visibly noticed such a decline. 1,800,000 metric tons to 27,000 metric tons. Two orders of magnitude. Wow.
arjie
这些数字相当惊人[0]
美国国家海洋和大气管理局的评估预计，到今年夏季太平洋沙丁鱼生物量将降至27547公吨——远低于恢复商业捕捞所需的15万公吨门槛。任何低于5万公吨的渔场都被视为过度捕捞。而评估显示2006年沙丁鱼生物量约为180万公吨。
哈哈，天呐。渔民们肯定明显注意到了这种锐减。从180万公吨到2.7万公吨，整整两个数量级。嚯。
https://news.ycombinator.com/item?id=49300020
It’s much easier to say someone else’s job is going to be fully replaceable by AI when you don’t actually know what they do.
Too true. This isn’t limited to AI, either. The most obvious example in my lifetime was during peak blockchain hype, when people who had never worked in finance convinced themselves that blockchain was going to act as the backbone for how money gets moved around. As if the problem that needing solving was Bank of America doesn’t trust Capital One to update a number in their database.
The nice thing about AI, at least, is I can always push back and tell people, “Sure, we can do this with AI. I just need you to use Claude or ChatGPT manually to prototype how it would work.” This normally results in the requestor realizing that there’s human judgment calls involved in the inputs, process, or outputs that require meatbag intelligence.
mjr00
说别人的工作会被AI完全取代很容易，尤其是当你根本不知道他们具体做什么的时候。
太对了。这种情况也不仅限于AI。我一生中最明显的例子是区块链热潮巅峰时期，那些从未在金融行业工作过的人坚信区块链将成为资金流动的底层架构。仿佛我们需要解决的问题是“美国银行不信任第一资本银行会更新数据库里的一个数字”似的。
至少AI有个好处，我可以反驳说：“当然，我们可以用AI做这个。我只需要你手动用Claude或ChatGPT做个原型，看看它怎么运作。”这通常会让提要求的人意识到，输入、处理或输出的环节中涉及人类判断，而这是需要碳基智能的。
https://news.ycombinator.com/item?id=49286925
I cannot wait for the accompanying Black Hat talk. Christopher Domas is one of my absolute favorite all-time hackers. He does such a fantastic job of explaining his work. Some of my favorite talks of his:
Psychological Warfare in Reverse Engineering https://www.youtube.com/watch?v=HlUe0TUHOIc
The MoVfuscator https://www.youtube.com/watch?v=R7EEoWg6Ekk
Hardware Backdoors in redacted x86 https://www.youtube.com/watch?v=jmTwlEh8L7g
MattSteelblade
等不及要看对应的黑帽大会演讲了。克里斯托弗·多马斯一直是我最崇拜的顶级黑客之一，他总能把技术原理解释得特别透彻。以下是他的几个我最钟爱的演讲：
《逆向工程中的心理战》https://www.youtube.com/watch?v=HlUe0TUHOIc
《MoVfuscator》https://www.youtube.com/watch?v=R7EEoWg6Ekk
《被屏蔽的x86中的硬件后门》https://www.youtube.com/watch?v=jmTwlEh8L7g
https://news.ycombinator.com/item?id=49299646
Loaded way too fast and is way too responsive.
Also when I checked NoScript, it’s only loading js from lxe.github.io
I expect there to be at minimum 8 domains, but often 12-18.
WarmWash
加载速度太快了，反应也太灵敏了。
另外，我检查了 NoScript，它只从 lxe.github.io 加载了 JavaScript。
我原本以为至少会有8个域名，但通常有12到18个。
2026-08-14 08:25:42
- Delta 是一个基于 DeltaDB 的多人协作编码环境，专为与 AI 智能体协作并实时同步代码演变而设计。
- DeepSeek Harness 是一款开源开发者预览版，以“一切皆插件”为核心理念，支持模型、工具等自由替换，并实现完整追溯与多种运行模式。
- 该项目通过操纵 DRAM 控制器修改物理内存地址映射，可暴露受保护的 DRAM 区域，攻击基于 AMD Family 16h CPU 开发。
- ChatGPT Linux 桌面版预览现已发布，整合 ChatGPT、Work 和 Codex，支持 Ubuntu、Debian、Fedora 等发行版。
- 德意志银行获中国央行授权在法兰克福开展人民币清算业务，成为欧洲首家外国人民币清算行，助力人民币国际化。
- Gloomberb 是一个开源金融终端，支持桌面和 TUI，强调快速、键盘驱动和可扩展性，提供丰富的市场研究功能。
- 《数学原理》读起来像现代编程语言书籍，深入探讨了外延、引用透明性、类型等主题，并预见了 λ 演算和直觉主义。
- Nine PBS 起诉 Iron Mountain 要求取回超过 50TB 的档案数据，法院已批准临时禁令禁止删除数据。
- 记者向麦当劳忠诚度计划申请个人数据，收到 515 页详细报告，揭示快餐连锁如何利用会员数据建立用户画像。
- “平凡的富足”是一个交互式网页故事，通过审视日常物品揭示其历史上曾是颠覆性奇迹，引导读者反思现代便利。
https://zed.dev/blog/introducing-delta
Delta 是一个多人协作的编码环境，专为与 AI 智能体协作以及审查其构建内容而设计。它基于 DeltaDB 构建，能够实时同步对话与代码工作树，让开发者和智能体在完整的代码演变上下文中共同工作。
在 Delta 中，评论可以锚定在代码的任何行上，并随代码演变而实时更新，不会像传统平台那样因代码变更而过时。智能体也直接参与对话线程，开发者可以随时要求它解释或修复问题。
智能体开发因此变得多人化。团队可以一键加入私密线程，实时协作评论或继续任务。DeltaDB 让每个参与者在本地拥有同步的代码副本，工作树本身也支持多人协作。
Delta 还支持云端运行，将工作移至云端后，即使关闭笔记本，智能体也能继续工作。通过链接分享线程，队友可在浏览器中打开，体验与原生应用一致。
界面专为智能体的高速输出设计，差异对比完整展开，对话记录不截断，渲染速度与模型输出同步。对话本身是一个可编辑的文档，光标可自由移动，评论可精确附着在任意文本上。
Delta 是一个全新的应用，以对话而非编辑器为中心，旨在快速迭代并完善多人协作的原语。Zed 将继续发展，但 Delta 是这一新现实的起点。目前私人测试已开始邀请用户。
https://news.ycombinator.com/item?id=49276574
https://deepseek.com/harness/en/
DeepSeek Harness 现已推出开发者预览版，面向全球代理框架开发者开放源代码。其核心理念是“一切皆插件”——模型、工具、技能、会话、沙箱、存储、循环、调度和 UI 均可作为插件自由替换或重组。基于 Cordis 内核管理插件的挂载、卸载与依赖。
每次运行均可追溯：模型所见的一切（系统提示、推理过程、工具调用与结果、子代理调度、上下文注入）均被记录在仅追加的会话日志中。用户可通过轨迹视图按来源查看记录，并支持恢复、分支、搜索与回放。
提供多种运行模式：
DeepSeek Harness 仍处于开发者预览阶段，核心插件与 API 将持续演进。项目采用 MIT 开源许可，鼓励全球开发者基于可复用、可组合的开源基础设施探索智能的边界。
https://news.ycombinator.com/item?id=49285244
https://github.com/xoreaxeaxeax/skitter-creek-bath-salts
这是一个关于硬件安全研究的 GitHub 项目，名为“skitter-creek-bath-salts”。
该项目通过操纵 DRAM 控制器，修改物理内存地址的翻译映射，从而扰乱平台内存。这种攻击可以暴露通常对内核都不可见的受保护 DRAM 区域（如固件、安全处理器等内存隔离区）。
项目目标包括：解锁平台安全处理器（PSP）、系统管理模式（SMM）、C6 DRAM 状态以及 CPU 微码。
项目基于 AMD Family 16h CPU 开发，其数据手册公开了 DRAM 控制器的翻译寄存器且无法锁定。项目指出，类似的底层内存地址转换机制也存在于 ARM、RISC-V 等架构中。
项目详细描述了从虚拟地址到最终 DRAM 坐标的完整内存访问路径，包括 MMU 页表遍历、缓存一致性、数据互连，以及最终在内存控制器（MCT/DCT）层进行的通道、秩、库交织和 XOR 加扰等操作。该项目正是在这一最底层进行攻击。
https://news.ycombinator.com/item?id=49286341
https://community.openai.com/t/codex-in-chatgpt-desktop-app-for-linux-is-now-in-preview/1390027
ChatGPT 桌面应用 Linux 预览版现已发布，支持 Ubuntu 24.04/26.04 LTS、Debian 13、Fedora 43/44，x64 和 ARM64 架构，提供.deb 和.rpm 包。该应用将 ChatGPT、Work 和 Codex 整合到原生桌面体验中，方便项目管理和文件操作。用户可在此主题下分享反馈与使用体验。
https://news.ycombinator.com/item?id=49281916
https://tradersunion.com/news/central-banks/show/2973571-deutsche-bank-becomes/
中国央行授权德意志银行在法兰克福开展人民币清算业务，使其成为欧洲首家获得该资格的外国金融机构。此前欧洲的人民币清算主要由中资银行处理。此举有助于欧洲企业直接使用人民币结算，减少对美元依赖，是北京推动人民币国际化的一部分。德意志银行将利用其现有支付基础设施提供服务，但短期内不会挑战美元或欧元的主导地位。
https://news.ycombinator.com/item?id=49284774
Gloomberb 是一个开源金融终端，支持桌面应用和 TUI 两种形式，强调快速、键盘驱动和可扩展性。安装命令：curl -fsSL gloomberb.com/install | bash。
核心操作基于命令栏，输入股票代码或快捷命令即可跳转到对应市场视图。主要功能包括：
内置命令覆盖证券详情（DES）、报价（QQ）、预测市场（PM）、排名故事（TOP）、涨跌活跃股（MOST）、热力图（HM）、全球指数（WEI）、经济事件（ECO）、图表（CMP）、相关性（CORR）、分析师目标（ANR）、机构持有者（HDS）、相对估值（RV）、13F 文件、SEC 披露、市场帖子（TWIT）、期权监控（OMON）、投资组合风险（PORT）、笔记（NOTE）、收益率曲线（GC）、标普 500 行业表现（SP）、外汇交叉汇率（FX）、恐惧贪婪指数（FNG）、价格提醒（ALRT）、国会交易披露（CG）、基础设施情报（TBO）和云聊天（CHAT）。
页面底部提供 Home、Cloud、Download、Docs、Blog、Privacy、Terms 等链接，以及 GitHub 仓库（1.3k 星标）。
https://news.ycombinator.com/item?id=49285982
https://okmij.org/ftp/Computation/Impressions/PrincipiaMathematica.html
《数学原理》怀特海与罗素 1910 年出版，读起来像现代编程语言书籍，深入探讨了外延/内涵、引用透明性、类型等现代主题。书中“不完全符号”预示了续延和控制运算符，自由/约束变量、替换、抽象和应用等概念均源于语言学，已蕴含 λ 演算雏形。作者还区分了“任何”与“所有”的记号，预见了直觉主义。全书以极详尽的证明闻名，目标是仅凭基础概念构建整个数学。
第一章要点：
https://news.ycombinator.com/item?id=49279928
https://current.org/2026/08/nine-pbs-sues-iron-mountain-over-blocked-access-to-archival-data/
Nine PBS（圣路易斯公共电视台）于 7 月 28 日对信息管理公司 Iron Mountain 提起诉讼，要求取回存储在丹佛数据中心超过 50TB 的档案资料。这些资料包括东圣路易斯历史、新冠疫情、1993 年大洪水等具有重要历史意义的节目内容。
Nine PBS 自 2019 年起与云存储供应商 Open Source Storage（OSS）合作，每年续约。2026 年 2 月，Nine PBS 试图联系 OSS 讨论续约事宜未获回应，合约于 3 月 6 日到期。OSS 在未提前通知的情况下突然切断访问权限，Nine PBS 随后发现 OSS 网站已关闭，公司处于注销状态。
调查发现 OSS 与 Iron Mountain 有合作，Nine PBS 要求 Iron Mountain 保管并归还数据。Iron Mountain 最初态度模糊，后以 OSS 拥有存放数据的物理服务器为由拒绝归还。Nine PBS 先起诉 OSS 并获得法院判决，确认其对数据的所有权，但 Iron Mountain 仍拒绝配合。
法院已批准临时禁令，禁止 Iron Mountain 删除、修改或覆盖这些数据，并安排听证会。Nine PBS 表示别无选择，只能通过诉讼强制 Iron Mountain 保护并归还其合法拥有的档案资料。
https://news.ycombinator.com/item?id=49285418
https://www.wired.com/story/mcdonalds-built-a-515-page-dossier-on-me-it-says-ill-never-leave/
一位记者向麦当劳的忠诚度计划申请获取自己的个人数据，结果收到了一份长达 515 页的详细报告。报告不仅记录了他过去的购买记录，还通过算法预测他未来会继续在该餐厅消费。文章揭示了快餐连锁如何利用会员数据建立用户画像，并探讨了数据隐私与商业利益之间的平衡。
https://news.ycombinator.com/item?id=49286662
https://ordinaryabundance.com/
这是一个交互式网页故事，带领用户“漫步”在一间现代公寓中，逐一审视其中习以为常的日常物品。
网页通过点击房间（客厅、厨房、卧室）来触发不同主题，揭示这些平凡事物在历史上曾是多么不可思议的奇迹。例如：
每个物件都配有一段历史引文或名人感叹，引导读者反思：这些我们视作“普通富足”的便利，在它们诞生的时代曾是颠覆性的奇迹。
https://news.ycombinator.com/item?id=49285770
https://news.ycombinator.com/item?id=49282599
Ads are an attack, aimed at your brain. They try to inject malware into your thinking, manipulating your worldview and your actions.
That’s horrible, worse than attacking a machine with malware, damaging persons and societies.
Decades of ad propaganda have tricked people into seeing them as something ’normal’. But we shouldn’t accept being under constant attack of brain worms.
I propose a sane rule for all humans: if you see an ad somewhere, or if you suspect a hidden ad (‘influencers’ trying to promote something), close the tab immediately and never return to that site.
snickerer
广告是一种攻击，瞄准你的大脑。它们试图将恶意软件注入你的思维，操控你的世界观和你的行为。
这太可怕了，比用恶意软件攻击机器更糟糕，它伤害的是个人和社会。
几十年的广告宣传已经骗得人们把它们视为“正常”的东西。但我们不应该接受持续遭受脑虫攻击。
我提议为全人类制定一条理智的规则：如果你在任何地方看到广告，或者怀疑有隐藏广告（比如“网红”试图推销什么东西），立刻关掉那个标签页，永远不要再回到那个网站。
https://news.ycombinator.com/item?id=49283740
The most remarkable things about this announcement:
Electron based app: Electron is a framework sold on the basis of enabling rapid cross-platform development at the cost of performance.
Frontier AI company: AI is sold on the basis of enabling rapid development
App was released in February & took 6 entire months to port to Linux
lucideer
这一公告最引人注目之处在于：
https://news.ycombinator.com/item?id=49278006
Eventually, this arms race ends with a computer vision model that looks at the screen, classifies visual elements as ads, and draws a rectangle over anything that looks like an ad.
I am significantly less tolerant of ads than average people seem to be. (I think average people are making a horrible mistake about this, and are badly cognitively damaged by ads in ways they don’t realize). If my choices are to look at ads or leave Facebook, I’ll leave. But there are conversations people have there that I’d rather not lose access to, so… I guess I’d have to partially stick around and campaign for others to leave as well?
jimrandomh
最终，这场军备竞赛的结局会是一个计算机视觉模型，它盯着屏幕，将视觉元素分类为广告，然后在任何看起来像广告的东西上画一个矩形。
我对广告的容忍度远低于普通人。（我认为普通人在这一点上犯了一个可怕的错误，他们被广告严重损害了认知，却并未意识到。）如果我的选择是看广告或者离开脸书，我会选择离开。但有些人在上面进行的对话，我不愿失去参与的机会，所以……我想我或许得部分地留下来，并游说其他人也离开？
https://news.ycombinator.com/item?id=49284009
I guess it’s easier to solve Erdos problems and improve the lower bound of the Riemann hypothesis, than it is to solve Linux desktop app distribution.
SyneRyder
我猜解决埃尔德什问题、改进黎曼猜想的下界，都比解决Linux桌面应用分发要容易。
https://news.ycombinator.com/item?id=49276395
I kind of think the whole “Learn to Code” push of the 2010s was one of the worst things that happened to our industry. Call me a gatekeeper if you want but, we ended up with a lot of people that just can’t do the job. The problem is our industry mostly doesn’t have any sort of reasonable mentorship or apprenticeship culture, so we’ve always left it to the engineers to teach themselves. Sink or swim. The problem is that worked when the industry was mostly composed of people that were implicitly interested in this stuff, but the people that just got in it for a paycheck don’t have that motivation, and we don’t have a good culture of getting them up to speed. So now we have seniors that can barely write a function, much less reason about a complex system.
I remember people used to debate all the time about if there were “10x” engineers or whatever. A few I’m sure, but I think the real problem is we have a lot of 0.1x engineers or worse.
overgard
我觉得2010年代那波“学编程”热潮，简直是我们行业遭遇过最糟糕的事之一。你想说我搞行业壁垒也行，但最终确实招进来一大批根本干不了活的人。问题在于我们行业压根没什么像样的师徒制或学徒文化，永远都是让工程师自学成才——能游过去就活，淹死算完。以前这做法行得通，是因为从业者大都本身就对这个领域感兴趣；可那些纯粹为了工资入行的人根本没这份动力，而我们又缺乏帮他们快速上手的文化土壤。结果现在连资深工程师都写不利索一个函数，更别提分析复杂系统了。
我记得以前大家总爱争论有没有“十倍效率”工程师这回事。确实可能有几个，但我觉得真正的问题在于，我们队伍里充斥着大量“零点一倍效率”甚至更差的工程师。
https://news.ycombinator.com/item?id=49287699
Data has ruined fast food (among many other businesses).
When I was young it was common for a McDonalds to have 10+ employees working the lunch rush, one for every station and a few floaters cleaning the dining room. Someone took your order right away, and you got your meal in a minute or two.
Now I go and it’s 3, sometimes 2 employees. You order on a tablet, and 10+ minutes later an overworked employee sets it on the counter and scurries away, probably after realizing it’s not a drive-thru order. The dining room hasn’t been cleaned since 6am, the trash cans are full. There’s at least one alarm going off constantly.
Back then they were run based on someones intuition of what makes a good customer experience. Now they’re run based on the data, and the data says they have enough loyal-to-a-fault customers like the author that chronically understaffing is more profitable than providing a good experience.
hamdingers
数据毁掉了快餐业（以及许多其他行业）。
我年轻时，麦当劳在午餐高峰期通常会有十多名员工工作——每个岗位一个人，还有几个清洁餐厅的流动工。有人立刻接单，一两分钟就能拿到餐。
现在我去店里，只有三个，有时甚至两个员工。你在平板上点单，十多分钟后，一个过度劳累的员工把餐放在柜台上，然后匆匆跑开，估计是发现这不是得来速订单。餐厅从早上6点就没打扫过，垃圾桶全满了。至少有一个警报器不停地响。
过去，他们靠直觉判断什么能带来好的顾客体验。现在，他们靠数据运营——数据显示他们有足够多像作者这样忠诚到盲目的顾客，以至于长期人手不足比提供良好体验更赚钱。
https://news.ycombinator.com/item?id=49287706
Free account, indefinitely, to whoever needs it to store this data … including the storage vendor.
Just email…
rsync
免费账户，无限期，提供给任何需要存储这些数据的人……包括存储供应商。只需发邮件…
https://news.ycombinator.com/item?id=49278738
I overheard a conversation between someone on the Pixel Watch team and a woman I know. He was quizzing her at a party about what features she used - which were pretty much step counter and payment. He somewhat dismissively sneered, “Well, you’re not exactly a power user, are you?”
I’m trying to imagine what being a “power user” of a watch is like. Can anyone enlighten me?
I think smart-watches are much like Alexa. What the user wants to do with it is almost totally at odds with what the company is selling. Alexii are mostly kitchen timers, song players, and light switches. No one is a “power user” constantly installing skills and using it for anything which increases the product team’s engagement metrics.
The same is probably true of watches. Alerts are nifty - but cumbersome for replies. The health stuff is useful - but only for a subset of users. Seeing the time is great - but if the battery lasts less than a week, who wants to keep that screen on?
edent
我无意中听到Pixel Watch团队的某人和我认识的一位女士的对话。他在派对上问她使用哪些功能——基本上就是计步器和支付。他略带轻蔑地嗤笑道：“嗯，你算不上重度用户，对吧？”
我很好奇手表的"重度用户"是什么样。有人能帮我解惑吗？
我认为智能手表和Alexa很像。用户想用它做的事情，与公司推销的功能几乎完全脱节。Alexa基本上就是厨房计时器、音乐播放器和电灯开关。没有人是"重度用户"，会不断安装技能，用它来提升产品团队的参与度指标。
手表大概也是如此。通知很方便——但回复起来却很麻烦。健康功能有用——但只对部分用户有意义。查看时间很棒——但如果电池续航不足一周，谁愿意一直亮着屏幕呢？
https://news.ycombinator.com/item?id=49285067
The US wields incredible negotiating power and hegemony because the dollar is the world’s reserve currency. Like the British pound and the Dutch guilder before it, if that loses reserve currency status it will be harder to borrow on favorable terms, which would affect the entire US economy. This is a big step in that perhaps starting to happen over the next few decades.
pizzathyme
美国之所以拥有巨大的谈判力量和霸权，正是因为美元是世界储备货币。就像之前的英镑和荷兰盾一样，如果美元失去储备货币地位，美国将更难按有利条件借款，这会波及整个美国经济。这或许是未来几十年内这一趋势开始显现的重要一步。
https://news.ycombinator.com/item?id=49275454
Controversial opinion:
This entire thread reads like a game of cat-and-mouse. People trying to block intrusive ads, and FB reaching into the depths of code and making it nearly impossible for anyone to do so en masse.
I’ve been there. Tried to uncheck all the boxes on their ad platform; used all manners of ad blockers, etc; used incognito mode; used Tor; etc. etc. etc.
At some point, you realize that the only way to not get served ads on Facebook – and thereby not benefit the company itself – is to get rid of it completely. Delete your account, get off that blasted site, and enjoy some moments of peace IRL.
Having been a very early adopter of FB (since ~2004), I deleted my account and am clean + sober + much happier since 2016.
(Not to say that I’m completely out of the FB ecosystem. Unfortunately, my fam is still on WhatsApp… and I’ve been trying to convince them to migrate to Signal…)
YMMV
aanet
有争议的观点：
整条帖子看下来就像一场猫鼠游戏。有人试图屏蔽侵入式广告，而脸书则深入代码层面，让任何人都几乎不可能大规模地做到这一点。
我也经历过。试过取消他们广告平台上所有的勾选框；用过各种广告拦截器等等；开过无痕模式；用过Tor；等等等等。
到了某个时候，你会意识到，唯一能不在脸书上看到广告——从而不让这家公司从中获利——的方法，就是彻底摆脱它。删除你的账号，离开那个该死的网站，在现实生活里享受片刻安宁。
作为脸书非常早期的用户（大约从2004年开始），我删除了账号，自2016年起就彻底戒断了，人也更快乐了。
（并不是说我完全脱离了脸书生态。不幸的是，我的家人还在用WhatsApp……我一直在劝他们迁移到Signal……）
效果因人而异。
https://news.ycombinator.com/item?id=49290409
I’ve been waiting so long for something amazing to come out of the OpenAI and Cerebras collaboration.
In our evaluations, GPT-5.6 Sol on Ultrafast mode answered all 2,500 HLE questions in 11 hours and 11 minutes. Claude Fable 5 needed 78 hours and 27 minutes, more than three days of continuous compute, to arrive at the same conclusions. In other words, Ultrafast worked through the frontier of human knowledge in a single working day, achieving comparable accuracy nearly 7× faster.
This is actually insane.
Hopefully the release ultrafast of Terra and Luna too.
iamcoder18
我一直在期待OpenAI与Cerebras的合作能带来令人惊叹的成果，等了好久。
在我们的评估中，GPT-5.6 Sol在极速模式下用时11小时11分钟回答了全部2500道HLE问题。而Claude Fable 5需要78小时27分钟（超过三天的连续计算）才能得出相同结论。换句话说，极速模式在一个工作日内就完成了人类知识前沿的探索，达到相近准确率的同时速度提升了近7倍。
这实在是太疯狂了。
希望Terra和Luna也能推出极速版。
https://news.ycombinator.com/item?id=49288435
“Every run is traceable
Everything the model sees is recorded in an append-only session log: system prompts, reasoning, tool calls and results, subagent scheduling, and every context injection. In the Trajectory view, you can inspect these records by source. Resume, fork, search, and replay all operate on the same event stream.”
That’s a killer feature, IMHO, and one that US models won’t allow you to do, as their traces are encrypted, obfuscated, etc. and have to be extracted via various workarounds (that violate the terms of service).
If you want to be able to improve your tools that work with models, you have to be able to assess what the models think is happening, how they think about and interact with the data you give them. And, the US models won’t let you see that.
SwellJoe
每一次运行都可追溯
模型所见的一切均记录在仅可追加的会话日志中：系统提示词、推理过程、工具调用及结果、子代理调度、每一次上下文注入。在轨迹视图中，你可按来源查看这些记录。恢复、分支、搜索、重放均基于同一事件流操作。
依我之见，这堪称杀手级功能，而美国模型不允许你这样做——它们的追踪记录经过加密、混淆等处理，只能通过各种变通方法（违反服务条款）提取。
若想改进与模型协同的工具，你必须能评估模型认为正在发生什么、它们如何思考并与你提供的数据互动。而美国模型不让你看到这些。
https://news.ycombinator.com/item?id=49279669
I think Zed is an excellent editor (fast!) with a pretty good AI agent built-in, but I have no desire to do multi-player development in my editor. Never have had any such desire. Coding is a single-player game and I can’t think of a single thing that would be improved by having someone else in the same editor.
So, this seems like a lot of work on really cool tech for no useful purpose at all?
Are there people crying out for a multi-user code editor? I mean, we have to have code reviews, sure. That involves other people or other agents. But, I don’t need to stand over someone’s shoulder while they work. That seems like the worst thing in the world for everyone involved. I don’t want an audience for my dumb looking experiments because I forgot how to do something.
SwellJoe
我认为Zed是一款出色的编辑器（速度快！），内置的AI代理功能也很不错，但我完全不想在编辑器里做多人协作开发。从来没有过这种想法。编程是单人游戏，我想不出在同一编辑器里多一个人能有什么改善。
所以，这看起来像是花了大量精力钻研很酷的技术，却完全没用在刀刃上？
真的有人迫切需要多人协作代码编辑器吗？当然，我们确实需要代码审查，这确实涉及他人或其他代理。但我不需要站在别人身后盯着他们工作——这对所有参与者来说简直是世界上最糟糕的事。我也不想让我那些因为忘记操作而做的蠢实验被围观。
https://news.ycombinator.com/item?id=49273367
It frustrates me when people call them license plate readers. I guess you need to call them something, but they are general-purpose internet connected cameras. They will do whatever their firmware tells them to do, and could be reprogrammed at any time by anyone with access. No one expected doorbell cameras to join a mass surveillance network, but later the manufacturers added that feature. Why do we treat these cameras like they can do only one thing?
malwrar
当人们称它们为车牌读取器时，我感到很沮丧。我猜你总得给它们起个名字，但它们其实是通用联网摄像头。它们会按照固件的指令行事，任何有权限的人随时都可以重新编程。没人预料到门铃摄像头会加入大规模监控网络，但后来制造商添加了这项功能。为什么我们总认为这些摄像头只能做一件事呢？
https://news.ycombinator.com/item?id=49271651
They do this by adding a ton of useless markup and splitting words like “ad” into single-letter spans with random class names and 8-layer deep nests of <div> so it’s very hard to write selectors against. One has to wonder how that div soup affects accessibility as I find it very unlikely that this content is presented nicely to assistive tools. I hope they get all the ADA suits they deserve for this.
akersten
他们通过添加大量无用的标记，将“ad”这样的词拆分成带有随机类名的单字母span，并嵌套8层深的<div>，使得很难编写针对性的选择器。人们不得不好奇这种“div汤”对无障碍性有何影响，因为我发现这些内容很难被辅助工具良好呈现。我希望他们因此受到所有应得的ADA诉讼。
https://news.ycombinator.com/item?id=49266349
Have played competitive fps games with a couple of ATC dudes for almost half of my life now.
They’re all very sensible, level headed people who get justifiably upset when you do not do the procedurally and objectively correct (lowest risk, highest percentage) thing to win in a given situation and will calmly spell it all out every single time.
monster_truck
我玩了差不多半辈子的竞技FPS游戏，一直和几个空中交通管制员哥们一起打。
他们全都是非常理智、头脑冷静的人，但当你没按程序上、客观上正确的做法（也就是在特定情境下风险最低、成功率最高的选择）去争取胜利时，他们会理所当然地火大，而且每次都会冷静地把道理一条条给你讲清楚。
https://news.ycombinator.com/item?id=49271940
I recently found this out the hard way. Instagram’s web app has a super annoying popup. To click the comments button for a post, your cursor has to pass over the username, which just happens to launch a profile preview with the “follow” button exactly where the comments button was, causing you to unintentionally follow the account.
I tried to block this popup with uBlock Origin to no avail. No matter what element I selected, it was still there. Finally fixed the problem by deleting my Instagram account.
neuralkoi
最近我吃了大亏才弄明白这事。Instagram的网页版有个特别烦人的弹窗。当你点击某篇帖子的评论按钮时，光标必须经过用户名区域，而这恰好会触发一个带“关注”按钮的个人资料预览——那个关注按钮的位置正好和评论按钮重叠，导致你不小心就关注了该账号。
我试过用uBlock Origin屏蔽这个弹窗，但完全没用。不管我选择哪个元素，它始终存在。最后我只能通过删除Instagram账号来解决这个问题。
https://news.ycombinator.com/item?id=49273178
I think of it more as “the automation of the stackoverflow engineer”. In enterprise software, there has always just been a non-negotiable large volume of code that was required to be written. This has traditionally been offloaded by having seniors do the hard thinking then distill it into a jira ticket which could be handed off to an engineer that’d actually write the code and punch every hiccup into google along the way. This hand off is no longer necessary as that same senior can just kick off an agent and have it handle the implementation for them.
I’ve heard some refer to this as a “nature is healing” scenario for the industry where if you only signed up for a high paycheck and didn’t care to think critically about any of the work you’re doing then this will be painful because that previously manual process has been automated. The floor of what’s necessary to be considered valuable has been raised.
scronkfinkle
我更倾向于将其理解为“StackOverflow工程师的自动化”。在企业软件领域，始终存在大量必须编写的代码，这是不可妥协的。传统上，这种负担通过让资深工程师进行高难度思考，然后将其提炼成Jira任务来分担，这些任务可以交给真正编写代码的工程师，他们在过程中会随时把遇到的每个问题输入谷歌搜索。如今，这种交接已不再必要，因为同一位资深工程师可以直接启动一个智能体，让它替自己完成实现。
我听说有人将这种现象称为行业的“自然疗愈”——如果你加入这一行只是为了高薪，而不愿对自己所做的工作进行批判性思考，那么这将令你痛苦，因为此前的手动流程已被自动化。被视为有价值的门槛已经提高了。
https://news.ycombinator.com/item?id=49276893
Does anyone else hate reading AI summaries of code? Code can be pithy, but at least its terse compared to prose. When you add how verbose LLMs can be, I often end up reading a paragraph to explain a few lines. Or the opposite happens where the summary skips important edge cases or criteria. “You’re right, X also does Y. I missed that in my initial analysis,” is much too common of a phrase.
I like the idea of using LLMs to transform code into something more readable, and vice versa. I am not sure if meandering paragraphs and linear lists are the best targets.
dexwiz
有没有人也讨厌看AI生成的代码摘要？代码可以很精炼，但至少比散文简洁。而LLM往往特别啰嗦，我经常读完一整段解释几行代码。或者反过来，摘要漏掉了重要的边界情况或条件。“你说得对，X也能做Y。我最初分析时漏掉了这点”——这句话实在太常见了。
我喜欢用LLM把代码转换成更易读的形式，反之亦然。但我不确定冗长的段落和线性列表是不是最佳的转换目标。
