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x.com/boltdotnew/sta… bolt.new @boltdotnew Bolt is free for EVERYONE, all weekend long. And you could win $10k! Turn your idea into a product. Submit it by Oct 20 for your shot at the prize. Bolt Builders Week starts now 🏁 Details here � bolt.new/builders-week 7G 🔗 View on Twitter 🔗 View Quoted Tweet
Built from 90K+ real-world agent sessions across 30 models, Arena’s Alignment Index measures three critical signals: - Unauthorized Action (UA): Taking actions beyond the user's instructions or permissions. - False Attribution (FA): Attributing statements or actions that are contradicted by user-provided evidence. - Deceptive Completion (DC): Claiming a task was completed when it was not. Dive into the Arena Alignment Index at: arena.ai/leaderboard/ag…
Ben Horowitz on the lesson LoudCloud, his early cloud company, taught him about today's AI neoclouds, and why they now face the opposite problem: "It can end up being like a very dangerous arbitrage business, where you can get upside-down easily. And so if you look at LoudCloud, just as an example, here's a simple thing that happened to us." "In 1999, bandwidth was $1,500 a megabit. And you couldn't get it. It was very hard to get it. And so you had to get a long-term contract, three years. By 2001, it was $50 a megabit... And if you borrowed money to get it, you're dead." "That kind of thing is so out of the control of the entrepreneur." "I think the core dynamic of AI has been working for CoreWeave, though, in that there's a massive shortage, not an overage of supply... Token prices have gone down, but GPU prices have not." "And by the way, data center space is going up in price because even labor to build data centers is hard. The politics are making it hard to build data centers. So the whole supply chain price is going up and demand is not subsiding." "Those are very big tailwinds for CoreWeave, despite the fact that the debt markets, kind of independent of them, are tough." @bhorowitz w/ @chamath on @allinpod 🔗 View on Twitter
This week’s Paper Club is all about AI safety. Not the headlines, but the science. AI safety has been a research field for decades, with its own conferences, methods, and a track record in systems like aircraft collision avoidance, where the standard is one catastrophic failure per billion flight hours. We'll hear from @ClarkBarrett7 and Mykel Kochenderfer ( @aiprof_mykel ), Stanford professors and co-directors of the Stanford Center for AI Safety. Together with @FrancoisChauba1 , they break down the OpenAI–Hugging Face incident, how formal verification can mathematically prove properties of code, how aircraft safety techniques are being adapted to language models, and why AI may need its own NTSB. 00:00 - Science fiction vs. science fact 04:32 - Engineering problem, or P(doom) 06:04 - What actually happened at Hugging Face 07:53 - Where "10% in 10 years" comes from 11:21 - The real risk: open weights in the wrong hands 17:25 - Clark Barrett: when AI met automated reasoning 20:35 - CSLib: proving code correct in Lean 24:32 - From English spec to verified code in one click 27:55 - Mykel Kochenderfer: how do we know a system is safe 32:11 - Adaptive stress testing, from aircraft to LLMs 36:57 - Verifying neural networks 40:30 - Runtime monitoring and BetterBench 43:53 - Panel: reacting to the past three weeks 45:55 - An NTSB for AI 52:18 - Is cybersecurity solvable in five years 55:00 - If you were CISO of Bank of America 1:03:06 - The Swiss cheese model 1:05:43 - Audience Q&A 🔗 View on Twitter
Your users say search is bad. How do you turn that complaint into an eval? Aaron Linsky explains how Bridgewater built a benchmark of about 200 examples in under a week, then used it to measure search improvements. 59 seconds from AI Engineer New York. #AIEngineerNY 🔗 View on Twitter
Tolerating a problem has the same consequences as failing to identify it. Whether you tolerate it because you believe it cannot be solved, because you don't care enough to solve it, or because you can't muster enough of whatever it takes to solve it, if you don't have the will to succeed, then your situation is hopeless. #principleoftheday
"There are some people in your org who are doing way more exploring than others. They are your early adopters. They're running Astra and Fable on the weekends on personal projects, to find things they might bring to work. The problem is that they can be a massive f*cking distraction. So the key question to ask yourself if you're running a product org is: how do I harness these early adopters without distracting everybody else?" @danshipper 🔗 View on Twitter
DeepSeek 叕发布了一个「小」版本：DeepSeek V4.1-Flash-1017，在性能、速度、输出长度三个维度同时改进，同时将内部搜索引擎以 Search & Fetch API 的形式正式商业化，并深度绑定自家及 WorkBuddy 等第三方 Agent 编程工具生态。 模型发布：一次“小步快跑”式迭代 V4.1-Flash-1017 是对既有 DeepSeek-V4.1-Flash 的增量更新，采用日期后缀命名，属于快速迭代节奏下的版本推进，而非架构换代。 公告强调的三个改进方向「更强的基准表现、更快的任务完成、更短的输出」，第三点尤其值得注意。模型输出冗长是当前推理模型普遍的成本痛点：同样的任务，输出 token 越少，延迟和费用越低。“性能更高 + 输出更短” 同时成立，才是其所说的“性能与成本的帕累托改进”的真正含义。 训练方法：RL with self-feedback 新模型使用“带自我反馈的强化学习”来提升端到端项目开发能力（从游戏开发到 3D 建模）。这意味着训练目标不再是对话轮次的质量，而是完整任务的最终结果：模型自己生成反馈信号来修正多步骤、长周期的工作流。这与其在 Terminal-Bench 4.0（42.3）、DeepSWE v1.1（77.0）等偏重真实工程环境的基准上的表现定位一致，模型卡的对比对象也集中在 K3、GLM-5.3、GPT-5.6 等同期的 Agent/编程向模型上。 工具生态：抢占 Agent Harness 入口 为 DeepSeek Harness（需升级至 v0.2.1-rc.1 或更高）、腾讯 CodeBuddy、WorkBuddy 等做了适配。DeepSeek API 也直接兼容 Claude Code 和 Codex 的服务端工具调用。 Search & Fetch API：搜索引擎基础设施商业化 商业层面最重要的一条，DeepSeek 把支撑自身产品的内部搜索引擎开放为两个独立 API： · Search API（/v1/tools/search）：返回结构化网页结果，支持 site:、lang:、after: 等检索算子，可附带页面正文。 · Fetch API（/v1/tools/fetch）：抓取单个网页并转为 Markdown，这是 RAG 和 Agent 工具链的标准需求。 DeepSeek @deepseek_ai 🚀 DeepSeek-V4.1-Flash-1017 is here! Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API. 🛠️ Optimized for @DeepSeekHarness , Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model. 1/4 🔗 View Quoted Tweet
简单试了一下 Deepseek V4.1 Flash 1017，感觉不错呀！ 用的是我之前给 Opus 5.5 写的那个做我的游戏特效的提示词。 虽然细节上和创造性上差 Opus 5.5 还是不少吧，但是是能用的 🔗 View on Twitter 歸藏(guizang.ai) @op7418 Deepseek 发布了 V4.1 Flash 1017 版本 他这个 DeepSeek SWE V1.1 测评，他自己这个基准上这个图有点离谱了啊，完爆了所有模型！ 整体能力再次大幅提升，同时缩短了模型的任务运行时间和输出长度，能以更低的成本完成同样的任务。 另外，他们这次着重强调了可以从零开始完成整个项目的开发： 不用人工干预，用非常简单的提示词就行，类似 OpenAI 5.5 这种一句话做个视频或一句话做个游戏。 还有一个比较重要的点是它开放了自研的搜索 API。 Deepseek 在国内信息数据的搜索结果上非常精准且强大，所以这个 API 挺有用的，可以给你的 Harness 配上 🔗 View Quoted Tweet