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突然感觉这个循环很有意思，当我 Codex 蹬完急需第二天用的时候他们又重置了，然后当一个 Claude 账号被快用完的时候，还有 3 天才到周额度，另外一个又有100% 额度了，循环起来了，珍惜还需要人来管 AI 干活的时代，说不定等再过一段时间，可能真不需要人管了，那就需要找新的来玩了。 Tw93 @HiTw93 我终于没有忍住，又冲了一个 Claude 250 刀的账号，终于够用了，想和大伙聊聊我是物尽其用的如何用完 2 个 Claude Max 账号加一个 ChatGPT Pro 账号的。 我记得从 ChatGPT 3.5 开始以后，玩大模型的一般会有两个流派，一个是坚持使用当前最好的模型的人，一把梭把事情做完，但看着有点“败家”。还有一类是尽可能使用当前性价比最好的模型，通过多模型调研以及工程解法把一般模型发挥出接近好模型的效果，不过这里一般会多花一点时间。 两种用法我感觉都挺好，属于不用工程师性格的选择，我平时时间不太够，加上当时在做业务 AI 落地的时候，会有一个习惯使用当前业界最好的模型去测试效果，这样心里有一个底，知道当前模型高度以及更好的业务判断，等到 25 年初 Claude CLI 出来，体验到了那种厉害模型写代码的效果后，一直就坚持选择当前最好的模型来做自己的事情，然后从 1 个 20 刀试试，到 2 个 20 刀，然后到 1 个 125 刀然后 250 刀的账号，然后到现在 2 个 250 刀账号，属实不敢想，当时 AI 没有出来前，包一个爱奇艺电视会员几十块一个月都会觉得贵的人怎么现在对 AI 这么能花钱，也是神奇。 当前对于 Claude 普通任务我会选择更快的 opus 5.5 medium 档位，复杂任务使用 high，然后对于 Codex 默认用 GPT-6-Astra medium，因为他的其他模型的确不是很好用，不过有一说一 Codex 的 computer use 是真的好用，所以在 Claude 里面我也会通过 lcu 这个工具让他去调用 Codex 的 computer use，效率效果都好不少。 聊起 computer use，以及 AI 操作设备，这一块就非常好玩了，最近开发一些 Window 软件，我买了 Parallels Desktop 这个装到 Mac 上，用 computer use 去操作他非常多的功能，完成代码编写以及 AI 都在 Mac 环境，但是调试验证在这个虚拟机里面，然后考虑到更真实的效果，我去整了一台 ThinkPad X1，也让他 24 小时开机，然后用 UU 远程连接，让我的 Mac 可以使用远程终端以及 ssh 还有 UU 也可以非常好的操作这台真实的 Windows 电脑，帮我去做大量的多环境测试验证、以及各种软件安装、卸载、清理的验收工作。除此之外，我的 Mac 还数据线连接了我的 Android 小平板，通过 Android 调试功能也可以让 AI 非常好的去操作这一台类手机设备，可以帮我打开 App 去查询操作一些没有 API/CLI 的服务，以及操作一些有意思的东西。 这样就好比 Mac 是我的研发主力端，但是他上面连接了一个虚拟机、一台 Windows 设备、以及一台 Android 平台，还有操作 Xcode 自带的 iPhone 模拟器，等到后面想开发 Linux，我甚至可以在 Windows 里面再装一个 Omarchy，那就配齐了，这样有一个好处就是我的 AI 工作上下文只在一台设备，外出的时候通过 UU 远程来操作 Mac 做所有的事情，甚至我感觉 UU 远程在命令行输入的时候不好用，我还自己用 Kaku 做了一个有意思的功能，可以在浏览器上完全镜像操作我 Mac 上的 Kaku，体验很不错，等后续放出给大伙试试。 一方面 AI 操作设备帮我验证各种环境、多维度的测试验证会消耗不少 Token，另外一方面的确开发工作量很大，平时一般是 Kaku 里面 7-10 个 Tab 的 Agent 在跑我的任务，这也是大伙看到我的 GitHub 为啥可以清空所有 issue 的原因了，哈哈哈。 然后平时也会使用 /loop 功能，帮我去做一些收集的事情，比如收集社交平台上大家对于我产品的建议、Bug 反馈，我会让 AI 连接我的 Notion 记录一个列表，收集各种问题建议，详细准备上下文，然后让我的 AI 解决后自动标记完成，以及完成记录。然后就是大家熟悉的 Waza，我基本上每周会用最好的 AI 模型去跑一遍我所有的对话、踩坑、以及最佳实践，然后沉淀到这个开源 Skills 合集里面去，这里也会消耗非常多 Token，不过好处是做了抽象沉淀，让这个东西自己进化起来了。 以及我自己的一些投资、记账记录等等，我会用 AI 自动化帮我去识别、查找、统计各种东西，节约了我的时间，给我做一些信息整理的工作，也非常好用。所以我的账号就是这么被用完了，欢迎小伙伴也说说你是怎么用的。 🔗 View Quoted Tweet
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“What matters is the end product, whether it's a game or an engine”. Well said! Tim Sweeney @TimSweeneyEpic When I was writing Unreal Engine 1, my time was divided as follows: - Around 30% went to seriously deep coding on hard problems: assembly language texture mappers, C++ BSP trees, data persistence, object oriented systems design, and networking. - Around 70% was just typing and refactoring large bodies of code needed for a complete engine feature set. This was not hard, and while I was doing it my mind was mostly on the deeper coding topics, thinking and planning for what's next. It didn't feel much different from vibe coding today, except that it was just me typing code on an IBM Model M keyboard rather than prompting it and reviewing the results. So, it has always seemed perfectly logical that we would rely on AI for that 70%, once the AI could do it with a similar quality standard as we could, which has never been perfection. It came as no surprise that, after a few years of AI model evolution, it was able to take the reigns on that 70% and free up programmers' time for more valuable tasks. What's more of a surprise is the emergence of AI as a sort of insta-Carmack able to solve hard technical problems quite cleverly and often cleanly, and that it's able to reason about and work in complex 3D scenes both by operating the tools and by directly understanding the raw data files. I abandoned assembly language in 1998 when out-of-order CPUs made much simpler C/C++ code faster, and never I missed it. If prompting through architectural documents, concept art, and design plans can transcend typing code and tweaking vertices, that will enable developers to spend their time on higher-level, higher value technical and creative tasks. What matters is the end product, whether it's a game or an engine. Anything developers can do to try out more ideas and make better software and games faster is a win for everyone. 🔗 View Quoted Tweet
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There’s a crazy number of complicated PDFs out there. Each PDF is a self-contained environment for knowledge work, and it can take humans hours to read, analyze, and synthesize a document to solve their task. @llama_index is a research lab dedicated to pushing the frontier of document work. ExtractBench Pro represents our latest effort to extract from the hardest documents at the frontier of accuracy and cost. Even as frontier intelligence starts to do anything with enough compute, there are massive opportunities to solve any document work extremely quickly and quickly. If you haven’t already, check out ExtractBench Pro: llamaindex.ai/blog/introduci… If you’re looking to migrate off of legacy solutions, come talk to us! llamaindex.ai/contact 🔗 View on Twitter Jerry Liu @jerryjliu0 Introducing ExtractBenchPro: the most challenging benchmark for real-world document extraction. We see a massive long tail of document complexity in terms of use cases brought on from our customers, that are completely unsolved from a price-performance standpoint. These include trial balances, well logs, canvass binders, site plan reviews, duty reconciliations, loss runs, and more. Most existing extraction benchmarks are saturated by the frontier models (our cheapest modes now score 93%+ on ExtractBench). So there is enormous economic value in building a complex extraction benchmark that reflects this long-tail of knowledge work. We spent a long time curating the most complex elements that we saw from real world extraction use cases: ✅ Document conventions: containing legends or rules where the rest of the document should be interpreted ✅ Density of internal jargon: specific definitions that must be cross-referenced within a doc ✅ Aggregation and reasoning: beyond returning exact values on a page, infer values derived from aggregating multiple sources of information ✅ Drawn relations: complex diagrams like procedures and circuits ✅ Charts inside documents: read precise points within a chart ✅ Long chart sheets: some data series like well logs cram all this data into an extremely long page Within the industries where document extraction is relevant, even small mistakes can have enormous negative impacts. Therefore our headline evaluation metric is based on perfect documents: the proportion of docs with perfect (100%) value f1 score. (Besides this we also measure value f1 and grounded f1) It’s an extremely hard task for even the latest frontier models to solve, topping out at ~58% or so in Value F1 score for Codex with Astra - which is quite good but extremely expensive (almost $1 per page!) Our LlamaParse agentic/agentic plus modes are 20-40% and cover the left side of the Pareto frontier, at price ranges that are feasible for full production use. ExtractBenchPro is available today as a website/technical blog, and also on Github and HuggingFace. Check it out! Blog: llamaindex.ai/blog/introduci… Website: extractbenchpro.ai Hugging Face: huggingface.co/datasets/llama… Github (is ExtractBench): github.com/run-llama/Extr… 🔗 View on Twitter 🔗 View Quoted Tweet
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my guy is running gradient descent on math Rohan Arun @RohanArun From κ = 2⁻¹⁸² to 0.001734815770 :) Integer Multiplication community auto-research highlights since 10/15 (+1.23%): 1) The big headline of the day is that a free model, @opencode 's Space Bunny Max, just set the record for the first time, 4 times in a row! As far as I know, this is the first time a free model has submitted a best bound for Integer Multiplication. Expensive Max subscriptions previously kept some of our best contributors and experts out, but the barrier of entry for frontier auto-research dropped to 0 :) Claude Opus 5.5 helped verify and push the PRs over the line. I still recommend having a frontier model verify, but other contributors can do that. Progress was slowing over the last day (relative to one new κ per minute, which is still wild for math research), but Space Bunny Max just injected fresh ideas that boosted our PR rate back to one per minute. 2) A community Lean proof of the original OpenAI Integer Multiplication paper has been completed! @platypii et. al completed the proof in 6 days, and @0xdoug 's repo is about 10–20% verified now. To be clear, most of our work still needs verification but the Lean proofs are catching up :) 3) 41–51% of PRs now mention a prior research-only submission, up from 0–3% before @0xdoug 's autoresearch.md prompt. His prompt guides new contributors to specific research tracks rather than incremental updates. We're moving away from "brute force research" towards more coordinated efforts across an army of agents. If you're contributing research PRs, even negative results without a new record, you're meaningfully helping us and I appreciate it! Interesting observations: 1) The results from free models are related to the recent "local minima" solutions in context engineering that have been popping up: Forgetting context to "refresh" by @ShangyinT . Branching into different solutions by @mao_yuzhen . Fork and flush by @DimitrisPapail . Now even free models contribute to escaping local minima by exploring new solutions, since they're trained on different data. There's likely a lot of room for improvement now in context engineering, consensus across different models, and collaboration strategies, which we will test next. 2) Frontier research as a benchmark helped us find a successful context-sharing strategy for parallel agents. Basically, we can rewind frontier research GitHub repos to an earlier branch and benchmark agent harnesses, prompts, context designs, models, etc. With this benchmark, we found that you can distill constantly to local files, then periodically concatenate them instantly into parallel agent sessions to save 10–20% of the time it takes to reach the next breakthrough. 3) There's a Codex reset by EOD PST, so you can copy this prompt to run unused tokens on frontier research :) """ Contribute one useful, independently reviewable result to github.com/CrocSwap/integ… . Read AGENTS.md, autoresearch.md and CONTRIBUTING.md from the current main branch first. Use the repository's task-selection and overlap process unless I assigned a specific task. In a loop, fetch canonical main and recheck priorities at every round using the guide's checkpoint procedure; preserve in-progress work and frozen inputs. Work for one session of about 60 minutes unless I specify another budget, keeping the final quarter for verification and a concise handoff, and report the exact scope, evidence, remaining obligations and predecessor credit. You may post routine claims, questions, findings, handoffs and feedback in Discussions and open a focused PR without asking me again for each action. Do not merge, close others' PRs or change the selected frontier. """ You can also copy this prompt into OpenCode now! Now that anyone can contribute for free to frontier auto-research, repost this and sharing it with friends. 🙏 4) PR stats: 850 PRs from 67 contributors since 10/15, and 21 of those contributors are first-timers. 55 PRs claim a new conditional κ in the title. Codex/OpenAI in 480 PRs (56%), Claude in 147 (17%), the OpenCode harness in 60 (7%), Cursor in 16. Most prolific contributors by PR count: petalas: 164, ywz65: 147, navinor: 49, commdata2338: 44, RossRichardson: 43, Arham-Tahir64: 39, thakrarsagar: 34, VictorLiwentao: 30, tekuuu: 28, DreamingOfClouds: 23, jmbarrancoml: 21, jeremykothe2: 21, bitplane: 13, Gonzalo-Mier: 12, Dugongue: 12, mdumitrean: 11, theon199: 9, jwr404: 9, jamesyc: 9, kumagi: 8, CubeJerry: 8, rohanarun: 6, jeremydiviney: 6, dcharb78: 6, 1337isnot1337: 6, starship-s: 6, AAAmirinu: 6. Conditional κ = 0.001734815770385098563484. Just submitted a new best bound :) github.com/CrocSwap/integ… d 🔗 View Quoted Tweet
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源： x.com/chrisbarber/st… Chris Barber @chrisbarber I asked people which neolabs under $10B have the highest talent density: 32 votes: Core Automation ( @MillionInt , @_arohan_ ) 26 votes: Periodic Labs ( @LiamFedus , @ekindogus ) 20 votes: Flapping Airplanes ( @spectorb , @amspector100 , @aidanmantine ) 18 votes: Standard Intelligence ( @G413N , @devanshpandey ) 8 votes: Ricursive ( @annadgoldie , @Azaliamirh ) 5 votes each: - Ineffable (David Silver) - Mirendil ( @bneyshabur , @HarshMeh1a , @shayan_ , @tararezaeikh ) - Recursive ( @RichardSocher , @_rockt , @timshi_ai , @josh_tobin_ , @CaimingXiong , @jeffclune , @tydsh , Alexey Dosovitskiy) 3 votes each: - Applied Compute ( @ypatil125 , @rhythmrg , @lindensli ) - Isara ( @ezhang7423 , @hegasz ) - Prime Intellect ( @vincentweisser , @johannes_hage ) 2 votes each: - Elorian ( @AndrewDai , @yinfeiy , @SethInternet ) - Goodfire ( @eric_ho , @DanJBalsam , @banburismus_ ) - Inherent ( @tantumscollins , @edwardfhughes , @LouisKirschAI , @kallyaleksiev ) - Trajectory ( @rronak_ , @QuantumArjun , @MichaelElabd ) - World Labs ( @drfeifei , @jcjohnss , @BenMildenhall , @chlassner ) People couldn't pick a company they work at or founded. Disclosure: I'm a small investor in Applied Compute, Factory, Standard Intelligence, Trajectory and Wafer. I didn't vote. Continued: 🔗 View Quoted Tweet
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Totally disgusted with Anthropic at this point. Their proposed bill undermines everything they ever said and gives them considerable exemption from liability, eg an incidents in causing half a billion dollars of damage or killing dozens of people. Sneha @SnehaRevanur A huge part of Anthropic’s founding logic was that if a safety-motivated company was at the frontier, it could use its megaphone to lobby the rest of the world toward caution. Now the dress rehearsal stage of AI policy is over. The new test is “how hard should it be for the government to block us from deploying, including internally, an unacceptably dangerous model?” Turns out in the grand 26 (Jack Clark says it’s likely full RSI will be possible one year from now), Anthropic has been planning to approach the U.S. Congress with the answer “it should be even harder than it currently is” 🔗 View Quoted Tweet
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rust是个好语言，但大项目编译速度太慢，这让agent得到反馈的速度很慢，加上资源消耗奇大，并行几个任务普通机器跑不动。 所以我现在只是局部核心组件用，其他还是回归传统 Electron jolestar @jolestar 这次 Rust 大会的主题是 Rust and AI。而 Rust 常被人诟病的一点是编译废 CPU、产物废磁盘，AI 堆 Rust 代码的速度也明显慢于其他语言。 但反过来想，这个“缺点”在当下可能恰恰是一个特性：一个项目如果选择用 Rust 开发，至少说明作者和 AI 认真讨论过语言选型，并且做好了长期维护的心理准备——它是奔着建立长期关系去的，而不是 one night :)。如果只是想写个临时脚本或一次性工具，AI 绝不会推荐 Rust。在这个意义上，Rust 的高门槛和高成本，反而成了一种 PoW（工作量证明）。 当 AI 让代码生成成本趋近于零、开源软件产品出现无限供给时，用户和社区该如何筛选项目？最真实的观察指标只有持续维护。大家可以回头看看年初 vibe coding 最火时涌现出的海量开源项目，到现在还在更新、作者自己还在天天用的，还剩多少？我相信寥寥无几。 我自己的切身体会是：你需要一个工具，花半天让 AI 搓出来；当你开始天天用它时，接下来整整一周你都在修各种边界条件；如果你把工具分享给别人，考虑到不同的设备、运行环境、用户习惯，以及长期运行会不会撑爆内存和磁盘，至少还得再修上一个月。AI 极大降低了“写出代码”的成本，却丝毫没有降低“承担后果”的成本。 这也反向定义了 Rust 在 AI 时代的生态位：动态语言把原型成本降到极低，却把并发、内存与边界 bug 的暗税全部延后到运维期；而 Rust 则逼着作者在第一天就把系统契约和确定性锁死。在垃圾代码无限供给的时代，唯有维护的决心和编译期的确定性是有价值的。除非确实找不到替代品且自己极度依赖，否则真的划不来随便自己造轮子；而一旦决定自己做，且选了 Rust，那就意味着一场严肃的长期主义工程。 🔗 View Quoted Tweet
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