options or anything of the sort. Don’t bet the farm on a single trade. And don’t do anything stupid, either financial or medical. Don’t test these drugs on yourself. Don’t do anything your grandma wouldn’t do. I’m not your financial or medical advisor. I’m not your friend. This is not financial advice, or medical advice. In fact, this is not advice. I’m only saying this for posterity, so if it happens, I can say Fingers crossed. And good luck.
反AI情绪中的“过去崇拜”与法西斯主义逻辑的相似性
近年来，科技界反AI情绪高涨，部分批评者怀念“过去真正的程序员”，认为现代大语言模型（LLM）是行业堕落的象征，用低质量代码和数量至上取代了精神层面的匠心。然而，有分析指出，这种对过去的崇拜和对现代技术的拒绝，其论证结构与法西斯主义经典特征高度吻合。文章引用朱利叶斯·埃沃拉、埃兹拉·庞德和希特勒的言论，发现反AI叙事中常见的“精神堕落”“机器统治”“数量凌驾于质量”等修辞，与法西斯文本如出一辙。尽管AI本身常被指责为法西斯工具，但作者提醒：反AI阵营将世界描绘成传统男子气概的精神力量与堕落资本之间的斗争，同样滑入了极权思维的陷阱。这种“过去崇拜”和“拒绝现代主义”正是乌姆贝托·艾柯定义的“原法西斯主义”核心特征。文章呼吁批判技术时需警惕自身论证中的潜在风险。 #反AI #法西斯主义 #技术批判 #过去崇拜 #意识形态 #科技评论 #文化分析 #LLM
近年来，科技界反AI情绪高涨，部分批评者怀念“过去真正的程序员”，认为现代大语言模型（LLM）是行业堕落的象征，用低质量代码和数量至上取代了精神层面的匠心。然而，有分析指出，这种对过去的崇拜和对现代技术的拒绝，其论证结构与法西斯主义经典特征高度吻合。文章引用朱利叶斯·埃沃拉、埃兹拉·庞德和希特勒的言论，发现反AI叙事中常见的“精神堕落”“机器统治”“数量凌驾于质量”等修辞，与法西斯文本如出一辙。尽管AI本身常被指责为法西斯工具，但作者提醒：反AI阵营将世界描绘成传统男子气概的精神力量与堕落资本之间的斗争，同样滑入了极权思维的陷阱。这种“过去崇拜”和“拒绝现代主义”正是乌姆贝托·艾柯定义的“原法西斯主义”核心特征。文章呼吁批判技术时需警惕自身论证中的潜在风险。 #反AI #法西斯主义 #技术批判 #过去崇拜 #意识形态 #科技评论 #文化分析 #LLM
C++ Vs Rust: Which is better for writing AI/ML code with LLMs
We do many conversions of AI models from the reference PyTorch implementation to GGML and C++. The reason is GGML/C++ produces a relatively tiny package we can run almost anywhere. The performance usually matches or exceeds PyTorch with a fraction of the dependencies. Personally I despise C++; canât stand it. I donât have to write it anymore though, LLMs do it. I sit above a layer of abstraction and only descend into the code to avoid worst case scenarios in much the same way I would randomly go read the code for a library I depended on before AI. Someone asked if we had considered doing these ports in Rust. I am well aware of Rust, got excited about it in the past, but had not seriously considered it because GGML is written in C++. In recent years I experimented by comparing Rust against Go and it was a slam dunk
We do many conversions of AI models from the reference PyTorch implementation to GGML and C++. The reason is GGML/C++ produces a relatively tiny package we can run almost anywhere. The performance usually matches or exceeds PyTorch with a fraction of the dependencies. Personally I despise C++; canât stand it. I donât have to write it anymore though, LLMs do it. I sit above a layer of abstraction and only descend into the code to avoid worst case scenarios in much the same way I would randomly go read the code for a library I depended on before AI. Someone asked if we had considered doing these ports in Rust. I am well aware of Rust, got excited about it in the past, but had not seriously considered it because GGML is written in C++. In recent years I experimented by comparing Rust against Go and it was a slam dunk
victory for Go on the project I was doing. Neither myself, LLMs or the person advocating for Rust seemed to understand how to solve basic problems without tripping over the type system. However I do like to explore options and it had occurred to me already that an experiment between Rust and C++ could be interesting, so having someone ask for it is the push I needed to burn some tokens. I in fact ended up doing a series of A/B experiments, none of which are very scientific, but I want to test realistic scenarios. Creating neat test environment tends to increase in difficulty the more realistic you try to make it. The first experiment was to pit C++ with GGML Vs Rust with Burn. They both had to reach parity, in terms of correctness and performance against the PyTorch implementation of FLOAT (it animates portraits given voice audio to make it appear they are talking). The final experiment was to create a pure CPU only implementation using SIMD libraries in C++ and Rust. Removing the confonders introduced by pitting GGML against Burn. The original suggestion was to use GGML from unsafe blocks in Rust, but I thought that this might put Rust at a disadvantage, nullifying its safety properties. Rightly or wrongly I decided to go with Burn instead and I can say right now Burn is great. Except that is on CPU, itâs terrible on CPU. On GPU though it is competitive with GGML and PyTorch on inference and it does training too. I have published Fableâs and ChatGPTâs analyses in the rust-vs-cpp-analysis repository , but here Iâll give my opinions and highly condensed version of events. I asked Claude to create 3 independent sub-agents; one to do an analysis of the PyTorch implementation, create a parity harness to do layer by layer numerical comparisons and do some Rust research to level the playing field with C++. You see, the thing is, the C++ agent would have a fair more prior art to go on, because we have done a bunch of these conversions and it helps to give access to those prior conversions. Iâm not going to withold that info because I want results. The best I can do is do some extra work upfront to try and convert lessons learnt on C++ projects into lessons for a Rust proejct. The other two subagents are the Rust and C++ implementors. They ran until they reached correctness and performance parity with the PyTorch implementation. Initially I thought I had walked into a decisive result proving Rustâs greatness. The Rust agent reported about 50% of the token usage while achieving the same performance. However it turned out the Rust agent benefited from some work the C++ agent did to debug the parity harness. The primary Fable agent also decided to do the GGML implementation in CUDA and the Rust one in Vulkan. Then blamed me for asking that GGML use CUDA and Burn use Vulkan. I donât recall asking for that, but in any case it is a confounder, but not a big one as it turns out. Regardless of whether we use Vulkan or CUDA, fused kernels are required in GGML and something similar in Burn to get parity with compiled PyTorch. After some iterations trying to get the Rust and C++ ports to be equivalent, I decided to do something more drastic as a final test. I asked to create two new ports, both in new repositories, but using lessons learned from the previous attempts. This time the ports would be CPU only and use SIMD libraries. I demanded that they be statically compiled and allow cross compiling to ARM. Do zero allocations during
computations and a bunch of other stuff. Again it initally appeared that Rust had achieved a victory, but it turned out that the rust agent simply decided not to create a C API. Possibly because I didnât explicitly ask for it, but of course there should always be a C API/ABI otherwise only other Rust code can consume the library. The Rust port was also a bit slower, because the agent simply decided to stop with perf optimizations sooner. Maybe Claude doesnât like optimizing Rust or more likely itâs just the random nature of LLMs coming through. In any case after forcing the agent to complete the C API/ABI and get perf up to parity, the Rust advantage disappeared. Finally I decided to do a couple of code reviews, one with Claude again and another with Codex. The argument in favor of Rust would predict that the Rust code would have less previously undiscovered bugs. Both Rust and C++ were fuzzed from the beginning, but still the review found plenty of potential bugs in both. I asked both Claude and Codex if Rustâs and C++âs tools and features had been used to their fullest to prevent bugs and the answer was no. Not even close. The agent just didnât decide to do it on the first round. The agent had made some spurious uses of unsafe, but actually most of the uses seemed to be valid. Removing the unnecessary unsafe blocks wouldnât have made a lot of difference to the analysis. So essentially both languages have more intrinsic or extrinsic features available to combat bugs. Both agents failed to use all of those features and both agents ended using similar amounts of tokens and time. From what I can gather there is no difference to an LLM between Rust and C++. The main difference between these lanauges is that Rust has some features built into the compiler whereas C++ relies on external tools. There are many problems with C++, but they get arbitraged with external tools like fuzzers, the address sanitzer, static anlysers and more ergonomic libraries. Rust is perhaps more prone to solving these issues in the language. In either case itâs not automatic that language features or external tools will solve the problems they can solve. The surprising part is that solutions which are enforced by the language donât really solve the original problem automatically. Letâs say you solve memory safety with a borrow checker, now you have a new problem which is working around the borrow checker. Except it is not really a new problem, you are still trying to solve the underlying problem, but with new constraints. That isnât to say the borrow checker canât help solve the problem, itâs just that it requires effort to do so. The question is does it require less effort than testing the code with the address sanitizer on, using smart pointers, a GC or using a combination. Possibly all of these techniques are mathematically and computationally equivalent when carried out by an LLM, except that it makes a difference when the computation occurs; in production, test time or compile time. Compile time and test time are practically the same it seems, so C++ and Rust are the same. They both move computation from runtime to development. I suppose the fundamental problem is that you have to model the real world task the software is trying to orchestrate. You have to express this model in the programming languageâs primitives. The real world, being as varied as it is, means that any general purpose programming language lacks primitives that
abstract or verify an arbitrary model. Even if the language does contain primitives to help with a large part of your model. You have to know how to use those features, which is not free. Especially when you view it through the lens of agentic codimg. You can put a token count on finding the correct primitives to express the model in. Intuitively I think one language is better, but I havenât been able to disprove the null hypothesis. Thatâs most likely because there are things that matter a lot more than whether you write your loops over arrays in C++ or Rust. Now both these languages are created for humans and there is the question of what happens if you systematically optimize a language to reduce token usage while completing practical tasks. It could be a deadend where the language always collapses into degenerate solutions or it could lead to something much better. My feeling is youâd hit diminishing returns quite quickly because the language would need to expand to encode solutions for an increasing number of problems, making recall the bottleneck. However the initial gains could be large.
长鑫科技上市首日暴涨471%，野村看高至116元
7月27日，国产DRAM龙头长鑫科技登陆科创板，开盘即大涨471.59%，报49.5元，市值达3.3万亿元，成为A股市值最大公司。此次IPO发行价8.66元，募资约579亿元，为科创板史上最大IPO。同日，野村证券发布首份覆盖研报，给予“买入”评级，目标价116元，较发行价上涨空间超12倍，对应市值7.76万亿元。野村认为，全球AI驱动内存需求呈指数级增长，而供应端受制于产能扩张瓶颈，供不应求将成为常态。长鑫科技作为中国最大DRAM制造商，市场份额将从当前约10%提升至2028年底的18%，逐步逼近美光。技术层面，公司计划2026年主流制程达16-17nm，2027年量产HBM3。野村预计2026-2028年营收和净利润复合增长率分别达63%和74%，目标价基于20倍2028年预期市盈率。但报告也提示地缘政治为主要风险。 #长鑫科技 #DRAM #IPO #芯片 #AI存储 #野村证券 #科创板
7月27日，国产DRAM龙头长鑫科技登陆科创板，开盘即大涨471.59%，报49.5元，市值达3.3万亿元，成为A股市值最大公司。此次IPO发行价8.66元，募资约579亿元，为科创板史上最大IPO。同日，野村证券发布首份覆盖研报，给予“买入”评级，目标价116元，较发行价上涨空间超12倍，对应市值7.76万亿元。野村认为，全球AI驱动内存需求呈指数级增长，而供应端受制于产能扩张瓶颈，供不应求将成为常态。长鑫科技作为中国最大DRAM制造商，市场份额将从当前约10%提升至2028年底的18%，逐步逼近美光。技术层面，公司计划2026年主流制程达16-17nm，2027年量产HBM3。野村预计2026-2028年营收和净利润复合增长率分别达63%和74%，目标价基于20倍2028年预期市盈率。但报告也提示地缘政治为主要风险。 #长鑫科技 #DRAM #IPO #芯片 #AI存储 #野村证券 #科创板
SUI、EIGEN、FF等代币下周将迎大额解锁，总价值超2300万美元
Token Unlocks 数据显示，SUI、EIGEN、FF等多款代币将于下周迎来大额解锁。其中，Sui（SUI）将于北京时间8月1日上午8点解锁约1372万枚代币，价值约990万美元；EigenCloud（EIGEN）将于北京时间8月1日中午12点解锁约3682万枚代币，价值约760万美元；Falcon Finance（FF）将于北京时间7月29日晚上9点解锁约1.02亿枚代币，价值约620万美元。大规模解锁通常会对代币流通供应量产生压力，可能引发市场波动，投资者需密切关注。 #SUI #EIGEN #FF #代币解锁 #加密货币 #市场动态 #TokenUnlocks
Token Unlocks 数据显示，SUI、EIGEN、FF等多款代币将于下周迎来大额解锁。其中，Sui（SUI）将于北京时间8月1日上午8点解锁约1372万枚代币，价值约990万美元；EigenCloud（EIGEN）将于北京时间8月1日中午12点解锁约3682万枚代币，价值约760万美元；Falcon Finance（FF）将于北京时间7月29日晚上9点解锁约1.02亿枚代币，价值约620万美元。大规模解锁通常会对代币流通供应量产生压力，可能引发市场波动，投资者需密切关注。 #SUI #EIGEN #FF #代币解锁 #加密货币 #市场动态 #TokenUnlocks
开发者为 OpenCode 构建静态验证器，阻止不安全 AI 工具调用
受 Erik Meijer 关于 AI 工作流形式化验证的论文《Guardians of the Agents》以及 Nada Amin 在 Guardians 仓库中实现的启发，一位开发者构建了 OpenCode 静态安全验证插件。该插件利用 OpenCode 的 TypeScript 插件系统，在候选工具调用（如 bash、read、edit、write）实际执行前进行拦截，并将工具参数发送到本地运行 Python 守护进程进行旁路验证，从而在代码运行层面实现静态安全性检查，有效防止 AI 代理执行危险操作。 #OpenCode #静态验证 #AI安全 #工具调用 #安全验证 #编程 #技术开发 #ShowHN
受 Erik Meijer 关于 AI 工作流形式化验证的论文《Guardians of the Agents》以及 Nada Amin 在 Guardians 仓库中实现的启发，一位开发者构建了 OpenCode 静态安全验证插件。该插件利用 OpenCode 的 TypeScript 插件系统，在候选工具调用（如 bash、read、edit、write）实际执行前进行拦截，并将工具参数发送到本地运行 Python 守护进程进行旁路验证，从而在代码运行层面实现静态安全性检查，有效防止 AI 代理执行危险操作。 #OpenCode #静态验证 #AI安全 #工具调用 #安全验证 #编程 #技术开发 #ShowHN
西门子与英伟达深化合作，推出自验证AI工作流用于芯片设计
西门子宣布扩展与英伟达的战略合作，将自验证的代理式AI工作流引入电子设计自动化（EDA）领域，帮助半导体和PCB工程团队从自主任务编排转向更可信、持续验证的工程成果。新功能基于西门子近期推出的Fuse EDA AI Agent系统，并整合了英伟达AI技术，使长期运行的领域专用AI代理能够针对确定性、基于物理的EDA引擎进行推理、行动并持续验证决策。该系统已集成至西门子新发布的Intelligence Center X企业工业AI环境中，支持在设计、制造和供应链间实现AI驱动的协同流程，增强数字孪生能力。双方表示，通过结合西门子的领域专用工业AI和物理引擎与英伟达的加速计算及开源模型，能够创建可信的自验证工作流，加速开发、提升设计质量，并增强对工程成果的信任。该方案支持多代理安全协调、实时反馈和高效收敛，覆盖半导体与PCB全生命周期。 #西门子 #英伟达 #EDA #AI工作流 #自验证 #半导体 #PCB #工业AI #数字孪生
西门子宣布扩展与英伟达的战略合作，将自验证的代理式AI工作流引入电子设计自动化（EDA）领域，帮助半导体和PCB工程团队从自主任务编排转向更可信、持续验证的工程成果。新功能基于西门子近期推出的Fuse EDA AI Agent系统，并整合了英伟达AI技术，使长期运行的领域专用AI代理能够针对确定性、基于物理的EDA引擎进行推理、行动并持续验证决策。该系统已集成至西门子新发布的Intelligence Center X企业工业AI环境中，支持在设计、制造和供应链间实现AI驱动的协同流程，增强数字孪生能力。双方表示，通过结合西门子的领域专用工业AI和物理引擎与英伟达的加速计算及开源模型，能够创建可信的自验证工作流，加速开发、提升设计质量，并增强对工程成果的信任。该方案支持多代理安全协调、实时反馈和高效收敛，覆盖半导体与PCB全生命周期。 #西门子 #英伟达 #EDA #AI工作流 #自验证 #半导体 #PCB #工业AI #数字孪生
无偏开放世界正则化
研究人员提出了一种名为“无偏开放世界正则化”的方法，用于改进自监督学习中的公平性。该论文由Léonic Nicollier等人提交至arXiv（2026年7月24日），核心思想是通过正则化技术减少模型在训练过程中对敏感属性（如性别、种族）的依赖，从而在开放世界环境下提升模型的泛化能力和公平性。与传统自监督学习方法不同，该方法无需依赖标签数据，而是通过引入无偏的约束条件，使模型学习到的表示更加鲁棒和公平。实验在多个基准数据集上验证了该方法的有效性，结果表明其在保持较高下游任务性能的同时，显著降低了偏见指标。该研究为构建更公平的机器学习系统提供了新的理论视角和实践路径，尤其适用于数据分布复杂、标签稀缺的真实场景。 #机器学习 #自监督学习 #公平性 #正则化 #AI #arXiv #论文 #算法 #无偏学习
研究人员提出了一种名为“无偏开放世界正则化”的方法，用于改进自监督学习中的公平性。该论文由Léonic Nicollier等人提交至arXiv（2026年7月24日），核心思想是通过正则化技术减少模型在训练过程中对敏感属性（如性别、种族）的依赖，从而在开放世界环境下提升模型的泛化能力和公平性。与传统自监督学习方法不同，该方法无需依赖标签数据，而是通过引入无偏的约束条件，使模型学习到的表示更加鲁棒和公平。实验在多个基准数据集上验证了该方法的有效性，结果表明其在保持较高下游任务性能的同时，显著降低了偏见指标。该研究为构建更公平的机器学习系统提供了新的理论视角和实践路径，尤其适用于数据分布复杂、标签稀缺的真实场景。 #机器学习 #自监督学习 #公平性 #正则化 #AI #arXiv #论文 #算法 #无偏学习
TriGlue
近日，研究团队在arXiv上提交了一篇题为《TriGlue: a Biology-Inspired Generative Model for Generating Molecular Glue-Induced Ternary Complex》的论文。该工作提出了一种新型生成模型TriGlue，其核心设计受生物学机制启发，旨在从头生成能够诱导蛋白质间形成三元复合物的分子胶。分子胶是一类能促使两种蛋白质发生相互作用的化学分子，在靶向蛋白降解等前沿药物研发中展现巨大潜力。TriGlue模型通过深度学习技术，有望突破传统筛选方法的局限，高效探索广阔的化学空间，从而加速候选分子胶的发现过程。这一成果将为蛋白质相互作用调控和新型治疗策略的开发提供有力工具。 #分子胶 #生成模型 #AI #药物发现 #蛋白质降解 #生物信息学
近日，研究团队在arXiv上提交了一篇题为《TriGlue: a Biology-Inspired Generative Model for Generating Molecular Glue-Induced Ternary Complex》的论文。该工作提出了一种新型生成模型TriGlue，其核心设计受生物学机制启发，旨在从头生成能够诱导蛋白质间形成三元复合物的分子胶。分子胶是一类能促使两种蛋白质发生相互作用的化学分子，在靶向蛋白降解等前沿药物研发中展现巨大潜力。TriGlue模型通过深度学习技术，有望突破传统筛选方法的局限，高效探索广阔的化学空间，从而加速候选分子胶的发现过程。这一成果将为蛋白质相互作用调控和新型治疗策略的开发提供有力工具。 #分子胶 #生成模型 #AI #药物发现 #蛋白质降解 #生物信息学