In this comprehensive coding tutorial, we explore Meta's Rebalancer assignment solver using its C++ core and Python bindings. Learn how to build datacenters, manage task placements across racks, handle constraints and goal priorities, and compare local search against the exact HiGHS solver using published benchmarks. The post How to Use Meta’s Rebalancer for Task Placement & Optimization appeared first on MarkTechPost .
Meta released Muse Wonder and Muse Wonder Full, 1M-context agent models. Muse Wonder costs $4/$20 per million tokens on Meta Model API. Wonder Full, on Muse Code plans, leads DeepSWE and SWE-Marathon. The post Meta Launches Muse Wonder and Muse Wonder Full: Benchmarks, Pricing and How to Use It appeared first on MarkTechPost .
AI-powered knowledge management systems face a fundamental challenge when businesses need accurate answers rather than plausible ones. Imago Platform Ltd. encountered that problem while developing an application to help customers navigate complicated product warranty policies. Imago’s first client, a lighting and electrical business, needed help resolving warranty claims across manufacturers with widely varying policies. The […] The post Imago Platform uses graphs to improve AI knowledge management appeared first on SiliconANGLE .
Chatbot prompts reveal the engineer’s alleged efforts to set up a covert listening device days before one was found in a meeting room used by high-level executives.
Hi HN, I'm Niz. Pulse Local is a budgeting app for Mac (Apple Silicon, beta) that answers questions about your spending with models that run on your machine. Local mode needs no account. The ledger and your questions stay on the machine, and the app sends no analytics or crash logs. I felt there are not enough consumer apps that run locally with no calls to the outside world that help us manage sensitive corners of our lives. This app in its local variant only downloads a couple of models and occasional updates, it does not send any analytics nor data. Features-wise, Pulse local is a complete budgeting app that offers an assistant built on top of a fine-tuned model and thousands of rules embedded in the code itself. I'm sharing it for free with options to help me continue experimenting with fine-tuning and expanding along this direction. How it works: you import a CSV from any bank, in any currency. When you ask a question, a tuned Qwen3 0.6B model reads it into a structured query, and code, not the model, does the money math against the ledger. A 4B checker model double-checks each money answer; when the two readings disagree, the app says it is not sure instead of guessing. That change took wrong answers from about one in five to 5 in 494 questions across our last three tests. The write-up covers how we test, the designs that failed, and the limits of the claim: https://pulseos.finance/pulse-local/writing/agree-or-not-sur... The tuned weights are on Hugging Face (PulseOS/pulse-local-qwen3-0.6b). Limitations: the first start downloads about 2.9 GB of models. With the checker on, a money answer takes 12 to 14 seconds (you can turn the checker off). Small models can still misread an open-ended question. The app answers in English. Mac only for now; Windows is next. The app is free. An optional Connected mode (Sync Pass, $29/year) syncs US banks through Plaid; transactions pass through our server's memory and are not stored. Feedback welcome, especially on wrong answers and on questions it should handle but does not. Comments URL: https://news.ycombinator.com/item?id=50080275 Points: 3 # Comments: 0
Industrial Next helps manufacturers automate complex, delicate tasks that have traditionally required human labor.
Cerebras CEO Andrew Feldman predicts a future where personal AI assistants become as commonplace as household appliances, running partly on local devices and partly in the cloud.
Max Hodak, the former president and co-founder of Neuralink, detailed on stage at TechCrunch Disrupt how his company's work with PRIMA is part of its path toward a novel brain-computer interface (BCI).
Research: Profiling web-edit with Python 3.15: flame graphs and source heatmaps I put the new Python 3.15 profiler through its paces by having GPT-6 Astra Extra High in Codex run it against my new web-edit app : I need a Python 3.15 flame graph and heatmap demo of the "web-edit" app from PyPI - research first then make me those demos Here's the resulting example flame graph and heatmap . Tags: profiling , python , codex
Release: llm-meta-ai 0.2 Meta AI release Muse Wonder (and Muse Wonder Full), their new "most capable models". These look like they may be Meta's first Fable class models - Artificial Analysis scores Muse Wonder (Max) as equivalent to GPT-6.1 Sol. I updated my llm-meta-ai plugin for the new model. Here are pelicans for Muse Wonder for the six reasoning levels: minimal, low, medium, high, xhigh, max. This is the max one: Blue cycling helmet, bicycle is missing one bar but otherwise an OK shape, has mudguards, nice background, OK beak. For comparison, here are pelicans for Muse 1.3 (released September 2nd) and Muse 1.2 (released August 5th). Meta AI are putting out a new model a month, and the improvements between each model (at least in terms of drawing pelicans) are material. Tags: ai , generative-ai , llms , meta , pelican-riding-a-bicycle , llm-release
Release: web-edit 0.1a1 Includes performance optimizations written by Claude Opus 5.5 after I fed it this Python 3.15 profiling report generated by GPT-6 Astra. Tags: profiling , projects
发布：llm-gemini 0.35 今天通过 Gemini API 以及其每月 100 美元订阅计划向客户提供了全新的 Gemini 4 Argon 模型，我为 llm-gemini 添加了对它的支持。下面是低、中、高三档生成的 pelican。如预期一样，它们都非常出色：Gemini 一直以来都很擅长生成优秀的 SVG，而且据我所知，这是唯一一家其员工专门投入 SVG 研究的实验室（他们以前也曾展示过自己生成的 pelican）。这是“高”档生成的插图，花费了我 17.166 美分： 时尚而准确的自行车车架。篮子里有一条鱼。帅气的围巾。双脚分列车架两侧，踩在脚踏板上。车轮辐条、一顶小帽子、漂亮的喙和喉囊，以及略显凶狠的表情。 定价很有意思。发布时，Argon 的价格是输入每百万 token 2 美元、输出每百万 token 10 美元——与 GPT-6 Sol 相同，是 Claude Opus 5.5 价格的一半，以及 Claude Fable 5.1 和 GPT-6 Astra 价格的五分之一。但从 12 月 1 日起，Argon 的价格将翻倍——届时会与 Opus 5.5 同价。 如今，这样的价格并不能真正直接比较，因为真正重要的是：在代理式运行框架中，模型要完成一个目标需要消耗多少 token。Artificial Analysis 发布了使用各模型运行其基准测试的总成本，这为我们提供了一个有用的参考。Argon 在这方面表现非常好：运行该基准测试的成本为 2,407 美元，接近 GLM-5.3，而且明显低于 OpenAI、Anthropic 和 Meta 的最新模型。即使价格翻倍，它仍会比 GPT-6 Astra 略便宜。 Artificial Analysis 的堆叠柱状图截图，对 14 个 AI 模型的美元成本进行比较，按从低到高排序；柱体按提供商着色，每个柱体下方显示提供商徽标，底部以倾斜文字标出模型名称。右上角的徽标为 Artificial Analysis。指向 Gemini 4 Argon 柱体的悬浮提示框显示：Gemini 4 Argon（High），2,407 美元；输出 313 美元；推理 815 美元；缓存写入 897 美元；缓存读取 264 美元；非缓存输入 118 美元；点击查看更多信息。 从左到右的柱体如下，每个柱体上方标有总成本，部分分段数值印在柱体内部：Qwen3.8 27B（xhigh），1,336 美元，Alibaba 徽标；Mistral Large 4 Preview，1,607 美元，Mistral 徽标；Gemini 3.8 Flash（high），1,623 美元，Google 徽标；Muse Spark 1.3（max），2,000 美元，其中一段为 769 美元，Meta 徽标；Gemini 4 Argon（high），2,407 美元，其中两段分别为 815 美元和 897 美元，Google 徽标；GLM-5.3（max），2,503 美元，其中一段为 790 美元，Z.ai 徽标；Kimi K3（max），3,658 美元，Kimi 徽标；Qwen3.8 Max（0902），4,935 美元，其中两段分别为 893 美元和 1,441 美元，Alibaba 徽标；Grok 4.7（xhigh），4,967 美元，其中两段分别为 1,861 美元和 1,618 美元，xAI 徽标；GPT-6 Astra（max），5,324 美元，其中两段分别为 778 美元和 978 美元，OpenAI 徽标；Muse Wonder（max），5,733 美元，其中一段为 1,748 美元，Meta 徽标；Claude Sonnet 5.5（max with fallback），7,260 美元，其中两段分别为 1,216 美元和 1,815 美元，Anthropic 徽标；Claude Opus 5.5（max with fallback），8,708 美元，其中一段为 921 美元，Anthropic 徽标；Claude Fable 5.1（max with fallback），13,129 美元，其中一段为 7,219 美元，Anthropic 徽标。 （我用 ChatGPT 编辑了那张图表！数据全部正确，我已经核对过了——我只是需要让 Gemini 4 Argon 的信息框显示出来，同时避免遮挡其他数字。） 标签：google，ai，generative-ai，llms，llm，gemini，llm-pricing，pelican-riding-a-bicycle，llm-release
Anthropic 关于重新设计源代码管理的一篇有趣文章，介绍了他们为何以及如何构建了一个自定义系统来替代 Git。这是因为过去六个月里，他们代码变更的速度大幅提升。2026 年 5 月，Anthropic 平均每天合并约 2,500 项代码变更；截至 9 月，这一数字已超过每天 30,000 项。他们还附上了一张展示这些变更的图表。我让 Claude 修改了他们的图表（代码见此处），加入了 2026 年各模型公开发布的标记： 堆叠柱状图，标题为“每天合并的变更”，展示 Anthropic 单体代码库在 2026 年 1 月至 9 月期间的情况。每天的柱子分为拉取请求（蓝色）和 Antique 变更请求（橙色），并以黑线表示 7 天平均值。工作日的合并量从 1 月份的约 1,000 次开始，缓慢攀升至 5 月下旬的约 3,000 次。Antique 变更请求于 5 月下旬出现，在 6 月中旬超过拉取请求，并在 7 月中旬几乎完全取代后者；此时工作日的合并量达到 6,000–8,000 次。7 月末标注为“安静的一周”的时期降至每天不足 1,000 次。此后增长变得陡峭：8 月中旬超过每天 10,000 次，9 月初约为 17,000 次，并于 9 月 9 日达到 30,401 次的峰值。虚线标出了 Claude 模型的发布：Opus 4.6（2 月 5 日）、Sonnet 4.6（2 月 17 日）、Mythos Preview（4 月 7 日）、Opus 4.7（4 月 16 日）、Opus 4.8（5 月 28 日）、Fable 5 和 Mythos 5（6 月 9 日）、Sonnet 5（6 月 30 日）、Opus 5（7 月 24 日，就在安静的一周之前），以及 Fable 5.1 和 Mythos 5.1（9 月 1 日）。这里展示的是公开发布日期，而不是这些模型可供 Anthropic 内部使用的日期。我猜，引入 Fable/Mythos 类模型是编码速度提升的一个关键因素。 “Antique”是他们新的代码审查系统。源代码管理系统名为 Scion，在我看来，它听起来像是沿着 Subversion 路线重新发明的集中式源代码管理系统。他们的要求相当极端： 仅主分支的历史记录就接近 100 GB，压缩后的工作副本超过 25 GB。我们每天为 CI 执行大约 2,500 万次代码检出，这还没算开发者环境、智能体沙箱和研究任务。如果以朴素方式获取，这每天将产生超过 600 PB 的 Git 流量。 这个新系统由一个 7 人团队构建完成，从 Git 迁移过来只用了 5 个月。 顺带一提，最近我一直在自己的项目中尝试对抗式审查：让一个模型尝试寻找缺陷（我经常让 Claude 和 GPT 互相对照）。文章中的这段话反映了我的亲身经历： Anthropic 也广泛采用对抗式审查。尽管这种方式在许多方面都极其有效，但也可能存在目光短浅的问题，因此需要有经验的工程师引导 Claude，避免它因应对假设性问题而对系统的某些部分进行过度工程化。 来源：Hacker News 标签：git、迁移、版本控制、ai、生成式 ai、llms、anthropic、智能体工程
Cerebras CEO Andrew Feldman has a message for AI leaders spreading doomsday warnings: Maybe stop talking? At TechCrunch Disrupt, he argued that the industry's focus on AI's risks is drowning out its potential benefits.
Google LLC today brought its Gemini 4 Argon frontier artificial intelligence model to Gemini app users on its Pro and Ultra subscription plans, two weeks after the model went first to vetted cybersecurity defenders. Subscribers on those plans also get a new tasks mode that keeps Gemini working on a job after they have stepped […] The post Gemini 4 Argon reaches Gemini app Pro and Ultra subscribers alongside a new tasks mode appeared first on SiliconANGLE .
Our first robotics pod with Dyna’s Chief Scientist on their big bet on World Action Models & the Next Frontier of Physical AI
https://x.com/OpenAINewsroom/status/2110095403198849132 Comments URL: https://news.ycombinator.com/item?id=50078141 Points: 199 # Comments: 189
Showing off Sugabots, an internal project I built at work that we've decided to open-source. I got tired of the team just copying context between agents and Slack. So I built an internal tool for shared chats where our team and agents could work together, using our choice of models and tools. The team is now actively maintaining and building on it to add all the features we're missing. We've shared a hosted offering with a few other teams and has proven useful for them too. Its open-source (MIT) and self-hostable if you want to use it yourself. Let me know what you think. Comments URL: https://news.ycombinator.com/item?id=50079791 Points: 1 # Comments: 0
1) For those who have let AI write (or rewrite) any part of their tech blog posts https://forms.gle/3zYuANeuCdcmFt9D9 2) For anyone who has used any level of AI assistance in any type of professional (tech) writing https://forms.gle/rnHUguku2mLDh1YNA Comments URL: https://news.ycombinator.com/item?id=50078659 Points: 1 # Comments: 0
An OpenAI engineer was arrested after the company said it discovered a "sophisticated recording device" hidden in one of its offices. The employee, 35-year-old Abdulrahman Alfozan, is charged with using or attempting to use a device to intercept oral communications, according to a report from The San Francisco Standard. OpenAI's post about the incident says […]
A federal judge ruled that Apple can access OpenAI's interview materials involving former Apple employees, as well as private messages and testimony from key figures in the case.
AI leaders joined whistleblowers and security leaders at a sweeping parliamentary session, while a few hundred meters away Britain’s spy chief warned AI could be used to create new bioweapons.
A service and open source tool to get open data streams in two commands. No sign up needed for subscribing. Streams to try: - https://hypermass.io/stream/_J0fm450s - extreme UV images of the sun from SOHO - visual data - https://hypermass.io/stream/_ATtCoEK - GDACS live disaster data (filtered to just the ongoing items) The CLI (Apache 2.0, https://github.com/hypermass-io/hypermass-cli ) tool allows you to subscribe to multiple live streams of files, with the files arriving in a local folder. The filenames are ULIDs so they can be easily sorted into time order in scripts and code. Publishing works similarly - drop files into a folder and they get read, published and deleted locally. The idea is that by being highly opinionated about the shape of the data (files, delivered in sequence, in real time) a bunch of the logic and edge cases get handled well in one place (things like ordering, retries, hash verification, reconnection, etc) - rather than requiring every subscriber to implement it themselves for every source. A high-speed version for things like departure boards, train positions is in the pipeline. Background: About 10 years ago, I worked for a few years supporting and developing some of the UK Rail Open Data systems. As a developer I was frustrated at how fragmented the open data space was, and how every open data system was designed as a blank-page exercise. I realised that a lot of data falls into an odd middle ground, too infrequent to economically poll on a large scale, too large for messaging protocols, but in some sense still real time. For example transport schedules, periodic solar imagery, disaster alerts - real-time but infrequent. Fairly often I just wanted a "just give me the data" option when the solutions I found were static catalogues with no nod towards real-time use, bespoke APIs, analytics systems and other things I didn't need. I couldn't find a simple approach that leaned into what I saw as the data's strengths (simple files, delivered in order, as/when they are published) - so I built it. About me: Solo founder based in the UK, full time for just over a year. Sustainability: Initially a "heavy users pay for more data allowance" model with a decent free tier. Ideally we later shift to institutional publishers pay to keep their streams free to download. Possibly a paid data marketplace to support costs, but the main focus would always be the free and open data. What I'd like to know: - Does the CLI approach solve any real problems for you? - Is the technical approach sane? - Would you use a service like this to publish data? - Recommendations to bootstrap users (particularly publishers)? Comments URL: https://news.ycombinator.com/item?id=50071200 Points: 5 # Comments: 1
https://celebratio.org/Appel_KI/article/796/#sender Comments URL: https://news.ycombinator.com/item?id=50077096 Points: 4 # Comments: 1
很快，更多 AI 代理将能够浏览 Shopify 商品列表并进行购买，这标志着该公司在代理式商务方面的野心进一步扩展。Shopify 周二宣布，将允许消费者配置自己的 AI 代理，代表他们购买商品，从而让更多代理能够访问 Shopify 商品目录中的超过十亿条商品信息。（该目录包括 […]