OpenAI 收购 TBPN，目标是加速围绕 AI 的全球对话，并支持独立媒体，进一步扩大与开发者、企业及更广泛技术社区的连接。
OpenAI acquires TBPN to accelerate global conversations around AI and support independent media, expanding dialogue with builders, businesses, and the broader tech community.
AI Gateway 现在支持网关级自动重试。当上游 provider 返回错误时，请求会按你配置的策略自动重发，无需修改客户端逻辑。可配置重试次数、重试间隔以及 Constant、Linear、Exponential 等退避策略。
AI Gateway now supports automatic retries at the gateway level. When an upstream provider returns an error, your gateway retries the request based on the retry policy you configure, without requiring any client-side changes. You can configure the retry count (up to 5 attempts), the delay between retries (from 100ms to 5 seconds), and the backoff strategy (Constant, Linear, or Exponential). These defaults apply to all requests through the gateway, and per-request headers can override them. This is particularly useful when you do not control the client making the request and cannot implement retry logic on the caller side. For more complex failover scenarios — such as failing across different providers — use Dynamic Routing. For more information, refer to Manage gateways.
Codex 现为 ChatGPT Business 和 Enterprise 提供按量计费选项，让团队能以更灵活的方式启动和扩展使用规模。
Codex now includes pay-as-you-go pricing for ChatGPT Business and Enterprise, providing teams a more flexible option to start and scale adoption.
Cloudflare 认为，每周超过 100 亿次的 AI bot 请求正在改变缓存设计的前提。文章讨论了 AI bot 流量与人类流量的差异，以及缓存系统需要如何相应演进。
2026-04-02ResearchCacheThe explosion of AI-bot traffic, representing over 10 billion requests per week, has opened up new challenges and opportunities for cache design. We look at some of the ways AI bot traffic differs from humans, how th…
OpenRouter 公布了 Auto Exacto，这是一项默认开启的自适应质量路由能力，用于在不同模型和请求场景下动态优化路由表现。
February Release SpotlightFebruary 23, 2026OpenRouter Outages on February 17 and 19, 2026February 20, 2026January Release SpotlightJanuary 9, 2026Distillable Models and Synthetic Data Pipelines with NeMo Data DesignerDecember 24, 2025Decem…
Google DeepMind 发布 Gemma 4 系列开放模型，主打多步规划和端侧 agentic workflow。配套还包括 Google AI Edge Gallery 和 LiteRT-LM 库，面向移动设备、桌面和 IoT 平台加速部署。
Google DeepMind has launched Gemma 4, a family of state-of-the-art open models designed to enable multi-step planning and autonomous agentic workflows directly on-device. The release includes the Google AI Edge Gallery for experimenting with "Agent Skills" and the LiteRT-LM library, which offers a significant speed boost and structured output for developers. Available under an Apache 2.0 license, Gemma 4 supports over 140 languages and is compatible with a wide range of hardware, including mobile devices, desktops, and IoT platforms like Raspberry Pi.
Google 更新了账号设置，允许美国用户修改自己的 @gmail.com 用户名且保留原有数据与收件箱。对开发者而言，如果应用仍只依赖邮箱地址做身份识别，可能会带来重复账号或访问丢失问题，因此建议迁移到 subject ID。
Google has updated its account settings to allow U.S. users to change their @gmail.com usernames while keeping all existing account data and inboxes intact. For developers, this means that while old email addresses will remain active as aliases, apps that rely solely on email addresses for identification may face issues with account duplication or lost access. To ensure a seamless user experience, Google recommends migrating to the "subject ID" as the primary user identifier and allowing users to manually update their contact information within app settings.
GitHub 回顾了近期以窃取 secrets 为目标的开源攻击，并介绍当前可用的防护措施，以及平台正在推进的安全能力。
Recent attacks on open source focus on exfiltrating secrets; here are the prevention steps you can take today, plus a look at the security capabilities GitHub is working on. The post Securing the open source supply chain across GitHub appeared first on The GitHub Blog.
Cloudflare 发布 EmDash beta，这是一款基于 Astro 6.0 的全栈无服务器 JavaScript CMS，试图把传统 CMS 的能力与更现代的插件安全模型结合起来。
2026-04-01Today we are launching the beta of EmDash, a full-stack serverless JavaScript CMS built on Astro 6.0. It combines the features of a traditional CMS with modern security, running plugins in sandboxed Worker isolates....Continue re…
/fleet 让 Copilot CLI 可以并行分派多个 agents。文章介绍了如何编写可按文件拆分的 prompt、声明依赖关系并规避常见坑。
/fleet lets Copilot CLI dispatch multiple agents in parallel. Learn how to write prompts that split work across files, declare dependencies, and avoid common pitfalls. The post Run multiple agents at once with /fleet in Copilot CLI appeared first on The GitHub Blog.
Reuters 报道称，美国国家劳资关系委员会裁定 Amazon 必须与代表 Staten Island 仓库约 5,000 名员工的工会进行谈判，Amazon 计划提出上诉。
Greg Bensinger / Reuters: The US NLRB rules that Amazon must negotiate with the Amazon Labor Union, which represents ~5K workers at its Staten Island warehouse; Amazon plans to appeal — Amazon (AMZN.O) must negotiate with a labor union representing some 5,000 workers at a company warehouse on Staten Island …
作为随 webpack 5 推出的开源微前端机制，Module Federation 2.0 带来了动态 TypeScript 类型提示、解耦运行时层、Node.js 支持等更新，并进一步增强了对不同 bundler 与框架的兼容性。
Module Federation 2.0, an open-source micro-frontend mechanism introduced with webpack 5, offers significant updates including dynamic TypeScript type hints, decoupled runtime layers, and Node.js support. It enhances compatibility across various bundlers and frameworks. Key features include a Side Effect Scanner and easier integration for remote modules, addressing previous adoption challenges. By Daniel Curtis
Matt Mullenweg 评论称，Cloudflare 推出的 EmDash 虽然开源，但核心目标更偏向带动 Cloudflare 服务销售，而不是像 WordPress 那样推动跨平台内容发布的普及。
Matt Mullenweg: Matt Mullenweg says EmDash, while open source, is designed “to sell more Cloudflare services” and lacks the cross-platform democratization of WordPress — So, two other Matts at Cloudflare announced EmDash — the spiritual successor to WordPress that solves plugin security.
Andrew Harmel-Law 与多位架构师讨论了 2025 年架构实践的变化，包括如何向利益相关方解释技术债务、如何通过 ADR 推动去中心化决策，以及现代技术领导者的职业路径。
Andrew Harmel-Law and a panel of expert architects discuss the shifting practice of architecture in 2025. They explain strategies for communicating technical debt to stakeholders, the benefits of decentralized decision-making through ADRs, and the career paths of modern leaders. The panel shares insights on bridging the gap between mobile and backend teams to ensure a holistic system. By Andrew Harmel-Law, Cat Morris, Diana Montalion, Shana Dacres-Lawrence, Vanessa Formicola, Elena Stojmilova, Peter Hunter
华尔街日报报道称，随着中美都将人形机器人视作战略产业，Tesla 等企业正在更多地向中国供应商采购相关零部件。
Raffaele Huang / Wall Street Journal: Sources: Chinese companies move to cement their role in humanoid robot supply chains as Tesla and others turn to China for components the US sees as strategic — Tesla and others turn to suppliers in China for components in an industry seen as strategic by Washington and Beijing
TBD By Saumya Tyagi
TBD By Saumya Tyagi
VentureBeat 报道称，Arcee AI 发布了 399B 参数的 Trinity-Large-Thinking MoE 模型，并以 Apache 2.0 许可开放，支持完全自定义与商业使用。
Carl Franzen / VentureBeat: Arcee AI releases Trinity-Large-Thinking, a 399B-parameter MoE AI model under an Apache 2.0 license, allowing full customization and commercial use — The baton of open source AI models has been passed on between several companies over the years since ChatGPT debuted in late 2022 …
GitHub 推出持续运行的 AI 工作流，用 GitHub Actions、Copilot 和 Models APIs 统一管理无障碍反馈、分析 WCAG 合规性并自动完成分诊，同时保留人工复核。
GitHub has launched a continuous AI-powered workflow to manage accessibility feedback at scale. Using GitHub Actions, Copilot, and Models APIs, the system centralizes reports, analyzes WCAG compliance, and automates triage while maintaining human validation. Teams now resolve feedback faster, improving inclusion and cross-functional collaboration. By Leela Kumili
<div class = 'img-link'><a href = 'https://martinfowler.com/articles/harness-engineering.html'><img src = 'https://martinfowler.com/articles/harness-engineering/card.png' width = '350px'></img></a></div> <p>上个月，<b class = 'author'>Birgitta Böckeler</b> 写下了她对 Harness Engineering 这一新兴概念的初步思考。过去几周里，她持续研究并深化了这一主题，如今给出了一套更完整的心智模型，帮助人们更有效地驱动 coding agents。</p> <p><a class = 'more' href = 'https://martinfowler.com/articles/harness-engineering.html'>更多…</a></p>
<div class = 'img-link'><a href = 'https://martinfowler.com/articles/harness-engineering.html'><img src = 'https://martinfowler.com/articles/harness-engineering/card.png' width = '350px'></img></a></div> <p>Last month <b class = 'author'>Birgitta Böckeler</b> wrote some initial thoughts about the recently developed notion of Harness Engineering. She's been researching and thinking more about this in the weeks since and has now written a <a href = 'https://martinfowler.com/articles/harness-engineering.html'>thoughtful mental model</a> for understanding harness engineering that we think will help people to drive coding agents more effectively.</p> <p><a class = 'more' href = 'https://martinfowler.com/articles/harness-engineering.html'>more…</a></p>
零售商已经意识到，搜索与发现环节正在重构；接下来会发生什么仍未定型。从嵌入式结账到新兴第三方入口，文章梳理了电商与 AI 领导者如何把 agentic commerce 落进实际流程。
Retailers know search and discovery have already shifted. What comes next is less settled. From embedded checkout to emerging third-party surfaces, here’s how ecommerce and AI leaders are integrating agentic commerce.
一个版本只花了 5 分钟，另一个用了 8 小时。作者解释了为什么更慢的那种做法，反而是更值得的决定。
One took 5 minutes. The other took 8 hours. Here’s why the slow one was the better decision.Continue reading on Medium »
身份与同意是现代系统中最关键的两类信号，但它们往往不是以“报错”的方式失效，而是以设计错位的形式失真。
Two of the most critical signals in modern systems — identity and consent — often fail not as errors, but as design misalignments.Continue reading on Medium »
在快速变化的数字世界里，企业需要的不只是一个基础网站，而是更强大、可交互、可扩展的 Web 应用能力。
In today’s fast-moving digital world, businesses need more than just a basic website. They need powerful, interactive, and scalable…Continue reading on Medium »
一篇面向初学者的 CSS 布局与响应式设计入门文章，延续作者前一篇 CSS 基础内容，帮助读者继续搭建前端基本功。
Halo temen-temen, konnichiwa!! Gimana nih kabarnya? semoga masih semangat buat belajar ya! Setelah kemarin kita belajar tentang CSS Dasar…Continue reading on Amikom Computer Club »
作者以初学者视角解释 Flutter 中的 Clean Architecture，梳理展示层、领域层、数据层的职责，以及从用户交互到 API 再回到 UI 的完整流转。
When I first started learning Flutter, my code worked, but it was messy! Everything was mixed together, like UI, API calls, logic, and debugging felt like a nightmare. Then I discovered Clean Architecture. At first, it looked complicated, but once I understood the flow, everything changed. Today, I want to share a simple, beginner explanation. 🧠 What is Clean Architecture? Clean Architecture is a way to organize your code into layers so that your app becomes: ✅ Easy to understand ✅ Easy to maintain ✅ Scalable for large projects In Clean Architecture, there are three layers. 🔵 1. Presentation Layer (UI) This is what users see and interact with. Screens (SignIn, SignOut) State Management (Provider, Bloc, GetX, etc.) 👉 Responsibilities: Take user input Show data Update UI automatically 🟡 2. Domain Layer (Brain) This is the core logic of your app. 👉 Includes: UseCases(SignIn, GetProfile) Repository (abstract class) 👉 Responsibilities: Decide what should happen Define rules and actions 🔴 3. Data Layer (Worker) This layer does the actual work. 👉 Includes: RemoteDatasource (API calls) RepositoryImpl (Implementation of Repository) Models (JSON to Dart) 👉 Responsibilities: Call backend API Convert JSON to objects Handle data logic 🔄 Full Flow (Step-by-Step) Here’s how everything works together: User Action (Button Click) ↓ UI (Screen) ↓ Provider (State Manager) ↓ UseCase (What to do) ↓ Repository (Contract) ↓ RepositoryImpl (How to do) ↓ RemoteDatasource (API Call) ↓ Backend Server ↓ JSON Response ↓ Model (Convert to Object) ↓ Provider (Update State) ↓ UI Rebuild (notifyListeners in Provider) 🎯 Real-Life Example Think of it like a restaurant 👤 User → Customer 🧑💼 Provider → Manager 🧾 UseCase → Waiter 🍳 API → Kitchen 📦 Model → Food Flow: Customer → Manager → Waiter → Kitchen → Food → Customer 💡 Why Use Clean Architecture? Because your future self will thank you! 🧠 Key Lessons I Learned ✔ Separate UI from logic ✔ Keep business logic in UseCases ✔ Use Repository as a contract ✔ Convert API response using Models ✔ Always update UI via state management 🚀 Final Thought At first, Clean Architecture feels complex… But once you understand the flow, it becomes powerful and addictive If you're learning Flutter, don’t skip this. This is what takes you from beginner → professional developer. 💬 Let me know: Have you tried Clean Architecture in your Flutter projects? If you have any suggestions for me, feel free to suggest them for me to do better. Thank You!
框架不只是样板代码，它还固化了项目结构、逻辑边界与请求处理方式。但“框架有用”并不等于“任何场景都该先上框架”。文章强调先理解语言本身，再决定何时引入框架，尤其是在小项目、受限部署环境或问题模型不匹配时。
Frameworks are good for more than just boilerplate. They encode decisions: how to structure a project, where logic belongs, how to handle requests. A developer picking up Laravel or Spring for the first time isn't just getting free code — they're inheriting years of hard-won conventions. That's valuable. It means a junior and a senior on the same team are solving the same problem in the same -almost- shape. But "frameworks are useful" doesn't mean "always use a framework." Knowing when not to reach for one is as important as knowing how to use one. When you're still learning the language This is the one that gets skipped most often, and causes the most damage later. When the only mental model is Laravel does it this way, it's not really programming — it's copying at a higher level. Instead of copying Stack Overflow snippets, copying framework patterns. The abstraction is more sophisticated, but the understanding underneath is the same. When a bug appears outside the framework's happy path, or something it doesn't support cleanly is needed, there's nothing to fall back on. A concrete example: webhook signature verification. // ❌ What you might write if you only know framework routing $expected = 'sha256=' . hash_hmac('sha256', $rawBody, $secret); return $expected === $received; // Vulnerable to timing attacks // ✅ What learning the language teaches you $expected = 'sha256=' . hash_hmac('sha256', $rawBody, $secret); return hash_equals($expected, $received); hash_equals() instead of ===. This is a language-level security detail that prevents timing attacks. No framework teaches this — it's just PHP. Learning PHP only through a framework, a developer might write === and never know it was wrong. Learn the language first. Write raw SQL before using an ORM. Handle routing yourself before adding a router. Not forever — just long enough to see what the abstraction is actually doing for you. When the project is small enough to not need one A framework has a cost beyond file size or boot time. The mental overhead of fitting your problem into its model is real. For a 200-line script, a standalone endpoint, or a cron job that reads a file and sends an email — that cost doesn't pay off. A script that runs once a day and calls one external service doesn't need routing, DI containers, or a migration system. It needs to work. The same principle applies to pulling in packages. PointArt's self-updater downloads release zips from GitHub with no HTTP client library: // ❌ Reaching for a package by default $client = new GuzzleHttp\Client(); $zip = $client->get($zipUrl)->getBody(); // ✅ PHP already handles this natively $ctx = stream_context_create(['http' => ['timeout' => 30]]); $zip = file_get_contents($zipUrl, false, $ctx); Knowing the language means knowing when the standard library is enough — and not adding a dependency graph to solve a problem that was already solved. The question isn't could I use a framework here? It's does this problem have enough surface area that shared conventions help me manage it? When the framework's model doesn't fit your problem Frameworks are designed around specific problem shapes. A web framework expects HTTP request/response cycles. An MVC framework expects controllers, models, views. If your project doesn't fit that shape — a long-running daemon, a CLI tool, a data pipeline, a batch processor — you spend as much effort fighting the framework as building the thing. When I built PointArt, I had to make this call explicitly: no middleware system, no async, single-process, designed for shared hosting. Not oversights — deliberate limits because the target problem is the web request/response cycle on constrained hosting. The AI angle There's a new version of the "framework without language knowledge" problem: using AI to generate framework code without understanding either. With enough prompting you can build something that looks like a working application. Frameworks help AI here — it's seen a lot of Laravel and Rails, so it generates plausible-looking code. But when something goes wrong, and it will, you have no model of what correct looks like. You can't debug what you can't read. You can't maintain what you don't understand. AI is a strong tool for developers who already know what they're doing. It fills in boilerplate fast. But the understanding it skips is exactly what you'll need when the generated code misbehaves. The practical signals Use a framework when: Multiple developers need shared conventions across a long-lived codebase The problem shape fits the framework's model well Skip it when: You're still learning the language fundamentals The project is small enough that the model costs more than it saves The deploy target rules it out Your problem shape doesn't match the framework's assumptions The goal is not to avoid frameworks. It's to know what they're doing — so you can choose when to use them, and know what to do when they stop working. I've been writing about building PointArt — a zero-dependency PHP micro-framework — from scratch. If you're curious about what it looks like to make these decisions at the framework level, you can take a look at PointArt Devlog Series.
随着 LLM 产出越来越多代码，团队开始用“认知债务”和“意图债务”来理解系统健康；Shaw 与 Nave 的论文把 AI 纳入 Kahneman 双系统认知模型，提出 AI 作为 System 3 的视角；Ajey Gore 则进一步指出，如果 coding agent 让写代码更便宜，那么真正昂贵的事情会变成 verification。Fowler 也补充说，在遗留系统理解与抽象命名层面，LLM 依然大有可为。
<p>As we see LLMs churn out scads of code, folks have increasingly turned to Cognitive Debt as a metaphor for capturing how a team can lose understanding of what a system does. Margaret-Anne Storey thinks a good way of thinking about these problems is to consider <a href="https://arxiv.org/abs/2603.22106">three layers of system health</a>:</p> <blockquote> <ul> <li>Technical debt lives in code. It accumulates when implementation decisions compromise future changeability. It limits how systems can change.</li> <li>Cognitive debt lives in people. It accumulates when shared understanding of the system erodes faster than it is replenished. It limits how teams can reason about change.</li> <li>Intent debt lives in artifacts. It accumulates when the goals and constraints that should guide the system are poorly captured or maintained. It limits whether the system continues to reflect what we meant to build and it limits how humans and AI agents can continue to evolve the system effectively.</li> </ul> </blockquote> <p>While I’m getting a bit bemused by debt metaphor proliferation, this way of thinking does make a fair bit of sense. The article includes useful sections to diagnose and mitigate each kind of debt. The three interact with each other, and the article outlines some general activities teams should do to keep it all under control</p> <p> ❄ ❄</p> <p>In the article she references a recent paper by Shaw and Nave at the Wharton School that <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6097646">adds LLMs to Kahneman’s two-system model of thinking</a>.</p> <p>Kahneman’s book, “Thinking Fast and Slow”, is one of my favorite books. Its central idea is that humans have two systems of cognition. System 1 (intuition) makes rapid decisions, often barely-consciously. System 2 (deliberation) is when we apply deliberate thinking to a problem. He observed that to save energy we default to intuition, and that sometimes gets us into trouble when we overlook things that we would have spotted had we applied deliberation to the problem.</p> <p>Shaw and Nave consider AI as System 3</p> <blockquote> <p>A consequence of System 3 is the introduction of cognitive surrender, characterized by uncritical reliance on externally generated artificial reasoning, bypassing System 2. Crucially, we distinguish cognitive surrender, marked by passive trust and uncritical evaluation of external information, from cognitive offloading, which involves strategic delegation of cognition during deliberation.</p> </blockquote> <p>It’s a long paper, that does into detail on this “Tri-System theory of cognition” and reports on several experiments they’ve done to test how well this theory can predict behavior (at least within a lab).</p> <p> ❄ ❄ ❄ ❄ ❄</p> <p>I’ve seen a few illustrations recently that use the symbols “< >” as part of an icon to illustrate code. That strikes me as rather odd, I can’t think of any programming language that uses “< >” to surround program elements. Why that and not, say, “{ }”?</p> <p>Obviously the reason is that they are thinking of HTML (or maybe XML), which is even more obvious when they use “</>” in their icons. But programmers don’t <em>program</em> in HTML.</p> <p> ❄ ❄ ❄ ❄ ❄</p> <p>Ajey Gore thinks about <a href="https://ajeygore.in/content/the-expensive-thing">if coding agents make coding free, what becomes the expensive thing</a>? His answer is verification.</p> <blockquote> <p>What does “correct” mean for an ETA algorithm in Jakarta traffic versus Ho Chi Minh City? What does a “successful” driver allocation look like when you’re balancing earnings fairness, customer wait time, and fleet utilisation simultaneously? When hundreds of engineers are shipping into ~900 microservices around the clock, “correct” isn’t one definition — it’s thousands of definitions, all shifting, all context-dependent. These aren’t edge cases. They’re the entire job.</p> <p>And they’re precisely the kind of judgment that agents cannot perform for you.</p> </blockquote> <p>Increasingly I’m seeing a view that agents do really well when they have good, preferably automated, verification for their work. This encourages such things as <a href="https://martinfowler.com/bliki/TestDrivenDevelopment.html">Test Driven Development</a>. That’s still a lot of verification to do, which suggests we should see more effort to find ways to make it easier for humans to comprehend larger ranges of tests.</p> <p>While I agree with most of what Ajey writes here, I do have a quibble with his view of legacy migration. He thinks it’s a delusion that “agentic coding will finally crack legacy modernisation”. I agree with him that agentic <em>coding</em> is overrated in a legacy context, but I have seen compelling evidence that LLMs help a great deal in <a href="https://martinfowler.com/articles/legacy-modernization-gen-ai.html">understanding what legacy code is doing</a>.</p> <p>The big consequence of Ajey’s assessment is that we’ll need to reorganize around verification rather than writing code:</p> <blockquote> <p>If agents handle execution, the human job becomes designing verification systems, defining quality, and handling the ambiguous cases agents can’t resolve. Your org chart should reflect this. Practically, this means your Monday morning standup changes. Instead of “what did we ship?” the question becomes “what did we validate?” Instead of tracking output, you’re tracking whether the output was right. The team that used to have ten engineers building features now has three engineers and seven people defining acceptance criteria, designing test harnesses, and monitoring outcomes. That’s the reorganisation. It’s uncomfortable because it demotes the act of building and promotes the act of judging. Most engineering cultures resist this. The ones that don’t will win.</p> </blockquote> <p> ❄ ❄ ❄ ❄ ❄</p> <p>One the questions comes up when we think of LLMs-as-programmers is whether there is a future for source code. David Cassel on The New Stack has an article summarizing <a href="https://thenewstack.io/ai-programming-languages-future/">several views of the future of code</a>. Some folks are experimenting with entirely new languages built with the LLM in mind, others think that existing languages, especially strictly typed languages like TypeScript and Rust will be the best fit for LLMs. It’s an overview article, one that has lots of quotations, but not much analysis in itself - but it’s worth a read as a good overview of the discussion.</p> <p>I’m interested to see how all this will play out. I do think there’s still a role for humans to work with LLMs to build useful abstractions in which to talk about what the code does - essentially the DDD notion of <a href="https://martinfowler.com/bliki/UbiquitousLanguage.html">Ubiquitous Language</a>. Last year Unmesh and I talked about <a href="https://martinfowler.com/articles/convo-llm-abstractions.html">growing a language</a> with LLMs. As Unmesh put it</p> <blockquote> <p>Programming isn’t just typing coding syntax that computers can understand and execute; it’s shaping a solution. We slice the problem into focused pieces, bind related data and behaviour together, and—crucially—choose names that expose intent. Good names cut through complexity and turn code into a schematic everyone can follow. The most creative act is this continual weaving of names that reveal the structure of the solution that maps clearly to the problem we are trying to solve.</p> </blockquote>
Cloudflare 表示，自 1.1.1.1 上线八年以来，他们持续把更快、更私密的互联网访问作为目标；最新独立审查结果显示，其隐私保护措施仍按承诺运行。
Eight years ago, we launched 1.1.1.1 to build a faster, more private Internet. Today, we’re sharing the results of our latest independent examination. The result: our privacy protections are working exactly as promised.
帖子汇总了多个 Gemma 4 GGUF 模型链接，方便社区用户直接在 Hugging Face 等渠道体验与部署。
https://huggingface.co/unsloth/gemma-4-26B-A4B-it-GGUF https://huggingface.co/unsloth/gemma-4-31B-it-GGUF [https://huggingface.…
Gemma 4 and Qwen3.5 on shared benchmarks
帖子展示了作者把 Optuna 与 autoresearch 进行对比实验的结果，并讨论新方法是否真的比经典超参调优更有优势。
[](https://preview.redd.it/is-autoresearch-really-better-than-classic-hyperparameter-v0-zgty2uy3ausg1.png?width=1118&format=png&auto=webp&s=aa1ca48a2422a0f2f69ed00a6cdfeefa87f4037d) We did experiments comparing Optuna & aut…
发帖者在多年产品开发后，希望重新投入独立研究，想了解在当下 ML 研究生态里，哪些方向更值得做、自己又能在哪些位置贡献更多。
After years of focus on building products, I'm carving out time to do independent research again and trying to find the right direction. I have stayed reasonably up-to-date regarding major developments of the past years (reading books, pap…
Google releases Gemma 4 models.
帖子补充了 Gemma 4 在 Hugging Face 与 Ollama 上的可用链接，并强调其开放权重与 Apache 2.0 许可带来的商用友好性。
The model is also available here: * 🤗 HuggingFace: https://huggingface.co/collections/google/gemma-4 * 🦙 Ollama: https://ollama.com/library/gemma4
Tried to buy a pint, Finding a Trojan: My First Malware Analysis
When the Category Leader Stalls: Postman and the Future of API Tooling
这个名为 WeThePeople 的 FastAPI 应用聚合了 40 多个政府公开 API，用于跟踪企业游说、政府合同、国会议员股票交易、执法行动和竞选捐款等数据。
**What My Project Does:** WeThePeople is a FastAPI application that pulls data from 40+ public government APIs to track corporate lobbying, government contracts, congressional stock trades, enforcement actions, and campaign donations acros…
Janus 是一个 Python 库，配合 Rust 后端，可以让开发者像分支、合并、时间旅行那样追踪复杂 Python 对象的状态变化。
Hello all. I've been working on a project and I wanted to share and know what your thoughts are on it. **TD;DR:** Janus is a python library that lets you branch, merge, and "time-travel" through the state of complex Python objects in a man…
先把 Codex 启动得更强，再在工作变复杂时让 OMX 接管更好的提示词、工作流和运行时辅助。
Start Codex stronger, then let OMX add better prompts, workflows, and runtime help when the work grows.
**Onyx** 是 LLM 的应用层，提供功能丰富、且任何人都能轻松自托管的界面。
**Onyx** is the application layer for LLMs - bringing a feature-rich interface that can be easily hosted by anyone.
TimesFM（Time Series Foundation Model）是 Google Research 开发的预训练时间序列基础模型，用于时间序列预测。
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
OpenScreen 是一个免费、开源的 Screen Studio 替代品。
OpenScreen is your free, open-source alternative to Screen Studio (sort of).
面向 AI agents（MCP）和 Neovim 用户的高速文件搜索工具，并内置记忆能力。
AI agents (MCP) | Neovim users A fast file search for your AI and neovim, with memory built-in
全球最大的开源提示词库之一，支持 ChatGPT、Claude、Gemini、Llama、Mistral 等多种 AI。
The world's largest open-source prompt library for AI Works with ChatGPT, Claude, Gemini, Llama, Mistral, and more formerly known as Awesome ChatGPT Prompts 🌐 Browse Prompts • 📖 Read the Book • 📄 View on GitHub • 🚀 Self-Host
可跨 400 多个社交网络根据用户名追踪账号，并提供 Debian、Ubuntu、Homebrew、Kali、BlackArch 等社区维护包。
Hunt down social media accounts by username across 400+ social networks Community-maintained packages are available for Debian (>= 13), Ubuntu (>= 22.10), Homebrew, Kali, and BlackArch.