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GitHub热门项目 | 本项目为xiaozhi-esp32提供后端服务，帮助您快速搭建ESP32设备控制服务器。Backend service for xiaozhi-esp32, helps you quickly build an ESP32 device control server. | Stars: 9,656 | 8 stars today | 语言: JavaScript

GitHub热门项目 | 🌐 The Internet Computer! Free, Open-Source, and Self-Hostable. | Stars: 41,904 | 138 stars today | 语言: JavaScript

GitHub热门项目 | 24 Lessons, 12 Weeks, Get Started as a Web Developer | Stars: 95,811 | 13 stars today | 语言: JavaScript

GitHub热门项目 | Simple HTML5 Charts using the tag | Stars: 67,458 | 7 stars today | 语言: JavaScript

GitHub热门项目 | The design language that makes your AI harness better at design. | Stars: 30,939 | 287 stars today | 语言: JavaScript

GitHub热门项目 | 🚀🤖 Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper. Don't be shy, join here: https://discord.gg/jP8KfhDhyN | Stars: 66,704 | 210 stars today | 语言: Python

GitHub热门项目 | MOSS‑TTS Family is an open‑source speech and sound generation model family from MOSI.AI and the OpenMOSS team. It is designed for high‑fidelity, high‑expressiveness, and complex real‑world scenarios, covering stable long‑form speech, multi‑speaker dialogue, voice/character design, environmental sound effects, and real‑time streaming TTS. | Stars: 2,051 | 53 stars today | 语言: Python

GitHub热门项目 | Public repository for Agent Skills | Stars: 142,410 | 686 stars today | 语言: Python

GitHub热门项目 | Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflows - all through natural language commands. | Stars: 127,228 | 323 stars today | 语言: Python

GitHub热门项目 | An agentic skills framework & software development methodology that works. | Stars: 210,568 | 1,726 stars today | 语言: Shell

GitHub热门项目 | A meta-skill that designs domain-specific agent teams, defines specialized agents, and generates the skills they use. | Stars: 3,710 | 68 stars today | 语言: HTML

GitHub热门项目 | DigitalPlat FreeDomain: Free Domain For Everyone | Stars: 170,105 | 1,769 stars today | 语言: HTML

GitHub热门项目 | An advanced guide to learn English which might benefit you a lot 🎉 . 离谱的英语学习指南/英语学习教程/英语学习/学英语 | Stars: 47,854 | 2,015 stars today | 语言:

GitHub热门项目 | Python tool for converting files and office documents to Markdown. | Stars: 126,876 | 1,263 stars today | 语言: Python

They started at $1,000. Then $700. Then $600 budget machines. Now, Qualcomm says the price of its Arm-based Windows laptops will hit $300 this year. Even though RAMageddon has yet to subside and PC prices keep climbing, the company says it's built a new budget laptop platform called Snapdragon C - "C" as in "Compute" […]

Intel is barely in the handheld gaming PC space - but that might be about to change. After the embarrassment that was the first MSI Claw and the excellent MSI Claw 8 AI Plus that followed it, Intel announced it would create custom handheld gaming chips. Today, it's formally announcing them as the Arc G3 […]

Xiaomi has announced the 17T and 17T Pro, two cheaper spins on its 17 series flagships. As is usual for the T-series, the focus is more on performance than photography, with the biggest batteries Xiaomi has included on any of its phones outside China yet. The 17T includes a 6,500mAh silicon-carbon battery, while the 17T […]

General Compute is betting SambaNova will be the next breakout chipmaker.

If you're building an AI agent that touches dates — booking flows, scheduling bots, "remind me on Friday" assistants — you've probably noticed: LLMs are terrible at dates. They hallucinate weekday-to-date mappings. They fencepost-error ranges. They forget what "next Friday" means in Ukrainian vs English. Asking the model to "be careful" doesn't fix it — what fixes it is moving date interpretation out of the model and into a deterministic tool. That's what whenis is. Use it as an agent tool Define a resolveDate(expression, reference) tool that calls whenis . Let the model invoke it instead of guessing. import { createParser } from ' @whenis/core ' ; import { uk } from ' @whenis/locale-uk ' ; import { booking } from ' @whenis/booking ' ; const parser = createParser ({ locales : [ uk ], plugins : [ booking ], options : { preferFuture : true }, }); const ref = new Date ( ' 2026-05-28 ' ); parser . parse ( " наступної п'ятниці " , { reference : ref }); // → { type: 'date', date: '2026-06-05', confidence: 1 } parser . parse ( ' з 5 по 10 червня ' , { reference : ref }); // → { type: 'range', start: '2026-06-05', end: '2026-06-11', nights: 6 } parser . parse ( ' після свят ' , { reference : ref }); // → { type: 'fuzzy', reason: 'holiday_ref', // metadata: { suggest_next_month: true } } English works the same way: import { en } from ' @whenis/locale-en ' ; const parser = createParser ({ locales : [ en ], options : { preferFuture : true } }); parser . parse ( ' next Friday ' , { reference : new Date ( ' 2026-05-28 ' ) }); // → { type: 'date', date: '2026-06-05', confidence: 1 } How it differs from chrono-node Multi-candidate output. A bare "Friday" mid-week emits both this Friday and next Friday with confidence scores. Your agent re-ranks using conversation context — no silent guessing inside the library. Locale as data. Adding RU/PL/CS is one source file with no engine changes. The Ukrainian locale ships full inflection: months × 7 cases, weekdays × 4 cases, pointers, conne

Platform team sprint planning in 2026 begins with budget allocation, not architecture review. The first question is no longer "what do we need to build?" — it's "what can we afford to run?" This is not FinOps adoption. This is authority displacement. The platform team became a finance team because the control plane for infrastructure decisions migrated from architecture governance to budget governance. Cost constraints don't inform architectural decisions anymore — they dictate them. And when financial systems gain veto authority over technical systems, resilience becomes the variable that adjusts. Platform team cost governance is now the primary control surface. Architecture is secondary. How We Got Here The timeline is sharper than most organizations admit. 2018–2022 was the cloud adoption phase. Platform teams built for scale. Multi-region resilience was standard. Observability was deep. Auto-scaling was elastic. Architectural requirements shaped cost models. The budget followed the design. 2023–2024 brought FinOps as a cost visibility layer. Teams could finally see where money was going. Dashboards got built. Anomaly detection got configured. Attribution models got refined. But visibility was still separate from authority. The FinOps team reported. The platform team decided. 2025–2026 is when cost governance moved from reporting to gating. The turning point: platform teams stopped asking "can we build this?" and started asking "can we afford this?" Engineering roadmaps became cost roadmaps. Feature requests now come with budget allocation approvals. Architecture reviews now include CFO sign-off gates. This shift introduced Budget-Normalized Architecture — systems designed around predictable monthly spend targets instead of operational resilience targets. The architecture no longer optimizes for failure domains, latency requirements, or recovery objectives. It optimizes for staying under the cost ceiling. Cost governance expanded because engineering governance fa