Fata.plus @FataPlus

前端终于有高级感的加载动画了！ 告别普通 spinner！ 分享一个超赞的 GitHub 开源项目 —— math-curve-loaders 专为追求极致美观与轻量前端项目而生，它把数学曲线变成极致优雅的加载动画！ 玫瑰曲线、李萨茹曲线、心形线…… 纯 HTML + CSS + JS 实现，零依赖、超轻量。 每个动画都附带数学公式 + 可实时调整参数 + 一键复制代码，直接抄走就能用！ 数学之美 × 前端动效，视觉党与代码党都爱了 ❤️ 地址：paidax01.github.io/math-curve-loa…

Introducing the Open Knowledge Format (OKF), an open specification that formalizes the LLM-wiki pattern into a portable, interoperable format. AI is only as smart as the context we give it. As we build more advanced, agentic AI systems, they need accurate metadata and context to be useful. But in most organizations, that context is locked inside fragmented data catalogs, isolated wikis, scattered code comments, or the minds of senior engineers. Every time a new AI agent is built, teams are forced to solve the exact same context-assembly problem from scratch. To solve this, we've announced OKF, a vendor-neutral, open specification that formalizes the "LLM-wiki pattern" into a portable, interoperable format. It provides a standardized way to represent the enterprise knowledge that modern AI systems rely on. — Just markdown: readable in any editor, renderable on GitHub, indexable by any search tool — Just files: shippable as a tarball, hostable in any git repo, mountable on any filesystem — Just YAML frontmatter: for the small set of structured fields that need to be queryable: type, title, description, resource, tags, and timestamp We’ve also shipped reference implementations to help you hit the ground running, including an enrichment agent for BigQuery, a static HTML visualizer, and live sample bundles on @github → goo.gle/4uGvAEe ➕ Knowledge Catalog can now natively ingest OKF! Stop reinventing data models and building bespoke integrations for every new AI tool. Here's more about how OKF works → goo.gle/4uGvBbg

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GLM-5.2 hasn't officially launched yet (we'll have a formal launch, as usual).

Z.ai @Zai_org

6 days ago

Intelligence should be open, accessible, and ready to build with, empowering every developer, everywhere. GLM-5.2 is now available to all GLM Coding Plan users, including Lite, Pro, Max, and Team plans. docs.z.ai/devpack/latest… As our new flagship model, GLM-5.2 delivers

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GLM-5.2. Open-source. 1M context. When one door closes, another opens. – GLM Team Coding Plan: live now – API: this week

Z.ai @Zai_org

6 days ago

Intelligence should be open, accessible, and ready to build with, empowering every developer, everywhere. GLM-5.2 is now available to all GLM Coding Plan users, including Lite, Pro, Max, and Team plans. docs.z.ai/devpack/latest… As our new flagship model, GLM-5.2 delivers

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GLM-5.2 is Fully Open, Frontier Intelligence Belongs to Everyone Today, the sudden restriction of certain frontier models is deeply regrettable. At a time when access to frontier models is abruptly cut off for non-technical reasons, we are even more convinced of one thing: science should be global. The path to AGI (Artificial General Intelligence) must never be enclosed by high walls. We have always believed that AGI should be the cornerstone for all of humanity to collaboratively explore the boundaries of intelligence and solve complex challenges, rather than a privilege monopolized by a few rules and subject to revocation at any moment. In the face of external blockades and restrictions, our attitude is one of radical openness. Frontier intelligence must remain open-source, accessible, and buildable, serving every dedicated developer. GLM-5.2 is Zhipu's most capable open-source model to date. It not only supports a truly usable 1M context window but also maintains a continuous lead in the independent completion of long-horizon tasks, providing solid foundational support for building complex agent applications. It also continues to be our main engine for creating the strongest domestic coding model. Tonight at 5:21—at this special moment—GLM-5.2 will officially be available to all GLM Coding Plan users (including Lite / Pro / Max). The API will also go live next week. A step closer to frontier intelligence for everyone. The future of AI is open, and it is for the people. ModelKey: GLM-5.2

GLM-5.2 is now fully available for GLM Coding Plan users. ZCode 3.0 is deeply optimized for GLM-5.2, bringing stronger Agent task execution, better long-context coding, and the new Goal feature for managing larger development objectives from planning to completion. Coding Plan subscribers get 150% usage quota inside ZCode. New users get 5 days free with 5M tokens per day. Download: zcode.z.ai

🚀 智谱的ZCode 3.0.0 新版来了，又是一个Codex的分X 每天有GLM5.2的300万免费token 全面切换自研 ZCode Agent 内核。 针对满血 GLM 深度优化长程推理、工具调用和大型工程执行链路，整体任务完成效果已显著优于第三方 Agent 新用户预置 GLM Start Plan 套餐和专属 150% 配额权益。 新用户即享连续 5 天 GLM 旗舰系列模型用量；升级或已订阅 GLM Coding Plan 用户应用内相比 API 调用专享 150% 配额 全新分组式任务工作区 ，支持拖拽折叠、跨区迁移、批量管理，高效管控多 Agent 并发任务，任务层级清晰可控 具体内容见 zcode.z.ai/cn

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GLM5.2免费用，智谱发布了新版 ZCode 3.0 ，每天有GLM5.2的300万免费token。希望大家试用之后，能在其他方面也能挖掘出好的用法不只局限于编程。 界面其实很像Codex，终于智谱借助强大的模型能力的东方也开始推自家的agent产品了。Mac、Windows都支持 zcode.z.ai/cn

Skills in Gemini Enterprise let you formalize a specific workflow—like applying brand guidelines or formatting a report—and save it as a reusable action for your whole team. Learn more about codifying your unique expertise into reusable Skills → goo.gle/4d5sUKL

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How to become so creative it feels illegal article summary in 4k dark mode

DAN KOE @thedankoe

3 months ago

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Claude Code fully dissected! Researchers from UCL reverse-engineered the leaked Claude source. What they found changes how you should think about agent design. Only 1.6% of the codebase is AI decision logic. The other 98.4% is operational infrastructure. Permission gates, tool routing, context compaction, recovery logic, session persistence. The model reasons. The harness does everything else. This is the opposite of what most agent frameworks do today. LangGraph routes model outputs through explicit state machines. Devin bolts heavy planners onto operational scaffolding. Claude Code gives the model maximum decision latitude inside a rich deterministic harness, and invests all its engineering effort in that harness. The core loop is a simple while-true. Call model, run tools, repeat. But the systems around that loop are where the real design lives: A permission system with 7 modes and an ML classifier. Users approve 93% of prompts anyway, so the architecture compensates with automated layers instead of adding more warnings. A 5-layer context compaction pipeline. Each layer runs only when cheaper ones fail. Budget reduction, snip, microcompact, context collapse, auto-compact. Four extension mechanisms ordered by context cost. Hooks (zero), skills (low), plugins (medium), MCP (high). Each answers a different integration problem. Subagents return only summary text to the parent. Their full transcripts live in sidechain files. Agent teams still cost roughly 7x the tokens of a standard session. Resume does not restore session-scoped permissions. Trust is re-established every session. That friction is the point. The bet behind all of this is simple. As frontier models converge on raw coding ability, the quality of the harness becomes the differentiator, not the model. Paper: Dive into Claude Code (arXiv:2604.14228) We've shared an article on Agent Harness and what every big company is building. Read it below.

Akshay 🚀 @akshay_pachaar

2 months ago

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This is really big news. Google introduced the Open Knowledge Format (OKF) - a standardized way to store information in a directory of markdown files. Makes it really easy to make a digital brain that agents can use. These files can serve as a living wiki. You can give agents the ability to query them or edit them. They can interlink. Seems to me this could replace Notion or Obsidian. I can think of so many uses for this. Google's blog post: cloud.google.com/blog/products/… An easier to understand explanation is the SPEC.md file: github.com/GoogleCloudPla… I gave those two links to Antigravity and asked how we could use it for any of the projects we're working on. It came up with so many ideas. I would imagine Claude Fable 5 would whip up some pretty amazing things based on this system. Currently creating an OKF library of our pepper garden. It's going to be a fun weekend.

Building a mobile app means wrestling with Xcode, Android Studio, certificates, builds, and more @expo removes most of that pain • Build iOS + Android apps with React Native • 100+ ready-made native APIs • Cloud builds and app store submissions • Push updates without waiting for app reviews • One workflow from development to deployment

Ever seen text change this satisfying? 👀 Just use Slot-text textmotion.dev

How to build your first AI agent (Full guide)

Rahul @sairahul1

a week ago

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Karpathy said something you'll regret ignoring: "Remove yourself as the bottleneck. Maximize your leverage. Put in very few tokens, and a huge amount of stuff happens on your behalf." Loop engineering is the exact thing that does that. In a hand-run session, the operator handles two things: - deciding what the agent runs next - and checking its output before the next step Both are manual, and both decide how far the agent gets on its own without the operator. Loop engineering moves both steps into the system. A core operating structure surrounds the loop, and the diagram below depicts it. - A schedule decides what to run - Loop is the maker that produces the work - A separate checker agent grades the output - A file on disk holds the state they both read. The loop runs until either done, max iterations, or an exhausted budget. Here are some practical engineering considerations: 1) A model grading its own output justifies what it already did instead of catching where it failed. That's why a separate checker's findings return to the maker as the next instruction. And the cycle repeats until the checker finds nothing left to fix. 2) A loop with no stop condition burns tokens, and the cost climbs fast once sub-agents and long runs add up. That's why the exit must be set before the loop runs, not while it is running. A simple exit could be: ↳ fix only the major issues, run one final pass, and stop after two loops, with "all tests pass and lint clean" as the rule that ends it. 3) State has to live on disk, not in context. The model forgets everything between runs, so an MD file or a knowledge graph holds what is done and what is still open. Each run reads it and writes back to it, which lets a loop pick up again after days. 4) The lower the verification bar, the safer the loop. Boring, repetitive checks like a stale version string or a missing test are trivial to verify, so a loop runs them with little risk while the operator is away. Judgment-heavy work is loopable too, but only as far as the checker can confirm the result. Let's look at how an unattended loop fails in two ways. 1) It reports done when nothing is actually verified. The separate checker exists to prevent it, but it merges code faster than anyone reads it, so over weeks, the team stops understanding its own codebase while every check stays green. Green tests say the code passed the tests, not that anyone knows what shipped. Someone still has to read what the loop merges. 2) The checker keeps a running loop honest, but it only catches failures inside a run. The harness around the loop, like the prompts, tools, and checks wrapped around the model, still drifts and breaks in production as models change. That repair loop is usually run by hand based on observability traces. My co-founder wrote a detailed walkthrough (with code) on making that harness repair itself, where a failing trace gets diagnosed, the fix is verified against the exact input that failed, and the failure is locked as a regression test so it cannot recur. Read it below.

Akshay 🚀 @akshay_pachaar

a week ago

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