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AI 资讯

AgentTrust ID is live

This weekend, AgentTrust ID went live in production. As of today, all five SDKs are published: pip install agenttrustid npm install @agenttrustid/sdkgo get github.com/agenttrustid/sdk/go cargo add agenttrustid # Maven / Gradle # id.agenttrust:agenttrustid:0.3.0 The SDKs are open source under Apache 2.0 at github.com/agenttrustid/sdk . The hosted platform is running at app.agenttrust.id in a controlled beta. Why I built this AI agents broke the assumptions that machine-to-machine security was built on. An API key answers one question: who is calling. It asks it once, at the door. An agent decides its next action at runtime, from context nobody wrote by hand. The same agent that summarized a document a second ago might now try to email it, delete it, or chain a task to another agent. A credential that only proves identity has no opinion about any of that. Agents need a decision at the action boundary : should this specific action happen, right now, on whose behalf . Answered at runtime, every time, with an audit trail and a kill switch. What's running Everything below is live in production today, not a roadmap: Per-action authorization. Every consequential action passes a pre-flight check. The Guardian pipeline routes each action by risk: deterministic rule checks for the common path, a policy engine for mutations, and AI-backed review for destructive operations. Fail-closed where it counts. Opaque, instantly revocable tokens. Credentials are at_ references with no standing authority of their own . The server decides on every use, so revocation is one call, effective immediately. Scoped delegation. When one agent hands work to another, the grant narrows instead of copying : subset scopes, independent TTLs, independently revocable, bounded chain depth. Read-only sessions with time-boxed elevation. Sessions start safe and rise only on approval, for a bounded window, then revert on their own. One model across surfaces. MCP tools, agent-to-agent calls, and direct API inte

2026-06-08 原文 →
AI 资讯

I shipped a support desk by deleting a dependency

I added a support desk to LaraFoundry this week. The first commit in the slice removed a package instead of adding one. LaraFoundry is a reusable SaaS core for Laravel that I'm extracting in public from an older app of mine. Auth, multi-tenancy, roles, activity log, notifications, billing seam, and now support tickets. The rule for every module is the same: lift the proven idea out of the old code, modernise it, harden it, and make it something you can composer require into a fresh Laravel app without inheriting a pile of assumptions. Tickets is where that rule got interesting, because the old code didn't own its ticket model. It leaned on a third-party ticket library. Why a ticket package is wrong for a reusable core A third-party ticket package is a perfectly reasonable choice when you're building one app. You get tables, a model, a status enum and a UI scaffold for free. It's the wrong choice for a core that other apps install. Pull it into the core and every host app inherits that package's migrations, its table names, its status vocabulary and its idea of what a ticket is. The dependency becomes load-bearing in projects that never asked for it, and the day it lags a Laravel release, every downstream app waits. So I cut it (decision D-4.2-1 in my notes) and wrote the model by hand. The model is about 180 lines. There is no magic. Two tables, a uuid, a status, a couple of scopes. The diff against "depend on the package" was less code in the core, not more, because I only kept the behaviour I actually use. Here's the top of the model, with the extraction notes I leave for future me: /** * A support ticket: the channel between a host user and the platform operator. * * Extracted from the donor App\Models\Ticket, which sat on a third-party * ticket package. That dependency is cut: this is a self-contained model. * Categories and labels are JSON slug arrays driven by config, not pivot * tables. The dead assigned_to column and the donor's invalid-operator * query are

2026-06-08 原文 →
开源项目

🔥 danny-avila / LibreChat - Enhanced ChatGPT Clone: Features Agents, MCP, Skills, DeepSe

GitHub热门项目 | Enhanced ChatGPT Clone: Features Agents, MCP, Skills, DeepSeek, Anthropic, AWS, OpenAI, Responses API, Azure, Groq, o1, GPT-5, Mistral, OpenRouter, Vertex AI, Gemini, Artifacts, AI model switching, message search, Code Interpreter, langchain, DALL-E-3, OpenAPI Actions, Functions, Secure Multi-User Auth, Presets, open-source for self-hosting. Active | Stars: 38,631 | 139 stars today | 语言: TypeScript

2026-06-08 原文 →
开源项目

🔥 hmjz100 / LinkSwift - 一个基于 JavaScript 的网盘文件下载地址获取工具。基于【网盘直链下载助手】修改 ，支持 百度网盘 / 阿里云盘

GitHub热门项目 | 一个基于 JavaScript 的网盘文件下载地址获取工具。基于【网盘直链下载助手】修改 ，支持 百度网盘 / 阿里云盘 / 中国移动云盘 / 天翼云盘 / 迅雷云盘 / 夸克网盘 / UC网盘 / 123云盘 八大网盘 | Stars: 15,746 | 84 stars today | 语言: JavaScript

2026-06-08 原文 →
AI 资讯

The NetSapiens inter-domain calling quirk that keeps showing up

Quick share. Been running into this enough times across NetSapiens deployments that it's worth flagging. Scenario: Call gets placed or transferred between two domains on the same NetSapiens platform. The call connects fine. Audio works. But the caller ID showing up on the receiving side is wrong. It's showing the local extension or domain user instead of the actual originating caller. If you check the NetSapiens Known Issues page, this maps to NMS-2518. It shows up specifically when calls cross domain boundaries during hold or transfer. The fallout is mostly cosmetic but it matters for anyone running multi-tenant reseller deployments. Your customer sees the wrong name in their call history. Voicemail attribution gets weird. Inter-domain analytics show calls from the wrong source. A few patterns I've seen work around it: Configure SIP header rewriting at the SBC layer to preserve the original From URI across domain hops For softphones that pull call history from NetSapiens API directly, verify which field the client is actually displaying. Some pull from orig_user , others from local_user . The discrepancy shows up clearer than you'd expect. If you're running a mobile softphone client, check whether the client is caching contact info locally and overriding what NetSapiens returns. A lot of "wrong caller ID" reports turn out to be client-side caching bugs, not platform bugs. The deeper fix is platform-side and depends on what NetSapiens version you're running. The cleaner softphone integrations route call history through the NetSapiens API rather than building it locally on the device, which sidesteps the worst of this behavior. White-label softphones that integrate natively with NetSapiens handle this more consistently than generic SIP clients. Tragofone is one example where the call history syncs through the NetSapiens softphone integration layer directly, so inter-domain caller ID is consistent with what the platform actually has. Other native integrations handle t

2026-06-08 原文 →
AI 资讯

I Built a Tool That Finds Package Equivalents Across Programming Languages

TL;DR: I built PackagePal — paste in any package from any language, pick your target language, and AI instantly finds the equivalent. No more Googling "what's the Node.js version of Python's requests ?" The Problem That Drove Me Crazy You know that moment when you're migrating a project — or just jumping between ecosystems — and you hit a wall trying to find the right package? I do. Every time. # You're used to this in Python import requests response = requests . get ( " https://api.example.com/data " ) And you move to Node.js and think: "Okay, what do I use here? axios? node-fetch? got? undici?" So you Google it. You find a Stack Overflow thread from 2019. Half the answers recommend packages that are now deprecated. You open 6 tabs. 20 minutes later you're still not sure which one is the current best choice. This wasn't a once-in-a-while thing for me. It happened constantly — switching between Python, JavaScript, Go, and Ruby on different projects. I was wasting real hours on a problem that felt completely solvable. So I built PackagePal . What PackagePal Does PackagePal uses AI to understand what a package actually does — its purpose, not just its name — and finds the best equivalent in whatever language you're moving to. The key insight: this isn't a lookup table. A simple mapping of requests → axios misses context. What if you're using requests for its session management? Or its retry logic? PackagePal surfaces options and explains why each one is a good match. Example searches people use it for: Python's pandas → JavaScript Ruby's devise → Node.js Go's cobra → Python JavaScript's lodash → Go Just type the package, pick the target language, and get results in seconds. 👉 Try it: packagepal.dev How I Built It Tech Stack 🤖 AI: Gemini Pro — handles the semantic understanding of what a package does and why an alternative matches ⚛️ Frontend: React + TypeScript ⚙️ Backend: Node.js + TypeScript on Google Cloud ⚡ Caching: Redis — so repeat searches (e.g., "requests → No

2026-06-08 原文 →
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