标签：#pens

GitHub热门项目 | Skills for Design Engineers | Stars: 2,102 | 258 stars today | 语言: TypeScript

In multi-turn agent loops, the full context re-sends on every API call. A tool result added at turn 3 gets billed again at turns 4, 5, 6, 7... forever. Most of it is never read again. Standard observability tools tell you the total token count. They never tell you what's in there or how much of it is waste . That's what ContextLens fixes. What it does ContextLens is a diagnostic profiler for LLM agent context windows. It: Decomposes the context window into regions: system prompt, tool schemas, tool results, retrieved chunks, user messages, assistant messages Tracks which blocks get re-billed across turns using SHA-256 content hashing Runs 5 waste detectors and ranks findings by dollar cost Prints a concrete one-line fix for each finding Renders an interactive D3 treemap report as a self-contained HTML file No API key required. Works offline on saved traces. The five detectors Detector What it finds Duplicate Same block re-sent verbatim across multiple turns Near-Duplicate >85% Jaccard similarity between distinct blocks Stale Tool Result Tool output never referenced by a later assistant message Unused Tool Schema Tool defined every turn but never called Redundant Retrieval Retrieved chunk with <15% overlap with model output ---Run the built-in demo (simulates a 30-turn agent loop, no API key needed): python -c "import contextlens; contextlens.demo()" python examples/demo.py Live capture — Anthropic import anthropic import contextlens as cl client = anthropic.Anthropic() with cl.capture_anthropic(client, model="claude-3-5-sonnet-20241022") as collector: for turn in range(20): client.messages.create( model="claude-3-5-sonnet-20241022", max_tokens=1024, system="You are a helpful assistant.", messages=build_messages(turn), ) report = cl.analyze_trace(collector.build_trace()) print(f"Recoverable waste: {report.recoverable_tokens:,} tokens (${report.recoverable_cost_usd:.4f})") Live capture — OpenAI import openai import contextlens as cl client = openai.OpenAI() with cl.ca

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

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

GitHub热门项目 | Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/ | Stars: 31,915 | 33 stars today | 语言: Rust

GitHub热门项目 | Slint is an open-source declarative GUI toolkit to build native user interfaces for Rust, C++, JavaScript, or Python apps. | Stars: 22,839 | 15 stars today | 语言: Rust

GitHub热门项目 | Simple Yet Powerful Anti-Detect Browser 🍩 | Stars: 2,724 | 21 stars today | 语言: Rust

GitHub热门项目 | Hyperlight is a lightweight Virtual Machine Manager (VMM) designed to be embedded within applications. It enables safe execution of untrusted code within micro virtual machines with very low latency and minimal overhead. | Stars: 4,384 | 24 stars today | 语言: Rust

GitHub热门项目 | The Open Standard for Generative UI | Stars: 6,776 | 47 stars today | 语言: TypeScript

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

GitHub热门项目 | Локальный API-прокси для Qwen AI с поддержкой сохранения контекста диалогов и управления сессиями через REST API | Stars: 207 | 13 stars today | 语言: JavaScript

GitHub热门项目 | Fast and low overhead web framework, for Node.js | Stars: 36,440 | 23 stars today | 语言: JavaScript

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

GitHub热门项目 | AI-assisted TradingView chart analysis — connect Claude Code to your TradingView Desktop for personal workflow automation | Stars: 3,470 | 31 stars today | 语言: JavaScript

GitHub热门项目 | Magenta RealTime 2: An Open-Weights Live Music Model | Stars: 1,418 | 86 stars today | 语言: Python

GitHub热门项目 | Free LLM API keys for GPT-5.5, Claude, DeepSeek, Gemini, Grok — copy, paste, use. Updated 3-5x daily. No credit card needed. | Stars: 1,782 | 143 stars today | 语言: Python

GitHub热门项目 | A visual, example-driven guide to Claude Code — from basic concepts to advanced agents, with copy-paste templates that bring immediate value. | Stars: 35,507 | 154 stars today | 语言: Python

GitHub热门项目 | Find the local LLM that actually runs and performs best on your hardware. Ranked by real, recency-aware benchmarks, not parameter count. One command, run it instantly. | Stars: 3,192 | 103 stars today | 语言: Python

GitHub热门项目 | PM Skills Marketplace: 100+ agentic skills, commands, and plugins — from discovery to strategy, execution, launch, and growth. | Stars: 12,389 | 112 stars today | 语言:

GitHub热门项目 | Agent Skills for Google products and technologies | Stars: 12,111 | 481 stars today | 语言: Python