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On the latest episode of Equity, we discuss why Sam Altman has calling on the industry to "pace the rate of AI development."

Building Laravel NATS: A Modern, Production-Ready NATS Integration for Laravel When building distributed systems, one of the biggest challenges is enabling services to communicate reliably without creating tight coupling. Laravel has excellent support for queues, events, broadcasting, and jobs, but when it comes to NATS , the ecosystem has been relatively limited. That's exactly why I built Laravel NATS . Instead of being just another wrapper around an existing PHP client, Laravel NATS aims to provide a Laravel-first developer experience while exposing the full power of NATS for modern event-driven architectures. In this article I'll explain: Why I built Laravel NATS Why you should consider NATS How Laravel NATS works Features that make it production ready Code examples Real-world use cases What makes this package different from existing solutions What is NATS? NATS is a lightweight, high-performance messaging system designed for cloud-native applications. Unlike traditional queues, NATS focuses on: Extremely low latency High throughput Simple publish/subscribe messaging Request/Reply APIs JetStream persistence Horizontal scalability Instead of applications calling each other directly: Order Service │ ▼ Notification Service Applications publish events: Order Service │ ▼ NATS Server │ │ ▼ ▼ Email Analytics Every service becomes independent. Why Laravel Needed a Better NATS Package Most existing packages expose the underlying PHP client almost directly. That means developers still have to understand: client lifecycle connections serialization subscriptions queue consumers JetStream APIs Laravel developers expect something different. We are used to APIs like: Cache :: put (); Queue :: push (); Event :: dispatch (); The goal of Laravel NATS was to make NATS feel just as natural. Installing Laravel NATS Installation is straightforward. composer require zaeem2396/laravel-nats php artisan vendor:publish --tag = nats-config Then configure your environment: NATS_HOST=127.0.0

Hello Everyone, I'm the creator of PyBotchi, an intent-based AI Agent Orchestrator. In this post, I will discuss some key concepts why I created it. A little bit of background first. I'm a solutions architect with 10 years of experience as a software engineer. Most of my work are high throughput, high reliability, low cost and low latency services. This is while making it simple and readable to improve it's maintainabality. When I'm designing a system, I usually prioritize these concerns. You may assume this is my bias in relates to AI Agent building. I'm also Claude Certified Architect (Foundation) and I found that PyBotchi aligns almost identical to Anthropic's core agent recommendations. TL;DR: PyBotchi is an lightweight, async-first Python framework that uses nested Pydantic models and OOP inheritance to turn LLM intent detection into clean, deterministic business logic without the overhead of complex graph orchestration. Why I created PyBotchi? I really believed that traditional coding can already solved what client's need. The only limitations we have is how we read the input and how we show the output. In most cases in web services, your API use JSON, XML, etc with their respective specification/structure. Input Analogy Assume you have created a Books CRUD endpoints (FastAPI with Pydantic). Your create endpoint will have a define specifications for book creation to have a validation and avoid user errors. Most of the time you will also validates sessions and permissions which also included in the request. If you want your chat bot to support those, you just need add those endpoint as intent (tools). If your model tool selection are able to detect intents. You are more "close" to being deterministic. "Your services will have 50 endpoints or more. You will flood your tool selection call" In your frontend UI, you segregate panels/forms/inputs in their respective pages. You don't usually join multiple intent in a same page. Cluttered UI will make your UX confusin

GitHub热门项目 | Zigbee 🐝 to MQTT bridge 🌉, get rid of your proprietary Zigbee bridges 🔨 | Stars: 15,462 | 133 stars this week | 语言: TypeScript

GitHub热门项目 | Open-source media request and discovery manager for Jellyfin, Plex, and Emby. | Stars: 12,106 | 127 stars this week | 语言: TypeScript

GitHub热门项目 | Rust‑first, cross‑platform wgpu/WebGPU renderer exposed to Python for fast, headless 3D rendering. Built in Rust, shipped as Python wheels. | Stars: 558 | 10 stars today | 语言: Rust

GitHub热门项目 | A bunch of lints to catch common mistakes and improve your Rust code. Book: https://doc.rust-lang.org/clippy/ | Stars: 13,405 | 6 stars today | 语言: Rust

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GitHub热门项目 | Open Brain — The infrastructure layer for your thinking. One database, one AI gateway, one chat channel — any AI plugs in. No middleware, no SaaS. | Stars: 4,357 | 14 stars today | 语言: TypeScript

GitHub热门项目 | Story Creation AI Agent for novel, scripts, translation, interactive games, and IP content | Stars: 8,614 | 55 stars today | 语言: TypeScript

GitHub热门项目 | 基于Dineshkarthik的项目， 电报视频下载，电报资源下载，跨平台，支持web查看下载进度 ，支持bot下发指令下载，支持下载已经加入的私有群但是限制下载的资源， telegram media download,Download media files from a telegram conversation/chat/channel up to 2GiB per file | Stars: 5,440 | 7 stars today | 语言: JavaScript

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GitHub热门项目 | Comprehensive production pipeline for quad-modal AI filmmaking with Seedance 2.0 | Stars: 5,846 | 101 stars today | 语言: Python

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GitHub热门项目 | Netflix-level subtitle cutting, translation, alignment, and even dubbing - one-click fully automated AI video subtitle team | Netflix级字幕切割、翻译、对齐、甚至加上配音，一键全自动视频搬运AI字幕组 | Stars: 18,012 | 48 stars today | 语言: Python

Two weeks ago, I published "Why Agent Evaluation Is Harder Than Model Evaluation." The core argument:...

Yesterday, backend-reviewer inspected pull requests with one model, read only the repository and public documentation, and stopped for human approval before proposing any change. Today it has exactly the same name. The model has changed, the system instructions have been rewritten, incident history is now available as a context source, memory persists between tasks, and database migration changes no longer require approval before they are proposed. The dashboard still shows the same team member. The engineer responsible for quality and risk is looking at a different agent. The identifier stayed. The behavior moved. That distinction is what NexFlow , an open specification for AI developer teams, is trying to make visible. The project does not currently provide a production runtime, a production CLI, or model-provider integrations. Its present job is narrower and, in my view, more important: give teams a language for reviewing agent changes before anything executes. A name answers the wrong question Agent names are useful to people. They distinguish a code reviewer from a documentation writer and establish a long-lived role inside the team. A name says very little about the configuration that produced a particular result. A model change can affect code quality, cost, latency, and the way uncertainty is handled. New instructions alter the order of analysis and the criteria for an acceptable answer. An additional source expands both available knowledge and the exposure surface. Memory carries the consequences of one task into another. A new permission changes more than output style: it changes what an error can damage. For audit purposes, “Which agent did the work?” is therefore incomplete. A second question matters just as much: which version of that agent's definition was active? In draft RFC-0004 , NexFlow separates stable agent identity from a versioned agent definition. Identity contains the role, description, and long-lived responsibility. The definition captures

How do you actually test an AI agent? Not "does it respond," but: does it route to the right tool, chain calls correctly, recover from failure, resist prompt injection, and stay within cost/latency budget? I spent weeks working through this on a running agent, and open-sourced the entire methodology — framework-agnostic , so it applies regardless of your language, runtime, or toolset. What's inside •⁠ ⁠ 61-source benchmark map — BFCL, GAIA, τ-bench, SWE-bench, WebArena, AgentDojo, LongMemEval and more, categorized by what they actually measure •⁠ ⁠ 58 universal test blocks across 7 tiers (L1–L4, Error Recovery, Multi-Turn, Security). Each block = a tool-agnostic capability definition + a concrete reference implementation •⁠ ⁠ Full OWASP Top 10 for Agentic Applications 2026 (ASI01–ASI10) mapped to 6 universal security test blocks •⁠ ⁠ Evaluation methodology — LLM-as-Judge biases, pass@k vs pass^k, trajectory vs end-state, observability (OpenTelemetry GenAI), automated red-teaming (garak, PyRIT, DeepTeam) •⁠ ⁠ Regulatory alignment — NIST AI RMF, MITRE ATLAS, EU AI Act, ISO/IEC 42001 How to use it Take Part II, replace the reference-implementation fields with your own agent's tool names and expected outputs. The universal capability definitions need no changes. Blank templates are included. PheronAgent (a macOS agent with 50+ native/MCP tools) is included as a real reference case study — but the methodology is the product, not the agent. No marketing narrative: ⁠ STORY.md ⁠ documents the real bugs, real test runs, and real corrections that shaped each version. Docs are CC BY 4.0, templates are MIT. Issues and PRs welcome. 👉 https://github.com/trgysvc/AgentTestMethodology

CleanSlate is an open-source platform for running coding agents across your local machine and the cloud. Today, it works through an IDE, CLI, and SDK. Agents can understand a codebase, make changes, run commands, browse the web, and verify their work. We are now building longer-running autonomous cloud agents that can continue working without keeping your machine active. CleanSlate supports multiple model providers and is not tied to a single ecosystem. GitHub: https://github.com/TheWariend/CleanSlate Website: https://thewariend.com/cleanslate The project is still early, and I would appreciate honest feedback from developers who use coding agents.