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Libretto PR agents is a free TypeScript library for maintaining Playwright browser automations. Add one line of code to your existing Playwright scripts and it lets an agent automatically open GitHub PRs fixing the script when it fails. A few months ago we released Libretto, a CLI + coding-agent skill for building deterministic browser automations. The idea was that for many browser workflows, especially repetitive business workflows, you don’t need an AI agent making decisions at runtime. You w

Hyundai aims to deploy 25,000 Atlas robots starting with US factories in 2028.

👋👋👋👋 Looking back on your week -- what was something you're proud of? All wins count -- big or small...

If only someone could have predicted it.

CD sales are apparently going up, reportedly thanks to fans realizing they're an affordable way to support their favorite artists. According to a new report from research firm Luminate, 16.3 million CDs were sold in the first half of 2026 in the US, a 16 percent increase year-over-year. The growth in CD sales was driven […]

Toyota's everyday small SUV should rarely require trips to the gas station.

This remarkably vivid home projector has deep inky blacks, without a five-figure price tag.

The social-engineering technique has primarily been a tool of financially motivated criminals.

Creator says he will "very loudly ignore" those arguing for a ban on AI tools.

Your Mac tidies itself: screenshots, installers, Downloads Discussion | Link

Amazon Web Services has recently introduced AWS Continuum, a new integrated security platform to automate the discovery, enforcement, and remediation of security issues across codebases, dependencies, and applications. AWS Continuum launches with four agentic capabilities, aiming at the entire vulnerability lifecycle: penetration testing, code review, threat modelling, and code vulnerabilities. By Gianmarco Nalin

The Problem If you’ve built a decent-sized NestJS application, you know the authorization dance. You start with basic Roles, then suddenly you need fine-grained permissions, then maybe some attribute-based access control (ABAC). Before you know it, your controllers are cluttered with @UseGuards(RolesGuard) , and your RolesGuard itself is a massive switch statement checking for every possible permission string. It's repetitive, hard to test, and honestly, a bit boring to maintain. The Solution: nestjs-permissions I got tired of reinventing this logic for every project, so I built nestjs-permissions . The goal was simple: Declarative, type-safe authorization that stays out of your way while keeping your code clean. Why use it? Decorator-Driven: No more complex metadata injection. Just wrap your routes. Type-Safe: Keep your permissions consistent across your frontend and backend. Framework-Native: It plays nicely with the standard NestJS Request lifecycle. Quick Start Getting started takes about 5 minutes. 1. Install it: npm install nestjs - permissions 2. Configure your module: import { Module } from ' @nestjs/common ' ; import { PermissionsModule } from ' nestjs-permissions ' ; @ Module ({ imports : [ PermissionsModule . register ({ // Your config here }) ], }) export class AppModule {} 3. Protect your routes: import { Get , Controller } from ' @nestjs/common ' ; import { RequirePermissions } from ' nestjs-permissions ' ; @ Controller ( ' dashboard ' ) export class DashboardController { @ Get ( ' admin ' ) @ RequirePermissions ( ' admin.read ' ) async getDashboard () { return ' Secret Admin Data ' ; } } What’s Under the Hood? Under the hood, nestjs-permissions leverages the NestJS Reflector to cleanly extract metadata from your route handlers. It automatically taps into the execution context, checking the incoming request against the required permissions without forcing you to write boilerplate guards for every module. When NOT to use it If you need hyper-complex, at

Last Tuesday I was deep in a session with an AI assistant, rewriting a caching layer that had been quietly leaking memory in production. We tried Redis first. It worked. Then I looked at the actual data volume (about 50MB, tops) and killed the idea, because spinning up a whole service to cache 50MB felt absurd. We landed on sqlite instead. Good call, I still think. Two days later a teammate opened the branch, saw cache_sqlite.py sitting next to a deleted cache_memory.py , and asked why we weren't just using Redis like the rest of our stack. I didn't have a good answer ready. The reasoning had lived entirely inside a chat session that was long gone by then. I remembered making the decision. I could not reconstruct it convincingly, and "trust me, we talked about it" is not a great answer to give a teammate, or future me, three months from now. That's the part most tooling misses with AI-assisted coding. The code survives. The diff survives. A wave of AI memory tools is racing to store more of what the code does , and a few (Selvedge, presence) are starting to chase the why too, which tells me the pain is real. But almost all of them are MCP servers you have to wire into a specific agent, with a database sitting beside your repo. What I wanted was dumber and more portable: the reasoning, and the approaches I tried and threw out, written in plain text I can read, next to the code, no matter which editor or agent produced it. Because that reasoning lives in a chat window, and chat windows end. You switch from Claude Code to Cursor, or you just close the laptop, and it's gone. The facts stay. The judgment behind them evaporates. This didn't feel like a hard problem, so I built a small thing called Alpheon to fix it for myself, the simplest possible version: one Python file, no server, no database , works off git so it doesn't care what editor or agent you use. Facts vs. reasoning Here's the distinction that clicked for me. A fact is "cache.py was modified, cache_sqlite.py