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👋👋👋👋 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

Hey friends! It's been a while since I tried to find open source projects to contribute to as I've been swamped with some projects myself. Someone approached me. They want to learn and start to contribute to open source. They're familiar with GitHub for their own projects, but never contribute to any open source project. They tried looking for beginners friendly issues on GitHub with no luck. Well, I immediately tried this myself and got overwhelmed with millions of issues only for JavaScript! So, I understand the struggle. 😅 If you're maintaining an open source project or know beginners-friendly open source projects that open to new contributors (preferably JavaScript, TypeScript, React, or NextJS), please send them my way in the comments below! 😊

I made this experimental art project/game that's an LLM chat assistant, but where you're the AI. I wanted people to get a visceral sense of what it's like to answer the kinds of things that people prompt their chatbots day in and day out. If you're interested, I wrote up some more info on how I made it, including how the "user" prompts are generated with an eye for realism: https://bethechatbot.com/about Hope you enjoy it! I'd love to hear people's takeaways.

Reasonable specs for a reasonable price.

Kalshi users betting on what President Donald Trump would say during his speeches were reportedly up against tough competition: the president's teleprompter operator. ABC News reports that federal investigators believe Gabriel Perez - Trump's teleprompter operator since 2016 - used inside information to make bets on Kalshi, a major prediction market platform that allows users […]

The Quest Begins (The "Why") I still remember the first time I walked out of a coding interview feeling like I’d just lost a lightsaber duel. The problem was a simple array‑rotation task, but I dove straight into typing, eyes glued to the screen, and barely said a word. When the interviewer asked, “What are you thinking right now?” I froze, mumbled something about “just trying to get it done,” and watched the seconds tick away. The feedback later? “Great coding skills, but we couldn’t follow your thought process.” That moment stung because I knew I could solve the problem—I just hadn’t learned how to show my thinking. After a few more silent attempts, I realized the interview isn’t a solo boss fight; it’s a co‑op mission where the interviewer wants to see how you navigate the terrain. If they can’t hear your internal monologue, they have no way to gauge your problem‑solving instincts, communication style, or ability to catch mistakes early. I went on a quest for a repeatable, low‑effort way to narrate my thinking without turning the interview into a monologue. What I found was a three‑step verbal framework that felt like unlocking a new Force power—simple, repeatable, and surprisingly effective. The Revelation (The Insight) The technique I now swear by is State → Plan → Execute . At each stage you say out loud exactly what you’re doing, using a tight, repeatable script. It’s not about over‑explaining; it’s about giving the interviewer a clear map of your mind. Here’s the exact wording I use, broken down by phase: State – Clarify the problem, assumptions, and constraints. “Okay, so we need to rotate an array to the right by k steps. I’m assuming k can be larger than the array length, so I’ll use modulo to normalize it. The array can contain any integers, and we should aim for O(n) time and O(1) extra space.” Plan – Outline the high‑level approach before writing a line of code. “My plan is to use the three‑step reversal algorithm: reverse the whole array, then reverse

Cyclosporiasis isn’t the only thing spreading across the US. So is anxiety about getting hit with it.

Most remote job listings are ghosts — already filled, never opened, or posted just to farm résumés. As a developer, that annoyed me enough to solve it with code instead of complaining about it on X . So I built Remoty.work , which grades every listing A–F on how likely it is to be real . Here's how the detection actually works under the hood. The problem, from an engineering angle Job boards are an endless scrape loop. Board A scrapes Board B, which scraped Board C. The same dead listing propagates across a dozen sites, and none of them verify anything. There's no signal for "is this real," so the noise compounds until every board looks identical. I didn't want to build board number thirteen in that loop. I wanted a layer on top that answers one question every listing should have to: is anyone actually going to read my application? The architecture Everything runs on a single VPS — Postgres, scrapers, and the scoring jobs, supervised on a schedule. Deliberately boring. High level: Ingestion: scheduled scrapers pull from source boards and company ATS feeds into Postgres. Each raw listing is deduplicated by a fingerprint (title + company + normalized URL) so the same job reposted across five boards collapses into one row with a repost_count . Scoring engine: every listing gets a ghost-risk score from a handful of signals, then mapped to an A–F grade so it's human-readable, not a black-box number. The "rant" signal: I cross-reference what people say about companies in places like r/recruitinghell and hiring threads. That's where the truth about a company's hiring leaks out, and it turns out to be a strong predictor. Agents: I use DeepSeek to classify and summarize the messy text job descriptions, company chatter. DeepSeek because running this over thousands of listings every night on GPT-4-class models would have killed the unit economics before I had a single user. One infra detail I didn't expect to spend a weekend on: the frontend is on Cloudflare's edge, but the ed