标签：#t

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

The National Highway Traffic Safety Administration is also investigating the crash

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

One of the best things about the Laravel AI SDK is that it doesn't lock you into a provider. Swap OpenAI for Anthropic, add Gemini as a failover, and your agent code barely changes. That's the entire pitch, and it mostly holds up. Until your agent needs to browse the web. The provider-agnostic promise has a hole in it The SDK ships two provider tools for giving agents access to the web: WebSearch and WebFetch . They're convenient, a couple lines and your agent can search or fetch a page. But they're not really part of the SDK's unified layer, they're implemented natively by whichever AI provider you're using, which means their availability depends entirely on which model you picked. WebSearch works on Anthropic, OpenAI, Gemini, and OpenRouter. WebFetch only works on Anthropic and Gemini. So if you configure a failover chain that includes Groq, DeepSeek, Mistral, or xAI, and one of those becomes the active provider, your agent quietly loses the ability to browse. Not an error, not a warning, just an agent that stops citing sources or stops noticing the page it was supposed to check even changed. That's a rough thing to discover in production. You built a failover chain specifically so a rate limit or outage wouldn't take your feature down, and it turns out one of your fallback providers was silently missing a capability the whole time. Even when it works, it's pretty shallow Set the provider problem aside for a second. Even on a provider where WebSearch and WebFetch are both available, what you get back is raw: search results or fetched page content that the model has to reason over itself. There's no schema. There's no guaranteed citation tracking. If you want your agent to return a structured, sourced answer, that reasoning happens somewhere in the model's own inference, not in a layer you control or can test. For a lot of use cases, that's fine. For anything where the answer needs to be defensible or reproducible, a research assistant, a competitive brief, anythin

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

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.

The company endorsed landmark AI transparency laws in California and New York last year, but its head of US state and local policy says they may already be outdated.

Metron was the Greek word for a measure: the standard you judge a thing against. It gives us metric, and metronome. Pick the wrong metron and everything you count against it comes out wrong, no matter how carefully you count. Ask most teams how they know AI adoption is working, and the answer is some version of: we’re shipping more. More PRs, more tickets closed, more velocity on the board. None of those numbers were ever measuring what people thought they were measuring. They were proxies, and bad ones, for whether the team was creating value. AI didn’t break that. It just made the proxies lie louder. An agent can produce PRs faster than any team can review them. It can close tickets all day. None of that tells you whether the thing built was worth building, or whether it fixed the actual problem, or whether anyone downstream is better off. Burning tokens isn’t the same thing as creating value. It just looks the same on a dashboard built to reward the first thing. The same DORA numbers I pointed at earlier are blunt about it: individual output climbs while delivery throughput and stability drop. Same bottleneck, counted at the wrong end . This is Theory of Constraints in one sentence: speeding up a step that wasn’t the bottleneck doesn’t speed up the system. It just piles more work in front of whatever the real bottleneck is. Implementation used to be the constraint. Now it isn’t. Review is. Coordination is. Making sure everyone agrees on what “done” even means is. Agents made the fast part faster and left the slow part exactly where it was, except now it’s buried under more output arriving to be reviewed by fewer people who understand any of it. Measuring individual speed after that shift is measuring the wrong clock. The team can be faster and worse off in the same quarter. The instinct, when this gets noticed, is to add another skill to the agent. Automate the review step too. Automate the coordination. Whatever’s slow, throw a capability at it. That doesn’t fix

I've been using TablePlus for years. It's good — but the connection limits on older licences drove me mad, and most alternatives are either Electron apps or haven't been updated since 2019. So I built my own. What is Stratum? Stratum is a native macOS database GUI — written in Swift, not wrapped in Electron. It connects to MySQL, MariaDB, PostgreSQL, and SQLite. What made me actually build it Three things: 1. Connection limits. TablePlus caps connections on older licences. Stratum has none. 2. Electron alternatives. Beekeeper Studio is good but it's an Electron app. On a Mac, that matters. 3. The MySQL setup friction. Most tools require you to install extra dependencies. Stratum connects natively — no brew install, no extra setup. What it does Table browser with inline editing — add, edit, delete rows without SQL Server-side pagination — stays fast on tables with 100k+ rows Query editor with schema-aware autocomplete Visual schema designer — create tables, add columns without writing DDL Full SQL export — DROP + CREATE + batched INSERTs iCloud sync for connections and snippets Laravel Valet auto-detection Import connections from TablePlus in one click SSH tunnelling The tech Built with SwiftUI and Swift 6. The PostgreSQL driver uses PostgresNIO. The MySQL driver implements the MySQL wire protocol directly over Network.framework — no Homebrew dependency, works inside the App Sandbox. Where it is now Currently in free beta. One-time purchase planned for the Mac App Store — no subscription. → stratum.mwn-digital.uk Happy to answer questions about how it's built.

I built Commit Cron , a small GitHub Actions experiment that creates one automated commit every day. The project updates a text file with the latest execution time, commits the change using the github-actions[bot] account, and pushes it back to the repository. View the project on GitHub: Commit Cron How It Works The workflow runs every day at 10:00 AM Asia/Manila time. on : schedule : - cron : " 0 10 * * *" timezone : " Asia/Manila" workflow_dispatch : The workflow_dispatch trigger also lets me run the workflow manually from the GitHub Actions tab. The workflow checks out the repository, creates the bot directory when needed, and updates bot/last-run.txt : mkdir -p bot printf "Last automatic update: %s \n " \ " $( TZ = Asia/Manila date '+%Y-%m-%d %H:%M:%S %:z (Asia/Manila)' ) " \ > bot/last-run.txt The file contains a timestamp similar to: Last automatic update: 2026-07-17 10:03:24 +08:00 (Asia/Manila) After updating the file, the workflow configures the GitHub Actions bot identity and creates the commit: git config user.name "github-actions[bot]" git config user.email \ "41898282+github-actions[bot]@users.noreply.github.com" git add bot/last-run.txt git commit -m "chore: daily automated update" Before pushing, it pulls the latest branch changes with rebase: git pull --rebase origin " ${ GITHUB_REF_NAME } " git push origin "HEAD: ${ GITHUB_REF_NAME } " This helps prevent the push from failing when another commit is added while the workflow is running. Repository Structure . ├── .github/ │ └── workflows/ │ └── daily-commit.yml ├── bot/ │ └── last-run.txt ├── LICENSE └── README.md Why I Built It Commit Cron is a small demonstration of: Scheduled GitHub Actions workflows Manual workflow triggers Automated file updates Bot-generated Git commits Repository write permissions using GITHUB_TOKEN The workflow uses: permissions : contents : write This allows the built-in GitHub token to push the generated commit. Important Note The automated commits only confirm that the work

There are, conservatively, nine thousand tools for running AI agents across your projects. This is number nine thousand and one. I know. Stay a second anyway. Eighteen years of backend work, most of it PHP, most of it through agencies — which is a polite way of saying I've spent my career as a line item in someone else's staffing spreadsheet. Several projects at once, different clients, different timezones, "resource" being the official word for what I am on a good invoicing day. So when AI agents got good enough to do real work, I didn't wonder whether one could write code. I wondered how I'd wrangle a whole fleet of them across projects — which, I eventually noticed, is me trying to become the manager of a little spreadsheet of resources, after eighteen years of being one. The snake found its tail. I built it anyway. Docket, then: a control plane for running OpenClaw agents across projects, made with heavy AI assistance on purpose — the point was to close my own gaps, not to prove I could type from memory. It started as a Bash CLI; it's now a tested Python package. On GitHub, beta, link at the bottom. This is me thinking out loud in public, not selling you anything. And somewhere in that rewrite, the doubt everyone expected me to have — is Bash the right language? — died, and a worse one took its place. That worse one is what this piece is actually about. Let me get there honestly. What I aimed for, not what I mastered I want to say this before the vocabulary makes me sound like I know more than I do. I did not sit down understanding context isolation, or git worktrees per agent, or anti-corruption layers. I sat down wanting clean, stable, maintainable, tested code — and I steered an AI toward that shape until it held together. The shape is real. My grip on every part of it is not the same. What the shape does, plainly: OpenClaw is a local-first agent daemon, great at running one agent. Run a fleet and you hit problems it doesn't solve. Docket runs pods — a Lead t

Most "I built a home SOC" posts show you the pretty dashboard and stop there. This one is about what happened after the dashboard loaded — because that's where the real learning was. 🖥️ The setup Three Mac Minis, one control machine, all Ubuntu. Mac Mini 1 runs the Wazuh manager, indexer, and dashboard — the security monitoring core. Mac Mini 2 handles AWS IAM permission scanning (Cloudsplaining, ScoutSuite). Mac Mini 3 is a documentation hub plus a custom vulnerability-intelligence pipeline pulling from the NVD API and NIST's CPRT compliance framework data. A fourth machine, my daily driver, orchestrates all three over SSH. Today's goal: get every machine reporting into Wazuh, so I'd have real, fleet-wide visibility — not just a single monitored box. 🐛 Bug #1: the silent feed stall After installing the Wazuh agent on Mac Mini 3, vulnerability scanning just... sat there. The log showed Initiating update feed process and then nothing. No error, no timeout, no progress. I ruled things out methodically: DNS resolved fine, curl could reach Wazuh's CTI feed endpoint and complete a full TLS handshake, disk space was nowhere close to full, and there were no stale lock files sitting around. Every individual piece of the pipeline worked in isolation — but the feed download itself never completed. The actual fix: upgrade Wazuh from 4.14.5 to 4.14.6. A newer point release fixed whatever was silently stalling the content-updater module. Once upgraded, the feed downloaded, decompressed, and the scanner came alive within minutes. Lesson: when every individual network/system check passes but a specific module still won't progress, check for a version-specific bug before going deeper down the debugging rabbit hole. 🔒 Bug #2: locking myself out with fail2ban Mid-troubleshooting, my control machine's SSH connection to Mac Mini 3 started getting flatly refused — no prompt, no error, just Connection refused. Ping worked fine. Every other port responded. Just port 22, silently dead. fai