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laws in California and New York last year, but its head of US state and local policy says they may already be outdated.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

SpaceX deorbiting satellites isn't a cause for worry, but scientists are still looking into the environmental impacts of frequent satellite deorbits.

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

Turn any Mac task into a step-by-step guide Discussion | Link

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

If the large, rocky planet is losing helium, then we can infer what is left behind.

New York Governor Kathy Hochul might have just signed a moratorium on new AI data centers in the state, but she's not against using the technology herself. During an interview with Bloomberg's Odd Lots podcast, Hochul said that her team is using "AI to analyze every single rule, regulation, [and] policy" to check for outdated […]

Sometimes it feels like keeping your floors clean is one of those never-ending chores, which is why it's nice to have a versatile robot vacuum take it off your hands. The Ecovacs Deebot X11 robovac / mop hybrid is designed to do just that, and it's now $699 ($400 off) at Amazon and directly from […]

Shopping browser with agentic checkout Discussion | Link