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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

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 […]

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BP Ventures is shutting down, ending a nearly 20-year run that was marked by reportedly lackluster returns.

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Uber has agreed to acquire Delivery Hero in a $14.8 billion all-stock deal that would nearly double the company’s global footprint and create one of the world’s largest food-delivery platforms outside China.

Beehiiv is launching an AI Copilot to help publishers with user growth and analytics.

Today, the European Union ordered Google to give its AI rivals greater access to Android, the open-source operating system that powers billions of devices worldwide. The demand is hardly surprising. It may look like a defeat on paper for Google, which has spent years resisting exactly this kind of access, but it is a regulatory […]

Roblox is about to let people make games with AI right inside its mobile app, which could make a platform that's already filled with content of questionable quality feel even more overloaded. The company has embraced AI with open arms, including a preview of an ambitious take on AI world models similar to Google's Project […]

X will use Grok AI to better detect stolen content, redirect payouts to original creators, and crack down on engagement bait.

OnePlus promises to continue supporting the phones it has already released.

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Travel agency Fora announced a $60 million Series D round led by Forerunner and Tactile Ventures, valuing the company at $1 billion.

Learn how OpenAI is making ChatGPT safer for teens with age-appropriate protections, learning tools, parental controls, and expert partnerships.

Google is giving its AI note-taking app a new name. The company announced on Thursday that NotebookLM is becoming Gemini Notebook, but will remain a standalone app even as it integrates more deeply across Gemini and Google Search. Google first revealed Gemini Notebook - then called Project Tailwind - in May 2023 before widely releasing […]

Google said users can soon access their notebooks through AI Mode in Search.