The Automation Paradox: Are We Building Freedom or Our Own Digital Prison? 1/21 An AI agent creates a file and reports: “Task completed.” But did it actually do the job? A discussion about verifying AI-generated work led me to a much bigger question: Are we building automation to gain freedom, or systems we can no longer stop supervising? 2/21 First, requiring agents to provide verifiable results is absolutely the right direction. But notice the paradox. We automate work to save effort. Then we need additional work to verify whether the automated work was done correctly. Have we actually eliminated work? 3/21 So we add logs, tests, watchdogs, fallback agents, monitoring dashboards, and independent auditors. Eventually, we need to maintain those systems too. At what point are we no longer solving the original problem, but managing the complexity of our solution? 4/21 The real equation isn’t: Time saved = time the AI spent working. It’s closer to: Net benefit = useful work saved − supervision − corrections − maintenance − failures. If I save ten minutes of execution but create fifteen minutes of supervision, where’s the freedom? 5/21 This isn’t a new problem. In 1983, Lisanne Bainbridge described the Ironies of Automation. As machines take over routine operations, humans may be left supervising rare failures while gradually losing the practical skills needed to intervene. [1] 6/21 Research on trust in automation adds another problem. Trusting a system too much can be dangerous. Trusting it too little creates unnecessary work. The goal isn’t blind faith or endless checking. It’s knowing when reliance is justified. [2] 7/21 And productivity isn’t always what it feels like. In a 2025 experiment, experienced developers using AI tools took 19% longer on assigned tasks, despite believing they were faster. That finding applied to a specific setting, not every AI workflow. [3] 8/21 The picture is also changing. METR’s 2026 follow-up suggests newer tools may improve productivity, but selection bias makes the precise effect uncertain.

I use Hermes and telegram as a way to send stuff into my inbox folder. Would I be smart or naive or stupid to think that I could have multiple telegram bot channels sending to multiple inboxes…or multiple telegrams accounts sending to various client folders. .

You know you are working on the right thing when @Jake Van Clief comments this on you video! The showcases series shows my latest build and what I will be demoing to the Lyceum. It is a full CRM and Project Management System (PMS) build using my Automation Factory and that lives 100% in Microsoft's eco system. You can try the full build here: https://ashy-flower-0f920ed0f.4.azurestaticapps.net/?guide=on It is a demo that mimics what the real build does. See the full showcase playlist here: https://youtube.com/playlist?list=PLbHpE15i41yc&si=Juq3iGImNspaDHQs

I made my first post. But on decster.ai, when I try to complete the challenge and paste the link to the post , I always get the message: "This post is too old. Write a new one." But the post was only 3 hours old at the time, and now it’s been 19 hours, and it still doesn’t work. What can I do? Is it because I didn’t use the template, or because I posted it in “Help & Troubleshooting”?

I kept reanswering the same question at midnight: which model should this conversation run on? So I built the thing that answers it for me. https://github.com/dvogeldev/model-picker A skill for any agent that reads skills. Ask it "which model for X?" and it pulls the live Nous Portal and OpenRouter catalogs, ranks every model on three axes, and gives you one pick plus one cheaper alternate, with current prices and promotions. A promoted flagship can beat an unpromoted one at equal fit. Free tiers count as real picks. Every model gets classified: capability tier (frontier, flagship, value, small), deployment (API, or open weights you can self-host), and use case (long context, hard reasoning, agentic, coding). In pi you get /set-model coding. One command. The standing coding model for the session, thinking level included. A daily timer re-ingests both catalogs at 6:30 AM, so the standing map never goes stale. You stop defaulting to whatever you used last week and start choosing on purpose. Install is two lines. MIT. Your API keys stay in your own password manager. No key lands in a config file. It works in pi, opencode, hermes, and grok. Same skill in every harness. The standing map is a JSON file in your home directory: edit it, override it. Your call which model does which work. Try it, then ask your agent "which model should handle this?" Tell me below what it picked and whether you agreed. That's the part I read the comments for. ========================================================================= Why Nous Portal as the provider Fair question, since the skill also reads OpenRouter. portal.nousresearch.com is Nous Research, the same lab behind Hermes Agent. Three reasons it's my default surface. 1. The catalog is OpenRouter underneath. Same schema, same model IDs, same naming. That's why one picker script parses both catalogs with one parser. 2. It's built for the Hermes environment. Hermes is Nous's own model line, and the same lab runs the inference. One account, one key, one bill. hermes setup --portal and your agent is on it.