u/sarox-dev

The school system was designed in 1800s Prussia to make factory workers who don't ask questions. Bell rings, sit in rows, memorize facts, repeat. It hasn't changed in 150 years. By 2030 we'll have AGI that can do most intellectual work. And we're still teaching kids to be obedient cogs in a machine that won't exist by the time they graduate.

I wrote a blueprint for an alternative. New education: financial literacy, critical thinking, emotional intelligence. Economic model: AI dividends replace lost jobs. Safety system: neural alignment so AGI can't be weaponized. Implementation: one private charter city that proves the model works, so well that the world has to copy it.

Singapore went from swamp to global hub in one generation by redesigning education. Dubai created free zones in the desert. One city is all it takes to prove a new system works.

I'm building this framework right now. Anyone else thinking about post-AGI society beyond "UBI and hope"?

The school system was designed in 1800s Prussia to make factory workers who don't ask questions. Bell rings, sit in rows, memorize facts, repeat. It hasn't changed in 150 years. By 2030 we'll have AGI that can do most intellectual work. And we're still teaching kids to be obedient cogs in a machine that won't exist by the time they graduate.

I wrote a blueprint for an alternative. New education: financial literacy, critical thinking, emotional intelligence. Economic model: AI dividends replace lost jobs. Safety system: neural alignment so AGI can't be weaponized. Implementation: one private charter city that proves the model works, so well that the world has to copy it.

Singapore went from swamp to global hub in one generation by redesigning education. Dubai created free zones in the desert. One city is all it takes to prove a new system works.

I'm building this framework right now. Anyone else thinking about post-AGI society beyond "UBI and hope"?

Had Qwen model running with OpenRouter for weeks. Cost maybe 0.20€ a day. Everything fine. Then one day I check my usage and it's 3€. In a single day. Provider silently adjusted their pricing.

I know you would say that 3€ is not much but I don't have money to afford that because I am still going to school and I don't have a stable income.

I spent the next two hours comparing models, rewriting configs, testing DeepSeek alternatives I found. That's when I realized I had zero visibility into what anything cost until I manually check the openrouter website.

So I built a system where Python script runs on a schedule, hits the OpenRouter API with my key, writes a daily .md file straight into my Obsidian vault with cost breakdown. My AI agent watches that file and alerts me if any line item jumps over a threshold I set. However no dashboard or database, just markdown files and one API call per day.

It works but it feels fragile. What do you use to track AI API costs? My setup is too manual and I'd rather use something that catches spikes before they turn into horrible costly surprises.

Had Qwen model running with OpenRouter for weeks. Cost maybe 0.20€ a day. Everything fine. Then one day I check my usage and it's 3€. In a single day. Provider silently adjusted their pricing.

I know you would say that 3€ is not much but I don't have money to afford that because I am still going to school and I don't have a stable income.

I spent the next two hours comparing models, rewriting configs, testing DeepSeek alternatives I found. That's when I realized I had zero visibility into what anything cost until I manually check the openrouter website.

So I built a system where Python script runs on a schedule, hits the OpenRouter API with my key, writes a daily .md file straight into my Obsidian vault with cost breakdown. My AI agent watches that file and alerts me if any line item jumps over a threshold I set. However no dashboard or database, just markdown files and one API call per day.

It works but it feels fragile. What do you use to track AI API costs? My setup is too manual and I'd rather use something that catches spikes before they turn into horrible costly surprises.

I run the usual stuff: Pi-hole, Jellyfin, NAS. The basics. Lately I've been diving into self-hosting AI tools and it's a different beast entirely compared to just running a media server.

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The hardware requirements alone are a conversation. A 4K movie stream uses your GPU for transcoding maybe 5% of the time. Running a local LLM pins your GPU at 100% for minutes straight. The power draw difference is noticeable.

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But the tradeoff is interesting: no API costs, no rate limits, no random service shutdowns. Once the hardware is in place, it's yours. You can throw a million requests at it and the only cost is the electricity.

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I'm curious what non-obvious things people in this community self-host. What's the weirdest or most useful thing you run that surprised you with its value?

Set up Hermes Agent on my local machine a while back. It runs tasks for me: content generation, file management, scheduled jobs. Nothing revolutionary, just consistent.

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What I didn't expect: it runs at 3 AM and I'm not even awake to see what it's doing. I wake up to files that were created while I was sleeping. Stuff that got written, organized, processed. It feels like having a very quiet coworker who works the night shift.

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The practical reality is less romantic. It hallucinates maybe 10% of the time. Sometimes it decides a file doesn't exist when it does. Sometimes it takes a reasonable instruction and does something I didn't intend. You can't fully trust it, but you also can't ignore the output because most of it is useful.

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The closest analogy I've found: it's like a very eager intern. Great at doing the boring stuff. Terrible at knowing when they're out of their depth. The trick is learning what to delegate and what to keep for yourself.

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Anyone else running AI agents locally? What do you actually use yours for vs what sounded cool on paper?

I've been using ChatGPT almost every day for the past year. Mostly for coding, troubleshooting, and occasionally bouncing ideas around.

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The biggest surprise wasn't how useful it is. It was how much my own thinking changed.

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I used to spend 20 minutes reading docs or StackOverflow to figure something out. Now I spend 2 minutes asking an AI, then about 18 minutes doubting the answer and checking it manually. Net time savings? Basically zero. But my process is completely different.

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I'm not faster. I'm just spending my time differently, less time reading, more time verifying. It's like having a junior dev who works at lightning speed but makes confident mistakes. You still have to review everything, but the review is different than writing from scratch.

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The really weird part is that I can't tell if this is better or worse. I feel more productive. But am I actually?

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Anyone else notice this pattern? You use AI constantly, but you're not sure if you're actually saving time or just rearranging how you spend it?

I noticed my own search habits changing without really deciding to.

For technical problems: straight to ChatGPT or Perplexity. The back-and-forth is better than clicking 5 StackOverflow links and hoping one of them matches my exact use case. (bad example because I think StackOverflow is abandoned now)

For finding places, opening hours, practical stuff: still Google. AI models don't know if the restaurant down the street is still in business or what time it closes today.

For research or "tell me about X": depends. If I want a broad overview, AI wins. If I need recent or verified information, I still end up on Google checking actual sources.

I'm somewhere around 60/40 split in favor of AI now.

Curious where everyone else lands. Have you cut Google out entirely, or is it still your default?

Got Hermes Agent running locally with an LLM that handles tasks for me throughout the day. It schedules jobs, writes content, manages files. Sounds great on paper. In practice? I spend half my time verifying it didn't hallucinate something.

Yesterday it told me a file didn't exist. I checked and the file was right there. It just decided to lie about it. And the bigger problem: you can't tell what you don't know. When a human messes up, you usually notice. When an AI messes up, it sounds confident doing it. So the things I didn't bother checking? Probably full of errors. Feels like we're all running experiments in production and pretending we're not.

Anyone else feeling this tension between how useful AI is and how little we actually trust it?

Spent the last few weeks trying to get a local AI agent running that could actually do what I needed. Looked at some of the tools that were popular.

Then found Hermes Agent. No docker-compose hell, no Python environment conflicts, no "make sure you have these 17 environment variables set". The wildest part? It's not even that special technically. It just doesn't get in its own way. You configure a model provider, give it tools, and it actually works. I spent more time fighting broken dependencies than I did setting up the thing that finally worked. Has anyone else noticed that the most hyped AI tools are often the least usable?

I usually want to search something online all the time; however, I like to keep things private from google for example.

I have a few questions related to my project that I am building.

1. Have you ever used bookmarks when exploring the wide web?

2. How often you search the web for a solution or some critical information?

3. Could asking AI for an answer be better than searching this information yourself?

I want to build some kind of a knowledge base where you can simply open the searched knowledge locally without ever searching for the same problem or information again.

Edit: The main goal is to save important context and not the website itself. I like the Obsidian file linking system so I am looking forward to connect the dots in my project in a way where you could find related websites and information in a very fast manner.