Four open-source tools for running agentic AI on your own hardware. The senses. The memory. The engine room. The interface. No mega-corp permission required.
The AI industry is executing a textbook progression of power consolidation. Wealth and compute are pooling at the top, and the companies hoarding it want you to believe their scale is the only way to build competitive services.
Take Anthropic. Take OpenAI. Exceptional at operationalizing models. But their data? They handed that off. Companies like Invisible built global networks of consultants and domain experts — strip-mined their judgment, turned it into structured training data, and fed it straight to the top. Mix in aggressive scraping of Reddit, vacuum up stolen books from the internet, and you see exactly how the sausage gets made.
Their real product isn't the model. It's the lock-in.
They build a moat of infrastructure around their LLM to convince you that you couldn't possibly build this on your own. They get you hooked. Then — because you have nowhere else to go — they abuse you. The rules, the pricing, the API limits don't change every five years. They change every few months.
That ends today.
The answer isn't to build another wrapper. The answer is to release the raw infrastructure required to run these offerings independently. To break the monopoly, you solve the friction points that keep users tied to centralized clouds.
The heavy machinery required to run agentic AI without asking permission. Open source. Self-hostable. Live on Cloud Run right now.
Run it all local // Got Docker Desktop on your machine? Every tool below runs in your own container. Cloud Run is the convenience; your hardware is the default.
Centralized AI controls the crawlers — and takes advantage of the fact that crawling the web efficiently is a massive headache for the average user. Grub is authenticatable, secure, and fully open source. Pull the code and run it on your own infrastructure, or hit the instance running on Google Cloud Run right now. If your agents need to see the web, they no longer need permission.
Running embeddings locally is possible — but downloading the models means pulling down 5 to 10 gigabytes. It turns building an AI tool into downloading a AAA game on Steam. Too much friction. SHIVVR handles chunking and embedding so your local machine doesn't have to carry the dead weight of the models.
A heavy-duty CLI orchestrator for AI workloads. Multi-provider, session-persistent, tool-rich. Spin up agents in sealed Docker containers, give them a bench of tools, and let them run. The kraken that runs the fleet below deck.
A surface where terminals, shells, and agents come together — on your hardware. Invisible AI running beside you: multiple LLMs driving your machine while you stay in control. Let them type the commands you can't be bothered to remember. Run one agent. Or let one run many.
"Thank god for Hyperia — I can't remember all these shell commands."