One workspace that assembles the interface each task needs, then does the task with you.
The same core, delivered three ways. Pick the one that fits the machine you already have.
Start from what changes for you, not from the architecture.
How the system is put together, and what it will and will not let an agent do.
Who is building this, where it came from, and where it is going.
MeghaOS speaks over 100 languages on your own machine. This site is available in full in every language listed here; our legal pages and blog posts stay in English.
Engineering decisions, architecture arguments, and the occasional thing we got wrong.
What MCP is, how the transport and primitives actually work, how servers are built and connected, and what you are granting when you connect one.
Voice is the fastest input humans have, and we use it for timers. The reason is not accuracy. It is that every mainstream assistant points a microphone at somebody else's data centre.
Why an agent's capabilities are its attack surface, how prompt injection turns tool access into a real exploit, and which mitigations actually hold under pressure.
What a read-only root filesystem actually changes, how A/B atomic updates and BTRFS snapshots work, and why the model suits agentic workloads in particular.
What hardware you need, how quantisation and context length drive memory, which model sizes suit which work, and where local inference stops being the right answer.
Every guarantee that matters for agentic AI (isolation, egress control, rollback, attribution) is enforceable at the OS layer and merely promised anywhere above it.
People withhold context from cloud assistants, correctly. That withholding is what makes those assistants mediocre.