With all the talk about AI companions and autonomous agents, I’ve been experimenting with building a more personal, always-on assistant that runs locally or on your own hardware.

The goal wasn’t just another chatbot — it was something that could handle voice conversations, manage ongoing tasks across different platforms (chat apps, scheduled triggers, etc.), remember context over long periods, and delegate work without constant babysitting.

What stood out in practice

• One consistent “brain” across everything — Whether you’re talking to it via voice, Telegram, a web interface, or it wakes up on a schedule, the core reasoning, memory, and tool use stay the same. This eliminated a lot of the fragmentation you see in many current agent setups.
• Modular extensions — Different capabilities (voice, different chat networks, external tools, long-term memory consolidation) plug in cleanly. This made it easier to add or swap things without rebuilding the whole system.
• Persistent and proactive — It can maintain memory across days/weeks, run background tasks, and even hot-reload its configuration when you change settings.

The result is something that starts feeling more like a digital collaborator than a question-answering box. A quick feel for the voice interaction style is here: https://youtube.com/shorts/NGIi8sliooU

I open-sourced the harness (called Maven) under an MIT license for anyone interested in running or extending their own version: https://ageneral.ai/maven

I’m curious how others are thinking about personal agent setups in 2026.

• Do you prefer fully local models, cloud APIs, or a mix?
• What capabilities feel most missing from today’s consumer AI assistants?
• How important is “owning” your agent data and runtime vs. using polished third-party services?

Would love to hear experiences or concerns from both technical and non-technical users.

With all the talk about AI companions and autonomous agents, I’ve been experimenting with building a more personal, always-on assistant that runs locally or on your own hardware.

The goal wasn’t just another chatbot — it was something that could handle voice conversations, manage ongoing tasks across different platforms (chat apps, scheduled triggers, etc.), remember context over long periods, and delegate work without constant babysitting.

What stood out in practice

• One consistent “brain” across everything — Whether you’re talking to it via voice, Telegram, a web interface, or it wakes up on a schedule, the core reasoning, memory, and tool use stay the same. This eliminated a lot of the fragmentation you see in many current agent setups.
• Modular extensions — Different capabilities (voice, different chat networks, external tools, long-term memory consolidation) plug in cleanly. This made it easier to add or swap things without rebuilding the whole system.
• Persistent and proactive — It can maintain memory across days/weeks, run background tasks, and even hot-reload its configuration when you change settings.

The result is something that starts feeling more like a digital collaborator than a question-answering box. A quick feel for the voice interaction style is here: https://youtube.com/shorts/NGIi8sliooU

I open-sourced the harness (called Maven) under an MIT license for anyone interested in running or extending their own version: https://ageneral.ai/maven

I’m curious how others are thinking about personal agent setups in 2026.

• Do you prefer fully local models, cloud APIs, or a mix?
• What capabilities feel most missing from today’s consumer AI assistants?
• How important is “owning” your agent data and runtime vs. using polished third-party services?

Would love to hear experiences or concerns from both technical and non-technical users.