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Happily, current AI is interacted with in a reactive loop. I open the Claude app, CLI, whatever, say my prompt, get an output.

I personally wanted an AI that was able to reach out to me about my life before I had to reach out to it. An example, a friend just emailed me asking to meet for at 1pm but I have class at 1:30, so a proactive AI would see that conflict and send me a notification about it, asking if the proposed email it drafted works, then I press send.

My personal setup tracks my mouse movement, keyboard, what’s on my screen, and keeps track of what I’m working on through files on my PC. It can update the backend and then restart it on it’s own, meaning I can develop the thing itself while being away from my PC.

The capabilities are more than what I’ve listed, but I want to avoid being too preachy about something I made. Here’s the repo if you wanted to take a look, it’s open-source and connects to the iPhone app:

https://github.com/getorb/Orb-Backend

8 comments

> An example, a friend just emailed me asking to meet for at 1pm but I have class at 1:30, so a proactive AI would see that conflict and send me a notification about it, asking if the proposed email it drafted works, then I press send.

I don't mean to downplay your work, but I think you should come up with a better example use case. Automating away interactions with friends is pretty much the last thing I want AI to do.

It's very common to experiment with ideas using your own data or personal niche, then branch out by finding analogies in real world business applications or (less commonly) a consumer tool with a cleaner pitch. The important thing is talking to other people to find those usecases or refine the public angle.

Good point, another example would be for when I was training my own small LM a while back, the target was about 170M parameters and was trained on 2B tokens worth of movie subtitles.

The run stalled mid-step around 80M parameters, Orb notified me that it stalled, asked if I wanted to resume at the last checkpoint and kill the stalled version. I simply press “yes” and continue doing whatever I was doing.

For non-technical users and non-antisocial people, remembering things you forgot so you don’t let people down. You told your sister you’d send her some pictures two hours ago but it can see you’re scrolling on reddit and the photos are on your desktop, so it assumes you forgot and reminds you.

The idea was that the biggest issue with the usefulness of an agent is that it has too little context about who I am, it needs more data. So I run all of my data through a smart router, then the local database, then the LLM reviews it and uses reasoning on what’s been collected.

> You told your sister you’d send her some pictures two hours ago but it can see you’re scrolling on reddit and the photos are on your desktop

Hmm, so your harness is processing my activity and personal data, uploading all that to AI vendors? That sounds like a privacy and security nightmare, if I understand correctly

That second one resonates a bit better for me.

Personally I don't think I'm ready to hand over total access to my digital life until I can self-host the model capable enough to act on it, but either way there is definitely some cool work to be done in the model harnesses for this.

I'm very interested in this kind of thing as a kind of ADHD brain augment, like it's monitoring my slack, github, email, calendar, active terminals, etc, and helps me prioritize what I should work on as well as weighing whether this or that ping is worth interrupting me for.

I assumed that's what openclaw basically was, but is Orb different from that? And is it fundamentally a different model from the request/response, or is it just request/response in an autonomous loop?

Exactly what my thoughts were when I first heard about Openclaw, that’s the exact idea of Orb that you pointed out. Letting you make less decisions, right now AI gives you answers but still requires decisions based on the outputs it gives you. This would deepen the actual ability of agents in those channels you listed.

On a fundamental level the backend was designed to do as little LLM calls as possible, for instance it’ll do scans of my screen every 15 seconds, log what’s on it and what’s going on, and store it in a local database, then Orb reviews the entire database every 6 hours for me. Then it’ll schedule wakeups for itself throughout the day, up to 4 so it doesn’t waste my tokens, and schedule notifications based on the last database dump it made.

I have my Claude Code, Codex, and Grok Build all useable by using the “claude -p; codex -p…etc” so you can also use multiple CLI’s in conjunction at the same time on different projects or the same project.

So your question about a loop is kind of right, but it really just collects your data all day and stores it locally on your PC then calls the LLM of your choice and it reviews all the data and makes those proactive moves we’ve discussed. You could theoretically get it to always be scanning by an LLM but that would be a drastic waste of money from what I’ve seen since most things don’t require a call.

Okay interesting, so maybe openclaw does a lot less than I thought it did; really I have no excuse not to be just trying it myself regardless, given that I'm sitting on a 9070 XT.

It doesn't look like you're building directly on openclaw, so is that coming from a place of different goals or philosophy, or what?

Openclaw is great from when I’d used it for a couple months, the key difference is that for Openclaw you’ll have to actually schedule the cron jobs or automated tasks yourself, so it’s a decision on your part to make the AI do a thing whereas Orb is a decision on the part of the AI (after your approval) to do the thing. So it’s a kind of shift of agency. Openclaw could definitely do many similar things, it just would do them only after you specifically instructed it to.

It’s built as it’s own backend and philosophy wise, I want my personal AI to connect to everything in my life and have complete context over all data that I own, be compiled into a neat stack of data accumulating, then when it thinks it’s appropriate to do/say something, it’ll do it without my involvement.

Nice card btw, I got my RTX 5070 a couple months ago and it runs like a dream.

I mostly got it to play Arc Raiders tbh, but I did kind of have in mind that it was worth stretching for 16GB so that local models would be on the table.

Hmm I might be completely missing something here (I’m not a machine learning person), but how is that “pro-active”? The mode is still taking in an input (or several inputs maybe in this case) and responding to that input, isn’t it? It doesn’t seem much different than current capabilities of the various agentic harnesses on the market right now. Again, apologies if I’m missing something obvious here…

OK really cool. I've been working on something similar but I started from the opposite direction. I first developed a database that pulls in data from as many personal sources as possible, and then runs it through a gradual annotation funnel going from coarse to fine, culminating in embedding for semantic and lexical search. At that point, it computes timelines and has resolution gradients along timelines (older = lower resolution, newer = higher resolution. Then I built an MCP server for that which can be used to query it. What's missing is the afferent arm, so these might be a really nice pairing.

>> Curious what you mean by proactive? Could you share a bit more?

> Happily, current AI is interacted with in a reactive loop. I open the Claude app, CLI, whatever, say my prompt, get an output.

"Current AI" is not limited to LLM offerings. There are many AI algorithms which can assist in what you specify thusly:

> I personally wanted an AI that was able to reach out to me about my life before I had to reach out to it.

Consider a forward chaining inference engine ("expert system") provided with relevant asynchronous percepts from the deployed environment to reason about. This could serve as an initiator of a "proactive AI".

But it's not proactive then? There's just a hidden loop or some cron-like signal that feeds data to a reactive loop..

I'd imagine proactive as something like, hmm, no signal from X, I wonder how they're doing...

What’s so innovative to send an auto message every x time or based on events: “wake up and check if you got anything to do”.

“Event x happened at y time”

How does this differ from Hermes / Openclaw / Vellum?