Launching today
MuleRun
Raise an AI that actually learns how you work
887 followers
Raise an AI that actually learns how you work
887 followers
MuleRun is the world's first self-evolving personal AI — it learns your work habits, decision patterns, and preferences, then keeps getting sharper over time. It runs 24/7 on your dedicated cloud VM, works while you're offline, and proactively prepares what you need before you ask.No coding. No setup. Just raise your AI and watch it evolve.
Having your AI be able to learn your patterns over time instead of starting from scratch every time is essential and the missing piece in a lot of AI platforms. Congrats on the launch! How long does it typically take before the AI starts feeling noticeable personalized to how you work?
MuleRun
@aya_vlasoff It depends on your usage frequency and specific needs. Why not start by trying out our Computer feature : ) It's going to pleasantly surprise you.
MuleRun
@aya_vlasoff Thank you — and you've put your finger on exactly the gap we set out to close!
On your question: personalization in MuleRun happens in layers, so there isn't one single "aha" moment — it builds progressively.
Most users notice the first signs quite early. Within your first few sessions, MuleRun starts retaining your stated preferences, communication style, and recurring task patterns. If you tell it you prefer concise summaries over long reports, or that you always want data sourced before recommendations, it carries that forward immediately — no need to repeat yourself next time.
The deeper personalization — where MuleRun begins anticipating what you need before you ask, proactively preparing relevant information, or suggesting workflow optimizations based on your habits — typically becomes noticeable after more sustained use, as the agent accumulates enough signal from how you actually work day to day.
The honest answer is: it compounds. The more tasks you run through it, the more behavioral data it has to work with, and the more accurate its model of you becomes. Users who engage with it consistently — especially across different types of tasks — tend to feel that shift most strongly.
Think of it less like configuring a tool and more like onboarding a new team member who gets sharper every week. We'd love to hear what your experience is like once you've had a chance to try it!
The self-evolving concept reminds me of what I wish every AI tool did. Actually learn from repeated use instead of starting from scratch each session. How do you handle cases where it learns a wrong pattern from the user?
MuleRun
@mehmet_kerem_mutlu Thanks for raising this — it's a critical question for any system that claims to "learn."
MuleRun's self-evolution works on two levels, and both have built-in correction paths:
At the individual level, MuleRun interacts with you
At the collective level, this is where it gets interesting. MuleRun has a Knowledge Network where effective solutions
On top of that, MuleRun's Heartbeat system actively analyzes your usage patterns and pro
The short version: you're the one raising
Would love for you to try it out and stress-test this yourself.
MuleRun
@mehmet_kerem_mutlu Really important question — and one we take seriously, because an AI that learns the wrong things confidently is worse than one that doesn't learn at all.
The safeguard is that the user always has the final word. When MuleRun acts on a learned pattern and gets it wrong, your correction is itself a learning signal — it doesn't just fix the immediate output, it updates the underlying model of how you work. One clear correction carries significant weight precisely because it's an explicit signal, not just passive behavior.
For higher-stakes tasks, MuleRun is also designed to surface its reasoning and confirm before acting, rather than silently executing on an assumption. The more consequential the action, the more it checks in.
And if a pattern is deeply ingrained in the wrong direction, users can directly update or reset specific learned behaviors — you're never locked into what the agent has accumulated. Think of it like course-correcting a colleague: a clear, direct conversation resets the expectation far more effectively than hoping they'll figure it out on their own.
The goal is earned trust, not blind automation.
Congrats on the launch! Curious — how is MuleRun different from traditional automation tools like Zapier when it comes to handling complex workflows?
MuleRun
@anaspis MuleRun replaces rigid "if-this-then-that" workflows with AI agents that understand what you want and figure out how to do it.
MuleRun
@anaspis Great question — and it's a distinction worth drawing clearly.
Zapier and traditional automation tools are fundamentally rule-based. You define a trigger, map a sequence of steps, and the tool executes that exact sequence every time. It's powerful for predictable, repetitive tasks, but it breaks the moment something falls outside the predefined logic. Every new workflow requires manual setup, and the tool has no understanding of context — it just follows instructions.
MuleRun operates on a completely different layer. Rather than executing fixed rules, your MuleRun agent understands the intent behind a task and figures out how to accomplish it. You describe what you need in plain language, and the agent determines the steps, selects the right tools, handles exceptions, and adapts when conditions change — without you having to anticipate every edge case upfront.
A few concrete differences:
No manual workflow mapping. With Zapier, you build the automation. With MuleRun, you describe the outcome and the agent builds and executes the path to get there.
Context and memory. MuleRun retains your working history, preferences, and domain knowledge across sessions. It gets better at your specific workflows over time. Zapier has no memory of you — every run is stateless.
Proactive vs. reactive. Zapier waits for a trigger. MuleRun can proactively identify what needs to be done based on patterns it has learned, and act before you ask.
Always-on execution. Because MuleRun runs on a dedicated 24/7 cloud VM, it can handle long-running, multi-step tasks that unfold over hours — not just instant trigger-response actions.
Think of Zapier as a very efficient set of pipes. MuleRun is closer to a digital employee who understands your business, learns your preferences, and figures out the plumbing themselves. You can see real workflow examples here.
How does MuleRun’s “self-evolving” feature actually learn and anticipate my needs over time, and can I control or review the actions it takes proactively? I'm just curious, not the secret sauce but in general.
MuleRun
@marcelino_gmx3c Thank you for your question! By continuously learning a user's work patterns, schedule, and communication habits, MuleRun builds a personalized profile that proactively recommends to-do items. Based on how the user has handled problems in the past, MuleRun intelligently anticipates solutions for similar issues and preloads the right tools, boosting task efficiency.
MuleRun
@marcelino_gmx3c Happy to walk through the general picture!
On the learning side, MuleRun builds a model of you through three main signals: what you explicitly tell it about your preferences and working style, how you actually behave across sessions — the tasks you run, the outputs you accept or revise, the patterns that repeat — and the feedback you give when it gets something wrong. All of this accumulates in your dedicated cloud VM, which persists across sessions rather than resetting.
On the proactive side, MuleRun has a built-in Heartbeat mechanism — it doesn't just wait for you to show up. It will proactively summarize what's been done, flag things worth your attention, and suggest next steps based on your habits. For recurring tasks you've set up, it executes on schedule without you needing to prompt it each time.
On control and transparency: yes, you can review what it's learned, adjust your preferences, and update or cancel any scheduled tasks at any time. For higher-stakes actions, it checks in with you before proceeding rather than acting unilaterally. The goal is that you always feel in the loop — proactive help shouldn't mean surprises.
So the short version: it learns from your behavior, acts on patterns it's confident about, and keeps you informed and in control throughout.
Congrats on the launch! The self-evolving angle is what makes this stand out; most AI tools are static from day one, and it's on the user to figure out how to get more out of them over time.
How does it handle domain-specific workflows, like financial analysis or structured research tasks? Does it get better with use, or is the learning more behavioral, adapting to how you work rather than what you're working on?
MuleRun
@andreitudor14 Thanks! To answer directly: both.
It learns how you work — your format preferences, communication style, tool choices, decision patterns. And it learns what you work on — your domain context, terminology, data sources, evaluation criteria.
For finance specifically, there's a dedicated Investment mode that comes preloaded with market monitoring, portfolio analysis, and daily briefing capabilities. But the real value compounds over time: it learns your risk framework, your sector focus, your analytical priorities. By month two, "run the usual analysis" just works.
Same applies to research workflows — it retains your methodology, your quality bar, your preferred report structure. Each correction makes the next output sharper.
The key: this isn't prompt-level memory tricks. It's persistent knowledge on a 24/7 dedicated VM that accumulates across every session and every channel. Static tools make you repeat yourself. MuleRun makes repetition unnecessary.
@MuleRun Hello, congrats on your launch. Interesting product, just a few questions. How do you enable the long term context to be functional? The context growth with the user, the context of the model is limited. How do you manage to keep it all together, working as intended?
MuleRun
@dingleberryjones Hey, thanks! Great question — and honestly one of the core architectural bets we made early on.
The short answer: we don't try to cram everything into a model's context window.
1. Persistent memory ≠ chat history.
2. Your agent has its own machine.
3. The system actively distills, not just stores.
And
We separated memory from model context at the architecture level. That's the fundamental answer
Curious about the feedback loop when it comes to "self-evolving" feature. How does it know what is the "correct" thing to learn and not pick up bad habits?
MuleRun
@tteer MuleRun's self-evolution is anchored to your explicit behavior, not unsupervised inference. It learns from what you correct, what you approve, what you repeat, and what you discard. When you tell it "no, use this tone instead" or "that analysis missed the point, here's what I actually need" — that's the training signal. It's building a model of your decision logic, your preferences, your standards.
So it's less "AI teaching itself" and more "you shaping a digital employee through daily work." The same way a junior hire gets better by watching how you react to their output — except MuleRun has perfect recall and never forgets the correction.
What prevents bad habit formation?
A few things by design:
You remain the authority. MuleRun doesn't silently lock in behaviors. When it acts on a learned pattern — say, auto-formatting a report a certain way because you've preferred it five times before — you can override it anytime. One correction updates the model. It doesn't argue with you or revert.
Transparency of learned context. MuleRun's long-term memory isn't a black box. Your preferences, established workflows, accumulated knowledge — these are inspectable. You can see what it "thinks it knows" about you and correct or remove anything that's wrong. Think of it as a profile you can audit.
The knowledge network acts as a quality filter. On the collective intelligence side, agents and solutions shared by users don't just get blindly propagated. They're weighted by validation — how many users have successfully applied them in similar scenarios. High-weight solutions surface; untested or poorly-performing ones don't. It's closer to peer review than viral spread.