Launching today
MuleRun
Raise an AI that actually learns how you work
607 followers
Raise an AI that actually learns how you work
607 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 'learns how you work' angle is what caught my attention — I've spent years building automation tools and the hardest part is always making them context-aware without manual setup. How does it handle domain-specific workflows that combine different tool stacks? Curious whether it can track patterns across Claude Code sessions and terminal commands, which is where most of my work happens.
MuleRun
@slavaakulov Hey, appreciate the thoughtful question — this is exactly the problem we built MuleRun to solve.
On context-awareness: MuleRun continuously learns your decision logic, work habits, and tool preferences across all interactions. There's no manual setup or config files — it builds your personalized profile through natural conversation and observation. Over time, it starts proactively predicting what you need and pre-loading the right tools before you even ask. We call this going from "wait for your command" to "already thinking ahead for you."
On domain-specific workflows: each user gets a dedicated 24/7 cloud VM with its own file system, pre-installed software, and hardware-level config. So it's not just a chat window — it's a persistent working environment where your agent can deploy services, run cron jobs, and handle long-running tasks autonomously, even when your browser is closed. This makes it particularly natural for combining different tool stacks within a single continuous workspace.
On the collective intelligence side — when users solve problems effectively, those solutions can flow into our Knowledge Network. The more people use it, the smarter everyone's agent gets for similar scenarios. Think of it as battle-tested workflows shared across the community.
For your specific developer workflow, I'd recommend trying our "Coding & Building" mode — it's designed for hosting and running services 24/7 on your dedicated VM. Would love to hear how it fits into your stack. Feel free to jump in and give it a spin.
MuleRun
@slavaakulov This is exactly the kind of use case we get most excited about — and your framing is spot on. Context-awareness without manual setup is the hard problem, and it's precisely what MuleRun's architecture is designed to address.
Here's how it handles complex, multi-tool workflows: every MuleRun user gets a dedicated cloud virtual machine with its own persistent file system, pre-installable native software, and configurable environment. This isn't a sandboxed chat interface — it's a real compute environment where your agent operates continuously. That means it can run terminal commands, manage files, execute scripts, and interact with your tool stack as a native process, not through fragile API wrappers.
On the pattern-learning side, MuleRun tracks not just what you ask for, but how you work — the sequence of operations, the tools you reach for in specific contexts, the outputs you accept versus revise. Over time, it builds a working model of your decision logic, so it can begin anticipating the next step in a workflow rather than waiting for instruction.
For a developer workflow spanning terminal sessions and coding environments, the practical implication is that your agent can observe recurring patterns — say, a sequence of build, test, and deploy commands you run in a particular order — and start preparing or executing those proactively. The 24/7 runtime also means long-running processes don't get interrupted when you step away.
That said, deep integration with specific tools like Claude Code is an evolving area and I'd rather be honest than overpromise. The best way to pressure-test it against your specific stack is to get hands-on — we'd genuinely value the feedback from someone with your background. Happy to get you set up if you want to dig in. You can explore the technical architecture further here.
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.
@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
“Raise your AI and watch it evolve” is such a cool framing! Curious how fast the learning actually happens in real usage.
MuleRun
@blink_66 I guarantee it will blow you away!
MuleRun
@blink_66 Glad that framing resonates — it really does capture how we think about the relationship between user and agent!
On learning speed: it's genuinely two-speed. Explicit preferences — tone, format, recurring instructions — are picked up immediately and carried forward from your very first sessions. The deeper layer, where MuleRun starts anticipating workflows and acting ahead of you, builds more gradually as it accumulates real behavioral signal. Most users notice that shift after consistent use over days and weeks rather than hours. The more varied the tasks you run through it, the faster that model of you sharpens. It compounds — which is kind of the whole point.