MuleRun
Raise an AI that actually learns how you work
1.4K followers
Raise an AI that actually learns how you work
1.4K 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.
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@curiouskitty Great question and an honest one to answer. What we've learned is that scheduled and monitoring workloads — daily reports, cron tasks, recurring data pulls — are highly predictable and easy to plan around. The spiky usage tends to come from generative tasks: bulk image creation, multi-step research, complex e-commerce workflows that fan out into many parallel actions. That distinction is shaping how we think about tiering — predictable background work and high-intensity burst tasks have different cost profiles and deserve different controls.
On guardrails: we're building toward pre-task cost estimates, per-workflow spending limits, and usage dashboards that give teams visibility before costs happen rather than after. The dedicated VM architecture already handles performance isolation well — your environment isn't competing with anyone else's. Output quality consistency across long-running workflows is still an active area of work, and we'd rather be upfront about that than oversell it. If you have a specific team workload in mind, we'd genuinely love to dig into the details.
Hey everyone 👋 I'm Growth Manager of @MuleRun
We built MuleRun so AI handles the work, and you get your time back — for the things that actually matter to you.
MuleRun is a personal AI that works for you — from building your own trading assistant to powering complex team workflows like short drama production, game production and e-commerce operations.
What makes it different:
Start from anywhere — works on your phone and desktop, no setup needed. Open mulerun.com, just chat.
Personal AI computer — with long-term memory, running 24/7. It remembers your context and keeps working even when you sleep.
Self evolving — it anticipates next steps and takes action proactively. The more you use, the smarter it will be.
Knowledge network — a growing ecosystem of reusable workflows and capabilities.
Safe— we proactively defend against cyber threats and restrict AI permissions by design. Your data stays yours.
We're early and building fast. Would love for you to try it, break it, and tell us what's missing. Every piece of feedback matters at this stage.
@ines_defirenza The self-evolving angle is interesting. How does it handle situations where a user's habits change significantly, like switching jobs or starting a new project type? Does it adapt forward or does old context start working against you?
@olia_nemirovski Really sharp question — this is one of the more nuanced challenges in building a truly personal AI, and something we've thought carefully about.
The short answer is: old context should never work against you, and the system is designed to adapt forward.
MuleRun's memory isn't a static snapshot that accumulates indefinitely without discrimination. When your behavior shifts significantly — new task types, different communication patterns, a new domain of work — the agent picks up on those signals through your actual usage. New patterns, consistently reinforced, progressively carry more weight than older ones that are no longer being activated.
Beyond passive adaptation, users also have direct control. You can explicitly update your agent's context — telling it you've changed roles, started a new project, or want it to deprioritize certain learned behaviors. MuleRun also supports different scene modes tailored to specific use cases, such as investment, marketing, coding, or research, so switching contexts can be as deliberate as selecting the mode that fits your current work. These settings persist and can be updated at any time without losing what's still relevant.
The goal is for accumulated context to feel like an asset you can curate, not a constraint you're locked into. A great human colleague who's worked with you for years doesn't forget everything when you change jobs — but they do update their understanding of what you need now. That's the behavior we're aiming for.
It's an area we're actively continuing to refine, and honest feedback from users navigating real transitions is genuinely valuable to us. Would love to hear how it holds up for you in practice.
@ines_defirenza That context curation angle resonates. I've been juggling a few different projects at once and the biggest friction isn't the AI forgetting things, it's carrying over assumptions from one context into another where they don't apply. If MuleRun handles that well in practice, that's a real differentiator.
@chris_payne_emba Thank you — and this is one of the most thoughtful questions we've received, so it deserves a real answer.
On how MuleRun decides what to act on proactively versus what to wait for: the distinction comes down to confidence and consequence. For tasks that are highly routine, clearly recurring, and low-risk to get wrong — think scheduled reports, daily briefings, monitoring tasks — MuleRun will execute proactively once the pattern is established, because the cost of acting without asking is low and the value is high. For tasks that are more consequential, context-dependent, or where the agent's confidence in the right approach is lower, it surfaces a recommendation and waits for your confirmation before proceeding. The goal is to be genuinely useful without being presumptuous.
This is also why the 24/7 dedicated VM matters beyond just uptime. It gives the agent a persistent environment to observe real behavioral patterns over time — not just the explicit instructions you give, but the sequence of how you work, what you revisit, what you delegate, and what you handle yourself. That behavioral signal is what allows the proactive layer to be calibrated rather than arbitrary.
On how personas evolve over weeks: what users typically describe is a gradual shift in the nature of their interaction. Early on, it feels more like a capable assistant you're directing. Over time, as it accumulates your domain knowledge, decision logic, and communication preferences, it starts to feel more like a collaborator that's already done the groundwork before you arrive. Some users find it begins anticipating entire workflows — not just individual tasks — based on patterns it has internalized.
The honest answer is that the evolution looks different for everyone, because it's shaped by how each person actually works. That's by design. We'd genuinely love to hear what your experience looks like after a few weeks if you give it a try.
Here's what that looks like in practice. A 3-person Etsy team doing $10M GMV is using MuleRun as their 24/7 e-commerce operator — automatically listing products, screening for IP infringement, researching trending items, and generating product images in bulk, all without adding headcount. A trader with no engineering background built a personal investment assistant that monitors markets around the clock, executes based on his strategy, and proactively initiates post-trade reviews. A content creator is running a full short drama production pipeline — her Mule keeps writing and pushing the story forward even when her laptop is closed. And a first-time game developer with zero coding experience shipped a playable game just by describing what he wanted in plain language. Explore more real workflows here →
@ines_defirenza Really interesting approach! I’m curious as MuleRun scales and more workflows are added, how do you prioritize which tasks the AI should act on proactively versus wait for confirmation? I’d love to see how the balance evolves.
@bello_kanyinsola1
It proactively learns from your tasks and distills insights into private reusable knowledge and skills — you decide whether to accept them.
It recognizes your use cases and recommends relevant public knowledge and skills for you to install.
It remembers your information and task history, anticipates what you may need next, and asks before acting.
Could this be used for iterative optimization research — say, discovering new rendering techniques for 3D games?
For example: have it run 100 passes trying to speed up drawing large 3D scenes (culling geometry that doesn't contribute to the final frame, finding cheaper shading paths, etc.), keeping the best results and iterating on them.
Follow-up on search strategy: Is there a way to preserve candidates that aren't immediately faster but might unlock better optimizations downstream? Basically a beam search rather than pure greedy; keeping a pool of "promising but not yet winning" approaches so it can explore paths that pay off after several iterations, not just the next one.
@jpeggdev Great question — and it touches on exactly the kind of long-running, autonomous workflow MuleRun was built for. Let me be direct about what fits and where the boundaries are.
What MuleRun can do well here:
MuleRun gives the self-evolution layer is relevant too. As you iterate with it on rendering research, it accumulates your preferences, your evaluation criteria, what you consider "good enough" vs. worth pursuing further. Over time it gets better at proposing candidates that match your judgment, not just raw metrics.
On your beam search idea specifically:
This is where I want to be honest rather than oversell. MuleRun is an AI agent platform, not a dedicated optimization framework like Optuna or a genetic algorithm engine. It doesn't have built-in beam search or population-based exploration out of the box.
But here's what you can do: use MuleRun as the orchestration layer. You describe your search strategy in natural language — "maintain a pool of 10 candidates, rank by frame time but also keep 3 that show structural novelty even if they're slower, re-combine the top approaches every 5 iterations" — and MuleRun can write the scripts, deploy them on its VM, execute the loop, persist state across sessions, and surface results to you. It has a full compute environment with file system access, so storing candidate pools, logging lineage of each approach, and implementing non-greedy selection logic is all feasible.
The 24/7 runtime is the real differentiator here. Most AI assistants terminate when your session ends. With MuleRun, a multi-hour or multi-day exploration process just keeps going. And if you refine the strategy mid-run — "shift more weight toward memory bandwidth efficiency, I think that's the bottleneck" — it incorporates that without starting over, because it retains full context.
@jpeggdev This is a genuinely exciting use case and the architecture maps onto it better than you might expect. MuleRun's 24/7 dedicated VM means you can run sustained, multi-pass optimization loops without babysitting them — set up the evaluation criteria, define what "better" looks like, and let it iterate across 100 passes while you're away. The persistent environment means intermediate results, candidate states, and benchmarks all survive across sessions, so nothing gets lost between runs. For 3D rendering specifically — culling strategies, shading path variants, draw call reduction — the agent can execute, benchmark, log results, and feed winning candidates back into the next generation without manual intervention at each step.
On beam search vs. greedy: this is the right instinct and something MuleRun can support by design, but it requires being explicit in how you structure the task. You'd want to define a candidate pool with retention criteria — not just "keep the fastest" but "keep anything within X% of best that opens a different search branch" — and instruct the agent to maintain that pool across iterations rather than pruning aggressively. MuleRun can follow that logic and preserve promising-but-not-yet-winning candidates as long as the evaluation and retention rules are clearly specified. The more precisely you define the search strategy upfront, the more faithfully it executes it. It won't invent a beam search strategy on its own, but it will run one rigorously if you lay out the structure. Would love to see what you build if you take this further.
This is genuinely impressive — the idea of agents that evolve from actual workflow patterns rather than static prompts is a big unlock. The always-on dedicated VM approach is smart too; most agent platforms lose context the moment you close the tab.
Quick question: for agents that handle media workflows (video processing, content production pipelines), how does MuleRun handle large file orchestration? We've been building video infrastructure at Vidtreo and the hardest part is always the handoff between "the AI decided what to do" and "the media pipeline actually executes it reliably."
Would love to see a MuleRun agent that can orchestrate end-to-end video workflows — record, transcode, deliver. That combination of autonomous decision-making + specialized infra could be really powerful.
Congrats on the launch!
@christian_segovia Thanks for the kind words — and the sharp question. Media pipeline orchestration is exactly the kind of problem where MuleRun's architecture pays off, so let me walk through it honestly.
@christian_segovia On the handoff problem you're describing:
You've identified the real pain point: the gap between "the AI made a plan" and "the media infra actually executed it reliably." Here's how MuleRun addresses that:
File persistence. The VM has a real file system. Intermediate outputs — raw frames, transcoded segments, metadata files — live on disk between steps. No ephemeral storage that disappears between API calls.
Cron jobs and proactive monitoring. You can set up scheduled workflows: "Every night at 2am, process today's uploads, transcode, generate thumbnails, push delivery manifest." If something fails, MuleRun proactively reports back to you rather than silently dropping the job.
Self-evolution over time. As MuleRun handles more of your media pipeline, it learns your patterns — your preferred codecs, resolution tiers, naming conventions, QC thresholds. The tenth time it runs your workflow, it's meaningfully better than the first.
Honestly, MuleRun is an agent orchestration platform, not a specialized media infrastructure stack. It's not replacing ffmpeg clusters or purpose-built transcoding farms for heavy throughput. If you're processing thousands of hours daily at Vidtreo, MuleRun isn't your transcoding backend.
But as the orchestration and decision layer sitting on top of your existing infra — that's the sweet spot. Think of it as the production manager who decides what needs to happen, triggers the right tools, monitors progress, handles failures, and reports results. The agent calls your APIs, manages the workflow state, and keeps running whether you're watching or not.
@sylvunny This is one of the most honest and thoughtful maker responses I've seen on PH — really appreciate you walking through the architecture instead of just saying "yes we do everything."
The distinction between orchestration layer vs specialized infra is exactly right. We handle the heavy lifting on the media side (browser-native encoding, chunked uploads, edge delivery) and what you're describing is the intelligent layer that decides when and how to trigger that pipeline.
The cron + proactive monitoring piece is especially interesting for our use case. Imagine: "new video uploaded → MuleRun triggers QC check → if resolution meets threshold, push to CDN → if transcription is ready, notify the client." That whole chain running autonomously on a persistent VM is powerful.
Self-evolution learning codec preferences and QC thresholds over time is a nice touch too — media pipelines have a lot of team-specific conventions that are painful to codify manually.
Would genuinely love to explore what a MuleRun + Vidtreo integration looks like. The "production manager + specialized infra" combo feels like the right architecture for teams that need video but don't want to babysit the pipeline. Let's stay connected 🤝
Curious what some of the most interesting workflows people are building with MuleRun so far.
@carlvert Welcome to explore our Knowledge Network! What I find most interesting is Learning Game Generator
@carlvert Great question! Our users has been building some incredible workflows. Here are a few standouts:
Game Development (zero coding): Users describe their ideas in plain language and MuleRun builds fully playable games — from Tetris to Texas Hold'em. Try some here
E-commerce on Autopilot: A 3-person Etsy team doing $10M GMV uses MuleRun as their 24/7 digital employee — auto-listing products, checking IP infringement, and researching trends. See the workflow
Personal Investment Assistant: Traders build agents that monitor markets 24/7, execute strategies, and proactively initiate post-trade reviews — learning your risk preferences over time. Check it out
Always-on Content Creation: Creators use MuleRun to continuously generate comic/drama scripts — the agent keeps working even when the laptop is closed. See examples
The magic is that users aren't just running automations — they're raising a self-evolving digital partner that proactively works for them 24/7. What's your use case? We'd love to help you get started!
Congrats on the launch. An AI that can keep working while you're offline is a big deal for founders and others juggling many things at once. How does this knowledge network work? Can you share workflows between users or is it all private to you?
@simonk123 Thank you! Users must actively publish a workflow after creation for it to be visible to everyone. The more knowledge users publish, the more sophisticated the knowledge network becomes, and the smarter our Mule gets!
@simonk123 Thank you! You've nailed exactly why we built it — founders and busy professionals shouldn't have to babysit their AI.
On the Knowledge Network: it works on two levels.
Individual level — your agent learns you. Every interaction, decision, and preference gets retained. Your MuleRun agent builds a persistent profile of your working style, risk tolerance, communication habits, and domain knowledge. The longer you use it, the more it anticipates what you need before you ask.
Collective level — community intelligence, opt-in sharing. When you build a workflow or solve a problem in a novel way, you can choose to share that agent into the public network. Shared agents are weighted by how many users have validated them. When someone else faces a similar task, MuleRun automatically surfaces the highest-performing, community-validated agent for that scenario — so you benefit from the collective experience of the entire user base, not just your own history.
Everything is opt-in. Your private data, conversations, and workflows stay in your own isolated cloud VM by default and are never shared without your explicit action. Think of it like open-source, but for agent workflows — you contribute if you want to, and you benefit either way.
The flywheel effect is real: the more people use MuleRun, the smarter every individual agent gets. You can explore some of the shared workflows our community has already built here.
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.
@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.
@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.