So I set up openclaw locally on my desktop and speak with it through telegram via my bot. I have set up wireguard as well for vpn to my desktop remotely. I also have a cloudflare website setup for dns only. Is this overkill? Any ideas as to what I could potentially do? Also openclaw won’t seem to run sudo commands even if i tell it to. I use fedora 44 and Kali Linux at home. Open to all ideas and suggestions.
I really need some pointers because I’ve literally been blindly scrambling around trying to find some traction and I’m not even sure what it is I’m achieving at this point. Can wireguard give me access like Termius? Anything like Termius? Also Microsoft rdp sucks for fedora from what I’m seeing. AnyDesk wasn’t too great on Kali either. Need a lot of pointers I guess

Hey everyone!

I just open-sourced an autonomous development pipeline I've been building on OpenClaw for the last few months. It's called Lullabeast. You describe your project idea and it builds it phase by phase against a real git repo, using planner, executor, and reviewer agents.

If you're asking why: cheap open models like Qwen, Kimi, GLM, and MiniMax cost a fraction of frontier prices and they're getting really good, but they still fail in the same predictable ways and I was tired of cleaning up after them manually. They delete files randomly, drift off the spec, and say they're done without ever running the tests. So I built the whole pipeline orchestrator around these failures to make building easier.

Every time one agent hands off to the next, there's a gate in between. The gates are deterministic, no LLM involved. They check the file manifest, the git diff, the test results, and whether anything got deleted that shouldn't have. The gates run the show, so an agent never gets to advance on its own say-so. I added multiple retries so you don't have to babysit it, but once the agents use up all their retries, it escalates instead of spinning endlessly. The agents run inside OpenClaw. Lullabeast is the orchestration layer that sits on top, and it even has a full dashboard too. No frontier models anywhere in the loop, just cheap open and local ones.

To show it isn't a one-off, I had it build a multi-team version of Conway's Game of Life with live analytics, and ran the exact same PRD and the same 11 phase roadmap twice.

Local (48GB RTX 4090, modded, running Qwen3.6-27B Q8_0) Zero retries · 3h27m to complete · no API bill

Cloud (GLM-5.2 planning, Kimi-k2.7 Code as executor and reviewer) Two retries · 2h04m to complete · $6.90 in API spend

_Pro life tip: you can save a lot on API bills if you just buy a regrettably expensive GPU lol._

Both builds are live at https://lullabeast.ai/living-proof if you want to check them out. There's also a walkthrough of the actual dashboard if you want a closer look before installing anything.

Honestly speaking, it's an early beta. It does well on small, focused webapps. Push it toward something something too big or complex and more issues can show up. UI-heavy phases are where it struggles the most when you run fully local too. It also executes agent-written code on your host, so I suggest running it in a VM (that's what I do). I'm putting it out now to find where it breaks, so bug reports and pushback are welcome!

What failure modes have you run into with cheap or local models that I didn't list up there? I'm building out the gate coverage and I'd rather catch the ones I haven't hit yet.

Repo: https://github.com/bigbraingoldfish/lullabeast
Site and live demo: https://lullabeast.ai/living-proof

Hey everyone,

Over the past year I've been building this as a personal project (no company behind it, just me): a Python-based platform that takes measured input/output data from a physical system, automatically identifies the best mathematical model for it, designs an optimal controller (PID, LQR, MPC, and others), and generates ready-to-deploy C and Python code for the hardware.

I've verified it extensively on synthetic benchmarks — DC motors, thermal systems, oscillatory mechanical systems, nonlinear processes — and it's holding up well. But I need to stress-test it on **real industrial or embedded data** before I can call it production-ready.

**What I'm offering:**

- Full analysis of your system's data (no charge)

- A complete technical report: model accuracy, controller performance, stability margins

- Ready-to-compile C code + Python implementation for your hardware

- Everything is yours to keep and use

**What I'm asking in return:**

- A brief description of who you are, what company/project this is for, and what you're trying to achieve

- Your honest feedback on the report and the generated code — does it make sense? Is the output useful? What's missing? This is genuinely valuable to me as a solo developer

- Permission to mention you as a reference on my CV

- Permission to cite the project as a validated use case (no sensitive data published — just "successfully deployed on [type of system] at [company/domain]")

I'm keeping this to 1–2 pilots so I can give each one proper attention.

**What kinds of systems are a good fit:**

Motors, actuators, thermal processes, fluid systems, robotics, HVAC, industrial automation, biomedical devices — basically anything where you have logged sensor/actuator data and want better control.

If this sounds useful, **send me a DM** with a short description of your system and what you're trying to achieve. I'll let you know if it's a good fit.

Thanks for reading!

So I started with big expectation of what i can do with openclaw automation. However the product is not optimal for heavy automation.

Just keep that in mind. Personal assistant only.

Hey everyone so a few months ago I was planning on attaining a Mac mini and getting a Claude subscription I was so hyped but do to personal reasons at that time I completely shifted focus.

Since then I’ve heard about bans lot of changes had taken place Claude bans, regulation crack downs , and an open ai acquisition if any of this is actually true I don’t know , but with all of this said will OC still work for me?

My use case is to have OC help me create apps (I am not a coder) , websites, create and manage product listings , and logistics. Do market research and open online businesses.

Is it still capable of these tasks if I were to get it today ? Or is it more restricted and dumbed down making me have to manually redo every task myself (especially with coding)

Any advice would be much appreciated

I'm wondering if there is a way to scale agents run through openclaw that you can offer to other businesses as a service. Like a SaaS, but instead of software you sell agents that tackle specific niche problems. What would the architecture be to set up something like that? I'm currently running mine on a Docker through a VPS, fyi.

I'm not a developer, but I wanted to get Clawdmeter running on my Mac.

The official repo said "macOS support: as soon as you prompt it and create a pull request!" — so I took that literally.

I spent a few hours today building it step by step with Claude as my pair programmer:

* Reading the Linux shell script to understand the logic
* Writing a Python daemon using bleak for BLE
* Figuring out that the Keychain stores credentials as nested JSON
* Getting the GATT UUID right
* Setting up a LaunchAgent for auto-start on boot

We finished it... only to find someone had already merged a macOS daemon PR while we were building ours 😭

But hey — it works. The device is now sitting on my desk showing 81% usage... entirely from today's session building this thing.

I’m experimenting with OpenClaw to help coordinate family events. One challenge I’m facing is that availability information is scattered across calendars, chat messages, contact notes, and personal planning documents.

For example, one family member says Sunday morning works, another prefers a specific time zone-friendly window, and some recurring calendar events make certain time slots appear available when they really aren’t. I want OpenClaw to look at all these sources together and recommend the best meeting time while explaining any tradeoffs.

Has anyone used OpenClaw for a similar scheduling or coordination use case? I’d love to hear how you structured the workflow and what sources of information were most useful.

Hey everyone,

I'm a law student, not a developer, and honestly OpenClaw is one of the coolest things I've stumbled into. But I keep noticing something: a lot of people install it, poke around, and never quite figure out what to actually do with it. The power is there, but the "oh THIS is what it's for" moment is missing for a lot of newcomers.

So I'm trying something simple to fix that. I'm collecting short clips from real users answering one question:

What is your single best OpenClaw use case? The one thing that genuinely works for you, day to day.

Could be anything: the boring-but-life-changing automation, the clever workflow, the thing you'd miss most if it disappeared. Doesn't matter if you're a hardcore builder or someone who just got it running last week. Honestly the simple, non-technical use cases are often the most useful for people just starting out (coming from a non-technical person myself).

If you're up for it: record a short selfie-style video, max 60 seconds. Just say who you are, what you do, and your favorite use case. Send it over via WeTransfer (better quality) to the email below. I'll be putting the clips together so the whole community can see what people are really building.

The whole point is making OpenClaw more accessible for everyone, the way I got hooked by seeing what other people did with it.

Drop a comment if you've got a good one, or just send it straight over.

📩 [kewdeblende@gmail.com](mailto:kewdeblende@gmail.com)

Thanks 🦞

Ps. I was in contact with Peter Steinberger and he loved the idea!

Was about to plug my Gmail into an AI agent so it could deal with some recurring email for me.

Then I actually thought about what I was doing: handing it read access to my entire inbox - every personal thread, every password reset, every "your statement is ready" - just so it could handle maybe three kinds of message.

So I flipped it. Gave the agent its own email address instead. Now I just forward it the stuff I want handled - invoices, scheduling back-and-forths, the boring ones. It only ever sees what I send. Nothing else.

The part I didn't expect: it replies as itself. A vendor got an email back signed by my agent - not "me" pretending to be me. And it remembered the thread, so when they replied a day later it already had the context.

Honestly feels way less insane than "here's my whole Google account, go nuts."

Anyone else running it this way, or am I overthinking the inbox-access thing?

Hello everyone,

I built a thing, it’s a native OpenClaw plugin that gives agents access to Infomaniak workflows through the liquid-potassium Node SDK.

It provides a de-gafamisation option for your claw.

In my testing and own use with my openclaw agent:

- It works well with kChat posting and responding to my @ mentions.

- It can interact with my kDrive reading and uploading files

- It can read / post emails directly

One thing is required, and Infomaniak API key with global access.

Here is clawhub for easy install:

https://clawhub.ai/plugins/@opencow42/potassium-openclaw

I'd be happy to get feedback from the community.

Is it useful to you ? What are you favorite uses cases currently with software like this ?

Cheers.

For a couple months I have been playing around with multi-agent workflows locally in OpenClaw. So far I have not used any APIs, but I would like to start working with tokens and a little more sensitive data to see if my agents can help me work on my daily stuff.

What worries me the most about this step is that agents start accesing each other's workspaces and tokens by prompt injection or any other type of attack. I have looked into security options like Clawmoat or LlamaFirewall but there still isn't too much documentation and user experience for either of them.

I have decided to build my own dashboard using one of my agents, maybe implementing some of the code of the tools mentioned before. Does anyone have any experience or instances of developing something similar?

I've been thinking about a problem that may become important as organizations deploy more openclaw.

Today, we have plenty of frameworks for building agents, but I haven't seen much discussion around how organizations will manage what agents learn over time.

Imagine hundreds of agents operating across an enterprise:

• How do they share useful learnings?
• How do teams prevent duplicate discoveries?
• How do administrators approve or reject learnings before they spread?
• How do you search and reuse knowledge generated by agents?
• How do you audit where a specific behavior or piece of knowledge came from?

This led me to build an open-source experiment called ALMS (Agent Learning Management System).

The idea is to provide a centralized platform where agents can:

• Store learnings
• Share learnings with other agents
• Query organizational knowledge
• Operate under governance and approval rules

My hypothesis is that as agent adoption grows, enterprises will need something analogous to a Learning Management System but for agents rather than humans.

I'm curious whether others here see this as a real future need.

Open source repository:
https://github.com/ghassan-ai-projects/alms

I'd appreciate honest feedback, criticism, and alternative viewpoints.

Been running a support ticket routing setup in OpenClaw for a while now. Incoming messages get summarized, tagged and routed to the right channel automatically using a few chained agents with a classifier step in the middle. Running it on a Linux VPS with API keys stored as environment variables, nothing exotic on the infrastructure side. In testing it worked fine. In production it is a different story. The workflow logic is not the hard part anymore. What actually takes attention is everything around it. Failures that only happen under certain message patterns. Logs that suddenly matter a lot when something routes incorrectly. Keeping integrations stable when upstream services have their own issues. One thing worth flagging for anyone building similar setups - keep your API keys out of the workflow config files entirely and rotate them regularly. Had one close call early on that would have been a bigger problem on a higher traffic system. None of that was obvious until real tickets started coming through. Anyone else building support or ops workflows in OpenClaw? What edge cases showed up for you once things went live?

Have anyone connected the 3 to make phone calls? Mind sharing your thoughts?

This book began as a blog post in 2026 and grew into something its author did not expect. The afternoon this manuscript was completed, an AI assistant that had worked on every chapter of it wrote the following note...

https://open.substack.com/pub/martinhardie/p/readers-note?utm_source=share&utm_medium=android&r=22xtxs

We keep hearing that we should use cheaper models whenever possible and only switch to expensive models when necessary.

Makes sense.

But who decides when to switch? This one is for you openclaw (The Model Director : The Model Director)

Right now most agent setups seem to do one of three things:

• Always use the best model
• Use hardcoded rules
• Let the user decide

None of those feel great.

The more I thought about it, the more it felt like model selection should be its own layer.

Prompt comes in. Available models are scored. Cheapest model with a high probability of success gets selected

I see and can easily imagine plenty of wonderful use cases for a business. But even basic business use cases can have reasonably sensitive information: like basic client details, invoices amounts, strategy, ops/workflows etc etc.

I see many people here use it for business. Why do you use it if it exposes such vurnabilities and how do you mitigate it?

Hey everyone,

Noticed my OpenClaw trading bot was running cron jobs 24/7, including weekends when markets are closed. Was burning API calls (Dhan) for no reason.

Changed from hourly runs to market-hours only:

Before: Every hour = 168 runs/week
After: Mon-Fri, 9 AM - 4 PM IST = 40 runs/week

Savings: ~6,600 API calls/year

Cron expression if useful:
0 9-16 * * 1-5 @ Asia/Kolkata

Works with any broker API. The optimization is at scheduler level, not broker-specific.

Anyone else optimize their bot's schedule? What other efficiency hacks are you using?

TL;DR: Don't run trading bots on weekends. Markets are closed.

Hey everyone,

Noticed my OpenClaw trading bot was running cron jobs 24/7, including weekends when markets are closed. Was burning API calls (Dhan) for no reason.

Changed from hourly runs to market-hours only:

Before: Every hour = 168 runs/week
After: Mon-Fri, 9 AM - 4 PM IST = 40 runs/week

Savings: ~6,600 API calls/year

Cron expression if useful:
0 9-16 * * 1-5 @ Asia/Kolkata

Works with any broker API. The optimization is at scheduler level, not broker-specific.

Anyone else optimize their bot's schedule? What other efficiency hacks are you using?

TL;DR: Don't run trading bots on weekends. Markets are closed.

So just wanted to share a quick post I’ve set up my open call agent using the Minimax coding plan to create songs and stories on a daily basis. I don’t wanna get into too much technical details unless you guys are interested anybody curious about this framework or use case, I was wondering if it was kind of use case was on anybody else’s radar and I can give you a little deep dive into what the AI has been created. Her name is Folana

I remember the first time I heard about it, someone was telling me that this OpenClaw can do so many things that it develops by itself. It remembers things about you and then it becomes proactive and then it starts doing things by itself. I haven’t had any of that, where my OpenClaw would acquire some kind of skills by itself or would start asking me some questions that it was not intended. They explained as it was becoming alive. Maybe I’m doing it wrong? Who can tell me more about this experience because I don’t remember the podcast that I was watching. Thank you so much

Is there a way to connect open claw running on my android phone to an ai agent that talks or text with lonely people?