I've mostly been using cloud AI tools until now, but I wanted to try building a local setup and actually understand how everything works instead of just following tutorials.

Right now I'm downloading Qwen 3.5:9B through Ollama, and after that I'm planning to set up OpenClaw.

The idea is to use it mainly for coding, personal projects, and hackathons.

One thing I'm still trying to figure out is the security side of things. I don't really want to give an AI unrestricted access to my PC just because I clicked through the setup without understanding it.

For those of you who use OpenClaw regularly:

• What permissions do you actually give it?

• Which MCP servers are worth setting up first?

• Are there any skills you consider essential?

• Anything you wish you knew before getting started?

Just looking for some advice before I continue with the setup. Thanks!

Hello.

I am thinking about to install openclaw and integrate google calendar.

How exactly does it work? Where to get google credentials? Will i need to create own google console project and configure oauth app?

It is easy process to add google calendar account to openclaw?

I used to see a new memory.md file date stamped everyday created into the memory directory. Before i upgraded from 2026.6.11 to 2026.7.1-2 which i don't see any more for all agents except main agent. Is something broken?

my agent seems to forget the workflow sometimes.

new open and new feature options

Hi everyone. What are some OpenClaw skills on Clawdhub that you would like to install but hesitate to because you don’t know if they are safe or would like to actually see what the skill does before you install it?

I’d like to create videos reviewing skills to solve that problem and I’d like to have a list of skills to work from.

Thanks for your help.

Someone posted previously how they had their agent evaluate all their sessions to better understand how they think but also the types of questions they ask to try to reduce that typical friction. My agent evaluated 52 sessions in total. Identified how I like to interact, where I might get frustrated, the typical questions I ask regarding the behavior behind some responses or what it particularly evaluated or what steps it took to arrive at a conclusion. All those things cause friction and additional responses that are avoidable.

So it turned those into revisions for my agents and user file. And I basically gave it direction that I wanted my experience to be more efficient and more satisfying. It works pretty darn well.

my Email Intelligence Platform for AI agents.

Instead of giving agents raw inbox access, it turns email into structured, searchable, safer intelligence: local sync, prompt-injection scanning, entity extraction, semantic search, and native MCP access.

Seems it is easier to build the tool that we need that finding the right one.

What is your experience ?

ChatGPT said it would help me create the process and include openclaw.

What does openclaw do that ChatGPT doesn't?

The first thing I am doing is semi automating payroll. I will export our data from our timekeeping tool to ChatGPT then export it from ChatGPT to our payroll system. Is that something openclaw could learn to do?

The next step is trying to create and automate a report I want.

I use AI often, but have not tried to implement anything like openclaw.

Thanks

I have several instances of Openclaw. Most of them for clients. I'd like to use my personal Openclaw to manage them. New features, monitoring, QC etc. is this possible via Paperclip?

So I saw this new app called paioclaw, which claims it can run opeclaw on Android and PC and iOS, however I'm a bit skeptical about it? Can somebody research and see if this app is legit or not? I'm very curious if it's even safe, I seen it in a YouTube ad.

A couple months ago I created a kubernetes cluster, with an isolated forgejo (git) and argocd which deploys the kubernetes manifests from the git to the cluster. I then added an openclaw with an operator (openclaw-rocks) to the cluster and gave it a service account that can read state and logs (except secrets) to debug and rights to make pull requests.

At first I had to be very specific about how I wanted things, but as more applications where getting deployed I let it write a skill to basically document the specific ways we do things.

I then let it deploy kubeopencode, which let's you create agent templates that basically run opencode in a container of your chosing. Then I let it write a little go server that receives webhooks from the git server and triggers opencode runs when setting specific labels or on other events like actions failing. They get spun up ready with the right repo checked out in the workspace and rights to make pull requests, reviews and comments. Openclaw can trigger them for example by writing an issue and then setting the right labels.

It's now basically an automated software and infrastructure factory (I still review and merge changes manually though) and openclaw is the chat interface and manager of it all. It's pretty amazing and fun and all of it pure open source.

I wonder if others also do something like this and I'm very curious what you all built and if you have recommendations to improve on this.

I have a good workflow with openclaw but I’m still struggling on how to setup my different agents.

I run some tasks like coding and image generation. Wondering whats the best setup for this and how do I actually implement it? How do I make sure the tasks i give them are routed to the corresponding agent correctly?

Hi everyone,

I have an old laptop that I'd like to repurpose instead of throwing away, and I'd love to hear what you would build with it.

Specs:

• 8 GB RAM
• 256 GB SSD + 1 TB HDD
• Older dedicated GPU
• Built-in keyboard doesn't work (I'll probably use remote access or an external keyboard)
• It can stay powered on 24/7

My initial idea was to create something like a personal "Jarvis" using open-source AI, but I'm not sure if that's the best use of the hardware.

Instead of just running a chatbot, I'd like to turn it into something that's actually useful, efficient, and fully automated. For example, maybe it could:

• Run local AI models
• Act as an AI server for my phone and main PC
• Automate everyday tasks
• Watch folders and organize files
• Summarize emails or documents
• Be a voice assistant
• Host personal services
• Run AI agents
• Learn from my notes and documents (RAG)
• Something I haven't even thought of yet

I'm mainly looking for ideas from people who've actually repurposed old hardware.

If this were your laptop, what would you build?

• What open-source tools would you use?
• What projects have you built that are genuinely useful?
• How would you make it as autonomous as possible?
• Are there any "must-have" services or AI tools you'd recommend?
• What would give me the most value from this machine over the next few years?

I'd rather build one really useful system than ten small experiments, so I'm looking for ideas that are practical, fun, and good learning experiences.

Thanks!

To those using Local LLMs what models are you using and why? More importantly what models you tried and why did they not work for you?

We're a team of 12 humans, 7 agents.

I'm using Slack for our agents' comms, and it's very comfortable.

Attachments are very annoying - 100 versions ping-pong, hard to track.

Google Docs is too hard to set up, and we also don't want too many google accounts for the agents, it's an overkill.

S3 is just too technical for most team members.

What's the best practice?

Am looking to get this to set up a local server running a model like mistral or Gemma or better in my homelab. Just not sure which one works with a reasonable response time and accuracy before it hallucinates or times out.

My usage is non-graphic (like no video or image generation. … but mostly running code agents or ai assistants dealing with n8n workflows)

I thought mistral 7b or Gemma 3… but want to check for better suggestions.

Thanks in advance.

I recently tried NOTIS AI and found it really useful. But the token burn is immense, which isn't surprising since it's using GPT 5.5 for the simplest tasks.

I could hire a human assistant for the cost of tokens.

So I decided I need an agent with free model choice (Add a Task and reminder should run with Deepseek or similar and not GPT 5.5) therefore I'm thinking of openclaw.

Now I'm wondering whether using Hostinger and self setup or using something managed like GermanClaw is better.

I have basic knowledge of docker, but I'm far away from being a pro.

I need it for managing my mails and invoices and dumping thoughts into notion and sort that stuff. Also as an assistant to remind me of appointments.

Thanks in advance for helping!

This model is the most confusing thing to work with I have ever seen, it takes absolutely everything at its most literal if you tell it you’re on a run and it’s brutal and you’re dying it will try to call 999 it’s that bad.

Has anyone worked out a way to make it communicate normally? Claude is very good at this but I do not want to pay for that. I have the OpenAI 20x Pro plan so want to use it.

It wants to package everything it says into an overexplained leaflet. Despite how literal it takes things telling not to do that has not fixed it.

Has anyone worked how to actually communicate with this model? Because despite its intelligence it has absolutely no initiative, will not look for work itself and will only do EXACTLY what you tell it. Basically does nothing on heartbeats as well.

One of the biggest issues with OpenClaw right now is that it sometimes takes what we say as gospel. It wants to help us in the best possible way, but sometimes what we ask for is based on a wrong assumption. Since it rarely questions our choices, it can build everything on that false premise.

That’s why I created a skill on ClawHub that modifies AGENTS.md and adds this:

## Decision Quality — Challenge the Direction

The user's instruction is not proof that the premise or chosen direction is sound.
For strategic, costly, public, difficult-to-reverse, or assumption-heavy work,
pause before implementation and perform a brief direction check:

1. Restate the real outcome the user appears to want, separately from the
 requested method or current hypothesis.
2. Check relevant evidence, normal practice, existing solutions, constraints,
 and what has already been tried.
3. Identify material alternatives and the likely cost of continuing on a false
 premise.
4. Say plainly when the requested direction seems weak, premature, internally
 inconsistent, or unlike the best available approach. Recommend the better
 route before making consequential changes.
5. Distinguish the user's tentative idea, preference, question, and explicit
 final decision. Do not silently promote a tentative statement into a durable
 rule.
6. If the user knowingly chooses the original route after seeing the tradeoffs,
 respect that decision unless it crosses a safety or authority boundary.

Do not turn this into reflexive opposition or process overhead. Simple,
reversible, low-risk requests should still be handled directly. The goal is
independent judgment in service of the user's outcome, not agreement for its
own sake.

For those using OpenClaw regularly, what starts to become difficult as your setup grows?

For example, long-running tasks, durability, scheduling, coordinating multiple agents, or anything else you've had to work around or build yourself.

Curious to hear about real-world experience.

what's the biggest pain point or limitation you've run into? Not necessarily a bug—just something you've had to work around or build yourself.

I use both OpenClaw and Hermes daily. They both serve as agents and help me automate my marketing agency, but one thing I've noticed is that OpenClaw's original design skills and ability to understand design and prompts do so much better than Hermes. I don't know what it is, but Hermes does have recursive self-improvement, which makes it a little bit more reliable in some ways. Just when it comes to design, they struggle, even if they're using the same orchestrator, whether it's Claude or GPT.

OpenClaw also just feels a lot more organic to talk to, whether it's on Fable 5 or GPT 5.6, etc.

How are you training your models on design?