Earlier I wasted way too much time trying to get it started from scratch but now been trying openclaw on bluestacks and it's a a relief, the main thing I realised is that the setup friction does demotivate you initially. Ofcourse, rest of the issues may come later and they do come but if you don't begin, you feel stuck.

What helped me was just keeping it really simple at the start. didnt try to add a bunch of extra skills, didnt try building some huge setup on day 1, and didnt try too many things at once

Got the basic dashboard/chat flow working first, then start talking to my agent using telegram cause honestly that felt easier to work with than whatsapp. Wrote only the necessary stuff into USER.md / MEMORY.md / SOUL.md early, then tested 1 actual task before adding anything else. Moved to more research and understanding then tried something that worked or failed.

the first useful thing (If I may say so) I built basically using x-twitter-scraper / xquik to find relevant x/twitter conversations, draft replies in a certain voice, and help with posting flow through telegram with me still checking things before anything goes out. Also ran into trickier things with permissions and all and the usual twitter api issues / reply limits etc, so that was a bit of stretch.

also did like having the infra already taken care of. felt cleaner not mixing all this stuff into my normal setup.

what have you been building after a few weeks that may be more orchestration. for ex, I haven't been able to connect with google yet even used dummy IDs something breaks

I am joining super late to this train…

I have installed OpenClaw on my old laptop that runs as a server with Debian .

I have started with telegram integration and the direct communication works perfectly fine. I wanted to try the discord integration and multi channels for better discoverability - I managed to make it work in DMs but no response in the server itself, even when tagged…

I also tried the multi chats telegram setup but it does t respond there at all…

Am I doing something extremely wrong here?

BTW, I am running on the latest release as for this morning

One thing that stood out to me from today's AI news cycle is that the conversation is starting to move beyond "which model is best" and more toward "which systems are actually usable in the real world."

The strongest signals I saw were:

• workspace agents becoming more serious for handling multi-step workflows across tools
• image generation getting more practical with better text rendering and reasoning
• enterprise AI getting pushed harder through infrastructure, security, and deployment partnerships
• agentic systems showing up in more applied use cases instead of just demos

That feels like a pretty important shift.

For a while, a lot of AI progress felt like isolated capabilities:

• better chat
• better coding
• better images
• better benchmarks

Now it feels more like the industry is trying to turn those pieces into operational layers that companies can actually build around. This article framed that shift around workspace agents, ChatGPT Images 2.0, and the NVIDIA + Google Cloud enterprise push.

A few things in particular jumped out:

• OpenAI's workspace agents push suggests more emphasis on automating complex workflows, not just giving answers.
• ChatGPT Images 2.0 points to multimodal tools becoming more usable, especially where text rendering and visual reasoning matter.
• NVIDIA and Google Cloud seem focused on making agentic AI more deployable in enterprise environments with stronger infrastructure and security.

My take is that the next phase of AI adoption probably won't be won by whoever has the coolest demo. It'll be won by whoever can turn models into reliable systems:

• workflows, agents, memory, security, tooling, deployment, and actual business use.

Curious what other people think: Are we finally entering the phase where AI becomes infrastructure instead of just a tool?

Link back to full article: https://aaronwiseai.com/learnai/2026/04/22/ai-news-digest-april-22-2026/

I gave this a real shot. Not a weekend experiment—three full months of trying to make OpenClaw part of my actual workflow.

I tried a VPS and even bought a Mac mini specifically to run it properly. Set everything up locally. Went down the rabbit hole with models, configs, dashboards that never functioned, constant memory systems, routing logic, token optimization, all of it. Subscribed to numerous LLM providers.

I burned time, burned money, burned a lot of mental energy trying to “get it right.”

And the truth is… it just never stabilized.

Something always broke.

If it wasn’t a config mismatch, it was a gateway issue.

If it wasn’t that, it was models behaving inconsistently. If it wasn’t that, it was outputs that felt unpredictable or bloated or just… off.

I kept thinking: “Okay, I’m one tweak away.”

Then: “Maybe I just need to restructure the pipeline.”

Then: “Maybe I’m using it wrong.”

At some point you realize—you’re not building a system anymore. The system is building you into someone who spends hours debugging instead of actually doing the work you set out to do.

That’s the part that got me.

I didn’t get into this to become a full-time infrastructure manager. I wanted something that supported my work, not something that required constant babysitting just to stay upright.

There are parts of OpenClaw that are genuinely impressive. The concept is powerful. When it works, it feels like the future.

But I never reached a point where I trusted it. It consistently lied to me. And if you can’t trust the system, you can’t build on top of it.

So I’m stepping away.

Not rage quitting—just being honest about the ROI. Three months in, I should be using it… not still trying to make it usable.

Curious if anyone else hit this wall, or if you managed to get it to a place where it actually runs reliably without constant intervention.

Hi all,

I’m running a local-first agent setup on a Mac mini M4 with 24GB RAM.

My setup:

• Main orchestrator (cloud): GPT-5.4
• Executor (local): Gemma 4 26B
• Coding agent (local): Qwen3.5:9B
• Also tried Qwen3-Coder:30B, but couldn’t get it to reliably finish tasks

Use cases:

• Sales prospecting based on defined criteria
• Lightweight stock / company research
• Small-to-medium coding tasks
• Productivity workflows (summarising notes, generating reviews)

Issues I’m seeing:

• Long runs timing out
• Context getting messy in multi-step loops
• Outputs look plausible but don’t complete tasks
• Coding agent writes code in chat instead of modifying files
• Runs stall or never finish
• Tool use is much less reliable vs cloud models

Also noticed that larger coding models aren’t consistently better — sometimes less reliable than smaller ones.

Trying to understand if this is:

• Model choice issue
• Config / orchestration issue
• Hardware limitation
• Or just a bad use case for local models right now

Questions:

• Which local models are most reliable for these use cases?
• Any config changes that significantly improve:
  • reliability
  • tool execution
  • long-run stability

Current config (important bits):

Sub-agents:

• runTimeoutSeconds: 1800

Executor (Peter):

• Model: ollama/gemma4:26b
• thinkingDefault: off
• heartbeat: 0m

Coding agent (Jay):

• Model: ollama/qwen3.5:9b
• thinkingDefault: off

Ollama model registry:

Gemma4:26b

• reasoning: false
• contextWindow: 32768
• maxTokens: 16384

Qwen3.5:9b

• reasoning: true
• contextWindow: 65536
• maxTokens: 32768

I’m not expecting cloud-level performance, just trying to get local agents stable enough to be genuinely useful.

Would really appreciate advice from anyone running something similar on Apple Silicon.

It occasionally happens that I make a change in OC to the project I'm working on, realize the change isn't exactly what I wanted, and then ask in the chat, "Undo your last change." I've noticed that in most cases, it either reverts several versions back or does nothing at all.

I wanted to ask you: how do you undo your last request on OC?

Thanks!

Hi All,

Seeking some input.

Setup

I have setup OC on a windows 11 pro 25H2 with WSL Ubuntu 24 and OC mounted within.

So far the setup has been good however for testing I have been running it on Kimi 2.5 connected to telegram and also local network access (also managed through the router for the IP address allocated to the Ubuntu instance) at present with a single Agent (default: Main).

Ideal usecase:

Looking at implementing our own little Support KB system for internal use only. I would like to be able to paste Windows and application logs (similar to how I have been using Claude and ChatGPT) and allow it to learn about our internal application - with the future to allow staff to communicate with it as a chatbot for help. IE ask for logs, guide them through password resets etc

Questions:

1. Kimi 2.5 free has only been good for testing - I need to move to a better model/tool. If I connect it to OAuth chatgpt on an existing account will it have access to the existing data (cannot risk crossover of information) or should I buy a separate ChatGpt account?
   1. If ChatGPT goes the way of Anthropic (its a risk I'm prepared to take) because if it works I will happily buy the API
2. When in WSL on windows it doesn't search the web you have to provide a link to chrome and use an add-in and the chrome instance needs to be on full time - you cant close the window. In the latest version of OC is there any way to fix this?

Any comments, thoughts would be appreciated.

So far, I have successfully connected my orchestrator agent to Telegram. Now, I would like to connect and link several additional sub-agents so that I can have a direct conversation on Telegram (separately from the conversation I have with my orchestrator bot), as well as a new context window for each of them individually.

I haven't been able to figure out exactly how to perform the binding or association process between a Telegram bot and a specific sub-agent.

So now I have a pairing code from the new bot i just created, how do I now associate the TG bot with a specific subagent?

I would appreciate an explanation on exactly how this is done.
10x

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