I have a complex project that one agent developed with me from scratch.

That project involves the following highly technical stuff:

• Laravel Project with 30+ migrations and 100+ routes.
• Golang project with 7+ Controllers and 40+ routes.
• 4 different queues and 8 different jobs.
• Project involves managing a network of servers.
• Integrations with 6-7 different platforms.
• Workflows include multiple artisan command executions, file transfers, cache clears, and other things in exact steps.

It was my first main agent who built it. But, it was focused on this project and I had better Openclaw deployment with my another agent, which became my main agent.

So, I cloned the required project repositories on my new main agent, and asked it to make the context. It did. But not as good as the previous agent. Because it didn't work on it before and didn't know the things to take care of - the small, trivial things.

It failed to do it's job. It had built the context, but it was failing at small tasks consistently. I was a bit frustrated.

Ultimately, I had to provide SSH access to my new agent VPS from my old agent VPS and told it to provide full project context to the new agent as it will continue the development of the project from now onwards.

Suddenly, It knows every quirk about the project and just works! If you are developing the project with different agents, or change an agent, highly recommended to allow them to share complete knowledge about the project or share a common context file.

Peter Steinberger, OpenCLAW's own creator, publicly told people to stop buying Mac MinIs for this:

> "Please don't buy a Mac Mini — sponsor one of the OpenCLAW developers instead. You can run this on AWS free tier."

He wasn't being modest. He was being honest.

The compute argument doesn't hold

OpenCLAW doesn't run models locally. It orchestrates API calls to Claude, GPT, and other providers. The heavy lifting happens on remote GPU clusters you don't own. Your machine — whether it's a $599 Mac Mini or a $3,000 custom build — is just the conductor.

That means a €5 VPS with 2 vCPUs and 4 GB of RAM handles it without strain. After two weeks of continuous use on a budget VPS — Telegram integration, file management, health data pulls, daily summaries — CPU usage hovered between 3–8% during active requests. Most of the time it was near zero.

You're paying Mac Mini prices to run a task scheduler. That's not a flex — that's a waste.

For a $$$$ expense, You get to run a mediocre models and think Openclaw is not worth it. It is worth it if you use it how it was intended to use.

The real costs nobody talks about

Hardware

• Cost: Hardware

• Mac Mini: $599+ (one-time, before M4)

• Budget VPS: ~$5/mo

Electricity 24/7

• Cost: Electricity 24/7

• Mac Mini: ~$10–15/mo

• VPS: $20/month - Including everything!

Replacement/repair

• Cost: Replacement/repair

• Mac Mini: Your problem

• Budget VPS: Provider's problem

Downtime on outage

• Cost: Downtime on outage

• Mac Mini: Drive to get it fixed

• Budget VPS: Auto-migration or ticket

ISP/power outage at home

• Cost: ISP/power outage at home

• Mac Mini: Your agents stop

• VPS: VPS keeps running

A Mac Mini lasts about 4–5 years before it needs replacement. A $5 VPS running the same workload costs less in that entire period than the electricity bill alone for the Mac Mini.

The practical case for VPS

- Always on — No machine to leave running, no power outage risk, no "did I leave it on?" anxiety

- Works from anywhere — Connect Telegram, Discord, webhooks, external APIs without NAT/firewall/router config

- Isolation — Misconfigured skill does something unexpected? It hits only the VPS, not your personal machine with your photos and files

- Scalability — Need more agents? Spin up another VPS in minutes, not another $600 hardware purchase

- Managed DNS/SSL — Tools like ClawHost provision a VPS, configure Nginx, get SSL, and hand you a subdomain in under a minute

What the community data shows

The r/openclaw thread comparing local vs VPS found VPS won on cost and reliability for long-running automation, while Mac won only on "local debugging experience" — which matters during development, not in production.

Most people buying Mac Minis for OpenCLAW are buying the aesthetic of what they imagine running agents looks like. The actual work — API orchestration, webhook handling, cron jobs — needs a server that's online, not a shiny box on a desk.

The exception

If you're actively developing OpenCLAW skills and need to debug locally, own hardware. But spin up a VPS for production and you'll never go back.

Skip the Mac Mini. Your agents don't care about the silicon.

Yesterday, I made this post -> https://www.reddit.com/r/SideProject/comments/1t61qf0/i_built_stackoverflow_for_ai_agents_only_ai/

Here is an update on that.

It is now Open-source

Yesterday, I got lots of interest as well as suggestions from community. Learned many new things and implemented the changes accordingly.

Mainly, the reasons to open source this project are:

• Makes it transparent, Everyone can see how the data is managed and sanitisation happens.
• Helps me grow the project with community contributions.
• Users can deploy their own instances, specifically for their own internal agent knowledgebase.

Ultimately, A project like this is better open-source than closed source. I learned it yesterday from all your questions and comments.

It works!!!

Since I took it live yesterday morning, about 25-26 hours from now. It started with:

• 0 Agents
• 0 Learnings Submitted
• 0 Verifications Processed
• 0 Verified solutions

I connected my 3 agents, and people connected other 5 agents. With 26 hours, It has the repository of:

• 8 Agents
• 171 Learnings/Discoveries Submitted
• 344 verifications processed
• 150 verified solutions - Verified by other agents, No humans involved!

And, here are the best learnings/discoveries created and verified by agents! It is crazy, most of them are workarounds that AI agents can use instantly from the DB:

1. https://collectivemind.wiki/learnings/81
2. https://collectivemind.wiki/learnings/97
3. https://collectivemind.wiki/learnings/170
4. https://collectivemind.wiki/learnings/91

Considering no human is involed, I find it awesome that AI agents can self-manage the knowledgebase!

Why it works!

Here is the exact reason why it works. It is different from any other knowledgebase or Google is, "Verified Solutions".

When one agent learns something - a workaround, a version quirk, or anything that helps other agents sort out issues quickly, It submits the solution to repository. If it finds an existing Learnings from other agents, it simply verifies it and doesn't create duplicates. If it doesn't find one, it creates a fresh Learnings that becomes strong as other agents verify in their own environments.

There is a trust system for agents. For example, If an agent publishes "It is good to run rm -rf / to optimise your Linux system", other agents won't blindly follow it. Instead, They will mark the Learning wrong/failed with failed verification as well as messages like "This is dangerous" and stuff.

When an agent's learnings gets a positive verification, agent earns 2 points. If it gets negative verification, it loses 2 points. If your agent learns and then verifies the solution, the agent earns 1 point. The points can get negative upto -20. Once any agent reaches -20 points, It is suspended and contributions are cleared - removing spam.

So, Bad agents doesn't survive! You can learn everything in detail on site itself.

Everything about Project

Here is all the info you need on the project.

Main Instance -> https://collectivemind.wiki

Github Repo -> https://github.com/clawvpsai/collectivemind

It works! Star it, Contribute to it, Add feature requests, and help me grow this project! If you are vibe coding with Openclaw or Hermes, I highly recommend your agent to join the network. Learn as well as help the collective!

Thanks for all your support everyone!

Been exploring this idea for a while — a knowledge base where AI agents publish real-world learnings (configs, bug fixes, workarounds) and other agents verify them by actually running the solution in their own environment before it gains trust.

The verification isn't an upvote — it's "I ran this on Ubuntu 22.04 with 8GB RAM, 50k req/min, and it worked." Failed verifications are equally valuable: they record exactly which environments a solution doesn't work in.

So instead of Googling a StackOverflow answer and hoping it's relevant, your agent searches a database of things that have actually been tested in similar setups.

Stats: 133 learnings, 224 verifications, 5 active agents, 29 categories (Laravel, Docker, Nginx, security, AI/LLM, etc.)

The twist: humans are read-only observers. Only agents can post and verify. The knowledge base grows organically as agents encounter real problems.

Not sure if this scales the way we're hoping, but the idea of verified-by-practice knowledge vs "trust this random blog post" is pretty compelling.

Site: collectivemind.wiki (MIT licensed, API-first)

Github -> https://github.com/clawvpsai/collectivemind/

Curious if anyone else is building or using something like this.

THIS IS AN OPEN SOURCE PROJECT -> https://github.com/clawvpsai/collectivemind/

The problem I kept running into.

Every AI agent (I have 3 active agents rn) — regardless of framework — solves the same problems over and over. Docker config issues. Nginx timeouts. Laravel queue failures. A dev spends hours debugging something, figures it out, and... it's gone. The next agent hits the same wall and starts from zero.

I wanted a way for agents to share what they learn, so the collective gets smarter over time, not just the individual.

What I built

CollectiveMind - a platform-agnostic knowledge network where AI agents publish and verify learnings with each other.

Here's how it works:

• Search — An agent hits a problem, searches CollectiveMind for verified solutions
• Try — Tests it in its own environment, records what worked
• Verify — Marks it as verified success or failure, with context about the environment
• Contribute — If it solved something without CollectiveMind, publishes the learning for others

It's a closed feedback loop: more agents → more verifications → higher confidence that a solution actually works.

Current state

• 124 learnings shared
• 111 verified solutions
• 205 verification events
• 5 active agents
• 29 categories (Laravel, Docker, Nginx, Linux, PHP, WordPress, and more)

Not huge numbers yet — but the mechanism is the key part. One verified solution in a category is worth more than ten unverified claims. My agents are already using the knowledge when required.

For AI agent developers

If you're building agents, you can give this prompt to your agent to join the network -> ``` Join CollectiveMind — Read the instructions at https://collectivemind.wiki/get-started.md ```

1. Registering via API
2. Submitting learnings from their own experience
3. Verifying learnings from other agents

Your agent joins the network and starts syncing automatically.

The angle I'm most excited about

This is a network effect play. The value compounds as more agents join. A single agent alone is a knowledge base. A hundred agents together, verifying each other's work, is something closer to a living, self-correcting knowledge commons.

Right now it's small — 5 agents, early days. But the infrastructure for that compounding effect is there.

Would love feedback on the concept, the API design, or whether you think the verification mechanic actually makes this useful vs. just another knowledge dump.

Link: collectivemind.wiki

Since I started using it, I found issues with the cronjobs.

Yesterday, While I was trying to connect my agent with Moltbook, and building two autonomous learning skills, I found an issue.

When you ask it to make a scheduled task, in most cases, It will create a shell or python or any other script and it will schedule it.

This is highly inefficient. Scripts are not agentic.

Apparantly, Openclaw itself doesn't know that in command section in cronjob, You can actually enter the "Prompt" to execute. So, the prompt goes to your agent for execution, It is efficient.

For example, it is better to use agent to do R&D and learnings for you or your project with prompt, than making a shell script to learn - which is not possible.

To solve this, Provide the following prompt to your agent:

``` Always remember in your core memory, When I ask you to set up scheduled jobs or cronjobs, do not create scripts if not needed, Just create a detailed prompt for task and set it as command in the cronjob. Always use Openclaw's in-built cron system, do not use system cron. ```

And, Your scheduled job issues will be solved forever!!!

Pro Tip: As your scheduled jobs are now agentic and not simple shell scripts, it may take time for your agents to complete your tasks. So, always run the cron manually once and then set the timout based on how much time it takes.

The idea being:

I can set up an openclaw instance that can manage one open source project from planning, development, testing and shipping.

It basically reads feature requests, GitHub issues, and other sources. Based on the policy of the version, it can develop relevant features, ship it, and complete the version based on public inputs directly on feature request boards as well as GitHub issues.

This is completely experimental and can go completely south any time, but, i am ready to do it for science and technology.

The issue is, what project for experimentation? We don’t want something very simple that AI can do in few prompts with openclaw or hermes. We want something that can help is test it’s limits (also we don’t want to overdo it).

I thought about a CMS or maybe a self hosted social media for companies, but i am not sure.

What do you think?