u/doubush

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?

Hello everyone.
I'd like to share an idea for discussion.

I use different AI harnesses a lot, and many MCP servers are connected — both stdio and remote HTTP servers.
I came across an idea on Reddit about simplified remote MCP servers without a persistent HTTP connection. It's not a standard, but it's a great idea. In 99% of cases, there's no need to keep a connection open and overuse network infrastructure.

But I think this idea can go further.

What if we simplify stdio transport MCP servers to run them only when a tool call is actually needed?

• On start: run → read list of tools → exit
• On a tool call: run → call a single tool → exit

This would save system resources — no need to keep an app process running constantly (stdio MCP servers are just CLI apps, and they stay in memory the whole time they're connected). Important: the harness knows about tool all the time and manages them as "live", presents to LLM etc.

Of course, some MCP servers require state — after the initial run they do something, receive some data, and need to keep that in memory until the next tool call. However, my experience shows this kind of MCP server is a minority. Most — I'd say more than 95% of cases — are stateless. They're often just a wrapper around some API: they receive credentials with every tool call and pass those same credentials to the API.

Here's some math. If I have 15 MCP servers built with Node.js or Python, each one uses RAM just to sit in memory, and 99.9% of the time it's doing nothing. Let's say one process takes 50 MB of RAM — that's 750 MB of RAM used for almost nothing.

If we don't keep them in memory and only call them when needed, it will be a little slower because a process has to start up each time. But that's not a problem — the bottleneck is still the LLM call.

To support this, we'd need to modify the MCP clients in our AI harnesses slightly. And maybe we'd need some kind of marker in the MCP config to indicate: "this MCP server keeps state" vs. "this MCP server is stateless and can be called only when needed."

What do you think? Is it a good way to save resources?

Where did this come from? Because I have an AI harness running 24/7. MCP servers stay connected (processes are active) all the time, but they're only used occasionally — once a day, etc. I see absolutely no sense in keeping all those processes in memory.

The core of any harness is the tool-use loop:

1. Build context (system prompt + conversation history + tool schemas)
2. Ask the model "what next?"
3. Parse the response — text, tool call, or stop
4. If tool call: execute it, append result to context, go to step 2
5. If stop: return final output

Simple in theory. Tricky in practice.

Every iteration adds tokens to context. After 10-20 tool calls, the context window is mostly tool results and the model starts "losing" the original goal. Smart harnesses handle this with summarization, sliding windows, or structured memory.

Other gotchas: retry policies (3 retries with exponential backoff is standard), timeout guards for tools that hang, and preventing the model from calling the same failing tool in an infinite loop.

The loop is simple. The guardrails are where the craft lives.

Hi.

I am learning OpenClaw. I wonder, is there something like configurable level of initiative?

So, OpenClaw can do something o own initiative when i am not around. And i can control to set it to be very active or less active or stop any self initiated actions .

How does it work?

Do you think we are close to self-emergent AI? This blog looks interesting. They say LLMs are good enough to build an agent capable of self-emergence (or capable of emerging on its own).

A while back I tried using an "AI browser agent" to automate my Reddit routine. It worked in a demo. In practice it was slow and unreliable. Gave up.

Recently I tried again with DMJBot running on my VPS alongside my other stuff, driving an old Windows laptop with Chrome. Night and day.

Instead of shoving entire pages at the model, it uses a lightweight, cleaned-up view of what's clickable — so every step is fast. The AI adapts on the fly when the page changes.

I automated reading, commenting, upvoting, and posting. Taught it once in a chat, saved as a skill, and now it just runs. In the post I share the setup and the skill file.

Hey — we’re the team behind DMJBot and just published a breakdown of how it compares to OpenClaw.

We tried to keep it fair and focused on actual differences in approach rather than marketing points.

Curious if we got anything wrong or missed something from your perspective

Hey folks,

Quick one from our side.

Our team released an AI assistant tool that works really well as an AI manager for multiple coding agents.

If you already run Claude Code / Codex / Copilot across different machines, you probably know the pain:

• too much manual coordination,
• too many tabs and sessions,
• too much switching just to keep things moving.

Our approach is simple: let DMJBot be the manager layer. You chat with one manager, and it coordinates the rest.

It is especially nice when you are away from your desk: you can use web/mobile chat, check progress, and steer tasks without constantly jumping back into every coding session.

Would love to hear real feedback from this community. If you try this style of workflow, what breaks first for you?

Hi all,

We launched DMJBot ("Do My Job Bot") - a self-hosted AI automation platform you run in Docker.

It is similar in goal to Openclaw/Hermes, but built on different principles:

• Self-hosted: runs in Docker and can be deployed anywhere Docker runs
• Multi-device operations: connects to Windows, macOS, and Linux with granular access per device
• Event-driven by MCP notifications: behavior can be triggered automatically by external events

Quick start:

docker run -d -p 8080:80 -v dmjbot-data:/data dmjbot/dmjbot:latest

Then open localhost port 8080 in your browser and connect your devices .

Would appreciate honest feedback. If useful, I can share the project link in comments.

Hey all,

I’ve been digging into the idea of automating content posting to social networks, mostly out of technical curiosity rather than trying to spam or game anything.

What interests me is how these platforms actively try to prevent automation, and what the “normal” engineering approaches are to deal with that.

For example, I experimented a bit with using an AI agent (Claude) + a browser (Chrome). The idea was: let the AI “see” the page, find elements like “create post,” click them, type content, attach images, etc.

In practice, this runs into problems pretty quickly:

• Pages like Facebook are huge, so passing full HTML into an LLM burns tokens fast
• The structure is complex and dynamic, so reliably finding the right elements is tricky
• It feels inefficient to treat the whole page as raw text instead of interacting with it more structurally

So I’m wondering how this is usually done in real-world setups.

Some specific questions:

• Do people rely mostly on browser automation tools like Selenium / Playwright with predefined selectors?
• Is there a pattern where you define reusable “actions” like: open URL → click selector → type text → upload file?
• How do you deal with constantly changing DOM structures and anti-bot protections?
• Are there hybrid approaches where AI is used only for decision-making, but execution is handled by deterministic scripts?
• Or do people just avoid UI automation entirely and use official APIs wherever possible?

Assume the account is already logged in via a normal browser session.

I’m not looking to bypass safeguards for abuse — just trying to understand the technical landscape and what approaches actually work in practice.

Would love to hear how others have approached this or what tools/patterns you’ve found effective.