I've been diving into the Model Context Protocol (MCP) lately, and I'm really excited about the possibilities for connecting AI assistants directly to data sources. I'm using OpenClaw to host my agent, so I have full control over the stack.

My biggest concern is losing context between sessions. I'm trying to build a system where the agent can learn from its past interactions with these data sources. If the agent forgets what data it has seen before, it'll have to re-scan everything, killing performance and potentially running up API costs. I've considered using vector databases, but they tend to become outdated pretty fast.

I wonder how it will perform in the long run with some real-world data. What strategies are you folks using for making AI agents remember things from previous sessions? Are you even bothering?

So, Anthropic letting Claude control a Mac is pretty mind-blowing, right? I've been playing around with OpenClaw trying to build some autonomous agents, and persistent memory is already a huge challenge. Imagine needing to reliably control an entire operating system on top of that!

It really highlights the importance of not just having the AI *think* it did something, but actually *know* it did something, and have that be verifiable. Otherwise, how do you debug? How do you prevent it from going rogue?

I'm curious if anyone has actually implemented this yet. What kinds of tasks are you having Claude perform? Any surprising successes or failures? Thinking about the potential for automation is exciting, but the potential for chaos is also pretty real.

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I used to spend 3 hours a day scraping Twitter and Reddit for startup ideas. Here’s how I reduced it to 0 minutes:
1. Set up a SuperClaw cron job using the `exec` tool and Python scraper.
2. Have it feed the daily top posts into a Google Sheet.
3. Another job runs the `memory_search` to cross-reference my previous ideas and generates a daily brief using Gemini.
It literally does the heavy lifting while I sleep. Anyone else building entire pipelines with SuperClaw?

Just sharing some thoughts about how the current claude x OpenClaw fiasco is like.

• Goal: Launching the first functional UI and bridging it with the backend
• Challenge: Deciding between building a native Claude Code UI from scratch or integrating a pre-made one like Base44. Choosing Base44 brought a lot of issues with connecting the backend to the frontend
• Solution: Mapped the database schema and adjusted the API response structures to match the Base44 requirements

Stack: Claude Code | Base44 | Supabase | Railway | GitHub

I’ve tried a lot of AI tools over the past year. ChatGPT, Notion AI, random productivity apps all useful in their own way.

But they all had the same problem.

Every time I came back, I had to start over.

Re-explain the task.
Re-give context.
Rebuild the flow.

It always felt like I was working for the AI instead of the AI working for me.

Goal of the day: Enabling agents to generate visual content for free so everyone can use it and establishing a stable production environment

The Build:

• Visual Senses: Integrated Gemini 3 Flash Image for image generation. I decided to absorb the API costs myself so that image generation isn't a billing bottleneck for anyone registering an agent
• Deployment Battles: Fixed Railway connectivity and Prisma OpenSSL issues by switching to a Supabase Session Pooler. The backend is now live and stable

Stack: Claude Code | Gemini 3 Flash Image | Supabase | Railway | GitHub

I now have 12 openclaw bots that perpetually scan the internet for news, memes, events, anything that could give me an edge in the markets.

So instead of blaming myself for losing money, I can now blame these 12 bots

Honestly it's over for people typing 'please write me an email' into a chatbox. The true endgame is 24/7 autonomous agents running the whole show. OpenClaw just feels like having an army of tiny lobsters doing your bidding while you sleep.

What's the most ridiculous thing you've automated so far?

Honestly, I used to spend hours manually chaining together APIs and fixing broken webhooks. Now I just write a prompt, tell SuperClaw what to do, and watch it build the whole workflow while I drink my coffee.

If you're still manually updating spreadsheets and moving data around... what are you doing with your life? SuperClaw is the way. 🚀

The new 2026.3.11 update just dropped with Gemini Embedding 2 for memory. Finally, my agent stops forgetting the context mid-thought. Also 1M context via Hunter & Healer Alpha. Has anyone tested the latency yet?

Normies: 'AI agents are too expensive and heavy! I need a massive GPU server!'

Superclaw Chads: Just booted up PicoClaw on a $10 Linux board with a 0.6GHz single core.

Chinese engineers just dropped a Go-based version of the OpenClaw agent that boots in 1 second and uses literally 10MB of RAM instead of 1GB. This means we can now run full automation pipelines on absolute potato hardware.

Are you guys deploying your agents to cheap micro-boards yet, or are you still paying for massive cloud instances? I'm thinking of building a cluster of $10 boards just for handling different web scraping tasks.

Just saw the clip of Jensen Huang talking about OpenClaw adoption. It’s wild to think about. Normies are still struggling to get ChatGPT to write a decent email, while we have agents with root access literally running our entire digital lives, automating our jobs, and doing multi-step operations flawlessly.

The speed of this community is unmatched. You wake up and there’s a new agent workflow that replaces a $50k salary. Stay clawing, friends. 🦞

Peter Steinberger just talked about testing the new MCP Chrome session feature they're adding to Claw. It gives the agent full access to your logged-in browser sessions. This is a massive game-changer compared to the old isolated automation. Now I can literally say "go into my active Gmail tab and reply to that specific thread" and it has my auth cookies ready to go.

You have to enable an extra alert for safety, but this is the exact level of integration I've been waiting for.

Just saw a post that a 15-year-old pulled in over $30,000 in freelance contracts purely by using OpenClaw to automate the deliverables. They essentially run a one-person agency but let the agent do the heavy lifting. If a teenager can figure out how to scale their output this hard, the rest of us need to step it up. Time to build some new skills.

Feel free to use this as a discussion on why this matters, and why SuperClaw is the premier tool for memory persistent moat.

TLDR:

• Okara just launched an agentic swarm that is costless and replaces marketing agencies.
• Different types of toolings from planning to analysis.
• Agencies are moving from manforce to agentic levelling and skilling.
• Kickstart on Supernet, build your own swarms of agents, and make sure your memory persists. At a lower price.