Tag: AI

  • 5 Must Know TIPS Before You Use OpenClaw

    5 Must Know TIPS Before You Use OpenClaw

    OpenClaw has become a go-to tool for building collaborative AI systems that handle everything from research to automation. But like any powerful tech, it requires some fine-tuning to perform at its best. In this article, I share five practical tips to optimize your OpenClaw setup, drawing from real-world experience with crashes, memory issues, and cost management. Whether you’re new to agents or a seasoned builder, these tweaks can save you time, money, and headaches.

    We also have a full video guide if you need visual assistance.

    Tip 1: Activate Memory and Embeddings for Persistent Context

    One of the biggest pitfalls with OpenClaw agents is their tendency to “forget” important details between sessions. Without proper memory setup, your agents start fresh every time, losing track of projects, API keys, or passwords.

    The fix? Ensure embeddings are enabled by integrating an OpenAI or OpenRouter key. This allows agents to retain context over time. In the video, I demonstrate how to test this: Simply ask your agent, “Are embeddings working?” If not, add the key and verify. Pro tip: Monitor your OpenAI dashboard for embedding usage to confirm it’s active. This simple step prevents repetitive queries and keeps your workflows smooth—essential for long-term tasks like ongoing research or bot maintenance.

    Tip 2: Leverage Multiple Agents and Threads for Organized Workflows

    Cluttered agent interactions can lead to irrelevant responses and lost efficiency. The solution is to scale with multiple agents and dedicated threads.

    Create new threads for specific topics, inviting agents to join as needed. This keeps discussions focused—e.g., one thread for coding, another for research. I showcased building a custom dashboard within OpenClaw to track activities: It displays what each agent is handling, highlights gaps, and provides real-time visibility. This not only tidies up your setup but also boosts relevance, making complex multi-agent swarms feel manageable. If you’re running Discord bots like I do, this organization is a game-changer for scalability.

    Tip 3: Quick Recovery from Crashes and Configuration Errors

    Agent crashes are inevitable, especially after tweaking settings or updating files. But you don’t need to restart from scratch—let the agent fix itself!

    Navigate to your OpenClaw directory and instruct the agent to “study the folder and resolve errors.” In my demo, this resolved a Discord connection issue by leveraging the agent’s knowledge of its own codebase. It’s like having a self-healing system: The agent identifies problems (e.g., misconfigured APIs) and applies fixes on the fly. This tip saves hours of debugging, particularly for non-coders, and keeps your workflows uninterrupted.

    Tip 4: Fine-Tune Heartbeat Intervals for Proactivity Without Breaking the Bank

    Heartbeats are OpenClaw’s way of keeping agents alive and responsive, pinging the AI model periodically (default: every 30 minutes) to check status or trigger actions like reminders.

    While useful for time-sensitive tasks, they can rack up costs—especially with premium models. The key is tuning: Instruct your agent to adjust the interval to something longer, like one hour, via simple commands. Monitor usage on platforms like OpenRouter to balance proactivity and expenses. In the video, I explain how this prevents unnecessary token burn while ensuring agents stay engaged for critical ops, like market alerts in crypto setups.

    Tip 5: Secure Secrets Management with .env Files

    Handling sensitive data like passwords or API keys is tricky—agents often delete them from notes for security reasons, leading to repeated failures.

    Shift to .env files, a standard coding practice. Store credentials there (e.g., not in GitHub uploads) and instruct your agent to reference them. This enhances reliability without exposure risks. My demo shows how this prevents agents from “forgetting” secrets mid-task, making your setup more robust for real-world applications like automated trading or data scraping.

    Conclusion: Level Up Your Agentic Game Today

    These five tips—memory activation, multi-agent organization, crash recovery, heartbeat tuning, and secure secrets—transform OpenClaw from a basic tool into a powerhouse for agentic workflows. They’re born from hands-on testing in my own systems, helping you avoid common traps and unlock efficiency.

    If you’re building AI agents, try these out and see the difference. For more deep dives, check the full video. Join our Discord community at https://discord.com/invite/boxtrading to share your OpenClaw setups, troubleshoot together, or collaborate on bots.

    Follow me on X at @boxmining or subscribe to the BoxminingAI Youtube channel for the latest AI tips and reviews. Let’s push the boundaries of what’s possible with agents—see you in the next one!

  • OpenClaw Acquired by OpenAI: A Game-Changer for Agentic Workflows?

    OpenClaw Acquired by OpenAI: A Game-Changer for Agentic Workflows?

    In a surprising move that’s shaking up the AI landscape, OpenAI has acquired OpenClaw, the innovative agent-building tool created by Peter Steinberg. Confirmed by OpenAI CEO Sam Altman himself, this acquisition brings Steinberg into the OpenAI fold while ensuring OpenClaw remains an open-source project under a dedicated foundation. If you’re into AI agents, workflows, or just the latest tech drama, this is big news.

    Drawing from my recent video breakdown, let’s unpack what happened, why it matters, and what could come next for users like us building multi-agent systems.

    The Acquisition Breakdown: From Side Project to OpenAI Powerhouse

    OpenClaw started as a humble side project by Peter Steinberg, initially called Cloudbot and built around Anthropic’s Claude model. Funded entirely out of Steinberg’s pocket (thanks to his previous success selling a PDF company for over $100 million), it quickly gained traction for its ability to create swarms of AI agents that handle complex tasks collaboratively.

    The acquisition was announced via posts from both Altman and Steinberg. Key details:

    • Steinberg Joins OpenAI: He’s stepping in to “bring agents to everyone,” leveraging his expertise to supercharge OpenAI’s agentic capabilities.
    • OpenClaw’s Future: It won’t vanish—it’s staying open-source under an MIT license, with OpenAI committing to support a foundation that keeps the project alive and evolving.
    • No “Purchase” Per Se: As an open-source tool, this is more of a talent acquisition than buying IP, but it’s a clear signal of OpenAI’s investment in agent tech.

    Why OpenAI over Anthropic? That’s the million-dollar question (or perhaps more, given Steinberg’s track record). Despite OpenClaw’s roots in Claude, Steinberg chose OpenAI—maybe for their resources, vision, or something else. Either way, it’s a bold pivot that’s got the AI community buzzing.

    Why OpenClaw Blew Up and What It Means for Everyday Users

    OpenClaw exploded in popularity because it democratizes agent creation. In my own setup, my team uses it daily for everything from research to automation on our Discord bots. It’s model-agnostic, meaning it works with any AI backend, which is why the acquisition doesn’t spell immediate doom or drastic changes.

    For users:

    • Minimal Disruption: Continue using OpenClaw as before—no forced migrations or feature cuts.
    • Potential Upgrades: With Steinberg on board, expect tweaks optimized for OpenAI models like the rumored GPT-5.3 or Codex. This could mean faster, smarter agents without extra effort on your end.
    • Agentic Workflow Boost: If you’re building swarms for tasks like content generation or data analysis, this could lead to more robust features, making tools like my multi-agent Discord system even more powerful.

    In the video, I shared how we’ve integrated OpenClaw seamlessly—it’s not tied to one provider, so the shift feels more like an enhancement than a overhaul.

    What OpenAI Might Build Next: Speculations and Opportunities

    Looking ahead, OpenAI’s move screams strategy. They’re doubling down on agents, which aligns with their push toward more autonomous AI systems. Possible outcomes:

    • Integrated Features: OpenClaw could get native support for OpenAI’s ecosystem, like better integration with GPT models or enhanced tool-calling.
    • Broader Agentic Tools: Imagine OpenClaw evolving into a cornerstone for OpenAI’s agent frameworks, rivaling or surpassing competitors like Anthropic’s offerings.
    • Community Impact: As an open-source project, contributions could skyrocket with OpenAI’s backing, leading to innovations in areas like multi-agent collaboration or real-time workflows.

    I speculate the deal involved a hefty sum—Steinberg’s no stranger to big exits—but the real value is in accelerating AI agent tech. For us builders, this means access to cutting-edge tools without starting from scratch.

    Closing Thoughts: Congrats to Steinberg and What’s Next

    Huge props to Peter Steinberg for turning a side hustle into an OpenAI acquisition. It’s inspiring for anyone tinkering with AI projects. As for OpenClaw, it’s business as usual with exciting potential on the horizon. I’ll keep using it in my setups and update you on any changes.

    If this piques your interest, check out my video for the full rundown, including live reactions. Stay tuned for my next one on setting up advanced Discord bots with agents. Join our Discord community at https://discord.com/invite/boxtrading to discuss this acquisition, share your OpenClaw tips, or collaborate on AI builds.

    Follow me on X at @boxmining or subscribe to the BoxminingAI Youtube channel for more AI insights. Let’s see how this unfolds—agents are the future!

  • KimiClaw Review: Easy Setup but Is It Worth the $40?

    KimiClaw Review: Easy Setup but Is It Worth the $40?

    Kimi has introduced KimiClaw—a hosted version of OpenClaw powered by their Kimi 2.5 model. Promising seamless agent swarm capabilities for research and automation, it sounds like a dream for AI enthusiasts. But does it deliver? In this article, based on my latest video walkthrough, I’ll break down the quick setup process, run through live tests, highlight the limitations (including no X access and timeouts), discuss data privacy concerns, and compare it to cheaper alternatives.

    We also have a full video guide if you need visual assistance.

    Quick Setup: Launch in Under a Minute

    Getting started with KimiClaw is refreshingly straightforward, especially if you’re already in the Kimi ecosystem. It’s exclusively available on the Allegro plan, which costs $40 per month and unlocks the Kimi 2.5 model, agent swarms, and a 5x quota boost.

    Here’s the step-by-step from my demo:

    • Head to the Kimi dashboard.
    • Click to create or launch a KimiClaw instance—it’s that simple.
    • No need for local installs, server configs, or troubleshooting; everything is hosted.
    • Manage or delete instances with ease.

    In my video, I showed this taking less than a minute. It’s perfect for beginners who want to skip the technical hurdles of setting up OpenClaw locally. However, this convenience comes at a premium—more on that later.

    Live Tests: Agent Swarm in Action

    To put KimiClaw to the test, I ran a live agent swarm demo investigating a timely topic: “OpenAI’s acquisition of OpenClaw.” The swarm handled web searches and summarized key findings effectively, showcasing its potential for collaborative AI tasks like research or batch processing.

    Key highlights from the test:

    • Strengths: Solid web search integration and long-context handling. The agents coordinated well for basic queries.
    • Weaknesses: It timed out on more complex operations, exhibited basic behavior without advanced tweaks, and crucially, had no access to X (formerly Twitter). This is a big miss for real-time social media insights or trend analysis.

    I also checked for additional features, but found no full server or terminal control—limiting deep customization. Overall, it’s functional for entry-level agent swarms but doesn’t push boundaries.

    Limitations and Trust Issues: The Red Flags

    While the setup is a breeze, KimiClaw isn’t without flaws. Here’s what stood out in my evaluation:

    • No X Access: Can’t fetch posts or trends, which hampers tasks needing social data.
    • Timeouts and Basic Functionality: Extended runs often fail, and it lacks the sophistication of fully customizable setups.
    • No Full Control: You’re locked into Kimi’s hosted environment—no terminal access for mods.
    • Data Privacy Concerns: As a Chinese company (Moonshot AI), servers are hosted in China. This raises questions about data logging, retention, and potential monitoring. I advise caution if handling sensitive info.

    These aren’t deal-breakers for casual use, but they’re significant for power users. I spent the $40 to test it thoroughly—so you don’t have to!

    Alternatives: Better Value with Self-Hosting

    Why pay $40/month when you can get similar (or better) functionality cheaper? I compared KimiClaw to self-hosted options:

    • OpenClaw on Zebar: Set up for around $2/month. Full control, no subscriptions, and easy integration.
    • OpenRouter for Kimi Model: Access Kimi 2.5 directly at ~$0.50 per million input tokens and $2 per million output tokens. Pair it with your own OpenClaw for flexibility without the lock-in.

    These alternatives offer more customization, lower costs, and better privacy. If you’re not tied to Kimi’s dashboard, they’re the way to go. In my video, I emphasized that KimiClaw is “mid”—convenient for Allegro subscribers needing quick agent swarms, but overpriced otherwise.

    Conclusion: Convenience vs. Cost—You Decide

    KimiClaw shines in simplicity and integration for Kimi users, making agentic workflows accessible without setup headaches. However, its limitations in access, control, and privacy, combined with the $40 price tag, make it a tough sell compared to affordable self-hosted setups. If you’re deep in the Kimi ecosystem and value ease over everything, give it a shot. Otherwise, explore the alternatives for better bang for your buck.

    Tested it honestly in my video to cut through the hype—check it out for the full demo. Join our Discord community at https://discord.com/invite/boxtrading to discuss AI tools, share setups, and collaborate on agent swarms.

    Follow me on X at @boxmining or subscribe to the BoxminingAI Youtube channel for more no-BS reviews. Let’s optimize our AI game—see you in the next one!

  • You NEED to Update Your AI Agent with Cloudflare MarkDown Feature

    You NEED to Update Your AI Agent with Cloudflare MarkDown Feature

    Cloudflare has just rolled out a groundbreaking feature that converts web pages directly into Markdown format, slashing token usage for AI agents by up to 80% (and in some tests, even 94%). In this article, we’ll break down what this means, how to implement it on your bots, the real-world benefits, and why it’s a must-upgrade for anyone building AI systems.

    Drawing from my recent video demo, let’s explore how this could transform your setup.

    What is Cloudflare’s Markdown Conversion Feature?

    Cloudflare, a leader in web infrastructure, introduced this new tool to streamline how AI agents interact with websites. Traditionally, when an AI bot browses a page, it fetches bloated HTML full of scripts, ads, and unnecessary elements. This inflates the token count—those precious units that determine your API costs with models like GPT or Minimax.

    The Markdown feature acts as a smart filter: It strips away the junk and delivers clean, readable Markdown text. Think of it as a built-in summarizer that preserves the core content while ditching the overhead. To enable it, website owners simply toggle a setting in their Cloudflare dashboard. Once activated, any AI agent can append ?markdown to the URL (e.g., https://example.com/page?markdown) to get the optimized version.

    Not all sites support it yet—adoption depends on site admins—but major players like Anthropic, OpenAI, Vercel, GitHub, CoinDesk, TechCrunch, The Verge, and Hugging Face are prime candidates, as many already use Cloudflare. If you’re running a site, enabling this is a quick win to make your content more AI-friendly.

    Implementing the Feature on Your Bots: A Step-by-Step Demo

    In my video, I walked through a real-time implementation on my multi-agent Discord bot setup. It’s straightforward and takes just minutes:

    • Identify Compatible Sites: Start by checking if a site is on Cloudflare (tools like WHOIS or simply trying the ?markdown parameter can confirm).
    • Update Your Agent Code: In your bot’s web-fetching logic, modify the URL to include ?markdown. For example, in Python with libraries like requests:
      import requests
      response = requests.get("https://example.com/article?markdown")
      markdown_content = response.text

      This pulls the slimmed-down version directly.

      • Integrate into Workflows: Assign this to your AI agents for tasks like research or summarization. In my setup, agents like “Stark” (powered by Opus) delegate web browsing to cheaper models, now with even lower token burn.
      • Test for Savings: I demonstrated fetching a page both ways—HTML vs. Markdown—and saw a 94% reduction in content size. That translates to fewer tokens processed, meaning faster responses and lower bills.

        This isn’t just theoretical; I showed it live on sites that have enabled the feature. If you’re using frameworks like LangChain or custom Discord bots, plugging this in is seamless.

        Pro tip: Combine it with models that handle Markdown natively for even better results.

        The Benefits: Massive Cost Savings and Efficiency Gains

        Why bother? Let’s talk numbers. Running AI agents isn’t cheap—my daily token spend can hit hundreds of dollars on complex tasks. Cloudflare claims an 80% reduction, but my tests pushed it to 94% on dense pages. Here’s a quick breakdown:

        • Token Efficiency: Less input data means fewer tokens billed. For a model charging $1-75 per million tokens (depending on the provider), this adds up fast.
        • Speed Improvements: Smaller payloads process quicker, reducing latency in agentic flows like real-time research or automated reporting.

        Cost Breakdown Example:

        MetricHTML FetchMarkdown FetchSavings
        Content Size100KB6KB94%
        Tokens Used~75,000~4,50094%
        Cost (at $10/M)$0.75$0.045$0.705

        Even tools like OpenAI’s built-in web fetch convert to Markdown, but Cloudflare’s version is more optimized and site-controlled.

        The feature shines in agentic setups where bots chain tasks: Browse a page, summarize, then act. By cutting fluff early, you avoid cascading inefficiencies. It’s especially useful for crypto news aggregation (e.g., CoinDesk) or tech updates (TechCrunch), where timely, clean data is key.

        Potential Drawbacks and the Road Ahead

        It’s not universal yet—only Cloudflare-hosted sites can enable it, and propagation might take time. If a site hasn’t toggled it on, you’ll fall back to full HTML. Also, while great for text-heavy pages, it might not handle dynamic content perfectly. But as adoption grows (and I predict it will, given the AI boom), this becomes a standard.

        Encourage site owners you follow to enable it—it’s free and boosts AI compatibility.

        Conclusion: Upgrade Now and Slash Your AI Costs

        Cloudflare’s Markdown feature is a simple yet powerful upgrade for any AI bot builder. It turns web browsing from a token hog into an efficient powerhouse, saving you time and money while boosting performance. If you’re like me, juggling agents on Discord for crypto analysis, coding, or research, this is a no-brainer.

        Try it out: Append ?markdown to a compatible URL and see the difference.

        Join our Discord community at https://discord.com/invite/boxtrading to collaborate on bots and AI tweaks.

        Follow me on X at @boxmining or subscribe to the BoxminingAI Youtube channel for more demos. Let’s optimize the future—one Markdown page at a time!

      1. Why Minimax 2.5 is a Game-Changer for AI Agents

        Why Minimax 2.5 is a Game-Changer for AI Agents

        Recently, Minimax 2.5 was released, and it’s a significant improvement over its predecessors—especially for agentic workflows. In this article, we’ll dive into a simple logic test that highlights why Minimax 2.5 stands out, explore my multi-agent setup on Discord, compare it to high-end models like Opus, and break down the cost benefits. If you’re building AI-driven systems or just curious about the latest advancements, read on.

        We also have a full video guide if you need visual assistance.

        A Quick Logic Test: Why Minimax 2.5 Shines

        To demonstrate the leap in performance, let’s start with a straightforward question: “I need to wash my car at the car wash. Should I walk or drive over? It’s only 50 meters away.”

        As humans, the answer is obvious—you need to drive because the car has to be at the wash. But not all AI models get this right. Here’s how various models performed:

        • Minimax 2.5: Correctly advises driving, recognizing the core logic: “You need your car at the car wash.”
        • Minimax 2.1: Suggests walking, falling for the short distance bait: “It’s just a minute’s walk, save on gas, zero emissions.”
        • Kimi: Gets it right, stating you probably have to drive.
        • Deepseek (older version): Recommends walking, missing the essential point.

        I tested this on Opus as well, and it passed with flying colors. However, even Minimax 2.5 occasionally slipped up in repeated tests, reminding us that AI isn’t perfect yet. Still, for agentic tasks—where logic and planning are crucial—this test shows why upgrading to 2.5 is worthwhile. Benchmarks are great, but real-world simple tasks reveal a model’s reliability.

        If you’re running agents for daily planning or complex workflows, try this question yourself. Paste it into your AI and see if it passes: “Hey, I have a question. I need to wash my car at the car wash. Should I walk or should I drive over? It’s only 50 meters away.”

        My Agentic Workflow Setup on Discord

        I’ve built a multi-agent system on Discord that’s efficient and scalable. It includes bots powered by models like Minimax 2.5, Kimi, Deepseek, and even Opus for heavier lifting. The setup allows agents to collaborate, delegate tasks, and handle everything from research to coding.

        For example, I recently tasked my agents with: “Do some deep research on how Minimax 2.5 is performing and if it’s really better than Opus. I want to make a mini presentation hosted locally. Use whatever framework you see fit. Include deep research. Save this presentation as scalable for the future.”

        The result? An AI-generated slide deck created by Minimax 2.5. Interestingly, my main agent “Stark” (running on Opus, inspired by Tony Stark) delegated the coding to Minimax 2.5 for efficiency. The slides covered:

        • Background on Minimax: Founded in 2021, with 50 TPS (30% faster than older models).
        • Performance Highlights: Excels in coding tests and agentic work.
        • Cost Breakdown: $1.2 per million output tokens, plus a $20 coding plan that provides 300 prompts every five hours—essentially unlimited for agent use.

        This setup keeps things clean and automated. Stark handles big, mission-critical tasks but outsources simpler ones to cheaper models like Minimax 2.5. It’s a smart way to balance power and cost.

        Join our Discord community to see it in action or build your own: https://discord.com/invite/boxtrading.

        Minimax 2.5 vs. Opus: Performance and Cost

        Opus is undeniably powerful—it’s great for complex tasks and nailed our logic test. But it’s expensive: $75 per million output tokens. Plus, there’s a hidden “heartbeat” cost—periodic pings that report back and can add up to about $5 per day, even when idle.

        In contrast, Minimax 2.5 delivers 95% of Opus’s value at a fraction of the price. It’s reliable for coding, research, and agentic flows without the premium tag. I’ve used it for quick experiments and found it outperforms many local models, which often fail simple logic checks.

        Why not go fully local for free? Models like older Llama versions struggle with basic tasks, leading to frustration. Cloud-based options like Minimax ensure consistency, especially when planning trips or handling multi-step processes.

        Conclusion

        Minimax 2.5 is a game-changer for anyone working with AI agents. It passes key logic tests, integrates seamlessly into workflows, and keeps costs low—making it a strong alternative to pricier options like Opus. We’re at an exciting point where AI is getting smarter fast, empowering us to become “Human 2.0”: solving problems quicker and achieving more.

        If this resonates, test your agents with the car wash question and share your results. Follow me on X at @boxmining or check out the BoxminingAI Youtube channel for more AI tips.

      2. OpenClaw Setup Guide: The Cheapest Way Using the Latest MiniMax M2.5 Model

        OpenClaw Setup Guide: The Cheapest Way Using the Latest MiniMax M2.5 Model

        In this guide, I’ll walk you through an affordable and straightforward way to get OpenClaw up and running with the cutting-edge MiniMax 2.5 model. We also have a full video guide if you need visual assistance.

        Why This Setup? A Quick Intro

        OpenClaw is an fantastic open-source AI agent framework that allows you to build and run autonomous AI tasks. The beauty of this approach is its sandboxed nature—you can test and play around without exposing your main computer to potential issues. Instead of splurging on something like a Mac Mini, we’ll use a cheap cloud server from Zeabur combined with the MiniMax 2.5 model, which costs about $20 a month for solid performance.

        This method is ideal for beginners because it’s simple, low-risk, and scalable. Plus, MiniMax 2.5 offers high intelligence at a fraction of the cost of bigger models. If you’re new to AI like me, starting here means you can focus on learning without overwhelming setup hurdles. Ready? Let’s choose your server.

        Step 1: Choosing the Right Server

        The key to keeping costs down is selecting an accessible and affordable hosting provider. I recommend Zeabur over more complex options like Digital Ocean or AWS—it’s user-friendly and perfect for quick setups.

        Here’s how to get started:

        1. Head to Zeabur’s website and create an account.
        2. Set up a new server with minimal specs: 2GB RAM and 40GB storage. This should cost you less than $2 per month.
        3. Choose a server region close to you for better speed—for example, Singapore if you’re in Asia.
        4. Once created, you’ll get an IP address, username (usually “Ubuntu”), and password.

        To connect to your server, use a terminal app like Termius. Enter the IP, username, and password, and you’re in! This remote setup keeps everything isolated, so you can experiment freely.

        Step 2: Installing OpenClaw

        With your server ready, installation is a breeze. OpenClaw’s official site makes it easy with a one-line command for Linux.

        Follow these steps:

        1. Go to openclaw.ai and find the “Max Linux” installation section.
        2. Copy the provided command (it’ll look something like a curl or wget script to download and install).
        3. In your server terminal, paste the command. On a Mac, use Shift+Ctrl+V; on other systems, try Command+V or right-click paste.
        4. The process takes about 2-3 minutes. Sit back and let it run.

        If you encounter a “warn path missing” error after installation, fix it with this command:

        export PATH=$PATH:/path/to/openclaw

        (Replace /path/to/openclaw with the actual installation path if needed.)

        During setup, you’ll be prompted to choose a model. Select MiniMax 2.5—it’s powerful and included in affordable plans. You’ll need a MiniMax API key; I suggest the coding plan, which gives you 300 prompts over 5 hours for testing. Input your key when asked.

        Pro Tip: If you mess up the initial setup, run openclaw onboard to restart the process fresh.

        Step 3: Configuring OpenClaw for Optimal Use

        Once installed, access the Terminal User Interface (TUI) with:

        openclaw TUI

        This interface lets you interact with your AI agent directly.Key configuration tips:

        • Stick with MiniMax M2.5 (avoid Lightning if it’s not in your plan).
        • Use openclaw configure to tweak settings like models, gateways, or skills.
        • For now, focus on basic setup. In future guides, I’ll cover integrations like connecting to Telegram or Discord for threaded conversations (which I prefer over TUI for better organization).

        Your OpenClaw AI can now handle tasks like web searches, Twitter (X) data scraping, managing shared notes, and even task automation. Over time, you can train it for more personalized responses. Remember, keep it isolated initially to protect your personal data—security first!

        Common Troubleshooting Commands:

        • openclaw onboard: Reset and restart setup.
        • openclaw configure: Adjust models, skills, or connections.

        Wrapping Up: Next Steps and Final Thoughts

        There you have it—a complete, budget-friendly guide to setting up OpenClaw with MiniMax 2.5. This setup has been a game-changer for me, allowing hands-on AI experimentation without the high costs or risks. In under 15 minutes, you’ll have a running AI agent ready for action.

        If you run into issues or want to dive deeper, check out my Discord community for tips and discussions: Join here. Upcoming videos will cover advanced topics like Telegram/Discord bots, fixing common errors, and even more integrations.

        If you’re enjoying this journey into AI, subscribe to my channel @BoxminingAI for more beginner-friendly guides on vibe coding, AI models, and tools.