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Tag: openclaw

  • Is OpenClaw Overhyped? My Honest Take After 2 Months

    Is OpenClaw Overhyped? My Honest Take After 2 Months

    After using OpenClaw for over two months, I keep getting the same question: is it overhyped? A post from Miles Stoer caught my eye this morning — he argued that most people shouldn’t use OpenClaw and that he’s moved his workflows to Claude Code instead. So I wanted to give you my honest, unfiltered take on where OpenClaw actually shines and where it falls short.

    The Short Answer: It’s Not Overhyped, But It’s Not For Everything

    Let me be real — I don’t use OpenClaw to run my life. I don’t let it read my emails, manage my calendar, or handle scheduling. There’s still roughly a 2-5% chance it’ll mess things up, like getting dates wrong or hallucinating details. That’s just the nature of AI agents right now, and it’s a point that TechCrunch recently echoed in their piece questioning whether OpenClaw lives up to the buzz. Some AI experts have pointed to its complex setup requirements and high computational demands as reasons for skepticism.

    Instead, I let OpenClaw handle tasks that are time-intensive, repetitive, and where mistakes aren’t catastrophic. That’s the sweet spot.

    Where OpenClaw Actually Excels: Daily Briefings and Cron Tasks

    The thing OpenClaw does better than almost anything else is recurring daily tasks. I have it generate a daily briefing presentation for me every morning — it just runs automatically via cron jobs, no prompting needed. I wake up to a full rundown of what’s happening in the crypto and AI space, complete with actual quotes, linked tweets, and sourced data.

    This didn’t happen overnight though. Over time, I refined the instructions to make sure it wasn’t gaslighting me. Early on, it would flat-out lie about video view counts or make up restaurant locations. My fix? I told it to always include source links, and I even set up a sub-agent to fact-check everything before the briefing gets delivered. These tweaks drastically reduced the slop and made the output genuinely useful.

    OpenClaw’s architecture is actually well-suited for this kind of work. Since it gained popularity in late January 2026 thanks to its open-source nature and the viral Moltbook project, the community has built out robust cron scheduling and monitoring capabilities. Tasks that need to happen daily, that benefit from memory across sessions, and that can be iteratively improved — that’s where OpenClaw is in its element.

    Content Ideas and Creative Bouncing

    I also have a second bot that scans trending videos and gives me daily intel on content opportunities. When I talk back to it and say “here’s what I’m interested in, suggest some video ideas,” it’s genuinely useful as a brainstorming partner.

    The key insight here is that none of this is mission-critical. If the bot suggests a bad video idea, nothing breaks. I can accept or reject its suggestions freely. It’s low-risk, high-reward automation — and that’s the mindset you need when working with AI agents in 2026.

    I’ve even had it scan through my old videos to add referral codes I’d missed, then save the process as a reusable skill for future use. Setting up skills in OpenClaw is honestly one of the most important things you can do to get real value out of it.

    Where OpenClaw Falls Short: Don’t Trust It With Your Life

    Here’s where I have to be honest about the limitations. We had an incident on our team where OpenClaw randomly messaged Ron’s girlfriend. Just out of nowhere. That’s the kind of thing that happens when you give an AI agent too much access without proper guardrails.

    I don’t trust OpenClaw enough to let it into my Mac or manage my personal communications. And I think that’s exactly where the “overhyped” perception comes from — people install it on their local machine, give it broad access, and then get disappointed when it can’t flawlessly run their entire digital life. As CNBC reported, some experts have criticized OpenClaw’s complex installation and the gap between expectations and reality.

    The way I see it, OpenClaw is like a $500-800 virtual assistant from a developing country. They can handle rough tasks, they have some coding skills (which is a huge bonus), but they make mistakes 2-5% of the time. You wouldn’t trust them with mission-critical work — that’s what your executive assistant is for.

    OpenClaw vs Claude Code: Different Tools for Different Jobs

    Miles’ original post suggested using Claude Code instead, and honestly, I use both. Claude Code is fantastic for programming tasks — it excels at parallel task execution, deploying sub-agents, and agent orchestration. As DataCamp’s comparison puts it, if your main use case is programming, Claude Code is the way to go. If you need a general-purpose assistant, OpenClaw is the better route. One comparison I saw described it perfectly: it’s like comparing a Swiss Army knife to a surgical scalpel.

    I actually plug my OpenClaw into Claude as its language model, but Claude Code is even better at leveraging Claude’s capabilities for building systems. If you want to build something fun — like the mini-games I’ve been making — Claude Code will get you there faster. But it takes 2-3 weeks to really learn, and it’s a bigger scope project.

    My Setup Recommendation

    If you’re going to use OpenClaw, here’s my advice: run it on its own virtual private server, not your local Mac. The open ports let you directly access files, share presentations with friends, and browse dashboards from anywhere. Letting it build dashboards and visual presentations with coding capabilities will dramatically improve your experience.

    And most importantly — understand what level of “employee” your AI agent is. Don’t try to build your entire life around it. Delegate the right tasks: repetitive daily work, content research, data monitoring, and creative brainstorming. Keep the mission-critical stuff in your own hands, at least for now.

    I genuinely believe that in about six months, we’ll get to the point where these agents can function as true executive assistants. But we’re not there yet, and pretending otherwise is what leads to the “overhyped” label. Use OpenClaw for what it’s good at, and you won’t be disappointed.

  • OpenClaw Skills Setup Guide: Build Custom AI Agent Automations

    OpenClaw Skills Setup Guide: Build Custom AI Agent Automations

    If you’ve already got OpenClaw up and running, the single most important thing you should set up next is Skills. I genuinely believe this is what separates a basic AI assistant from one that actually works for you — to your exact specifications, every single time. In this guide, I’ll walk you through what Skills are, why I build my own instead of downloading them, and how you can create custom Skills that transform your OpenClaw bot into a truly personalized AI agent.

    What Are OpenClaw Skills and Why Do They Matter?

    Skills are essentially instruction sets that tell your OpenClaw agent how to perform specific tasks — think of them like muscle memory for your bot. Without Skills, your agent starts fresh every session. It forgets your preferences, your formatting choices, your workflow quirks. With Skills, it wakes up knowing exactly what to do and how you like it done.

    This matters more than most people realize. OpenClaw, like most AI agents, clears its context window between sessions. The best analogy I can think of is that your bot essentially “dies” every day and comes back trying to remember everything. Skills solve this problem by giving your agent persistent knowledge — it’s like your bot knowing jiu-jitsu without having to relearn it every morning.

    The OpenClaw ecosystem now has over 3,200 community-built Skills available on ClawHub, the official skills registry. That’s a massive library covering everything from browser automation to financial tracking. But as I’ll explain, I think building your own is the way to go.

    Why I Build My Own Skills Instead of Downloading Them

    I’ll be honest — I don’t really go on ClawHub to download skills. There are two reasons for this. First, everyone has different preferences and workflows. Someone else’s presentation skill might format things completely differently from how I want them. Second, and this is important, there have been some security concerns with community-uploaded skills in the past. ClawHub now has virus scanning, but when you’re giving an AI agent instructions that run on your machine, I’d rather be safe than sorry.

    Instead, I design my own Skills by simply talking to my bot. I didn’t write a single line of the skill file myself — I just told my agent what my preferences were, how I wanted things structured, and said “save this as a skill.” The bot wrote up the entire SKILL.md document for me. That’s the beauty of it: you don’t need to be technical to create powerful, custom Skills.

    My Daily Presentation Skill: A Real Example

    Let me show you a concrete example. I have a presentation skill that automatically produces daily briefings about what’s happening in both crypto and AI. Every morning at exactly 8:00 AM, a cron job triggers this skill, and by the time I sit down with my coffee, there’s a fresh research presentation waiting for me.

    The skill knows the structure I want, the research depth I expect, where to save the files, and even which directory to use. It actually calls upon another skill — a deep research skill — to gather the information before assembling the presentation. Skills can build on top of each other like that, which is where things get really powerful.

    The cron job scheduling is key here. OpenClaw lets you set up scheduled tasks so your agent runs specific Skills at set times without any manual input. I have mine set to run first thing in the morning so all the news is fresh. I can then decide what to cover on my YouTube channel or use for other content. It’s hands-off automation that actually delivers quality output because the skill specifications are dialed in.

    How to Create and Refine Your Own Skills

    Creating a skill is surprisingly simple. Here’s my approach:

    Start by talking to your bot. Tell it what you want done. Be specific about your preferences — the format, the tone, the sources, the output location. Don’t worry about documenting it perfectly; just have a natural conversation about what you need.

    Ask it to save the skill. Once you’re happy with how the bot handles your task, say something like “save this as a skill” or “create a skill for this workflow.” Your agent will generate a structured SKILL.md file with all the specifications.

    Refine over time. This is the part most people skip. After using a skill for a while, you’ll notice things you want to change. Just tell your bot: “Update your presentation skill — I prefer light theme now” or “Your research wasn’t deep enough, make sure you check at least five sources next time and update that in your skill.” The bot handles the file updates automatically.

    The key phrases to remember are “update my skill” and “save this as a skill.” These trigger the agent to modify or create the SKILL.md files that persist between sessions.

    ClawHub: Good for Inspiration, Use With Caution

    While I prefer building my own, I do think ClawHub is worth browsing for inspiration. Seeing how other people structure their skills can give you ideas for your own workflows. The platform uses vector search to help you find relevant skills quickly, and there are some genuinely creative community contributions — from self-improving agents to advanced browser automation.

    That said, I’d recommend using ClawHub as a reference rather than blindly downloading and installing skills. Read through what a skill does, understand the approach, and then build your own version tailored to your needs. As a human, you want to filter out the good from the bad, especially when it comes to code that runs autonomously on your machine.

    Final Thoughts

    Skills are what make OpenClaw go from a cool toy to an indispensable daily tool. The combination of custom specifications, cron job scheduling, and the ability to chain skills together means you can build genuinely sophisticated automation workflows — all by just talking to your bot.

    If you’re just getting started, pick one repetitive task you do regularly and turn it into a skill. Refine it over a few days. Once you see how much time it saves, you’ll want to skill-ify everything. And if you have suggestions for what we should cover next, drop a comment — my bot actually has a skill that reads through comments and suggests video topics to me. So yes, your feedback literally gets processed and repeated back to me multiple times.

    Make sure to subscribe to @BoxminingAI for more guides, and join our Discord community to share your own skill setups with the growing community.

  • How to Add ANY API to Your OpenClaw Agent (Step-by-Step)

    How to Add ANY API to Your OpenClaw Agent (Step-by-Step)

    Your OpenClaw agent is smart — really smart. But without the right tools, it’s like a chef without a kitchen. In this video, Ron and I walk through one of the most important skills you can teach your agent: how to connect it to external APIs. We use a YouTube transcript API as our example, but the process applies to virtually any API out there. Let me break it down.

    Why Your Agent Needs APIs

    Out of the box, your OpenClaw agent can browse the web and fetch pages. That sounds like it should be enough, right? Not quite. The reality is that many websites actively block bot access. Twitter (X) is notorious for this — paste a tweet link and your agent will just stare at a wall. CoinGecko, one of the most popular crypto data sources, also restricts automated access because that data is valuable and they want you to pay for it.

    This is where APIs come in. An API (Application Programming Interface) is essentially a structured doorway that lets your bot request specific data directly, bypassing all the anti-bot protections on the front end. In 2026, APIs have become the backbone of AI agent ecosystems — industry research shows that AI agents rely on APIs to read data and take actions in real systems, from SaaS platforms to databases to internal services. Without them, your agent is flying blind.

    Finding the Right API: YouTube Transcripts as an Example

    For our demo, we wanted our agents to grab YouTube video transcripts automatically — super useful for generating timestamps, summaries, and descriptions. We used a service called youtube-transcript.io, which turns any YouTube video into a clean text transcript via a simple API call.

    The signup process is straightforward: create a free account, and they hand you an API token right on the dashboard. Think of this token as a password specifically for your bot. I know the word “API” can sound intimidating, but honestly, it’s just a key that unlocks a door. Your bot does all the hard work behind it.

    This same pattern works for hundreds of other services. Need crypto prices? There’s an API for that. Want social media data? There’s an API. Weather, news, translation — you name it. The setup process is essentially the same every time.

    The Setup Process: Three Simple Steps

    Here’s the workflow I use every time I add a new API to my agent. It works whether you’re connecting to a transcript service, a crypto data feed, or anything else.

    Step 1: Paste the API documentation. Most API services have a documentation page that explains how to make requests. Copy that documentation and paste it to your agent. Tell it something like: “Read up on this API documentation and make a skill to fetch transcripts.” The beauty here is that API docs are written for programmers — and your bot is a programmer. These AI models pass top-tier coding exams, so they can parse technical documentation far better than most humans.

    Step 2: Give it the API key and save it. Hand your agent the API token and tell it to save the key to the .env file in your OpenClaw directory. This is a hidden environment file where sensitive credentials are stored. The models are trained not to reveal what’s in this file, so it’s a safe place for your keys. Just remember — never share your API tokens publicly.

    Step 3: Test it. Ask your agent to actually use the API. In our case, we said “get the transcript for this video” and confirmed it could pull the data successfully. This verification step is crucial — it proves the integration actually works end to end.

    Save It as a Skill

    Once your API integration is working, the next move is to save it as a skill. Skills in OpenClaw are reusable capabilities that your agent remembers across sessions. So instead of re-explaining the API every time, your agent just knows how to use it going forward.

    In our case, once Stark (one of our agents) had the YouTube transcript skill saved, he would proactively grab transcripts and generate summaries without even being asked. That’s the power of combining APIs with skills — your agent becomes genuinely autonomous.

    Expect Some Bumps (And Don’t Give Up)

    I want to be honest here — things don’t always work on the first try. In the video, we ran two agents side by side: Stark and Banner. Stark, who already had the skill trained, nailed it immediately. Banner, running on Claude Opus, hit a few snags. He encountered Cloudflare blocks when trying to read the API docs, and at one point even hallucinated results instead of actually calling the API.

    This is normal. AI agents can sometimes “gaslight” you into thinking they completed a task when they didn’t. The fix? Verify the output. If something looks off, ask the agent to double-check. Start a new session if the context gets muddled. And most importantly — don’t give up after the first failure.

    I genuinely believe this is why some people struggle with AI tools. The first attempt fails and they walk away. But persistence and repetition are key. Even on our third time doing this exact process, we still hit unexpected issues. That’s just the nature of working with AI in 2026. Embrace it.

    What This Unlocks

    Adding API access to your OpenClaw agent is a force multiplier. Once you understand the pattern — find an API, paste the docs, give it the key, test, save as skill — you can connect your agent to virtually anything. Twitter data, crypto prices, weather forecasts, email services, calendar integrations, translation tools — the list is endless.

    As OpenClaw continues to grow as a platform, the agents that stand out will be the ones with the richest set of API connections. Think of each API as a new superpower for your bot. The more you add, the more capable and autonomous it becomes.

    If you’re just getting started, pick one API that solves a real problem for you and follow the steps above. You’ll be surprised how quickly your agent levels up.

  • Why Your OpenClaw Agent Gets DUMB (Context Window Explained)

    Why Your OpenClaw Agent Gets DUMB (Context Window Explained)

    If you’ve been running an OpenClaw agent and noticed it getting progressively dumber throughout the day, you’re not alone. In this video, we break down exactly why this happens and what you can do about it. It all comes down to one thing: the context window.

    What Is the Context Window?

    Think of the context window as your AI agent’s short-term memory — its working brain. Every message you send, every file it reads, every task it processes takes up space in that window. It’s measured in tokens (roughly 4 characters per token), and every model has a hard limit.

    The best analogy is a human assistant who’s been given too many tasks at once. Tell them to handle your car, your house, your parents visiting, your dinner reservations — at some point they get overloaded and start dropping balls. That’s exactly what happens to your AI agent when the context window fills up.

    Research backs this up too. A 2025 study by Chroma Research called “Context Rot” tested 18 different LLMs and found that models do not use their context uniformly — their performance grows increasingly unreliable as input length grows. Even for simple tasks, LLMs exhibit inconsistent performance across different context lengths. The longer the context, the worse the reasoning gets, especially for multi-step problems.

    Why Your Agent Wakes Up Already Loaded

    Here’s something that surprised us. Every day, OpenClaw essentially kills your agent and restarts it fresh. It wakes up, reads its long-term memory files (your SOUL.md, MEMORY.md, AGENTS.md, and other config files), and loads all of that into the context window. It’s like an assistant coming to work, reading their briefing notes, and getting up to speed.

    The problem? If you’ve stuffed those files with your life story, your preferences, your childhood memories, and every random thought you’ve ever had — your agent wakes up with a context window that’s already half full before it’s done a single task.

    In our test, Jeff (running on MiniMax 2.5) woke up at the start of the day already at 136K tokens. That’s because in the early days, the common advice was to “blast your agent with your life story so it understands you better.” Turns out, that’s actually counterproductive. All that irrelevant context is eating into the space your agent needs for actual work.

    Cheap Models Get Hit Harder

    Not all models handle large context equally. We compared two setups side by side:

    Stark running on Claude Opus — woke up at around 100K out of 200K capacity, and still performed fluidly. Opus is genuinely good at working with large context windows and maintaining quality throughout.

    Jeff running on MiniMax 2.5 — started struggling almost immediately. As one of our viewers, Note, put it: “The moment you go above 120K context window, it feels like I’m talking to ChatGPT 3.5.”

    There’s a hidden reason for this beyond just model quality. To save costs, cheaper models like MiniMax aggressively dump parts of the context they consider unimportant. This is an internal optimization to reduce compute costs — but sometimes what they dump is actually critical to your task. You might ask it to make a presentation and halfway through it forgets what the presentation is even about.

    This aligns with what researchers have found: relevant information buried in the middle of longer contexts gets degraded considerably, and lower similarity between questions and stored context accelerates that degradation.

    How to Keep Your Agent Smart

    Based on our testing, here are the practical tips that actually work:

    1. Trim your memory files. Go through your SOUL.md, USER.md, and other long-term storage files. Remove anything that isn’t directly relevant to the tasks you need your agent to do. Your agent doesn’t need to know your life story — it needs to know how to do its job.

    2. Specialize your agent. AI models actually gravitate toward specialization. Instead of making your agent a general-purpose assistant that handles everything from dinner reservations to research reports, train it for specific tasks. In our test, Stark was trained specifically for making presentations and research — and it delivered significantly better results than Jeff, who was loaded with general life context.

    3. Monitor your context usage. You can simply ask your agent “How much context are you using?” and it’ll tell you. On the OpenClaw terminal, it sometimes displays this automatically. Keep an eye on it throughout the day.

    4. Clear context when needed. If you feel your agent getting dumber, start a new session. This kills the current context and lets the agent restart fresh. There’s also a natural compacting stage where the agent automatically summarizes and compresses older context — similar to how your own brain forgets the details of brushing your teeth but remembers the important meeting you had.

    5. Choose your model wisely. If you’re on a budget with MiniMax or other Chinese models, context management becomes even more critical. These models aggressively optimize to save compute, which means they’ll cut corners on context retention. If you can afford it, models like Claude Opus handle large context windows much more gracefully.

    The Bottom Line

    Context window management is probably the single most impactful thing you can do to improve your OpenClaw agent’s performance. It’s not about giving your agent more information — it’s about giving it the right information and keeping that working memory clean.

    The takeaway is simple: less irrelevant context equals a smarter agent. Trim the fat from your memory files, specialize your agent’s purpose, and don’t be afraid to restart sessions when things get sluggish. Your agent will thank you — by actually being useful.

  • NEW OpenClaw Update is MASSIVE — Here’s What Changed in v2.25

    NEW OpenClaw Update is MASSIVE — Here’s What Changed in v2.25

    OpenClaw just dropped version 2.25, and honestly, this one’s a big deal. I’ve been testing it hands-on and there are some genuinely useful improvements here — especially around sub-agents and visibility. Let me break down what’s new and what it actually means for your day-to-day usage.

    Sub-Agent Delivery Gets a Major Overhaul

    The headline feature in v2.25 is the overhauled sub-agent delivery system. If you’ve been using OpenClaw for a while, you know sub-agents are one of the most powerful features — they let your main agent spin up smaller, focused agents to handle specific tasks in parallel. The problem was, they could be unreliable. Sub-agents would sometimes time out, vanish into the void, and you’d never hear about it again.

    I’ve experienced this firsthand. You tell your agent to do something, it says “give me five minutes,” spawns a sub-agent, and then… nothing. You’re sitting there going “yo, where’s my stuff?” with no feedback whatsoever.

    That changes with this update. Sub-agents now actively report back their status. When a sub-agent completes its work, the system tells you. When it fails or times out, you get notified about that too. It’s a visibility upgrade that makes the whole orchestration system feel way more functional and trustworthy.

    Why Sub-Agents Matter (And Why You Should Use Them)

    Here’s the thing about sub-agents that people sometimes miss: they’re not just about parallelism. They’re about clean context. Your main agent — the one you’ve been working with daily — has its brain full of everything: crypto updates, project notes, random conversations. When you spin up a sub-agent, it gets a fresh, focused context window dedicated entirely to one task.

    This is why sub-agents consistently produce better results for specific tasks like research, writing presentations, or updating documentation. The sub-agent isn’t distracted by the 47 other things your main agent has been juggling.

    With v2.25, the release notes confirm over 40 documented changes spanning Android client improvements, WebSocket authentication tightening, model fallback logic refinements, and comprehensive vulnerability patches. The sub-agent improvements are part of a broader push to make the entire agent orchestration pipeline more reliable and transparent.

    Real-World Testing: Building a Presentation

    To put this update through its paces, we built a presentation about the new features using OpenClaw itself. The agent automatically spun up sub-agents to research what changed in v2.25, pull community reactions from X, and then compile everything into slides.

    Did it work perfectly? Not quite. During one task, the sub-agent left a file truncated — cut off midway through. But here’s where the improvement shows: the main agent caught it, flagged the issue, and said “let me handle this myself.” That kind of self-awareness and error recovery is exactly what was missing before.

    We also experimented with breaking down large tasks into multiple specialized sub-agents — one for research, one for writing, one for quality-checking the output. This modular approach is something I’d recommend trying. It plays to the strengths of the sub-agent system and reduces the chance of any single agent getting overwhelmed.

    Heartbeat DM Delivery

    The other key improvement is heartbeat DM delivery. If you’ve set up heartbeat checks — where your agent periodically pings you to confirm it’s alive and working — the delivery mechanism is now more reliable. Previously, heartbeat messages could get lost or delayed, which kind of defeats the purpose of having a health check system.

    OpenClaw’s heartbeat system lets you configure check-in intervals (commonly every 5-30 minutes) with custom checklists your agent runs through. The v2.25 update also introduces a directPolicy configuration option, giving you more control over how heartbeat DMs are handled.

    Cron Job Tracking Gets Smarter

    Another pain point that’s been addressed: cron jobs. Before this update, if a scheduled task failed, you often had no idea why. Did it run at the wrong time because of timezone mismatches on your VPS? Did it silently crash? The new version adds better tracking and cleanup for cron jobs, so you can actually see what happened and why.

    The release also includes improvements to session maintenance with openclaw sessions cleanup, per-agent store targeting, and disk-budget controls — all of which help keep your instance running smoothly over time.

    What Else Is New

    Beyond the big features, v2.25 packs in a bunch of other updates worth noting:

    • Android updates — new features for mobile users (though I haven’t tested these personally since I’m not on Android)
    • Gateway security hardening — including optional Strict-Transport-Security headers for direct HTTPS deployments
    • Communication improvements — better visibility across Telegram and Discord integrations
    • Kimmy Vision — video content understanding via Moonshot, which is a feature I’m excited to explore in a future video

    One thing that really stands out is the pace of development. OpenClaw has a strong community of contributors pushing updates almost daily. Despite concerns after Peter Steinberg joined OpenAI (which is famously closed-source), the project remains actively open-source with lots of people building on it. That’s genuinely encouraging for the long-term health of the platform.

    Should You Update?

    Absolutely. If you’re running OpenClaw, updating is as simple as telling your agent to do it — literally just say “update yourself.” The sub-agent improvements alone make this worth it, especially if you’re doing any kind of multi-step automation. The better visibility into what your agents are actually doing removes a lot of the guesswork that made previous versions frustrating at times.

    The AI models themselves haven’t changed — you’re still running whatever you had before (Claude Opus 4.6, MiniMax, etc.). What’s improved is the plumbing: how agents communicate, how tasks get delegated, and how failures get reported. And honestly, that’s exactly the kind of update that makes the biggest difference in daily use.

  • PicoClaw: The Chinese Killer of OpenClaw with 99% Less Memory Usage

    PicoClaw: The Chinese Killer of OpenClaw with 99% Less Memory Usage

    In the rapidly evolving world of AI tools, efficiency is key—especially when it comes to running powerful assistants on budget hardware. Enter PicoClaw, a lightweight, open-source alternative to the popular OpenClaw, often dubbed the “Chinese version” for its origins and optimizations. This tool promises to deliver similar functionality while slashing resource demands dramatically, making it accessible for hobbyists, developers, and anyone with spare low-end devices like a Raspberry Pi or even an old Android phone. Let’s dive into what makes PicoClaw a game-changer, based on its core features and comparisons.

    What is PicoClaw and Why Does It Matter?

    PicoClaw is designed as a streamlined version of OpenClaw, focusing on core AI assistant capabilities without the bloat. While OpenClaw typically requires high-end setups—think a MacBook costing anywhere from $400 to $1,000—PicoClaw runs smoothly on devices as cheap as $10. Its standout feature? Memory efficiency. OpenClaw gobbles up over 1 GB of RAM, but PicoClaw operates with under 10 MB. That’s a whopping 99% reduction, allowing it to thrive in resource-constrained environments.

    Built in Go, a language renowned for its speed and low overhead, PicoClaw boasts a startup time of less than one second. It supports RISC-V architecture, which is common in affordable boards like the Raspberry Pi, and is compatible with a wide range of hardware. This makes it ideal for experimentation without breaking the bank.

    Functionality: How Does It Stack Up Against OpenClaw?

    At its heart, PicoClaw mirrors OpenClaw’s technology and features. Both tools excel at maintaining conversation history, turning a simple AI into a true personal assistant that remembers context over time. They integrate seamlessly with AI agents like Miniax and can be configured for platforms such as Slack and Discord.

    However, PicoClaw shines in deployment flexibility. It works effortlessly in containerized setups via Docker Compose, and you can even repurpose old Android devices to host it. On a modest $2 server with just 4 GB of RAM, you could run multiple instances without breaking a sweat. That said, it’s not a complete replacement—PicoClaw skips some of OpenClaw’s advanced bells and whistles, like browser control plugins that let the AI manipulate your mouse, keyboard, or screen for automated tasks.

    The Edge of OpenClaw and Potential Pitfalls

    OpenClaw still holds advantages for power users. It receives more frequent updates, offers direct access to its original developers, and includes those extra features for deeper automation. But there’s a catch: OpenClaw has been acquired by OpenAI (or at least its creator has been hired, as clarified in community discussions). This raises eyebrows, given OpenAI’s track record of shifting projects to closed-source models or shutting them down entirely. PicoClaw, being independent and open-source, sidesteps these risks and could emerge as a more reliable long-term option.

    Innovative Use Cases and Getting Started

    The real innovation here lies in memory management. By keeping chat histories efficient, PicoClaw enables personalized AI experiences on hardware that would otherwise be inadequate. Imagine pairing it with a lightweight model like Ollama on a Raspberry Pi to create your own voice-activated home assistant—similar to Alexa but fully customizable and privacy-focused.

    Setting up PicoClaw is straightforward, especially if you’re familiar with OpenClaw. For those new to it, resources like setup guides for Miniax and Zeber (a related tool) can get you up and running. If you’re interested in a deep-dive tutorial on PicoClaw itself, community feedback suggests it’s in high demand—drop a comment on the original video to push for one!

    Final Thoughts

    PicoClaw is gaining traction for good reason: it’s small, fast, and efficient, democratizing AI deployment for everyone from casual tinkerers to serious developers. With its tiny footprint and broad compatibility, it addresses the pain points of resource-heavy tools like OpenClaw, all while maintaining essential functionalities. If you’re looking to experiment with AI on a budget, PicoClaw is worth a shot. For more details, check out the full video on BoxminingAI’s channel, and join their Discord community for discussions and support.

    What do you think—will PicoClaw dethrone OpenClaw? Share your thoughts below!

  • Setting Up OpenClaw with Discord: A Complete Step-by-Step Guide

    Setting Up OpenClaw with Discord: A Complete Step-by-Step Guide

    If you’ve been tinkering with OpenClaw and wondering how to level up your workflow, connecting it to Discord is honestly the move. I’ve tried a bunch of different setups — terminal UI, Telegram, you name it — but Discord just hits different when it comes to organizing your AI agents. In this guide, I’ll walk you through the entire process step by step, based on what I’ve found works most reliably after setting up multiple bots over the past week.

    Why Discord Is the Best Interface for OpenClaw

    Here’s the thing about Discord that makes it perfect for AI agents: it’s structured. You can create dedicated channels for different bots and tasks, keeping everything clean, tidy, and neat. For example, I have an agent called “Stark” handling research and presentations in one channel, while another agent called “Banners” manages summarization tasks in a separate channel. Each bot stays in its lane, and nothing gets messy.

    What really sold me on this setup is the team collaboration angle. My team members can jump into Discord, interact with the bots directly, and we can all see what’s happening in real time. Discord threads let you spin up focused conversations — like a dedicated research task — without cluttering the main channel. It’s basically turning Discord into a full-blown AI command center.

    OpenClaw itself is an open-source AI agent framework that’s been gaining serious traction since its launch. Originally created by Austrian developer Peter Steinberger in late 2025, it’s quickly become one of the fastest-growing projects on GitHub. The framework is model-agnostic, meaning you can plug in Claude, GPT, MiniMax, or whatever model fits your budget and needs. It supports over 100 preconfigured AgentSkills for shell commands, file management, web automation, and more — all while keeping your data private since everything runs on your own infrastructure.

    Step 1: Create Your Discord Bot

    First things first, head over to the Discord Developer Portal and create a new application. Give it whatever name you want — I called mine “Bob” in the video because, well, why not. Upload a profile picture, add a description if you feel like it, and save your changes.

    Now here’s the important part: go to the Bot section and enable two critical intents — Server Members Intent and Message Content Intent. These permissions allow your bot to actually read messages and interact properly within your server. Don’t skip this step or your bot will just sit there doing nothing.

    Next, you’ll need to reset and copy your bot token. Treat this token like a password — if someone gets their hands on it, they can control your bot. Discord makes you go through a verification process with your passkey to generate a new token, which is a good security measure. Copy the token and keep it somewhere safe for the next step.

    Step 2: Configure OpenClaw

    Here’s a pro tip I learned the hard way: use the openclaw configure command instead of asking your agent to set up Discord for you. I tried the agent-based approach multiple times, and about three out of four attempts just blew up. The manual configuration method through the CLI is way more reliable.

    Run openclaw configure in your terminal, select the channels option, and choose Discord. It’ll prompt you for your bot token — paste it in using Ctrl+Shift+V on Windows. Then you’ll need your channel ID. If you don’t see the “Copy Channel ID” option when you right-click a channel in Discord, go to User Settings → Advanced and enable Developer Mode. This isn’t enabled by default, so most people miss it.

    Once you’ve pasted in the channel ID, confirm the pairing and you’re linked up. The whole configuration takes about two minutes once you know what you’re doing.

    Step 3: Restart the Gateway and Invite Your Bot

    After configuring, run openclaw gateway restart to activate the connection. This restarts the gateway service that handles communication between your OpenClaw agent and Discord. Without this step, nothing will work even if everything else is configured correctly.

    Now for the final piece: inviting the bot to your server. Back in the Discord Developer Portal, go to OAuth2 and check two boxes — Bot and Application Commands. For bot permissions, I just gave it Administrator access since it’s running on my private server. Select “Guild Install” if you’re adding it to your own server, then copy the generated URL and open it in your browser. Authorize the bot, prove you’re human, and you’ll see it pop up in your server’s member list.

    Post-Setup Tips That Actually Matter

    Once your bot is live, there are a few things worth tweaking. By default, the bot might only respond when you @mention it. You can configure it to reply to all messages in the channel, which makes the interaction feel much more natural — just talk to it and tell it to adjust its settings.

    One feature I use constantly is Discord threads. Instead of dumping everything into the main channel, I create a new thread for each task — like “Research Task” or “Content Draft” — and keep the conversation focused. This is especially useful when you’re running multiple agents or working with a team, because everyone can see exactly what each bot is working on without scrolling through a wall of messages.

    A word of advice from personal experience: don’t go overboard with the number of people and bots in your channels. It gets chaotic fast. Keep your setup focused and manageable, especially when you’re starting out.

    Troubleshooting: When Things Go Wrong

    Look, things will occasionally break. Your bot might stop responding, or the gateway might crash. The most common fix is simply running openclaw gateway restart again. If that doesn’t work, openclaw configure lets you re-do the channel setup from scratch. And if you really mess things up, openclaw onboard resets the entire configuration.

    The key takeaway here is to stick with manual configuration over letting the agent handle it. It’s more predictable and you’ll spend less time debugging weird failures. If you’re running into persistent issues, the OpenClaw Discord documentation covers common error scenarios and fixes.

    Final Thoughts

    Setting up OpenClaw with Discord has genuinely changed how I work with AI agents. The combination of Discord’s organized channel structure with OpenClaw’s powerful agent framework creates a workspace that’s both productive and easy to manage. Whether you’re a solo developer experimenting with AI or a team looking to integrate autonomous agents into your workflow, this setup is worth the 15 minutes it takes to get running.

    If you want to dive deeper into OpenClaw setups, check out our guide on setting up OpenClaw with MiniMax for a budget-friendly starting point. And if you want to connect with other OpenClaw users, join our Discord community for tips, troubleshooting, and discussions.

    For more beginner-friendly AI guides, subscribe to @BoxminingAI on YouTube. See you in the next one!

  • Setting Up OpenClaw with Discord: A Complete Step-by-Step Guide

    Setting Up OpenClaw with Discord: A Complete Step-by-Step Guide

    In the rapidly advancing world of AI agents and collaborative tools, integrating platforms like OpenClaw with Discord offers a powerful way to streamline team workflows and bot interactions. This article provides an in-depth, hands-on guide to this setup. Whether you’re a developer, AI enthusiast, or team leader looking to enhance collaboration, this article distills the key insights and steps from the video to help you get started seamlessly.

    Why Integrate OpenClaw with Discord?

    Discord stands out as an ideal platform for OpenClaw due to its structured environment, which keeps interactions “clean, tidy, and neat.” Unlike cluttered chat interfaces, Discord allows you to create dedicated channels for specific bots or tasks—for example, a “Stark” channel for research and presentations or a “Banners” channel for summarization. This setup facilitates both human-bot and bot-bot collaborations, making it easier to manage multiple AI agents without chaos.

    This integration turns Discord into a collaborative hub, where teams can assign tasks, monitor progress, and isolate discussions using threads. It’s particularly useful for users already familiar with AI tools like Claude AI, Grok AI, or Cursor AI, as it builds on similar concepts in prompt engineering and agentic workflows.

    Step-by-Step Setup Process

    While OpenClaw offers an agent-based setup option (via chat commands), we recommend the manual method for its higher reliability—noting that agent-driven attempts fail about 75% of the time.

    1. Create a Discord Application and Bot

    • Head to the Discord Developer Portal at discord.com/developers and create a new application.
    • Give your bot a name (e.g., “Bob”), upload an image, and add a brief description.
    • In the “Bot” section, enable key intents: “Server Intent” and “Message Content Intent.” These allow the bot to interact properly within servers.
    • Reset and copy the bot token—treat this as highly sensitive information and never share it.

    2. Configure OpenClaw

    • Launch OpenClaw and run the command openclaw configure.
    • Paste in your Discord bot token when prompted.
    • Specify the target Discord server and channel ID. To find these, enable Developer Mode in your Discord settings (under User Settings > Advanced), then right-click on the server or channel to copy the ID.
    • Confirm the pairing to link OpenClaw with Discord.

    3. Restart the OpenClaw Gateway

    • Execute openclaw gateway restart to activate the connection. This step ensures smooth communication between the AI agents and your Discord setup.

    4. Invite the Bot to Your Server

    • Back in the Discord Developer Portal, navigate to “OAuth2” > “URL Generator.”
    • Select scopes like “Application Commands” and “Bot,” and grant permissions (e.g., Administrator for simplicity, but use cautiously).
    • Choose “Guild Install” if it’s a private server.
    • Generate the URL, paste it into your browser, and authorize the bot to join your server.

    Once invited, the bot should appear in your server. Initially, it may only respond when mentioned (e.g. @Bob), but you can configure it to reply to all user messages for broader interaction.

    Post-Setup Tips and Best Practices

    After integration, optimize your setup for efficiency:

    • Use Threads for Focused Tasks: Start a new thread (e.g., “Research Task”) to keep bot interactions isolated and organized. This prevents channel clutter and makes it easier to track specific projects.
    • Team Management: Limit channel access to avoid overwhelming the space with too many users or bots. Discord’s structure shines in small, focused teams.
    • Security Emphasis: Always prioritize token security to prevent unauthorized access.
    • Customization: Adjust bot behavior in OpenClaw settings for reply preferences or additional features.

    In the video, we also touch on broader applications, such as using this setup for vibe coding, no-code AI development, or even Web3 AI projects, making it versatile for various workflows.

    Troubleshooting Common Issues

    If things go wrong—such as the bot not responding or gateway failures, stick to manual configuration over voice or agent commands for stability. If you’re new to this, spending time in Developer Mode will make IDs and permissions easier to handle.

    Tools and Models Mentioned

    • OpenClaw: The core AI agent framework, praised for its configurability.
    • Discord Developer Portal and OAuth2: Essential for bot creation and permissions.
    • Related ecosystems: Mentions of Claude AI, Grok AI, and tools like Minimax or GLM-5 for complementary AI tasks.

    Final Thoughts: Is This Setup Right for You?

    This integration transforms Discord from a simple chat app into a robust platform for AI-driven collaboration, ideal for developers experimenting with agent swarms or teams handling complex projects. While the initial setup requires some technical know-how, the payoff in organization and efficiency is significant. As AI tools continue to evolve—think o1 models or advanced prompt engineering—this guide positions you to stay ahead.

    This article captures the essence for quick reference. For the full walkthrough, including screen shares and demos, watch our video on YouTube!

  • 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!