Boxmining v2

Tag: ai automation

  • 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.

  • 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.