Ditch Mac mini Hosting: Sub-$500 AI Edge Computers Enable 24/7 Stable Operation for Clawdbot with AI Inference

Keywords: Clawdbot, OpenClaw, Edge AI, Edge Computing, AI Agent, InHand Edge Computer, EC3320, LLM Edge Deployment, Industrial Automation, Digital Employee, AI Inference, Local Deployment, Smart Monitoring, AI at the Edge, AI Edge Solutions

Meta Description: Discover how InHand Edge Computers, priced under $500, provide a 24/7 stable AI edge deployment solution for Clawdbot (OpenClaw), enabling efficient AI inference and industrial automation, transforming AI Agents into true digital employees.

Introduction: Clawdbot (OpenClaw) - Your Intelligent Digital Employee

Recently, the open-source AI Agent project Clawdbot (now OpenClaw), designed for efficient LLM on edge deployment, has gone viral globally. It's not an ordinary chatbot; it's the kind of AI that actually gets things done, transforming into a true digital employee through Edge AI Inference.

OpenClaw: The AI That Actually Does Things.

It clears your inbox, sends emails, manages your calendar, and checks you in for flights. All from WhatsApp, Telegram, or any chat app you already use.

By reading files, running commands, modifying code, calling APIs, and even gaining direct system permissions, Clawdbot performs automated operations for you. It transforms the AI Agent from a "temporary tool" into a true Digital Employee.

But when you truly intend to let it stand guard 24/7—monitoring equipment, watching for alerts, generating weekly reports, and performing automated inspections—the question becomes: Where should such a long-running Agent be deployed to maximize productivity, especially for running large language models on Edge Computing?

Some choose the cloud, while others use a Mac mini or small PC for hosting. However, the cloud involves recurring rental costs, and consumer PCs are not designed for 24/7 continuous operation.

Clearly, when a task requires long-term reliability and efficient Edge Computing for Clawdbot, the most stable approach is to give Clawdbot a dedicated Edge Computing Node or an Edge AI device. This is crucial for LLM edge applications.

InHand Edge Computers come pre-installed with a Linux distribution, further simplifying the deployment process. Within a budget of sub-$500, you can build an edge-side deployment form factor that is better suited for long-term monitoring and close-to-site operation. These are excellent computers supporting Clawdbot with edge computing and provide superior edge inference hardware performance.

Choose an InHand Edge Computer and instantly have a digital employee on call 24/7.

(Tutorial uses the AI-accelerated Edge Computer EC3320 as an example)

Clawdbot Installation and Deployment Tutorial

InHand EC series edge computers run standard Linux distributions and come with common runtime environments pre-installed. Once the device is connected to the internet, Clawdbot can be installed with a single command:

bash curl -fsSL https://molt.bot/install.sh | bash

Next, we will use a simple example to demonstrate how to build a "Power Distribution Room Transformer Data AI Monitoring" application based on Clawdbot:

Scheduled Data Collection → Real-time Analysis → Automatic Result Generation → Email Alert Push, achieving automated inspection and smart monitoring. This showcases a practical AI edge deployment solution.

Configuring Large Language Models (LLM) for Local Clawdbot Deployment

Use the command clawdbot onboard and follow the prompts to configure your LLM provider under "Model/auth provider," as shown below, to enable hardware for running private large language models.

Defining Email Sending Skill for Clawdbot

After installation, create a new SKILL.md in the following directory:

/home/edge/.clawdbot/skills/email-sender/

This is used to register the Email Sending Skill (foxmail-sender). Clawdbot will call a local Python script via this skill to implement automated email notifications.

Clawdbot Reference Configuration:

This skill allows the Clawdbot agent to send emails via the Foxmail (Tencent Enterprise Email) SMTP server. When a user requests to send an email, you need to use the run_command tool to execute the foxmail_sender.py script. The script constructs and executes a Python command to send emails with the extracted recipient, subject, and body.

Based on the image above, the foxmail-sender Skill completes the email delivery by calling the local script email_notifier.py. Please create this script and place it in:

/home/edge/.clawdbot/skills/email-sender/

(Directories and filenames can be adjusted as needed) The SMTP server, email address, and authentication code required for sending emails are configured via environment variables:

• FOXMAIL_SMTP_SERVER
• FOXMAIL_EMAIL
• FOXMAIL_AUTH_CODE

Defining Transformer Data Acquisition Tool for Edge Computing

At the end of the ~/clawd/TOOLS.md file, add a new tool definition to describe how sensor data is acquired. Clawdbot can call system tools based on this configuration to automatically read the required data, a key aspect of Edge AI Inference.

Tool description includes:

• Tool name
• Parameter description
• Return data format

Adding Agent Prompt Instructions

Add scenario-specific prompt instructions to the end of the ~/clawd/AGENTS.md file to define the Agent's operating rules and task boundaries. It is recommended to use Markdown format. This is vital for refining LLM on edge interactions.

Example below for a "Power Distribution Room Maintenance Assistant" using Clawdbot:

Creating Scheduled Tasks for Clawdbot

Access the device browser at: http://127.0.0.1:18789 Log into the Clawdbot console, go to Cron Jobs → New Job, and create a new scheduled task. In the System Text field, fill in the task description to define the data collection and analysis logic, and call the foxmail-sender Skill for notifications. This demonstrates Clawdbot local deployment in action.

After saving, the system will automatically generate a task configuration file, which can be viewed at the following path:

/home/edge/.clawdbot/cron/jobs.json

This configuration is essential for running large language models on edge computing autonomously, as shown in the image below:

After saving, restart Clawdbot to apply settings:

bash systemctl --user restart clawdbot-gateway.service

After the restart is complete, Clawdbot will automatically begin scheduled periodic inspections, data analysis, and sending notification emails, and the task will officially enter a long-term monitoring state, fully leveraging Edge Computing and Edge AI Inference capabilities.

Advantages of Edge Deployment: A 24/7 Stable Digital Employee

When an Agent moves from "non-production testing" to "continuous monitoring," the deployment location is just as important as the model's capability. Compared to the cloud or a personal PC, the edge side is closer to the on-site system: the network path is shorter, there are fewer dependencies; independent device operation makes isolation and control easier. This highlights the advantages of Edge Computing for robust Edge AI Inference with Clawdbot.

InHand Edge Computers are built on industrial-grade reliability, supporting 7x24 stable operation. With the built-in DeviceSupervisor™ Agent, they enable multi-protocol data collection and remote maintenance.

From collection to analysis to automated execution, Clawdbot can now reside long-term at the edge, rather than being a temporary "hosted" process. It truly becomes a Digital Employee stationed on-site.

Conclusion

Through InHand Edge Computers, Clawdbot achieves efficient and stable Edge AI deployment, maximizing the potential of AI Agents and making it an ideal choice for enterprise industrial automation and smart monitoring.

OpenAI's $6.5B Acquisition of Jony Ive's IO: The Dawn of Embodied Intelligence

In a groundbreaking move that signals a major shift in artificial intelligence strategy, OpenAI announced on May 22, 2025, that it would acquire IO Products—the hardware startup founded by former Apple design chief Jony Ive—in an all-stock transaction valued at approximately $6.5 billion. This acquisition represents not just OpenAI's largest deal to date, but a pivotal moment marking the evolution of AI from cloud-based models to physical hardware.

The Strategic Pivot from Cloud AI to Physical Presence

Over the past two years, we've witnessed remarkable advancements in large language models, from ChatGPT to Gemini and Claude, with tech giants engaging in an arms race focused on parameter count, inference speed, and generation quality. However, as model capabilities increasingly converge, the next competitive frontier in AI is shifting from "who has the strongest model" to "who truly owns the user relationship."

This paradigm shift redefines the significance of edge AI hardware. If large language models represent the "brain" of next-generation intelligence, hardware devices serve as their "body" and "interface." The entity that controls the user entry point ultimately controls the data, feedback loops, interactions, and ecosystem development.

Sam Altman, OpenAI's CEO, clearly recognizes this reality. He has publicly stated that for AI to reach its full potential, users must be able to "interact with AI in natural, everyday ways." The current approach of accessing ChatGPT through mobile apps or web browsers is not a sustainable long-term strategy.

Unlike Google with its Android operating system and Chrome browser, Meta with its social networks and smart glasses, or Apple with its system-level AI features, OpenAI lacks native distribution channels. By continuing to rely on these platforms, OpenAI effectively surrenders control of ChatGPT's "user relationship" to third parties. Every interaction, subscription, and usage data must flow through channels defined by others—a costly arrangement that prevents true ecosystem closure.

The Birth of AI Companion Devices

OpenAI's solution is to build its own distribution system—not by developing another app, but by directly entering the hardware layer to create an "AI-native entry device." This explains the collaboration with IO to develop what industry insiders call an "AI Companion Device."

This device represents a departure from conventional smartphones, tablets, or wearables. Instead, it's an entirely new form of AI interaction terminal: always present, potentially screen-free, voice-interactive, contextually aware, and deeply integrated into users' daily lives—becoming the "first point of contact" between AI and humans.

According to multiple media reports, including The Information, WIRED, and TechCrunch, the OpenAI-IO collaboration draws inspiration from the 2013 film "Her," which depicted a future where an omnipresent AI operating system could understand human emotions, engage in conversation, provide companionship, and even form emotional connections. A decade later, we may be just one AI device away from this reality.

The device under development is reportedly:

• Screen-free or minimally dependent on visual interfaces
• Designed to be constantly present (wearable, pocket-sized, or desktop form factor)
• Capable of environmental awareness, recognizing context, tone, location, and emotions
• Primarily voice-interactive, requiring no manual operation or wake words
• Able to continuously learn user behaviors and preferences, evolving toward "understanding the user"

This represents a fundamental shift in how we interact with AI—from "you go find it" to "it's always with you." The AI's presence transitions from cloud computing centers to every scenario and moment of your daily life.

Jony Ive: Redesigning the Human-AI Relationship

The partnership with Jony Ive is central to realizing this vision of "companion intelligence." Technical capability is merely the foundation; the true breakthrough lies in redesigning the human-machine relationship—precisely why OpenAI chose to collaborate with Ive.

Ive isn't simply "designing a device shell" for OpenAI; he's fundamentally reconstructing how AI and humans interact, starting from first principles. According to TechCrunch, Ive and his design firm LoveFrom have taken complete control of OpenAI's design work, leading the user experience and visual language of both hardware and software products.

This isn't an outsourced collaboration but a strategic design co-creation. Ive, the visionary behind the iPhone, iPad, and Apple Watch, now aims to create an AI device "more disruptive than the iPhone." The difference is that he's not building a communication tool but constructing an "intelligent companion with physical form."

This transformation is particularly crucial in design language:

• Shifting from "technological feel" to "lifestyle feel" and "emotional warmth"
• Moving from "functionality" to "presence" and "emotional affinity"
• Evolving from "user operation" to "device proactive understanding and collaboration"

As Ive stated: "I increasingly feel that everything I've learned over the past 30 years has led me to this moment." This marks not just a designer's return but a rewriting of the human-machine interface philosophy.

Embodied Intelligence: The Next Evolution of AI

We can describe this new type of device as an "Embodied Intelligence Agent" with three core characteristics:

1. Emotional Interface: Building "warm" communication through voice, tone, rhythm, language content, and semantic understanding
2. Perceptual Capability: Understanding the user's environment, emotions, needs, and historical behaviors
3. Behavioral Autonomy: Not just passively responding but actively assisting, reminding, and planning—becoming an "intelligent agent" rather than a "toolbox"

This signals the evolution of AI hardware from "passive tool" to "active partner," from "intelligent terminal" to "everyday intelligent agent."

In this process, hardware is no longer just a carrier for models but an extension of personality. AI doesn't just answer your questions; it coexists, empathizes, and lives alongside you.

When ChatGPT acquires a "body," AI ceases to be merely "technology" and becomes a "presence" in your daily life.

The Dual Path to Embodied Intelligence: B2B Edge AI and B2C Companion Devices

In the development of AI hardware, today and tomorrow aren't disconnected extremes but intertwined in a "double helix structure"—one path being the rapidly deploying edge B2B industry-specific AI devices, and the other being the emerging AI companion assistants.

One represents current technological reality, while the other points toward a reconstruction of human-machine relationships. Together, they drive large AI models from "cloud brains" toward "intelligent existence in the real world."

Edge industry-specific AI devices represent the most realistic path today. The essence of edge B2B AI devices is deploying lightweight, edge-optimized artificial intelligence models to various IoT terminals, giving these terminals a certain degree of local perception, processing, and decision-making capabilities. Compared to traditional cloud computing-dependent AI solutions, edge devices offer three major practical advantages: faster response, energy efficiency, and privacy-friendliness.

Currently, edge industry-specific AI devices have been implemented in multiple scenarios. In smart cities, cameras, traffic signals, and public facilities are achieving real-time recognition, event detection, and automatic scheduling through edge AI. In industrial settings, edge devices are deployed in production lines, robots, and sensors, performing quality inspection, predictive maintenance, and other tasks. In smart homes, voice assistants, smart speakers, robot vacuums, and door lock systems are gradually incorporating local AI models to improve response speed and user experience.

According to predictions released by Transforma Insights, by 2033, over 900 million edge AI devices will be connected to networks globally. For most enterprises, edge devices are the first step in bringing AI into the physical world, allowing data to be processed and responded to in real-time locally rather than just being uploaded to the cloud for analysis.

However, while edge B2B AI devices achieve "object intelligence," their intelligence is distributed and fragmented—"point intelligence" in the environment. They focus more on scenario efficiency, device coordination, and system optimization rather than understanding and accompanying "people themselves." This is precisely what AI companion assistants aim to complement.

B2C AI companion assistants represent the long-term vision of AI entering daily life. If edge industry-specific AI devices represent "scenario intelligence," then B2C AI companion assistants embody "user intelligence."

The former embeds AI into the environment, while the latter brings AI closer to human bodies and consciousness. Their differences lie not only in technological form but also in the depth of interactive relationships and the possibility of emotional connections.

The AI companion assistant (Companion Device) is an intelligent terminal driven by large models, designed not to control lights, adjust temperature, or monitor data, but to understand you, accompany you, serve you, and gradually build an interactive relationship with you as an "intelligent agent."

It may not have a screen or even a clear form, but it's always by your side, ready to respond, understand context, participate in conversations, provide suggestions, and silently record and learn your behavioral preferences.

The significance of AI companion assistants is that they make AI "embodiment" possible and allow AI to evolve from a tool to a "second self."

Technologically, it relies on the perception, reasoning, and language capabilities of AI large models; in design, it requires highly contextualized and humanized interaction logic. Commercially, it means AI is no longer just software subscriptions or cloud services but a "resident interactive presence"—an "intelligent individual" that can be carried, worn, and trusted.

From this perspective, B2B edge AI hardware solves "how AI enters the real world," while AI companion assistants solve "how AI truly enters human life." One is system-level, the other personal-level; one distributed, the other resident; one emphasizing efficiency, the other connection.

As revealed by the OpenAI-Ive collaboration to create AI companion assistants, future AI hardware is no longer just about "making devices smarter" but about "giving intelligence a body, warmth, and resonance." This is an evolution that crosses the boundaries of technology, design, and philosophy.

Therefore, edge B2B AI hardware and B2C AI companion assistants aren't mutually exclusive paths but double helix routes jointly constructing the future form of AI hardware: the former builds infrastructure, the latter defines the ultimate form. Together, they drive AI from "visible device intelligence" toward "felt intelligent presence."

If edge industry-specific AI hardware is the first step in making AI "visible," then AI companion assistants are the endpoint of making AI "perceptible." Each step of evolution between them represents a closer relationship between humans and artificial intelligence.

The Future of AI Hardware: From Tools to Companions

Today when we discuss artificial intelligence, the focus remains largely on model strength, parameter count, and inference speed. But what truly determines whether AI can deeply integrate into human life has never been just algorithms and computing power, but how it exists in our daily routines.

AI hardware is becoming the physical embodiment of artificial intelligence's "presence."

It's not just a terminal executing instructions but an entry point for understanding, accompanying, and coexisting with humans. The next decisive platform won't be faster CPUs but devices that understand you better. Through them, AI will gain its own "body," and humans will welcome truly meaningful "intelligent companions."

OpenAI's acquisition of IO represents more than just a business transaction—it signals a fundamental shift in how we'll interact with artificial intelligence in the coming years. As the boundaries between digital and physical continue to blur, we're witnessing the dawn of embodied intelligence that promises to transform our relationship with technology in ways we're only beginning to imagine.

Edge AI: Ushering in a New Era of Smart Manufacturing

With the rapid development of artificial intelligence (AI) technology, edge computing and edge AI are becoming hot topics in the industry. In particular, InHand Networks' edge AI products provide more efficient and energy-saving solutions for the Industrial Internet of Things (IIoT), driving the progress of smart manufacturing.

The Rise of Edge AI

In recent years, edge AI has gradually become an important technology to solve the challenges of running large AI models on edge devices. For example, STMicroelectronics (ST) recently acquired Deeplite, a company specializing in AI model optimization, quantization, and compression, helping large models run faster, smaller, and more energy-efficient on edge devices. This technology has made edge computing a key direction for AI application deployment.

InHand Networks is actively expanding in the edge AI market by launching efficient edge AI solutions. Its edge AI products can run deep learning algorithms on smart sensors, cameras, drones, and other devices, enabling real-time data processing. This greatly enhances the automation and efficiency of smart manufacturing.

Industry News and Major Acquisitions

ST Acquires Deeplite: Accelerating Edge AI Adoption

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InHand Networks, one of the leading pioneers of Internet of Things (IoT), is excited to announce the InGateway502 has been certified by UL and UL - C1D2 (class 1 division 2 hazardous locations). With these two certifications, they assure the InGateway502 is explosion proof and its operation safely can be ensured in potentially hazardous environments, including oil & gas, electric power and chemical industries or areas that any inflammable materials/combustibles may exist.

Underwriters Laboratories (UL) is an independent and non-profit organization that tests and certifies components and products for safety in U.S. Its UL certification is based on area classification and explosion protection methods for either Division or Zone installations, which is widely perceived as the non-incentive standard in North America relating to products for use in hazardous locations (HazLocs). In terms of C1D2, it is a part of a safety classification system that defines what type of technology that is permissible and safe to use for all workers.

July 12, 2022 – We are proud to announce that the VG710 multi-network vehicle gateway is now ITxPT compliant and listed in their catalog.

June 29, 2022 - We are proud to announce that InHand 5G devices - Edge router ER805-NRQ3 and vehicle gateway VG710-NRQ3 are now ready for use on FirstNet®, which means our InVehicleG710 Gateway and EdgeRouter805 can work with enhanced reliability, expanded coverage, and game changing 5G speeds. In this sense, we can provide our first responder customers with a network traffic prioritization and preemption services when needed anytime anywhere.   

What is FirstNet? FirstNet is a cellular network communications system designed to deliver priority and pre-emptive communications for first responders and other organizations involved in critical infrastructure and public health and safety. It offers technology, features and functionality designed to properly handle the rigorous and unique demands for first responders without concerns about network congestion. And FirstNet Ready® means InHand devices have the access to a physically separate and dedicated FirstNet network core, which enables FirstNet First Priority capabilities -- the always on priority in FirstNet Band 14 spectrum especially for first responders.   

In this IoT-powered world, everything - and everyone on board should undoubtedly be constantly connected. Given a wide range of devices with varying interfaces operating in uncertain environments, how to deliver reliable connectivity to them all and make public transport systems more intelligent?

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15, May 2022 – We are pleased to announce the certification of our Cloud-based edge router ER805-NRQ3-WLAN for use on T-Mobile network. And we are excited about the commercial potential for ER805, as it brings 5G speeds to business internet applications. Learn more to unlock the power of high-speed connectivity!

InHand has been extremely proud to work closely with T-Mobile as a hardware provider. InHand has 4 Million + devices deployed worldwide, already deploying thousands of T-Mobile connected devices, to enterprises across the country. T-Mobile’s offering nicely complements our offering of cellular routers and gateways, further bolstering the company’s initiative of leading Internet of Things (IoT) market in resources, products, and solutions.

Urban development is facilitating increasingly advanced public transport systems. With multiple on-board devices requiring intercommunications, security concerns as well as growing demand for passenger experience, it is expected that networking of public transport be not only fast and reliable, but also intelligent and interoperable. In an era where everything is connected, how to deliver high-performance connectivity for vehicle operators as well as passengers?