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The future of business is AI-driven. I specialize in creating AI-powered solutions that automate processes, integrate seamlessly with your existing tools, and generate content effortlessly. Whether it's WhatsApp and Telegram automation, AI voice agents, or AI-generated videos and images, I help businesses stay ahead of the curve. Let's explore how AI can work for you.

About Me

With over 25 years of experience in IT consulting and over 15 years in photography and videography, I've always been at the forefront of technology and creativity. My journey from visual storytelling to AI innovation has given me a unique perspective on how automation, AI integrations, and content generation can revolutionize businesses.

I now focus on:

•Developing AI-powered mobile apps
•Automating workflows with WhatsApp, Telegram, and CRM integrations
•Creating AI-generated content for businesses, including video and image automation
•Leveraging local LLMs for secure and powerful AI solutions

Businesses today need to embrace AI to stay competitive. Let's connect and explore how AI can transform your operations.

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AI-Powered Mobile Apps

Custom-built AI applications that streamline operations, enhance efficiency, and provide innovative solutions tailored to your business needs.

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Seamlessly integrate AI into your business operations with WhatsApp, Telegram, email marketing, and CRM automation.

Voice AI Agents

Enhance customer interactions with AI-driven voice agents, providing automated responses and intelligent customer support.

Local LLM Solutions

AI chatbots and tools that run locally, ensuring privacy, security, and speed for businesses needing on-premise AI.

AI-Powered Content Generation

Revolutionize social media and marketing with AI-generated videos, images, and automated content creation.

Past Work Experience

While I've built a strong foundation in photography and videography over the past 15 years, I've now refocused my expertise on AI solutions and mobile development to help businesses innovate and grow.

Photography

Photography Collection

A selection of high-quality commercial and artistic photography work.

Videography

Video Production

Professional video content for businesses and creative projects.

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Latest AI News

Claude Opus 4.6 Agent Teams Explained: A New Era of Multi-Agent AI Workflows

Feb 12, 2026

Agent Teams in Claude Opus 4.6 feel less like a feature update and more like a shift in how we think about AI collaboration. Until recently, working with sub-agents meant operating inside a single shared session. They could perform tasks in parallel, but they all relied on one shared context. You could not directly address one specific sub-agent. Everything flowed through one central brain. That was already powerful. But Agent Teams in Opus 4.6 move beyond that model entirely. If you test this inside a terminal environment like tmux or iTerm2, you can literally watch multiple reasoning threads unfold in parallel. Separate sessions spin up. Agents coordinate. Tasks get delegated. And suddenly, it no longer feels like one AI with helpers. It feels like a coordinated team. <ul> <li><a href="#what-changed">What changed from sub-agents to Agent Teams</a></li> <li><a href="#architecture">How Agent Teams are structured</a></li> <li><a href="#terminal">Why testing in tmux or iTerm2 makes it click</a></li> <li><a href="#workflows">Practical workflow examples</a></li> <li><a href="#engineering">Implications for engineering teams</a></li> <li><a href="#research">Research and analysis use cases</a></li> <li><a href="#automation">Automation and orchestration potential</a></li> <li><a href="#risks">Limitations and things to watch</a></li> </ul> <h2 id="what-changed">What changed from sub-agents to Agent Teams</h2> Previously, sub-agents operated inside one shared context. They could perform different subtasks, but: <ul> <li>They relied on a single memory space</li> <li>You could not directly communicate with one specific sub-agent</li> <li>Coordination happened implicitly through one main session</li> </ul> Agent Teams introduce a fundamentally different structure. <ul> <li>Multiple separate sessions are launched simultaneously</li> <li>Each agent maintains its own smarter individual context</li> <li>Agents communicate with each other</li> <li>You can directly address one specific agent</li> </ul> This changes the mental model from “one AI multitasking” to “multiple specialists collaborating.” <h2 id="architecture">How Agent Teams are structured</h2> Agent Teams operate more like a distributed system. Each agent can: <ul> <li>Maintain its own reasoning history</li> <li>Focus on a clearly defined role</li> <li>Pass structured outputs to other agents</li> </ul> In practice, you might see: <ul> <li>A Builder agent writing code</li> <li>An Analyzer agent reviewing logic</li> <li>A Validator agent testing edge cases</li> <li>A Planner agent thinking ahead about architecture</li> </ul> Instead of collapsing all reasoning into one continuous stream, responsibilities are separated. That separation reduces cognitive overload and improves clarity. <h2 id="terminal">Why testing in tmux or iTerm2 makes it click</h2> When you run Agent Teams inside environments like tmux or iTerm2, you can visually observe parallel sessions. Each pane can show a different agent executing its task. <ul> <li>One pane running tests</li> <li>Another generating implementation</li> <li>Another summarizing results</li> </ul> This visual parallelism changes the experience. You are not waiting for one long response. You are orchestrating a live system. For developers used to distributed systems, microservices, or CI pipelines, this feels familiar. It mirrors how real teams work. <h2 id="workflows">Practical workflow examples</h2> <h3>Example 1: Refactoring a large codebase</h3> Imagine you need to refactor a complex backend module. <ul> <li>Agent A scans the entire codebase and maps dependencies</li> <li>Agent B proposes a new architecture</li> <li>Agent C rewrites the implementation</li> <li>Agent D generates tests</li> </ul> All of this can happen in parallel sessions. Instead of one monolithic response, you get coordinated outputs that can be reviewed individually. <h3>Example 2: Technical due diligence</h3> <ul> <li>Agent A analyzes financial documentation</li> <li>Agent B reviews contracts</li> <li>Agent C checks regulatory constraints</li> <li>Agent D compiles risk summaries</li> </ul> Because each agent has its own context window, it can focus deeply without polluting the reasoning of others. <h2 id="engineering">Implications for engineering teams</h2> For software teams, this unlocks new possibilities: <ul> <li>Parallel feature implementation</li> <li>Dedicated testing agents</li> <li>Continuous review loops</li> <li>Architecture validation in real time</li> </ul> It begins to resemble a small AI development team working alongside human engineers. Especially when combined with Claude Opus 4.6’s expanded context capabilities, entire repositories can be processed with greater coordination. <h2 id="research">Research and analysis use cases</h2> Agent Teams are not limited to coding. In research-heavy environments: <ul> <li>One agent gathers primary sources</li> <li>Another validates credibility</li> <li>A third synthesizes findings</li> <li>A fourth challenges assumptions</li> </ul> This structured division reduces hallucination risk and improves cross-checking. <h2 id="automation">Automation and orchestration potential</h2> For automation builders, Agent Teams represent a new orchestration layer. Instead of one workflow trying to handle everything: <ul> <li>Agents can specialize</li> <li>Tasks can be delegated dynamically</li> <li>Outputs can be validated before proceeding</li> </ul> This opens doors for: <ul> <li>Complex business process automation</li> <li>Multi-stage data analysis pipelines</li> <li>Autonomous long-running workflows</li> </ul> The key difference is coordination. Not just execution. <h2 id="risks">Limitations and things to watch</h2> Agent Teams are powerful, but they are still experimental. <ul> <li>Coordination errors can compound quickly</li> <li>Misaligned prompts can create conflicting outputs</li> <li>Monitoring parallel sessions requires discipline</li> </ul> This is not a plug-and-play replacement for structured engineering processes. It is an acceleration layer. The real advantage comes when humans remain in control of orchestration while delegating execution. Agent Teams in Claude Opus 4.6 mark a shift from single-threaded AI interaction to distributed collaboration. It feels less like chatting with a model and more like directing a team. And once you see multiple reasoning threads running side by side in your terminal, it becomes hard to go back.

10 Essential Security Steps for OpenClaw Users to Prevent Hacks

Feb 11, 2026

Security is not a feature when you run OpenClaw. It is the foundation. If you are using ClawdBot (now called OpenClaw), you are effectively running an AI agent with access to API keys, infrastructure, logs, automations and possibly production systems. That is powerful. It is also risky. This article walks through 10 essential security steps that are not “nice to have” tweaks. They are baseline rules. Ignore them, and you are inviting trouble. Apply them, and you dramatically reduce your attack surface. <ul> <li><a href="#why-security-matters">Why OpenClaw security is different</a></li> <li><a href="#separate-device">1. Never run OpenClaw on your main device</a></li> <li><a href="#no-root">2. Disable root access</a></li> <li><a href="#change-port">3. Change default ports</a></li> <li><a href="#private-network">4. Keep it off the public internet</a></li> <li><a href="#ssh-fail2ban">5. SSH keys and Fail2Ban</a></li> <li><a href="#firewall">6. Lock down your firewall</a></li> <li><a href="#allowlist">7. Use an allow-list</a></li> <li><a href="#log-monitoring">8. Make OpenClaw monitor itself</a></li> <li><a href="#no-groups">9. Never use group chats</a></li> <li><a href="#least-privilege">10. Apply least privilege</a></li> <li><a href="#interfaces">Popular interfaces: WhatsApp, Slack, Telegram and more</a></li> <li><a href="#risks">Realistic risk scenarios and prevention</a></li> </ul> <h2 id="why-security-matters">Why OpenClaw security is different</h2> OpenClaw is not a passive tool. It can execute commands, interact with APIs, access tokens and perform automated actions. That means: <ul> <li>If compromised, it can expose API keys.</li> <li>It can modify data.</li> <li>It can run commands on your server.</li> <li>It can pivot into other systems.</li> </ul> Think of it as giving a junior sysadmin access to your infrastructure, except this one works 24/7 and follows instructions instantly. <h2 id="separate-device">1. Never run OpenClaw on your main device</h2> Do not install OpenClaw on your daily laptop that stores: <ul> <li>Personal photos</li> <li>Saved passwords</li> <li>Browser sessions</li> <li>Private documents</li> </ul> Use: <ul> <li>A separate VPS</li> <li>A dedicated Mac mini</li> <li>A cloud server with isolated credentials</li> </ul> Risk example: A malicious prompt injection causes OpenClaw to read local files. If it runs on your personal laptop, it could access sensitive directories. Prevention: Isolate it. If something goes wrong, you wipe the machine. No personal damage. <h2 id="no-root">2. Disable root access</h2> Never let OpenClaw operate as root. Root means full system control. Instead: <ul> <li>Create a limited user</li> <li>Disable root SSH login</li> <li>Grant only required permissions</li> </ul> Risk example: If OpenClaw executes a malicious command as root, it can modify system binaries, delete logs or install backdoors. Prevention: Limited user accounts contain the blast radius. <h2 id="change-port">3. Change default ports</h2> Default ports are scanned constantly by bots. Changing the port does not make you invincible but it removes you from automated mass scans. Risk example: Automated scanners find your default gateway and start brute-forcing credentials. Prevention: Change the port and combine it with firewall restrictions. <h2 id="private-network">4. Keep it off the public internet</h2> Expose nothing publicly if possible. Use a private networking solution like Tailscale to create a private mesh between your devices. This way: <ul> <li>OpenClaw is invisible to the public internet</li> <li>You can access it securely from anywhere</li> </ul> Risk example: A publicly exposed dashboard with weak auth gets indexed or brute-forced. Prevention: Zero public exposure. <h2 id="ssh-fail2ban">5. SSH keys + no passwords + Fail2Ban</h2> Passwords can be guessed or leaked. Instead: <ul> <li>Use SSH keys only</li> <li>Disable password authentication</li> <li>Install Fail2Ban</li> </ul> Fail2Ban blocks repeated failed login attempts automatically. Risk example: Botnet attempts 10,000 login attempts per hour. Prevention: Keys + automatic IP banning. <h2 id="firewall">6. Lock down your firewall</h2> Use UFW or similar firewall tooling. Close everything except: <ul> <li>SSH from your private network</li> <li>Specific required services</li> </ul> Risk example: An exposed database port becomes accessible externally. Prevention: Default deny. Only open what is absolutely required. <h2 id="allowlist">7. Use an allow-list</h2> Configure OpenClaw to respond only to approved identities. Allow-list specific: <ul> <li>User IDs</li> <li>Phone numbers</li> <li>Slack user IDs</li> <li>Telegram handles</li> </ul> Risk example: A random user discovers your bot endpoint and starts sending commands. Prevention: Only pre-approved users can trigger actions. <h2 id="log-monitoring">8. Make OpenClaw monitor itself</h2> Let OpenClaw monitor: <ul> <li>Auth logs</li> <li>System logs</li> <li>API usage spikes</li> </ul> Trigger alerts when: <ul> <li>Unknown IP connects</li> <li>API usage jumps unexpectedly</li> <li>Unexpected commands execute</li> </ul> This turns your AI into a watchdog instead of just an executor. <h2 id="no-groups">9. Never use group chats</h2> Only use Direct Messages. In group chats: <ul> <li>Someone can accidentally trigger commands</li> <li>Someone can inject malicious prompts</li> <li>Logs become messy and ambiguous</li> </ul> Risk example: A team member jokingly writes “delete everything” and the bot interprets it literally. Prevention: Strict DM-only command channels. <h2 id="least-privilege">10. Always apply least privilege</h2> Start with minimal permissions. Do not: <ul> <li>Grant full filesystem access by default</li> <li>Give admin API tokens unnecessarily</li> <li>Allow unrestricted command execution</li> </ul> Add permissions only when required. Risk example: Compromised bot token has admin-level API access across systems. Prevention: Scope-limited tokens and role separation. <h2 id="interfaces">Popular interfaces: WhatsApp, Slack, Telegram and more</h2> OpenClaw often integrates with: <ul> <li>WhatsApp</li> <li>Slack</li> <li>Telegram</li> <li>Discord</li> <li>Custom web dashboards</li> </ul> Each interface increases your attack surface. Security steps must apply to each integration separately. <h2 id="risks">Realistic risk scenarios and prevention</h2> <h3>Scenario 1: Prompt injection via Slack</h3> A malicious user embeds hidden instructions in a document link shared in Slack. OpenClaw processes it and executes unintended actions. Prevention: <ul> <li>Restrict file access scope</li> <li>Scan inputs for suspicious instructions</li> <li>Use confirmation layers for destructive actions</li> </ul> <h3>Scenario 2: API key leakage</h3> Logs accidentally expose API keys in debug output. Prevention: <ul> <li>Mask sensitive data in logs</li> <li>Rotate keys regularly</li> <li>Limit key permissions</li> </ul> <h3>Scenario 3: Compromised VPS</h3> Unpatched server vulnerability allows remote execution. Prevention: <ul> <li>Regular updates</li> <li>Automated patch management</li> <li>Infrastructure isolation</li> </ul> Security is not something you “add later.” If you treat OpenClaw like a toy, you will eventually get burned. If you treat it like production infrastructure from day one, you reduce risk dramatically. Security is not a feature. It is your foundation.

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