AI-Powered Automation & Content Creation for Businesses

Helping businesses leverage AI, automation, and integrations to streamline workflows and supercharge content creation.

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.

Automations & Integrations

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

Anthropic Cloud Code Security: AI-Powered GitHub Vulnerability Scanning Explained

Feb 23, 2026

Claude Code Security is a big step toward making security scans less manual and more actionable inside real developer workflows. <ul> <li><a href="#what-it-is">What Claude Code Security is</a></li> <li><a href="#what-it-does">What it actually does (and what it doesn’t)</a></li> <li><a href="#how-it-fits">How it fits into a GitHub-based workflow</a></li> <li><a href="#use-cases">Concrete use cases for teams</a></li> <li><a href="#business-benefits">Business benefits beyond “finding bugs”</a></li> <li><a href="#limitations">Limitations and realistic expectations</a></li> <li><a href="#rollout-checklist">A practical rollout checklist</a></li> </ul> <h2 id="what-it-is">What Claude Code Security is</h2> Claude Code Security is Anthropic’s new security scanning capability designed to analyze code across your GitHub repositories and generate structured findings with clear prioritization. The core value is not “yet another scanner.” It’s the workflow layer around the scan results: context, explanations, and actions that help developers move from detection to remediation faster. <h2 id="what-it-does">What it actually does (and what it doesn’t)</h2> From a practical perspective, Claude Code Security focuses on three things teams care about: <ul> <li>Coverage: it can scan multiple repositories rather than making you run checks one by one.</li> <li>Clarity: it presents findings with severity and prioritization so teams can focus on what matters.</li> <li>Actionability: it helps you understand exactly where the issue sits and what a reasonable fix could look like.</li> </ul> What it does not do is magically guarantee security. No tool can. It will miss things, it can misclassify issues, and it can suggest fixes that require human review. Think of it as a fast, always-on security teammate that reduces the cost of “first pass” security review. <h2 id="how-it-fits">How it fits into a GitHub-based workflow</h2> The most useful way to think about Claude Code Security is as a workflow accelerator, not a compliance checkbox. Here is how it fits into a typical team loop: <ul> <li>Before merge: run scans on pull requests (or on the target branch) so high-risk issues are caught early.</li> <li>After merge: scan the main branch on a schedule to catch new dependency risks or newly introduced patterns.</li> <li>Backlog hygiene: create tickets for critical items and auto-triage the rest into “fix soon” vs “monitor.”</li> </ul> If your team already uses CI plus something like CodeQL or Snyk, Claude Code Security can still add value by translating raw findings into understandable fixes and making remediation faster. <h2 id="use-cases">Concrete use cases for teams</h2> Here are realistic ways teams can use Claude Code Security without turning it into noise: <ul> <li>Onboarding a new repo: scan a newly acquired or inherited repository and produce a “top 10 risks” snapshot before you ship changes.</li> <li>Pre-release hardening: run scans across all repos involved in a release train and focus only on critical/high findings that impact customer data.</li> <li>Dependency hygiene: identify high-risk dependency usage patterns (outdated auth libraries, unsafe crypto usage, risky deserialization).</li> <li>Multi-repo consistency: find repeated patterns across repos (same insecure helper function copy-pasted everywhere) and fix them systematically.</li> </ul> Example: A team maintains 12 microservices. One service introduces a permissive CORS configuration and a weak token validation helper. Claude Code Security flags the exact files and highlights the shared helper pattern. The team fixes the helper once, rolls the change across services, and prevents the same issue from reappearing. <h2 id="business-benefits">Business benefits beyond “finding bugs”</h2> Security tooling is often framed as “risk reduction,” but the business impact is usually operational: <ul> <li>Lower review burden: fewer hours spent on manual triage and explaining issues across the team.</li> <li>Faster remediation: clearer findings mean engineers spend less time reproducing and more time fixing.</li> <li>More predictable releases: fewer last-minute security surprises right before launch.</li> <li>Better knowledge transfer: findings that explain the “why” help junior developers learn secure patterns faster.</li> </ul> For teams shipping frequently, “time-to-fix” is often the KPI that matters most. Anything that compresses the path from alert → understanding → patch is a direct productivity gain. <h2 id="limitations">Limitations and realistic expectations</h2> To use Claude Code Security well, it helps to set expectations with your team: <ul> <li>False positives happen: treat the scanner as a filter, not a judge.</li> <li>Severity is contextual: “critical” depends on exposure, data sensitivity, and runtime environment.</li> <li>AI suggestions need review: a suggested fix can introduce regressions or shift risk elsewhere.</li> <li>Security is broader than code: IAM, secrets management, network controls, and runtime monitoring still matter.</li> </ul> If a team treats scan output as a hard gate without review, you risk slowing development with noise. If a team treats it as an assistant that accelerates review, it becomes leverage. <h2 id="rollout-checklist">A practical rollout checklist</h2> If you want to implement Claude Code Security in a way that sticks, start simple: <ul> <li>Pick 2–3 repos first: one high-traffic service, one legacy repo, one typical project.</li> <li>Define a triage policy: what gets fixed immediately vs scheduled vs ignored (with documentation).</li> <li>Decide where results live: GitHub issues, Linear, Jira, or a security backlog.</li> <li>Add a human review step: AI can propose, but humans approve merges.</li> <li>Track one metric: time-to-fix for critical/high findings over 30 days.</li> </ul> If the signal-to-noise ratio stays high in the pilot, expand to more repos. If it doesn’t, adjust thresholds and workflows before rolling out broadly. Claude Code Security is not a “perfect security solution.” But if you run multiple repositories and want faster, clearer remediation loops, it’s a meaningful upgrade in how security work gets done.

Anthropic Bans OpenClaw: What It Means for AI Builders and SaaS Founders

Feb 19, 2026

Anthropic banning OpenClaw is not just a policy clarification. It signals the end of a grey zone that quietly powered a large part of the AI builder ecosystem. For months, developers were running serious agent workflows and even SaaS products on top of Claude consumer subscriptions. That door is now officially closed. <ul> <li><a href="#what-happened">What exactly happened</a></li> <li><a href="#what-openclaw-enabled">What OpenClaw enabled</a></li> <li><a href="#why-this-is-strategic">Why this is a strategic shift</a></li> <li><a href="#ecosystem-impact">Impact on the AI agent ecosystem</a></li> <li><a href="#cost-structure">What this means for cost structures</a></li> <li><a href="#builders">What builders and founders must change</a></li> <li><a href="#production-phase">The move from hack phase to production phase</a></li> </ul> <h2 id="what-happened">What exactly happened</h2> Anthropic has clarified that Claude consumer accounts (Free, Pro, Max) may not be used through external automation tools such as OpenClaw. This includes setups where OAuth tokens from standard user accounts were used to power agents, automation pipelines, or SaaS products. Enforcement is now active. This is not a minor wording adjustment in terms of service. It is a clear separation between human-facing subscriptions and product-facing infrastructure. The line is now explicit: <ul> <li>Claude consumer plans → for human usage</li> <li>Claude API → for products, automation, and SaaS</li> </ul> The grey zone is gone. <h2 id="what-openclaw-enabled">What OpenClaw enabled</h2> OpenClaw allowed developers to use Claude Code and consumer Claude accounts as the backend brain for agents and automated systems. This made it possible to: <ul> <li>Run multi-step agents</li> <li>Build automation workflows</li> <li>Prototype SaaS tools</li> <li>Operate AI-driven internal systems</li> </ul> And often at a fraction of official API costs. For early-stage builders, this was powerful. You could test ideas, build MVPs, or even run revenue-generating tools using a $20 or $100 monthly plan. That economic model no longer holds. <h2 id="why-this-is-strategic">Why this is a strategic shift</h2> This move is fundamentally about infrastructure control. AI companies do not want large-scale commercial products running on consumer subscriptions. From their perspective, this creates: <ul> <li>Unpredictable load</li> <li>Distorted pricing structures</li> <li>Infrastructure stress</li> <li>Unclear governance boundaries</li> </ul> By forcing builders onto the official API, Anthropic ensures: <ul> <li>Usage-based billing</li> <li>Scalable infrastructure planning</li> <li>Enterprise-ready permission models</li> <li>Clear separation between personal and commercial usage</li> </ul> This is not emotional. It is structural. The real battle in AI is not about chat interfaces. It is about infrastructure ownership. <h2 id="ecosystem-impact">Impact on the AI agent ecosystem</h2> OpenClaw was not a niche experiment. It became a core building block for many agent-based workflows. Examples include: <ul> <li>Automated research agents</li> <li>Code-generating pipelines</li> <li>Spreadsheet automation systems</li> <li>Social media analysis agents</li> <li>Financial modeling assistants</li> </ul> Many of these relied on consumer accounts for cost efficiency. Now, those setups must migrate to API-based architectures. For some builders, this means minor adjustments. For others, it means complete restructuring. <h2 id="cost-structure">What this means for cost structures</h2> The most immediate impact is financial. API pricing is usage-based. At scale, this can be significantly more expensive than a fixed subscription. Consider a small SaaS product generating 500,000 tokens per day through automated workflows. Under a consumer subscription, this might have been absorbed within a fixed monthly cost. Under API pricing, costs scale directly with usage. This affects: <ul> <li>Gross margins</li> <li>Pricing models</li> <li>Investor projections</li> <li>Operational risk management</li> </ul> Business models built on “cheap backend intelligence” must now be recalculated. <h2 id="builders">What builders and founders must change</h2> If you are building AI products today, you must think like an infrastructure engineer. This means: <ul> <li>Designing API-first architectures</li> <li>Implementing proper authentication flows</li> <li>Building cost-monitoring systems</li> <li>Structuring usage tiers intentionally</li> <li>Avoiding reliance on consumer interfaces</li> </ul> Shortcuts that worked during the experimentation phase are no longer viable. Production systems require production-grade foundations. <h2 id="production-phase">The move from hack phase to production phase</h2> The early AI wave was experimental. Builders tested limits, found loopholes, and optimized around subscription economics. That phase is ending. We are entering a production infrastructure phase. This phase is defined by: <ul> <li>Compliance clarity</li> <li>Permission boundaries</li> <li>Cost transparency</li> <li>Enterprise-grade scaling</li> </ul> The shift is subtle but fundamental. We are moving from: “How can I use AI cheaply?” to: “How do I build durable AI infrastructure?” For serious builders, this is not a setback. It is a maturation event. Infrastructure thinking is now the real leverage.

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