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.

Services

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 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.

Claude Sonnet 4.6 for OpenClaw: Should You Replace Opus?

Feb 18, 2026

Claude Sonnet 4.6 is one of those releases that changes the economics of running AI agents. Not because it suddenly becomes “smarter than everything else”. But because it gets very close to Opus-level performance on agent workflows, while being dramatically cheaper and faster. If you are using OpenClaw, Claude Code, or any tool-driven automation setup, this matters more than most benchmark charts. Because when the cost drops, you stop hesitating to run the workflows that actually create business value. <ul> <li><a href="#what-changed">What changed in Sonnet 4.6</a></li> <li><a href="#why-it-matters">Why this matters for teams and workflows</a></li> <li><a href="#agent-performance">Agent performance vs Opus: what “similar” means in practice</a></li> <li><a href="#cost-impact">The real win: cost changes behavior</a></li> <li><a href="#openclaw-default">Why Sonnet 4.6 should be the default in OpenClaw</a></li> <li><a href="#use-cases">Practical use cases you can run today</a></li> <li><a href="#model-strategy">A simple model strategy: when to still use Opus</a></li> <li><a href="#guardrails">Guardrails to avoid waste and runaway automation</a></li> </ul> <h2 id="what-changed">What changed in Sonnet 4.6</h2> Sonnet 4.6 is positioned as a fast, scalable model for agentic work. The big headline is not “it beats Opus at everything”. The big headline is: it’s close enough on most tool-heavy workflows that it becomes the rational default — especially when you pay per token or you run long sessions. What users tend to notice immediately: <ul> <li>Faster responses in multi-step tasks</li> <li>Lower costs for long-running agent workflows</li> <li>Less hesitation to run automation overnight or continuously</li> </ul> <h2 id="why-it-matters">Why this matters for teams and workflows</h2> Teams rarely use AI for a single one-shot answer. They use it for workflows: <ul> <li>Monitoring and summarizing signals</li> <li>Drafting and iterating content and docs</li> <li>Tool orchestration across systems</li> <li>Research loops</li> <li>Automation jobs that run daily or hourly</li> </ul> These workflows become expensive fast if the model is too costly. Sonnet 4.6 shifts that. It makes “always-on agent behavior” much more realistic for normal budgets. <h2 id="agent-performance">Agent performance vs Opus: what “similar” means in practice</h2> When people say Sonnet 4.6 is “similar” to Opus for agentic work, they usually mean it performs well on the boring-but-important parts: <ul> <li>It follows tool instructions reliably</li> <li>It can plan multi-step tasks without constant babysitting</li> <li>It keeps context across a workflow without losing the thread</li> <li>It stays stable when it needs to iterate and retry</li> </ul> That is exactly what matters in OpenClaw-style setups. In practice, “agentic quality” is less about perfect prose and more about making the right next step, using the right tool, avoiding infinite loops, and returning a structured result you can act on. <h2 id="cost-impact">The real win: cost changes behavior</h2> This is the part that actually changes your output. When your model is expensive, you avoid running the high-value workflows because they “feel too expensive”. Examples of workflows teams often avoid with expensive models: <ul> <li>Running a nightly competitor scan across multiple channels</li> <li>Doing daily analytics collection when no API exists (manual browser work)</li> <li>Letting an agent iterate on an internal tool for hours to clean up edge cases</li> <li>Long document processing (contracts, policies, technical specs) with structured summaries</li> </ul> With a cheaper model, these workflows become normal. And once they become normal, your output compounds. <h2 id="openclaw-default">Why Sonnet 4.6 should be the default in OpenClaw</h2> OpenClaw is an orchestration environment. Its strength is not “one clever answer”. Its strength is delegation, parallel tasks, tool usage and automation routines. If Sonnet 4.6 delivers near-Opus agentic performance at a fraction of the cost, it becomes the practical default model. A simple rule of thumb: <ul> <li>Use Sonnet 4.6 for the day-to-day operational agent work</li> <li>Keep Opus as an escalation option for the rare “high-risk, high-complexity” tasks</li> </ul> <h2 id="use-cases">Practical use cases you can run today</h2> <h3>Use case 1: Trend monitoring that turns into real actions</h3> Instead of “summarize what’s trending”, run it like an operational workflow: <ul> <li>Scan X and Reddit for a specific niche (example: AI agents for marketing ops)</li> <li>Extract recurring pain points and repeated questions</li> <li>Propose 3 automation ideas your team could implement</li> <li>Create a short task plan for the best one</li> </ul> Business value: <ul> <li>You get market research that turns into backlog items</li> <li>You reduce guesswork when deciding what to build next</li> <li>You can run it nightly without worrying about runaway cost</li> </ul> <h3>Use case 2: Overnight “maintenance agent” for a repository</h3> This is not “AI writes your entire product”. It’s a realistic maintenance workflow: <ul> <li>Check dependencies that are outdated</li> <li>Open a PR for safe version bumps</li> <li>Update docs where they are obviously out of sync</li> <li>Run linting and fix low-risk formatting issues</li> <li>Produce a morning report of what changed and what to test</li> </ul> Business value: <ul> <li>Your repo stays healthier with less manual overhead</li> <li>You reduce “maintenance debt” that causes future outages</li> <li>Developers spend time on features, not housekeeping</li> </ul> <h3>Use case 3: Internal reporting without building dashboards first</h3> Example workflow: <ul> <li>Pull weekly metrics from existing sources (docs, sheets, exports)</li> <li>Generate a structured summary: wins, losses, anomalies, action items</li> <li>Post to Slack in a consistent format every Monday morning</li> </ul> Business value: <ul> <li>Less meeting time spent “figuring out what happened”</li> <li>Faster decisions because the story is already summarized</li> <li>Better accountability because action items are explicit</li> </ul> <h2 id="model-strategy">A simple model strategy: when to still use Opus</h2> Sonnet 4.6 can be your default, but there are still moments where Opus is worth it: <ul> <li>Complex architectural decisions that require deep reasoning</li> <li>High-risk refactors across many modules</li> <li>One-shot implementation where you want maximum quality in a single pass</li> <li>Cases where mistakes are expensive (production systems, sensitive data flows)</li> </ul> In practice: Sonnet for 80–90% of operational tasks, Opus as the “senior consultant” when it truly matters. <h2 id="guardrails">Guardrails to avoid waste and runaway automation</h2> Cheaper models increase usage — which is good — but it also makes it easier to accidentally run wasteful workflows. Basic guardrails that help: <ul> <li>Hard limits: maximum steps per task</li> <li>Budget caps per day per workflow</li> <li>Approval gates for destructive actions (delete, overwrite, revoke)</li> <li>Logging: store every tool call and every external action</li> <li>Scheduling discipline: not everything should run hourly</li> </ul> The goal is simple: make it cheap enough to run often, but controlled enough to trust. The bottom line: Sonnet 4.6 is not exciting because it is “the smartest model ever”. It’s exciting because it makes serious agent workflows affordable enough to become normal. And once these workflows become normal, teams start working differently.

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