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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

Why Google’s gws CLI Matters for AI Agents, Automation, and Workspace Workflows

Mar 10, 2026

Google’s new gws CLI is one of those developer tools that looks small at first and then slowly reveals how important it could become. On the surface, it is “just” a command line interface for Google Workspace. In practice, it points toward something much bigger: a future where AI agents can work with Gmail, Drive, Calendar, Docs, Sheets, and more through one consistent interface instead of a patchwork of separate integrations. For developers, automation builders, and teams working on agentic workflows, that is a meaningful shift. <ul> <li><a href="#what-is-gws">What gws actually is</a></li> <li><a href="#why-it-matters">Why this matters for AI agents and automation</a></li> <li><a href="#one-interface">One interface instead of many APIs</a></li> <li><a href="#dynamic">Why the dynamic discovery model matters</a></li> <li><a href="#agent-actions">What agents can realistically do with it</a></li> <li><a href="#team-workflows">Concrete workflow examples for teams</a></li> <li><a href="#business-value">Business value and operational impact</a></li> <li><a href="#limits">Current limitations and what to watch</a></li> </ul> <h2 id="what-is-gws">What gws actually is</h2> gws is a new command line interface that brings together a large part of the Google Workspace ecosystem into one developer facing tool. Instead of building and maintaining separate handling for Gmail, Drive, Calendar, Sheets, Docs, Chat, Admin, and other services, developers can work through one command layer with structured output. That is the real appeal. The value is not simply that it talks to many Google services. The value is that it does so in a more unified way. For AI systems, consistency matters. Agents work better when tools behave predictably, return structured results, and do not require custom logic for every single service. <h2 id="why-it-matters">Why this matters for AI agents and automation</h2> Most agent workflows break down at the integration layer. The reasoning model may be strong, but the workflow becomes fragile once it has to connect to multiple APIs, manage different schemas, handle different authentication patterns, and translate outputs between systems. That is where gws becomes interesting. If one tool can act as a consistent bridge into Google Workspace, developers spend less time building glue code and more time designing useful workflows. For a solo builder, that means faster prototyping. For a team, it means lower maintenance overhead and fewer brittle automation chains. <h2 id="one-interface">One interface instead of many APIs</h2> This may sound like a technical detail, but it has practical consequences. Without a unified interface, an agent that needs to: <ul> <li>Read an email</li> <li>Check a calendar event</li> <li>Open a document</li> <li>Update a spreadsheet</li> </ul> usually needs four different integrations, four different ways of thinking about data, and a lot of custom handling. With gws, the promise is much simpler: one interface, one general operating model, and structured JSON output that an AI model can reason over more easily. That does not remove complexity entirely, but it reduces a major source of friction. For developers building internal tools, executive assistants, support automations, or scheduling workflows, this simplification can save a surprising amount of engineering time. <h2 id="dynamic">Why the dynamic discovery model matters</h2> One of the most interesting parts of gws is that it is described as being dynamically built from Google’s Discovery Service. That matters because Workspace products evolve constantly. New endpoints appear, capabilities change, and integrations that felt current six months ago can become outdated quickly. In a more traditional setup, every change creates maintenance work. In a dynamic model, new Workspace capabilities can potentially become available much faster without waiting for a separate client tool to be manually rebuilt and redistributed. That is especially valuable for agent builders, because agents become more useful when the tool layer keeps pace with the platform they depend on. It also suggests something important about the direction Google is taking: this is not just a CLI for developers. It looks increasingly like infrastructure for agent ready workflows. <h2 id="agent-actions">What agents can realistically do with it</h2> There is a difference between what sounds possible in a demo and what is realistically useful in daily work. The strongest use cases are not “AI does everything.” They are focused, bounded tasks where agents save time without creating chaos. Examples include: <ul> <li>Scheduling meetings after reading context from an email thread</li> <li>Updating a Google Sheet after a support interaction</li> <li>Finding and organizing files in Drive</li> <li>Summarizing a document and drafting follow up notes</li> <li>Creating a daily digest from Calendar, Gmail, and Docs</li> </ul> These are not speculative. They are the kinds of repetitive, structured tasks teams already do every week. The difference is that gws makes it easier to expose those actions to an agent through one common layer. <h2 id="team-workflows">Concrete workflow examples for teams</h2> Workflow 1: Sales follow up assistant A sales rep finishes a meeting and drops a short note into a system. An agent then uses gws to: <ul> <li>Read the previous Gmail thread</li> <li>Pull the next available time slots from Calendar</li> <li>Draft a follow up email</li> <li>Update a tracking sheet with status and next step</li> </ul> The rep reviews and sends. This saves time without removing human control. Workflow 2: Executive daily briefing Each morning, an internal agent can gather: <ul> <li>Today’s calendar events</li> <li>Unread high priority emails</li> <li>Recent updates from a shared document</li> <li>Open tasks from a project sheet</li> </ul> Then it produces a concise morning briefing. This is a simple workflow, but it is exactly the kind of thing that becomes much easier when one tool can access multiple Workspace surfaces consistently. Workflow 3: Support operations assistant After a support case is resolved, an agent can: <ul> <li>Create or update a shared troubleshooting doc</li> <li>Log the case outcome in Sheets</li> <li>Send an internal summary to Chat</li> <li>Schedule a follow up reminder in Calendar if needed</li> </ul> That is not glamorous. But it is operationally valuable. Workflow 4: Document driven project coordination A team working from Google Docs and Sheets often has scattered status updates. An agent using gws can: <ul> <li>Read the latest planning document</li> <li>Extract action items</li> <li>Match deadlines against Calendar</li> <li>Update a project sheet with the latest responsibilities</li> </ul> Instead of asking humans to manually sync everything, the system helps keep the operational layer tidy. <h2 id="business-value">Business value and operational impact</h2> The biggest value of gws is not convenience. It is operational leverage. For teams, that leverage shows up in three ways. First, faster prototyping. Developers can build and test agent workflows more quickly when one tool gives them access to many Workspace services. Second, lower maintenance. Fewer custom integrations means fewer places where workflows break when APIs shift or authentication logic changes. Third, better workflow design. When the tool layer is simpler, teams can spend more energy deciding what should be automated and where human review still matters. This is what separates useful agent systems from flashy demos. The real win is not “look what AI can do.” The real win is “this now fits into how our team actually works.” <h2 id="limits">Current limitations and what to watch</h2> At this stage, gws still appears to be positioned as an experimental developer example rather than a fully mature enterprise platform. That means teams should be careful not to confuse promising direction with finished infrastructure. Things worth watching closely: <ul> <li>Authentication and access control patterns</li> <li>Permission scoping for sensitive Workspace data</li> <li>Logging and auditability for agent actions</li> <li>Stability of behavior across products and updates</li> <li>How well it integrates into larger agent frameworks over time</li> </ul> In other words, the direction is exciting, but the right mindset is still developer preview, not blind trust. Used thoughtfully, though, gws could become one of the more important building blocks in the next wave of agentic productivity tooling.

Claude Code Agent Loops Explained: What /loop Really Does and What It Doesn’t

Mar 8, 2026

There is a lot of hype around Claude Code’s new Agent Loops feature and, unfortunately, a lot of confusion too. Some posts make it sound like Anthropic just released a fully autonomous 24/7 background agent that runs forever. That is not what this update is. Agent Loops are genuinely useful, but their value comes from focused, session-based automation, not from unlimited autonomy. <ul> <li><a href="#what-it-is">What Agent Loops actually are</a></li> <li><a href="#what-it-is-not">What Agent Loops are not</a></li> <li><a href="#how-it-works">How /loop works in practice</a></li> <li><a href="#real-use-cases">Useful real-world use cases</a></li> <li><a href="#limitations">The limitations that people keep skipping</a></li> <li><a href="#schedule-vs-loop">When to use /loop vs scheduled tasks</a></li> <li><a href="#team-workflows">How teams can use it without disrupting work</a></li> <li><a href="#bottom-line">The bottom line</a></li> </ul> <h2 id="what-it-is">What Agent Loops actually are</h2> Claude Code now supports scheduled prompts inside an active session through /loop. The feature allows developers to run prompts repeatedly, poll for status updates, and set one-time reminders within the same coding session. This means Agent Loops are best understood as session-scoped recurring tasks. They help Claude repeat useful work while your session remains open, rather than turning Claude Code into a permanently running background daemon. In plain language: this is very good for “keep an eye on this and tell me when something changes.” It is not the same thing as “run my business forever while I sleep.” <h2 id="what-it-is-not">What Agent Loops are not</h2> The biggest misconception is that /loop creates a truly persistent autonomous agent. In reality, Agent Loops are tied to the session where they are created. They are designed for temporary automation during a working session, not for permanent background operations. <ul> <li>A forever-running background worker</li> <li>A persistent service that survives terminal restarts</li> <li>A cloud agent that keeps operating after your local session disappears</li> <li>A replacement for production schedulers or durable automation infrastructure</li> </ul> <h2 id="how-it-works">How /loop works in practice</h2> The /loop command allows you to schedule a prompt to run repeatedly at a given interval. You can specify a time interval and a prompt, or simply provide a prompt and let Claude repeat it periodically within the current session. Typical examples include: <ul> <li>“Check every 10 minutes whether the deployment finished.”</li> <li>“Every 20 minutes, re-run my PR review command.”</li> <li>“In 45 minutes, check whether integration tests passed.”</li> </ul> These loops run between interactions with Claude. If Claude is busy answering another request when the scheduled time arrives, the task will run once the system becomes idle. <h2 id="real-use-cases">Useful real-world use cases</h2> Deployment monitoring during release windows Instead of manually refreshing dashboards, you can instruct Claude to periodically check whether a deployment finished and summarize any changes. This reduces context switching and allows developers to stay focused on the work that actually requires attention. Pull request babysitting During active coding sessions, loops can monitor CI status, watch for new review comments, or alert you when builds finish. This is especially helpful when waiting for feedback or test results while continuing other tasks. Monitoring long-running builds or tests Integration tests and large builds often require occasional attention. A loop can periodically check logs or build status and notify you when something changes. <h2 id="limitations">The limitations that people keep skipping</h2> There are several important constraints that often get overlooked in hype posts. <ul> <li>Loops automatically stop after a maximum of three days.</li> <li>They do not survive restarts of the terminal or Claude Code.</li> <li>They exist only inside the active session where they were created.</li> <li>If Claude is busy when a scheduled moment passes, the task runs once when the system becomes idle rather than catching up multiple missed runs.</li> </ul> <h2 id="schedule-vs-loop">When to use /loop vs scheduled tasks</h2> The key is choosing the right tool. /loop works best for repeated checks during a live working session. If you need automation that continues running after restarts or independently of your terminal session, scheduled tasks in the desktop environment or external schedulers are better options. <ul> <li>/loop : short-lived repetition inside an active session</li> <li>Desktop scheduled tasks : longer recurring tasks that survive restarts</li> <li>External automation tools : unattended production workflows</li> </ul> <h2 id="team-workflows">How teams can use it without disrupting work</h2> For development teams, the best way to think about Agent Loops is as a session helper. <ul> <li>Monitoring deployments during release windows</li> <li>Watching CI pipelines while coding</li> <li>Tracking review activity on specific pull requests</li> <li>Running repeated checks during debugging sessions</li> </ul> They are not designed to replace infrastructure-level automation tools. <h2 id="bottom-line">The bottom line</h2> Agent Loops are not useless, and they are not a gimmick. They are simply narrower than the internet is suggesting. What Claude Code introduced is a practical mechanism for repeating prompts and monitoring tasks within an active coding session. It may not be a “24/7 autonomous agent.” But it is still a meaningful productivity upgrade for day-to-day development work.

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