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

Nvidia GTC 2026 Explained: AI Agents, AI Factories, and the Future of Work

Mar 17, 2026

Nvidia’s GTC 2026 keynote was not just another product announcement. It was a clear signal that AI is moving from tools into systems that actually perform work. If previous years were about building models, this year was about deploying them as workers. And Nvidia positioned itself right in the middle of that shift. <ul> <li><a href="#new-phase">AI enters a new phase: inference and agents</a></li> <li><a href="#ai-factories">The rise of AI factories</a></li> <li><a href="#demand">Why demand is exploding so fast</a></li> <li><a href="#hardware">New hardware and the Vera Rubin architecture</a></li> <li><a href="#full-stack">Nvidia becomes a full stack AI company</a></li> <li><a href="#open-models">The push into open models and ecosystems</a></li> <li><a href="#openclaw">OpenClaw and the new agent layer</a></li> <li><a href="#gas">From SaaS to agents as a service</a></li> <li><a href="#robotics">AI moves into the physical world</a></li> <li><a href="#autonomous">Why autonomous driving is back</a></li> <li><a href="#philosophy">The core philosophy behind it all</a></li> <li><a href="#implications">What this means for teams and businesses</a></li> </ul> <h2 id="new-phase">AI enters a new phase: inference and agents</h2> For years, most of the focus in AI has been on training models. Bigger datasets, larger models, and better benchmarks defined progress. At GTC 2026, the message was different. The focus has shifted to inference and agents. This is the transition from AI that generates content to AI that performs tasks. A simple way to understand the evolution: <ul> <li>Early AI tools focused on generating text and images</li> <li>Reasoning models improved decision making and structured thinking</li> <li>Agent systems now take actions and execute workflows</li> </ul> This is not a small improvement. It changes the role of AI inside organizations. Instead of being a tool that supports humans, AI starts acting more like a digital worker that can complete parts of a process independently. <h2 id="ai-factories">The rise of AI factories</h2> One of the most important concepts introduced was the idea of AI factories. Data centers are no longer described as infrastructure for running software. They are being reframed as production systems that generate tokens. In this model: <ul> <li>Input is data and compute</li> <li>Output is tokens, which represent AI work</li> </ul> This leads to new performance metrics: <ul> <li>Tokens per second</li> <li>Tokens per watt</li> <li>Cost per token</li> </ul> This shift matters because it turns AI into an economic system rather than a purely technical one. Companies will start optimizing AI infrastructure the same way factories optimize production lines. <h2 id="demand">Why demand is exploding so fast</h2> Nvidia highlighted an extreme increase in compute demand. The scale mentioned was on the order of a million times growth in a very short period. At the same time, projections point toward a market of more than one trillion dollars in AI infrastructure within the next few years. This explains several trends that are already visible: <ul> <li>Rapid expansion of data centers</li> <li>Increased investment in chips and networking</li> <li>Rising importance of energy efficiency</li> </ul> For teams and businesses, this means AI is not slowing down. It is accelerating into a core layer of the economy. <h2 id="hardware">New hardware and the Vera Rubin architecture</h2> Nvidia introduced a new generation of systems built around the Vera Rubin architecture. The focus is not only raw performance, but also balance between speed and scale. Key improvements include: <ul> <li>Significant performance gains compared to previous systems</li> <li>Advanced interconnect technologies for scaling workloads</li> <li>Better handling of low latency inference</li> </ul> This matters because agent systems require both fast responses and the ability to handle large volumes of tasks. It is no longer enough to train a model once. Systems must continuously run and respond in real time. <h2 id="full-stack">Nvidia becomes a full stack AI company</h2> Another clear message was that Nvidia is no longer just a hardware company. Its role now spans multiple layers: <ul> <li>Chips and processors</li> <li>Complete systems</li> <li>Networking infrastructure</li> <li>Software platforms</li> <li>Models and frameworks</li> </ul> This vertical integration gives Nvidia control over the entire pipeline from compute to application. For enterprises, this simplifies adoption. For competitors, it raises the barrier to entry. <h2 id="open-models">The push into open models and ecosystems</h2> Nvidia is also investing heavily in its own AI ecosystem. This includes different model families for different domains: <ul> <li>Language models for general tasks</li> <li>World models for simulation</li> <li>Robotics models for physical systems</li> <li>Biology focused models for scientific use cases</li> </ul> The strategic goal is clear. Provide alternatives to closed systems while enabling companies and even countries to build their own AI capabilities. This idea is often described as sovereign AI. For organizations, this means more control over data, customization, and deployment. <h2 id="openclaw">OpenClaw and the new agent layer</h2> One of the most important themes was the emergence of a new layer for AI agents. OpenClaw was presented as a foundational system for building and running agent based workflows. The comparison was made to technologies that defined earlier waves of software: <ul> <li>Linux for operating systems</li> <li>HTML for the web</li> <li>Container orchestration for cloud infrastructure</li> </ul> The implication is that agent systems will need a similar standard layer. For developers and teams, this suggests that understanding agent orchestration will become as important as understanding web frameworks or cloud platforms. <h2 id="gas">From SaaS to agents as a service</h2> A major shift discussed was the transition from software as a service to agents as a service. Instead of selling tools, companies will increasingly provide AI systems that perform work. Examples include: <ul> <li>Agents that handle customer support workflows</li> <li>Agents that manage internal operations</li> <li>Agents that assist with development and data processing</li> </ul> This changes how value is delivered. Users are not paying for access to software. They are paying for outcomes produced by AI systems. <h2 id="robotics">AI moves into the physical world</h2> The keynote also showed strong progress in robotics. Large numbers of robots were demonstrated, supported by a full stack that includes simulation, training, and deployment. This signals an important transition. AI is no longer limited to digital environments. It is starting to operate in the physical world. Industries such as manufacturing, logistics, and healthcare are likely to be affected first. <h2 id="autonomous">Why autonomous driving is back</h2> Autonomous driving was presented as reaching a new level of maturity. New partnerships with major automotive companies indicate renewed confidence in the technology. The key difference compared to previous cycles is the capability of modern AI systems. Improved perception, reasoning, and decision making make large scale deployment more realistic. <h2 id="philosophy">The core philosophy behind it all</h2> Underneath all announcements was a simple but important idea. Tokens are becoming the unit of value in AI systems. Compute generates tokens, and tokens represent work. This creates a direct connection between infrastructure and economic output. In this model: <ul> <li>More efficient compute means more output</li> <li>Better systems mean lower cost per task</li> <li>Scaling infrastructure means scaling productivity</li> </ul> This is why Nvidia frames data centers as factories. They are producing digital labor. <h2 id="implications">What this means for teams and businesses</h2> The biggest takeaway is not about hardware or specific products. It is about how work itself is changing. Three practical implications stand out. First, agent workflows will become a core capability. Teams that learn how to design, evaluate, and manage AI agents will have a significant advantage. Second, infrastructure knowledge becomes strategic. Understanding how AI systems run, scale, and cost money will matter more than ever. Third, value shifts from tools to outcomes. Businesses that can deliver results through AI systems will outperform those that only provide software access. This is why the shift matters. It is not just about better technology. It is about a new model of how companies operate.

How Auto Research Can Make Claude Code Skills Improve Themselves

Mar 14, 2026

Claude Code Skills are powerful, but anyone who has used them for a while knows they are not always perfectly reliable. Some runs produce exactly what you want. Others feel completely off. A new idea is starting to change that. By combining Claude Code Skills with Auto Research techniques, developers can turn skills into systems that gradually improve themselves through repeated testing and evaluation. <ul> <li><a href="#why-skills-struggle">Why Claude Code Skills sometimes struggle</a></li> <li><a href="#auto-research">What Auto Research actually means</a></li> <li><a href="#karpathy">Why Andrej Karpathy’s idea matters here</a></li> <li><a href="#self-improving-skills">How Claude Code Skills can improve themselves</a></li> <li><a href="#metrics">Why metrics are the key to better skills</a></li> <li><a href="#evaluation">Building a simple evaluation system</a></li> <li><a href="#optimization">Turning prompt iteration into optimization</a></li> <li><a href="#broader-impact">Why this idea matters beyond Claude Code</a></li> </ul> <h2 id="why-skills-struggle">Why Claude Code Skills sometimes struggle</h2> Claude Code Skills allow developers to package instructions and workflows into reusable tools that the model can execute. They are extremely useful for automating tasks inside development environments. However, many users notice something quickly. Skills are powerful but not always perfectly consistent. A typical experience looks like this: <ul> <li>Most runs produce good results</li> <li>Some runs produce confusing or incomplete output</li> </ul> This does not mean the skill is broken. It simply reflects the probabilistic nature of language models. Slight differences in context or interpretation can lead to different outputs. The challenge is improving consistency without manually rewriting instructions again and again. <h2 id="auto-research">What Auto Research actually means</h2> Auto Research is an approach where agents repeatedly test variations of a process and evaluate the results in order to improve performance over time. Instead of relying on intuition or manual tuning, the system experiments automatically. Each iteration generates outputs, evaluates them, adjusts parameters, and tries again. The cycle looks like this: <ul> <li>Run the skill</li> <li>Evaluate the output</li> <li>Adjust the prompt or instructions</li> <li>Run the skill again</li> <li>Keep the best performing version</li> </ul> Over time the skill becomes more reliable because the system learns which instructions consistently produce better outcomes. This turns prompt engineering into a measurable optimization process rather than a guessing game. <h2 id="karpathy">Why Andrej Karpathy’s idea matters here</h2> The Auto Research concept gained attention after being shared by Andrej Karpathy. Karpathy is widely known in the AI world. He was one of the early members of OpenAI and later served as Director of AI at Tesla. His work in deep learning and neural networks has influenced many modern AI development practices. The original experiment focused on improving machine learning pipelines through autonomous experimentation. What makes the idea exciting is that the same principle applies extremely well to AI workflows such as Claude Code Skills. If a system can measure whether an output is good or bad, it can attempt to improve the instructions that produced that output. <h2 id="self-improving-skills">How Claude Code Skills can improve themselves</h2> When combined with an evaluation framework, Claude Code Skills can evolve through repeated testing. A simple improvement loop might work like this: <ul> <li>The skill generates several outputs for the same task</li> <li>An evaluation system scores each output</li> <li>The system modifies the skill instructions</li> <li>The updated skill runs again</li> <li>The best performing configuration is stored</li> </ul> This process gradually identifies instructions that produce more reliable results. Instead of manually guessing how to improve the prompt, the system discovers improvements through structured experimentation. <h2 id="metrics">Why metrics are the key to better skills</h2> The most important requirement for Auto Research is an objective metric. The system needs a clear way to measure whether a result is better or worse. For Claude Code Skills this might include: <ul> <li>Evaluation pass rate</li> <li>Task completion accuracy</li> <li>Formatting correctness</li> <li>Compliance with defined rules</li> </ul> Without a metric the system cannot improve itself because it has no signal telling it what success looks like. Once a metric exists, however, the system can compare different prompt variants and gradually move toward higher scores. <h2 id="evaluation">Building a simple evaluation system</h2> An evaluation system does not need to be complex. A basic setup might generate several outputs for a given prompt and evaluate them against a checklist. For example: <ul> <li>Did the output follow the correct format</li> <li>Did it include the required information</li> <li>Was the reasoning correct</li> <li>Did it satisfy the task constraints</li> </ul> If each criterion produces a score, the system can combine those scores into a total result. That score then becomes the signal used to determine whether a new prompt version performs better or worse. <h2 id="optimization">Turning prompt iteration into optimization</h2> Once a scoring system exists, the improvement loop becomes surprisingly powerful. Imagine a setup where: <ul> <li>Ten outputs are generated per run</li> <li>Each output is evaluated on four criteria</li> </ul> This creates a maximum score that the system can aim to improve. Over multiple iterations the optimization process may gradually move the average score upward. The goal is not only higher quality output but also greater consistency. Consistency is often the missing piece when turning AI prototypes into reliable tools. <h2 id="broader-impact">Why this idea matters beyond Claude Code</h2> The Auto Research concept is not limited to development tools. Any workflow that produces measurable results can potentially benefit from the same optimization loop. Examples include: <ul> <li>Improving website performance experiments</li> <li>Testing different marketing messages</li> <li>Optimizing landing pages</li> <li>Refining prompts used by AI agents</li> <li>Stabilizing creative generation workflows</li> </ul> The key insight is simple. If something can be measured, it can often be improved through automated experimentation. For AI systems this creates an important shift. The value is no longer only in the prompt or the model itself, but also in the history of experiments and improvements that produced the best results. Over time that improvement data becomes one of the most valuable assets in an AI workflow.

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