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

Nano Banana 2 vs Nano Banana Pro: Speed, Quality & Real-World Tests

Feb 27, 2026

Nano Banana 2 (also known as Gemini 3.1 Flash Image) is Google’s latest image generation model, positioned as “Pro-level intelligence at Flash-level speed.” That claim sounds ambitious. So instead of repeating marketing points, this article focuses on real-world tests: generation speed, text rendering, translation, subject consistency, instruction following, aspect ratios, 4K output claims, and web-grounded knowledge. <ul> <li><a href="#what-is-nb2">What Nano Banana 2 actually is</a></li> <li><a href="#speed-tests">Speed comparison: Flash vs Pro</a></li> <li><a href="#text-rendering">Text accuracy & complex layout rendering</a></li> <li><a href="#translation">Translation & localization inside images</a></li> <li><a href="#consistency">Subject & object consistency tests</a></li> <li><a href="#instruction-following">Instruction-following precision</a></li> <li><a href="#resolution">Resolution & 4K claims</a></li> <li><a href="#grounding">World knowledge & web grounding</a></li> <li><a href="#workflow-impact">Workflow impact for creators & teams</a></li> <li><a href="#final-verdict">Final verdict: Is Pro still needed?</a></li> </ul> <h2 id="what-is-nb2">What Nano Banana 2 actually is</h2> Nano Banana 2 is Google’s Flash-tier image generator designed to deliver near-Pro quality at significantly faster generation times. It is available inside Gemini and is expected to be accessible via AI Studio, Google Cloud (Vertex), and Flow. In Gemini, it appears to be widely available in many countries and can be used without a paid plan, while Nano Banana Pro is restricted to Pro/Ultra subscriptions. The positioning is clear: make Flash good enough that most people rarely need Pro. <h2 id="speed-tests">Speed comparison: Flash vs Pro</h2> In repeated side-by-side testing: <ul> <li>Nano Banana 2 generated in roughly 13–15 seconds.</li> <li>Nano Banana Pro generated in roughly 25–35 seconds.</li> </ul> That’s roughly 2× faster in most practical cases. Editing tasks (e.g., changing colors, adding logos, lighting adjustments) showed similar speed ratios. For teams producing high volumes of visual iterations — product mockups, ad variations, thumbnail tests — this speed difference compounds quickly. If you generate 50 variations per day, saving 15–20 seconds per render translates into real workflow efficiency. <h2 id="text-rendering">Text accuracy & complex layout rendering</h2> One of the most difficult tasks for image models is rendering structured, legible text. A complex test prompt included: <ul> <li>A laptop mockup</li> <li>A fictional pricing page</li> <li>Exact headlines and subheads</li> <li>A three-column comparison table</li> <li>Precise row labels</li> <li>Footnotes and fine print</li> </ul> Both Flash (Nano Banana 2) and Pro rendered the text correctly, with no gibberish, no spelling errors, and correct alignment. In this case, Flash performed at parity with Pro — but twice as fast and without requiring a paid plan. For designers creating UI mockups, landing page visuals, or presentation graphics, this is a meaningful improvement. <h2 id="translation">Translation & localization inside images</h2> A second test involved generating an English event poster and then translating it into Spanish while preserving layout, spacing, and typography. Both models handled the translation cleanly. In fact, in one instance, Flash produced a slightly more accurate localized phrasing than Pro. This suggests that for multilingual marketing teams, Nano Banana 2 is fully capable of producing localized campaign assets without manual design adjustments. The key advantage is speed: translation edits completed in ~15 seconds. <h2 id="consistency">Subject & object consistency tests</h2> Consistency across frames is critical for storytelling, short films, and multi-scene content. A test included: <ul> <li>Five distinct characters</li> <li>Fourteen objects in a room</li> <li>Follow-up prompts modifying actions and camera angles</li> </ul> Initial consistency was strong: <ul> <li>Characters retained wardrobe and facial features.</li> <li>Objects remained present and recognizable.</li> </ul> However, camera-angle changes revealed limitations. While character identity was preserved, spatial consistency of the room sometimes broke. Conclusion: strong identity retention, moderate environmental continuity. For short-form storytelling and multi-shot scenes, this is usable — but complex cinematography may still require iterative prompting. <h2 id="instruction-following">Instruction-following precision</h2> Instruction-following performance was tested with strict constraints: <ul> <li>No logos</li> <li>No extra objects</li> <li>Specific lighting direction</li> <li>Precise lens look (85mm f/5.6)</li> <li>Symmetry requirements</li> </ul> Nano Banana 2 followed these instructions accurately. Even rotational edits (e.g., rotate 15° to the right, no other changes) were executed precisely. This makes it highly practical for product photography mockups, ecommerce previews, and controlled brand visuals. <h2 id="resolution">Resolution & 4K claims</h2> Google suggests outputs up to 4K are possible. However, in testing, downloads consistently produced images at approximately 2752×1536 — not true 3840×2160 4K resolution. Even explicitly requesting 4K did not produce higher resolution outputs. That does not mean image quality is poor. Detail levels are strong, and images hold up well at full zoom. But strictly speaking, current output resolution does not appear to reach native 4K dimensions in direct export. Teams requiring exact 4K outputs for broadcast or large-format printing may need additional upscaling workflows. <h2 id="grounding">World knowledge & web grounding</h2> A test involving real-world landmark annotation (Petco Park in San Diego) showed mixed results. While the model correctly identified nearby landmarks, spatial positioning was imperfect. This suggests: <ul> <li>Good semantic knowledge</li> <li>Imperfect geographic precision</li> </ul> For infographics or educational visuals requiring strict factual mapping, human verification remains essential. <h2 id="workflow-impact">Workflow impact for creators & teams</h2> The most important shift is not feature-based. It’s behavioral. If Nano Banana 2 delivers ~95% of Pro quality at half the time and free access, it becomes the default daily driver. For teams: <ul> <li>Faster iteration loops</li> <li>Reduced cost barriers</li> <li>More experimentation</li> <li>Lower dependency on premium tiers</li> </ul> Creative workflows become less constrained by rendering time and subscription gating. Pro may remain relevant for: <ul> <li>Ultra-realism</li> <li>More advanced grounding</li> <li>Edge-case cinematic outputs</li> </ul> But for the majority of marketing, social, product mockup, and UI tasks, Nano Banana 2 appears sufficient. <h2 id="final-verdict">Final verdict: Is Pro still needed?</h2> Nano Banana 2 is not a dramatic leap beyond Pro in capability. It is a dramatic leap in accessibility and speed. Pro still edges ahead in subtle realism and some grounding tasks. But for roughly 90–95% of real-world use cases, Nano Banana 2 is fast, accurate, and consistent enough to become the default model. That makes this release strategically important. Not because it changes what’s possible but because it changes what’s practical at scale.

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.

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