This interview is with Mr Henry Ramirez, Editor-in-Chief, TECNOLOGIA GEEK.
For readers meeting you for the first time, how do you describe your role at Tecnologia Geek and the lens you bring to covering mobiles, AI, and gadgets?
I am the Founder and Editor-in-Chief of Tecnología Geek.
My lens is distinct because I don’t just look at how ‘shiny’ a new gadget is; I look at how safe it is. Coming from a background in Cybersecurity and Intelligence Analysis, I approach every mobile launch, AI tool, or gadget review with a ‘Zero Trust’ mindset.
Most tech coverage today is just regurgitated press releases. My role is to cut through that hype. I ask the uncomfortable questions:
- Where is this data going?
- Can this smart home device be weaponized?
- Is this AI actually useful or just a privacy nightmare?
I bring this rigorous, security-first perspective specifically to the US Hispanic community—a massive, tech-savvy audience that is often underserved by mainstream English-language media. I bridge the gap between complex security concepts and everyday consumer tech.
What experiences—from cybersecurity work to editorial leadership—most shaped the way you evaluate and report on consumer tech today?
My background in Intelligence Analysis and Cybersecurity taught me a hard truth: the user is always the weakest link. In the security world, we spend days patching servers, but attackers get in by simply tricking a human.
This experience completely changed how I report on consumer tech. I don’t look at a new smartphone or AI tool as just a ‘gadget’; I see it as a potential attack vector. When I review a product, my first question isn’t ‘Is it fast?’ but rather ‘Does this default setting expose my readers to risk?’
Transitioning to Editorial Leadership, I realized that technical specs are meaningless if they don’t serve the human experience. My time leading newsrooms for the Hispanic market showed me that my audience needs protection as much as they need innovation. Consequently, my evaluation process is heavily weighted toward privacy-by-design and usability. If a device is powerful but impossible to secure for an average parent or business owner, I cannot recommend it. That ‘security-first’ filter is what defines my voice in the industry.
When an AI-first phone or gadget lands on your desk, what is your end-to-end review workflow to separate real utility from marketing promises?
My workflow is designed to break the ‘demo mode’ illusion. It breaks down into three distinct phases:
1. The Privacy Audit (Day 1): Before I test performance, I test permissions. I set up the device and monitor exactly what data it demands. Does the AI require access to my contacts to function? Does it upload voice data to the cloud or process it on-device? If a gadget demands excessive privileges just to turn on the lights, it fails my utility test immediately.
2. The ‘Daily Driver’ Stress Test (Days 2-7): I don’t test gadgets in a lab; I integrate them into my actual workflow. I force myself to use the AI features for critical tasks—drafting emails, summarizing meetings, or editing photos. This quickly separates marketing gimmicks from real tools. If I find myself instinctively reaching for my old phone because the ‘AI way’ is three seconds slower, the device has failed.
3. The ‘Hallucination’ Check: For AI-first devices, I intentionally feed them ambiguous or complex queries to see if they break. I want to know if the AI will admit it doesn’t know an answer or if it will confidently lie to me. In 2026, an AI that hallucinates facts is not a tool; it’s a liability.
You’ve referenced running “privacy stress tests” on connected devices—can you walk us through your process from unboxing to issuing a risk rating?
My process is aggressive because modern devices are deceptive. I call it the ‘Zero-Trust Unboxing’, and it follows four strict phases:
1. The Terms of Service Audit (Before Power-On): I don’t turn the device on until I’ve read the data policy. I look for two red flags: ‘Third-party data sharing’ and ‘Forced arbitration.’ If a smart toaster demands the right to sell my usage habits to advertisers, it starts with a negative score.
2. The Network Interrogation: Once powered on, I connect the device to a monitored guest network. I watch the traffic. Is the device ‘phoning home’ to servers in jurisdictions with weak privacy laws? Is it sending encrypted packets when it should be idle? If a security camera uploads data when it’s supposedly ‘off,’ that’s an immediate fail.
3. The ‘Refusal’ Test: I systematically deny every permission request. Does the app really need my ‘Contacts’ to control a lightbulb? If the device refuses to function without unnecessary access, I classify it as ‘High Risk.’ Good software degrades gracefully; spy software holds features hostage.
4. The Risk Rating: Finally, I assign a rating based on the Utility-to-Exposure Ratio. If a device offers high convenience but demands total surveillance, it gets a ‘Do Not Buy’ rating. In 2026, privacy is a feature, and I rate it as critically as battery life.
Based on your field testing, in what scenarios does on‑device AI (NPUs) genuinely improve the mobile experience without wrecking battery life?
In my field testing, on-device AI (NPUs) genuinely shines in scenarios where latency and radio silence are critical. The cloud is powerful, but the 5G modem is a massive battery drain. The NPU wins when it eliminates that radio transmission.
I see the most genuine improvement in real-time computational photography and videography. When you shoot 4K video, the NPU performs semantic segmentation (separating the subject from the background) and noise reduction frame-by-frame. Doing this on a general CPU would melt the phone, and doing it in the cloud is impossible due to latency. The NPU handles this heavy math efficiently, delivering professional results without killing the battery.
The second scenario is offline voice processing. Live translation and voice-to-text used to require a constant data ping. Modern NPUs handle this locally. My tests show that keeping the modem idle while the NPU transcribes a meeting locally saves significantly more battery than offloading that work to a server, with the added benefit of absolute privacy.
What single in‑store or at‑home camera test would you recommend to validate a phone’s AI photo claims before buying?
I recommend the ‘Complex Edge Portrait Test.’
In the store, most people take a photo of the counter or a static wall. This is a mistake because it’s too easy for the AI. Instead, ask a store employee or a friend to stand in front of a busy, cluttered background (like a shelf full of accessories). Ask them to hold up a hand with fingers spread or to mess up their hair slightly. Then, engage ‘Portrait Mode’ and take the shot.
The Test: Zoom in 100% on the stray hairs or the gaps between the fingers.
The Verdict: If the AI has blurred the space between the fingers or cut the hair off like a helmet (looking like a bad Photoshop cutout), the phone’s AI is weak. It is failing to perform accurate semantic segmentation. However, if the edges are crisp and the background blur (bokeh) rolls off naturally around those fine details, the AI is genuinely processing depth maps in real-time, not just applying a cheap filter.
Tell us about a time when a post‑launch firmware update forced you to change a verdict on a device.
The most significant turnaround for me was the Google Pixel 6 Pro.
At launch, I had to issue a ‘Pass’ rating, which was controversial for such a hyped device. My reasoning was based on security hygiene: the in-display fingerprint sensor was so slow and unreliable that during my testing, I found myself wanting to disable biometrics entirely and revert to a simple PIN just to access the phone faster. In my view, friction is a security vulnerability—if a security feature is too annoying to use, users will bypass it, leaving them exposed.
However, Google released a series of firmware updates over the following months that recalibrated the sensor and fixed the modem’s standby battery drain. I revisited the device six months later and had to completely reverse my verdict to ‘Editor’s Choice.’ It was a humbling lesson that in the era of software-defined hardware, a review is a snapshot in time, not a permanent judgment. A device can literally ‘heal’ itself after it leaves the factory.
What repeatable benchmark or test rig have you built to measure mobile AI features (for example, live translation or summarization) alongside thermal and battery impact?
I developed a protocol I call the ‘Local AI Isolation Loop.’
Standard benchmarks like Geekbench are too bursty; they don’t capture the sustained thermal soak of modern AI tasks. My rig is designed to measure the ‘AI Tax’—the specific battery cost of intelligence.
The Setup: I take a standardized 30-minute high-density audio file (a fast-paced debate). I place the phone in Airplane Mode with the screen brightness locked at 200 nits.
The Test: I force the device to perform live, on-device transcription and summarization of that file. I don’t just look at the battery percentage drop; I use a FLIR thermal camera to measure the backplate temperature every 5 minutes.
The Insight: The critical metric isn’t just battery life; it’s thermal throttling. I’ve found that many flagship phones handle the first 10 minutes fine, but by minute 20, the NPU heat forces the screen to dim or the system to stutter. If a phone becomes uncomfortable to hold or throttles performance just to summarize a meeting, it fails my test regardless of how ‘smart’ the AI is.
At fast-moving events like CES, what playbook helps you cut through launch hype and publish useful, link‑worthy analysis within 24 hours?
My playbook relies on ‘Asymmetric Reporting.’ I know I can’t out-publish the giant tech sites on volume, so I out-flank them on depth and skepticism.
1. Pre-Show Recon (The ‘Ignore List’): I filter out 80% of the noise before I even arrive. I skip the ‘biggest TV’ or the ‘flashiest booth.’ Instead, I target the infrastructure and emerging tech sectors where the real risks lie.
2. The ‘One-Question’ Rule: On the show floor, I cut through the PR script by asking every rep the same specific security question: ‘Does this device function 100% offline?’ or ‘Can I see the data retention policy right now?’ The awkward silence or the ‘I’ll get back to you’ is often the real story.
3. The ‘Counter-Narrative’ Sprint: To publish within 24 hours, I don’t write a recap; I write a warning. While mainstream outlets are copy-pasting specs, I publish pieces titled: ‘Why You Should Wait to Buy [Hyped Product] Until This Privacy Flaw is Fixed.’ Specific, actionable advice travels faster than general news. My readers know that if Tecnología Geek publishes a ‘Green Light’ from CES, it actually means something.