About

About The Practical CTO

Hi, I'm WooSuk — a technology professional based in Korea working in enterprise software and AI systems.

I started The Practical CTO because most AI and tech coverage is either pure hype or too abstract to act on. I write for developers and technical teams who need practical, honest takes on what's actually changing — and what it means for their work.

What you'll find here: Breakdowns of model releases with real numbers, cost and performance comparisons, and implementation guides based on how these tools behave in practice — not just what vendors claim.

My take: I'm not neutral. After watching AI tools succeed and fail in production environments, I've formed opinions. When benchmarks say one thing and real-world use says another, I go with what I've seen.

If something's wrong or outdated, call it out. That's how this gets better.

Connect:
🔗 LinkedIn — WooSuk Jung
📧 jws1624@gmail.com

Comments

AWS vs Azure vs GCP in 2026: Which Cloud Platform Should You Choose?

The cloud platform decision is one of the most consequential technology choices an organization makes, and in 2026 it's also one of the most misunderstood. Most of the debate I see in enterprise architecture forums reduces to "we're an AWS shop" or "we go Azure because of Microsoft" — neither of which is a strategy. A platform choice made primarily on inertia or existing vendor relationships is a choice that will cost you for years. I've spent significant time in all three major cloud environments — AWS for scale workloads and data engineering, Azure for enterprise SAP and Microsoft-integrated architectures, and GCP for AI-intensive and analytics-heavy use cases. My goal in this guide is to give you a genuine, nuanced comparison that goes beyond feature lists and into the practical realities of choosing and running a cloud platform in 2026. I'll cover market position, each platform's honest strengths and weaknesses, how to match workloads t...

EU AI Act Compliance in 2026: What Every Enterprise Needs to Do Now

EU AI Act Compliance in 2026: What Every Enterprise Needs to Do Now The EU AI Act entered into force on August 1, 2024. The first provisions took effect six months later, and the full implementation timeline runs through 2027. If you're building, deploying, or using AI systems in or for the European Union, this law applies to you — and the window for being caught unprepared is closing fast. I've spent the past year working with enterprise clients on AI governance programs, and one pattern shows up again and again: organizations badly underestimate how much operational work compliance actually takes. It's not a checkbox exercise. It's a rethink of how you develop, document, deploy, and monitor AI systems. This guide is what I wish someone had handed me when I started — the substance of the law, the practical requirements, the deadlines that matter, and the mistakes I keep watching enterprises make. Photo by Petrit Nikolli on Pexels Photo by Karolina Gra...

GPT-6 Astra Is Here: $10/M Tokens, 100% on ExploitBench, and What It Actually Means for Developers

Photo by Michał Robak on Pexels Photo by Tara Winstead on Pexels OpenAI launched GPT-6 Astra on September 3rd, and unlike the usual cadence of incremental updates, this release ships with benchmarks that are hard to look past: 100% on ExploitBench, 98–99.9% on FrontierMath Tier 4 and ARC-AGI-3, and 72.6% on OSWorld 2.0. OpenAI is calling it their most capable model yet — and specifically their best for computer use, coding, and professional work. If you manage an API budget, the real question isn't whether the benchmarks look impressive. It's whether switching your workloads over saves money or burns it. Here's how the numbers actually shake out. The Numbers Behind the Launch Here are the headline specs from OpenAI's announcement: FrontierMath Tier 4: 98–99.9% (previous frontier models sat in the 60–70% range) ARC-AGI-3: 98–99.9% — a benchmark built specifically to resist memorization ExploitBench: 100% — which tripped OpenAI's Preparedness F...

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