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There is a new phrase catching wind in the AI community: loop engineering. It’s best explained when compared to the first generation of large language models, the interaction was simple: User asks a question -> Model gives an answer The focus was on writing better prompts. If the answer was not good, we adjusted the prompt. We called it prompt engineering. But agentic AI works differently. We no longer ask the AI only to answer a question. We ask it to complete an objective.

I was reading a post in Medium.com talking about evolution of AI framework and one of the questions it raises is: "If the models are this capable now, and we have MCP and Skills, do we still need frameworks like LangChain?" I think this is an interesting question. Frameworks come and go, most of time they die down in usage before we realise. I felt it's a great opportunity for me myself to reflect on this. My experience recently tells me that LangChain is no longer the automa

I am sure many of my friends have seen Jensen Huang's announcement at Computex 2026 of RTX Spark, an Arm-based system combining a Grace CPU, Blackwell GPU, and up to 128GB of unified memory capable of delivering an astonishing 1 petaflop of AI compute. Predictably, the conversation immediately centered around new normal: Human -> Application -> OS -> CPU vs. Human -> Agent -> Tools -> OS -> RTX But I believe such superficial take is far from what Jensen's really building up.

As a casual investor, I feel like we’re all drowning in AI information overload right now. One day a stock skyrockets by double digits; the next, another loses half its value. Honestly, I can’t remember the last time the market was this exciting. Driven by sheer curiosity, I decided to look at the AI value chain systematically—partly to make sense of what we're actually investing in, and partly to see what the future holds. I’m definitely no expert, so think of this as just a

I went into the National AI Student Challenge thinking of it as a kind of national service. Not the big heroic kind. More the quiet Singaporean version: show up, do your part, contribute back, encourage some students, sit through a few demos, give fair scores, maybe say something useful at the end. I did not go in expecting to be surprised. That was my mistake. The challenge problem from Certis was not an easy one. It was not “build a chatbot” or “make a dashboard”. The stude

I started thinking seriously about this because someone close to me works at Meta. They weren't affected by the recent rounds of layoffs. Their job is safe, at least for now. But over the past year, I've been hearing stories from the inside—not necessarily about people losing jobs, but about the atmosphere that follows. The uncertainty. The constant restructurings. Teams being merged, roles being redefined, managers disappearing, priorities shifting overnight. Even when you'r

A few weeks ago, my dog developed a frightening combination of symptoms: bloody stool, vomiting, and a persistent dry cough. Like most pet owners, I panicked. I rushed her to a local veterinary clinic. The consultation quickly escalated into X-rays, blood tests, and a series of diagnostic procedures. The bill came to roughly $800. The diagnosis was tentative—possibly food poisoning—and we were sent home with a small pharmacy's worth of medication: anti-nausea drugs, probiotic

This post took me almost a week to finish, partly because the idea kept expanding as I wrote. It started with a simple request from my chairman: could we build an OpenClaw-style personal assistant? I have been playing with OpenClaw since November 2025, back when it had just been announced and was still changing quickly — not only in its technology, but even in its name and positioning. More recently, NVIDIA’s announcement of NemoClaw, an enterprise version of OpenClaw, pushed

I keep having this thought that I honestly wish I could shake off. We keep talking about AI as if it is just another industrial revolution. Like steam engines. Like electricity. Like computers. Like the internet. Every time, some jobs disappeared, new jobs came, and somehow society moved on. That has been the comforting story. But I am no longer sure that story works this time. Because this time, I don’t think we are just building better tools. With the rapid advancement and

The news lately has been depressing. Hell, it has been depressing for, I guess, too long. Mark Twain supposedly said, "History doesn't repeat itself, but it often rhymes." If you look at the geopolitical and economic macro-trends of the world right now, the early 21st century feels like a full-blown cover song of the early 20th century. Let's look at the scoreboard. 1. The Global Plagues A century ago, the Spanish Flu brought the world to a grinding halt, exposing the fragili

When ChatGPT was released in late 2022, the AI industry appeared deceptively simple. The winners would be the companies that built the most intelligent models. OpenAI, Anthropic, Google, Meta, and others entered an intense race to push the frontier of artificial intelligence. Model quality became the primary differentiator. Every benchmark mattered. Every improvement in reasoning, coding, multimodal understanding, and context length was celebrated as a competitive advantage.

Most articles about agentic AI read like a medical textbook on cellular biology. They explain the synapses, the nervous system, and the metabolic pathways: ReAct loops, reflection, tool calling, routing, planning, and multi-agent orchestration. Useful, yes. But if you are a chief medical officer running a busy hospital, your first question isn't “How do the individual neurons fire?” Your first question is: Do we need a rapid-response triage team, a methodical surgical unit, o

As the father of a 10-month-old baby, I’ve started thinking more seriously about the future he’ll grow up in. With AI advancing so quickly, it’s hard not to wonder: what if AI ends up doing the job he might one day choose? Then I had a simple thought—if AI is part of the challenge, maybe it can also be part of the solution. So I collaborated with Gemini to help write this post. I have to say, it gave me, as a new parent, some refreshing ideas. If knowledge is cheap, what is s

Lately, opening my inbox or checking my WhatsApp notifications feels less like catching up on work and more like drowning in a sea of pristine, immaculate... nothing. Many of the messages I receive seems to have fantastic formatting, flawless vocabulary, and pristine English. On the surface, it looks like executive-level communication. But when I actually parse the sentences, I realize the content is entirely hollow. It’s completely nuts. In the pre-AI days, telling a stupid

I was reading the news this morning about the massive 2026 "Super El Niño" currently brewing in the Pacific, and it kinda trigger a few interesting thoughts. The "cloud" that we have shifted over >90% of enterprise workload to is made of concrete, copper, water, and an absolutely staggering amount of electricity. And as we barrel into the 2026 that is more and more defined by AI, the physical constraints of planet Earth are crashing headfirst into our algorithmic ambitions. H

Everyone is talking about AI assistants. Upload some documents, connect an LLM, ask questions, and magically get answers. At least that's the promise. We developed and launched an enterprise knowledge assistant called Alto to 20,000 Certis staff in Mar across both frontliners and office-based staff. The idea is straightforward: teams can create knowledge channels around a particular topic, project, function, or community, upload or link relevant content, and then chat with th

One thing I find strangely under-discussed in AI right now is this: Why is the open-model movement increasingly concentrated in China… while most leading US frontier models are becoming more and more closed? The divide is becoming very obvious. On one side, you have OpenAI, Anthropic, Google DeepMind — highly closed ecosystems, tightly controlled APIs, limited transparency. On the other side, you see DeepSeek, Qwen and others pushing open-weight models aggressively into the m

One thing I’ve observed across many leadership conversations about AI is that people often arrive with very different mental models of what AI actually is — and those differences shape expectations in profound ways. Some see AI as a "solve-it-all", while others are more suspicious. In reality, both perspectives are understandable. A large part of this disconnect comes from how most of us first experience AI: through consumer tools. We experiment with chatbots that can draft e

Step into any executive strategy session right now, and one title dominates the conversation: the Chief AI Officer (CAIO). According to recent data from IBM, a staggering 76% of global enterprises have now rushed to appoint a CAIO. On paper, the logic seems ironclad—AI is moving at a breakneck pace, and we need a single executive to grab the wheel. But as someone who is intimately familiar with how big enterprise work, I’m here to wave a massive (and unpopular) red flag. If

On July 17, 2025, OpenAI flipped a switch that effectively killed the traditional corporate chatbot. By integrating their Computer-Using Agent (CUA) infrastructure directly into ChatGPT, they launched the ChatGPT Agent. If you are leading strategy or tech right now, pay attention. We just crossed the boundary between "AI that talks" and "AI that does." Here is a breakdown of what just happened, stripped of the marketing noise, and why our enterprise roadmaps need to change im