Enterprise Technology & Applied AI

After seeing diffusion appear repeatedly across robotics papers, I initially thought it was simply another fashionable AI architecture imported from image generation. But the more interesting explanation is that diffusion happens to fit one of robotics’ deepest problems unusually well: In the physical world, there is rarely one correct next action. There are many plausible ways to act, and a robot must generate, choose, execute, and continuously revise one of them. That makes

I spent today at IROS listening to researchers talk about foundation models, world models, energy-based models, and compositional planning for robots. At first, these sounded like separate research topics. But after seeing the examples, I realized they are converging on a much bigger idea: Perhaps the next leap in robotics will not come simply from training a bigger model to produce better actions. It will come from robots that can imagine possible futures, reason about them,

For years, the popular story about AI risk has been remarkably simple. AI gets smarter. Eventually it becomes smarter than humans. It develops goals of its own. It tries to preserve itself. And somewhere along that trajectory, we lose control. It is a compelling story, partly because it is so human. We imagine intelligence producing ambition, ambition producing power, and power producing danger. But a recent incident involving OpenAI agents and Hugging Face suggests another p

Imagine a factory in Ohio around 1900. The owner has just replaced his steam engine with an electric motor. No more coal. No more boiler. No more worker spending his day keeping the fire alive. He is proud of the upgrade. His factory has entered the electrical age. But walk inside, and almost everything looks the same. The new motor turns the same long metal shaft near the ceiling. Leather belts still connect that shaft to the machines below. The machines still crowd around t

A few days ago, this infographic on the evolution of data systems appeared in my feed. I thought it was one of the better visualizations I've seen in a while. Not because it lists technologies—we have enough of those—but because it tells a story. Every generation of data systems solved a fundamental limitation of the previous one, and in doing so, unlocked the next era of computing. That said, I also think it misses one important piece of the puzzle, especially as we enter th

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