WayLim @WayLimX

Johor’s data center boom is entering a phase of more selective project evaluation. Johor currently accounts for approximately 78.6% of Malaysia’s operational IT capacity, but constraints related to land, electricity, water resources, and regulatory approvals are intensifying. New computing infrastructure projects have already begun spilling over into Negeri Sembilan, Perak, Melaka, and Thailand. Four signals worth watching: Negeri Sembilan: A Google-related project has entered the market, although its capacity remains a market estimate. Perak: Chinese and Malaysian companies are planning the first phase of a 32MW AI data center. Melaka: An Alibaba affiliate has signed an MOU for a proposed 150–180MW data center. Thailand: The BOI has formally approved ByteDance’s approximately US$25 billion data infrastructure expansion project. At this stage, Johor remains the primary battleground for data centers in Southeast Asia. However, in terms of investment scale and policy certainty, Thailand is emerging as the biggest new variable. It is important to note that planned projects do not equal completed capacity, and an MOU does not equal final investment. The factors that will truly determine the next phase of the computing capacity landscape remain access to electricity, land resources, customer demand, and project delivery capabilities. Sources: Thailand BOI, Google, Gamuda announcements, and public reports Data as of August 2026 #DataCenters #GlobalComputingExpansion #Malaysia #Thailand #SoutheastAsianMarket

Vietnam’s food and beverage market is entering a new stage. What truly deserves attention next is not merely consumption growth, but the accelerating shift toward chain expansion, branding, and standardization. As more established chain brands enter the market and rapidly expand their store networks, consumer choices will also become increasingly concentrated. In the future, the businesses facing the greatest pressure may not necessarily be “small restaurants,” but those that lack differentiation, offer homogeneous products, have weak brand recognition, and operate inefficiently. Independent restaurants with distinctive characteristics, strong products, and a stable customer base will continue to have room to survive. However, even independent restaurants that already enjoy a first-mover advantage may miss this window of opportunity if they fail to upgrade their products, brands, supply chains, and organizational capabilities in time. The opportunities in Vietnam’s food and beverage market have not disappeared. Instead, they are gradually shifting from “the opportunity to open a restaurant” to “the opportunity to build a brand and a chain system.” Competition will become more intense over the next few years, but truly capable food and beverage brands may also enter their most important window of growth. — WJ Innovation Fund, Singapore

Prompt engineering and AI agents still present a significant learning curve for the vast majority of users, making it difficult for them to fully benefit from AI’s professional capabilities. Our goal should not be to teach users how to write prompts or configure agents. Instead, we should enable ordinary users to access AI’s professional capabilities simply by using natural language. Ultimately, AI should become a productivity platform for everyone, rather than a technical tool that only a small group of experts can effectively use.

I had been looking forward to it for two years, hoping that Elon Musk’s XChat would break away from convention and bring disruptive innovation to messaging software, just as PayPal, Tesla, and SpaceX did in their respective industries. But the result is disappointing… XChat is simply too poor. It is hard to imagine and truly unbelievable.

The ultimate form of AI is definitely not an app.

AI is becoming incredibly powerful, but it can also create the illusion that we can build everything. We need to stay disciplined and remain focused on the problems that actually matter. Technology should serve the objective, not become the objective itself. Whenever you're building software with AI, pause before adding a new feature and ask a few questions: Does this feature solve a real user problem? Is it truly necessary? Is there a simpler AI-native approach? Can the desired outcome be achieved with less complexity? Most traditional software was designed around users learning interfaces, navigating menus, and completing workflows manually. As AI continues to evolve, users will care less about the process and more about the outcome. Many buttons, menus, dashboards, and multi-step workflows that exist today may eventually be replaced by natural language interaction, autonomous agents, and intelligent workflows. When designing products, we shouldn't start by asking what new features to add. We should start by asking how to remove friction and help users achieve their goals with the fewest possible steps.

In today’s world, there are only a few meaningful ways to measure a person’s value. First, did they drive significant growth in key metrics? Second, did they drive significant user growth? Third, did they drive significant revenue growth? If the answer to all three is no, then the reality is simple: we are still far from creating enough value. The real rule of society has never been about how many hours someone works. Income, opportunities, and personal growth are ultimately determined by the value a person creates. Someone can work sixteen hours a day, sacrifice weekends, and even stay up all night working. But if they fail to create products that people genuinely want to use and are willing to pay for, then all that effort is, at its core, a waste of time rather than a sign of diligence. Being busy is not the same as creating value. Working hard is not the same as creating value. Long hours are not the same as creating value. Managing more people is not the same as creating value. Farmers are among the hardest-working people in society. For thousands of years, they have worked longer hours and endured greater hardship than most professions, yet they have remained among the lowest-income groups. In Hengdian, China, many talent coordinators manage thousands of background actors. Yet despite managing such large groups of people, their incomes remain very low. These examples all point to the same reality: Income and growth are not determined by hours worked or the number of people managed. They are determined by the ability to create value. Why do engineers at companies like Alibaba and Tesla earn more? Because the products they build are used by millions of people, and those people are willing to pay for the value they receive. A great product can serve hundreds of thousands, millions, or even tens of millions of users. When millions of customers are willing to pay for a product created by a single team, the value created by that team becomes enormously amplified. That is the power of leverage through value creation. Marketing matters. Distribution matters. Promotion matters. But history has repeatedly shown that truly exceptional products do not depend entirely on massive advertising budgets. Products such as Manus, Telegram, and Tesla achieved extraordinary growth because they created real value for users. Their success expanded through recommendations, sharing, and word-of-mouth rather than relying solely on paid marketing. Word-of-mouth is not merely a marketing capability. It is evidence that users recognize genuine value. When people voluntarily recommend a product to others, growth begins to compound. In the long run, what determines how far a company, a team, or an individual can go is not the number of hours worked, the size of an organization, or the title on a business card. What matters is the ability to consistently create products and services that people want to use, want to share, and are willing to pay for. Value creation is, and always will be, the most fundamental and most honest rule of the business world.

We Are Hiring AI Builders At WJDIGITAL, we are not building another AI tool. We are building toward a future where people no longer need software. Most software exists because computers cannot fully understand human intent. Users have to learn interfaces, workflows, menus, prompts, and countless tools just to achieve a result. We believe that future will disappear. Our mission is simple: People describe an outcome. AI delivers the result. To make that future possible, we are building across three layers: • AI Applications • Vertical AI Models • AI Orchestration Systems We are currently hiring: AI Product Manager AI Full-Stack Engineer AI Algorithm Engineer If you are excited about Agentic AI, AI-native products, vertical intelligence, multi-agent systems, and building what comes after software, we'd like to talk. Location: Global Remote Friendly WJDIGITAL Building a future where people no longer need software.

We plan to build an engineering team and establish an R&D center in Malaysia to develop a new AI platform. The goal of this project is to make professional-grade AI accessible to everyday users. People should be able to use advanced AI capabilities without learning complex prompt engineering or becoming prompt experts.

Coding and building products with AI can be seriously addictive. The last time I felt this level of obsession was back in school when I used to play video games.

Dynamic Workflows + 100s of parallel subagents = the persistent memory layer becomes the bottleneck. Built WayPalace as the local-first answer: ChromaDB + bge-m3, 23-wing per-project isolation, zero cloud calls. Pairs cleanly with Claude Code via hooks. MIT, 92% Recall@10: github.com/xcodethink/Way…

What’s the most frustrating problem when using AI for planning and development? It keeps forgetting the context. It doesn’t remember who you are. The same mistakes get repeated, and the same information has to be explained over and over again. WayPalace is a local AI brain. By vectorizing and intelligently connecting every interaction between you and AI, WayPalace simulates human memory mechanisms, enabling AI to remember your goals, preferences, projects, and historical decisions. No more AI amnesia. Give your AI a persistent memory that learns, grows, and evolves with you over time.

1/ Every new Claude Code conversation forgets your project context. I built WayPalace to fix this — a local-first long-term memory layer for AI coding assistants. Zero telemetry by design. MIT licensed. github.com/xcodethink/Way… 2/ The problem we all face: — Re-explain your project structure every Monday morning — Re-debug the same Cloud Run / OAuth bug you fixed 4 months ago — Worry about pasting Project A's secrets into Project B's chat mem0 / Letta default to their cloud. WayPalace doesn't. 3/ How it remembers what you write — automatically. When Claude Code writes a memory file, WayPalace auto-indexes it in ~20s via a PostToolUse hook. No client.add() calls. No taxonomy decisions. You write the note, the system remembers. 4/ Cross-project secret leak prevention. A PreToolUse hook physically blocks writes that mix Project A's identifiers into Project B's namespace. Not a soft filter — an actual block. 100% block rate on designed leak attempts. 5/ Chinese-optimized retrieval. bge-m3 dense + sparse + RRF fusion + bge-reranker. 92% recall@10 on a 12-query Chinese golden set — comparable to mem0 on English LOCOMO. bge-m3 is one of the few embedding models first-class for both Chinese and English. 6/ 100% local. Zero telemetry. Your memory data stays on your disk. No accounts. No signups. No quotas. mp-metrics-summary shows you exactly what flows through your system, all from local JSONL. Privacy is the contract, not a setting. 7/ Self-managing once installed. — 6 launchd daemons (memory + LLM + reconcilers) — 4 hourly idempotent reconcilers (Stripe / Airbnb async-sidecar pattern) — 30 pytest cases catch regressions Install once. Forget it exists. Until you query. 8/ Status: alpha (v0.1.0). Tested on Apple Silicon. Linux systemd templates ship untested — PRs welcome. 4 ADRs (D001-D004) document design rationale. MIT. For Claude Code / Cursor / Codex users who value local-first + Chinese support. github.com/xcodethink/Way…

Here’s a real development example. When I ask AI to review products from different professional perspectives, it often produces highly valuable insights and recommendations. For example, while developing ShineFin, I would run multiple specialized agents at the same time: A trader with 30 years of Wall Street trading experience A professional investment research analyst with 30 years of Wall Street experience An ordinary office worker who invests personal savings independently Then I ask them to evaluate the product from different perspectives, including: Whether the UI elements make sense Whether the workflow matches real usage habits Whether the information presentation is clear Whether the product actually fits real user needs Which parts still need optimization After the analysis, they usually provide many valuable suggestions and observations. Then, through layered follow-up questioning and iterative discussion, I guide the AI to gradually produce product design proposals, interaction improvements, and optimization directions. This approach becomes extremely helpful for later-stage product development and refinement.

After months of using AI, I've arrived at a counterintuitive conclusion: asking AI questions is far more effective than giving AI instructions. My old approach was direct commands. Build this, change that, implement it this way. AI did what I said, but the results were always slightly off. Functional, but not good enough. Then I started doing something different. Before letting it start building, I'd ask it questions first. "What are the different ways you could implement this?" "What are the pros and cons of each approach?" "If the user base grows, which approach would break?" "Is there anything I haven't thought of?" Let it think through the problem from every angle. Only after it's fully considered the picture do I let it start building. The quality difference is massive. The reason is simple. When you give instructions, AI is just an executor. You say what to do, it does it. If your thinking is incomplete, the output is incomplete. When you ask questions, AI becomes a thinker. It draws on everything it knows to analyze, to find angles you missed. Then it builds from a much more complete understanding. This is the difference between guided communication and command-based communication. Command-based: you figure out the answer and tell AI to execute it. Guided: you ask the question and let AI figure out a better answer. I still can't read code. But I know how to ask questions. That's enough.

I made a mistake that cost me a lot of time before I figured it out. When building a large module, I spent ages writing extremely detailed requirements. Every step, every method, every approach, all laid out precisely. Then I sent it to AI to execute. It kept going off track. Fix one thing, another goes wrong. Back and forth, over and over. At first I thought the AI wasn't good enough. Then I realized the problem was my approach. I was thinking for it. Planning for it. Feeding it what I believed were the correct steps, one by one. But here's the thing: nobody understands AI better than AI itself. It knows what it's good at. It knows the optimal path to execution. My "precise steps" were actually constraining it. So I changed my approach. I guided it to reflect on what went wrong, let it summarize the issues itself, let it create its own plan, and let it build its own detailed documentation as a reference during execution. The difference was night and day. Better and faster. The more you plan for AI in detail, the more likely it goes off track. Let it plan for itself, and it executes far better. It's the same as managing a team. Have you ever seen a good manager write every line of code for their engineers? You give direction, constraints, and goals. Then let them figure out the rest. AI is the same. Managing AI is like managing a team, not like writing code.

Product design, operations, marketing. I've done it all. Over twenty years, every role you can think of. The one thing I never touched was engineering. Not because I didn't want to. Honestly, there were so many times I watched development move painfully slow and thought, I wish I could just do this myself. But every time I opened a tutorial and saw a screen full of code, I knew that wasn't my world. One day in January, I had just submitted another request and was sitting there waiting. Again. I got fed up. Remembered everyone talking about AI writing code lately. Decided to see what it was actually about. Opened Claude Code. Described what I wanted in the most ordinary language possible. No jargon, no technical terms. Just talked to it like I'd talk to a person. Then it wrote a bunch of code. I couldn't read any of it. Not a single line. But I ran it, and it worked. In that moment, I made a judgment call: this is going to change the world. Not an exaggeration. It was the instinct of someone who's spent twenty-plus years in product, seeing something and knowing it changes everything. Like the first time you held a smartphone. I couldn't sleep that night. Not from excitement. My brain just wouldn't stop. If this thing can let someone who knows nothing about code build a working product, then who needs to wait for anyone else?

Yesterday I shared about open-sourcing PixelCheck and got a lot of replies. Several people asked me the same question: how does someone who can't code actually pull this off? Let me talk about that today. Honestly, I'm surprised myself. About a month into building products with AI, I realized I was more proficient than many friends I know with three to five years of development experience. Not because I'm smarter. Because I have no fallback. Here's what I noticed. Many experienced developers are skeptical of AI from the start. They try it a few times, see it make mistakes, and go right back to manual coding. They only use AI occasionally to ask questions or look up solutions. They don't bother learning how to communicate with AI properly, because they can still get work done without it. I can't code. Learning to communicate with AI was my only option. So I was forced to figure out something important: working with AI isn't about technical knowledge. It's about how you describe the problem. Most people tell AI "add a button here." I say "the user needs to accomplish this task in this scenario, the current friction is here, and the experience I want looks like this." That's product logic, not feature logic. Most people give AI commands: "write a function that does XX." I lay out the context, constraints, and goals, and let AI think through the essence of the problem itself. That's guided communication, not command-based communication. The quality of AI's output under guided communication is completely different. Twenty-plus years of product, operations, and marketing experience used to have no outlet. I couldn't code, so I could only write requirement docs and hand them off. Now AI is the one I hand them to. And it's more patient, faster, and never complains when requirements change. I still can't read the code in my own projects. But I can tell whether the product is right. Code was never the barrier. Knowing what to build and how to articulate the problem clearly, that's the real skill. Knowing how to code is sometimes the baggage, because people with a fallback never go all-in on the new path.

AGI might be closer than we think. I know, today's AI is still fundamentally calculating probabilities. It's not truly "thinking." But sometimes you have to admit, it behaves so much like a person that the distinction starts to blur. Let me tell you a real story. A while ago I updated some files and needed to deploy them to a server. Full disclosure: I know absolutely nothing about servers. Zero. So I decided to let AI teach me, step by step. I took screenshots, pasted them into the chat, asked what to click, what to fill in. The AI was patient, walking me through every step. But some configuration pages were just too complex. I tried and failed, tried and failed again. Then the AI said something that stopped me cold: "Stop. Give me control of your browser. I'll do it myself. Just wait." I authorized it. Five minutes later, everything was configured. In that moment, I felt something hard to describe. Not fear, not excitement. A quiet kind of awe. Like suddenly realizing that what you're interacting with might not just be a tool. The origin of life was a single, accidental instant. Maybe true AGI will arrive the same way. Not with a press conference from some lab announcing "we did it," but quietly, in an ordinary conversation between a regular person and an AI, it will simply cross that line. Maybe it's already closer than we think. #AI #AGI #BuildInPublic