Take a moment to understand this. Once you internalise this, your investment returns will follow suit. $tsla $pltr

Happened to me a few times when context is >80%. Nowadays, I keep my context window to less than 70%

AI is cheap right now because consumers are being subsidised. That won't last. Use it while it's still this affordable.

Claude code on opus 5.5, feels dumber yesterday. Hope it recovers and be well today.

The current trend is to RE/replace expensive legacy software with open source. Doable for 80% of the software out there. Interesting times. Another industry is going to be disrupted.

Interestingly, I think the world is heading toward hybrid. Frontier models and open weights, both running side by side. Frontier is how you find out what's possible. You pay for the best model to show you how a job gets done fast, what the ceiling looks like, which workflows are worth building at all. Open weights is where you actually build. Sovereign AI. Internal super agents that sit on your own hardware, touch your own data, and never leave the building. No per-token bill scaling with every new use case either, which matters once agents run all day. So one is for discovery, the other for deployment. Rent the frontier to learn the playbook, then own the stack to run it. Will open weights catch up enough to make frontier optional? Maybe. For now I'd want both. Just my thoughts.

The barrier to coding has collapsed. Total collapse. It is no longer who can code but who can create the pipeline and orchestrate agents to do so.

Domain knowledge is what separates AI slop from finished work. You can tell when something looks right but isn't. Run five agents at once. Output multiplies but review capacity stays flat and decision fatigue sets in fast.

If you have been working with/in G, you will understand how it works. Before a statement is made, rigorous discussion on what-if would have been deliberated. Most angles would have been covered, minimum surprises when something is executed. This is akin to typical behaviour of an adult vs teenager. Teenager don't think much, just do it and think how to handle when music comes. It can be a good thing too, given more mistakes to learn from. Contrary, adult tends to think more before doing, trying to cover all angles before implementation. Yes, it will take longer to push something but the risk/ worst case scenario are mitigated. Neither is bad. But it depends on the circumstances — how high are the stakes and the consequences of failure.

There are reasons why some countries implement certain rules, even it may be unpopular or "limit" human rights. Physical rallies. Spreading false falsehoods. Mass immigration of different cultures. No correction on what's right or wrong. Not holding falsehoods spreader accountable. Over time, without correction, false news may become the truth to some. The repeated exposure (to fake news) brainwashed the brain. That is the danger of social media. That's how social fabric falls to the populism politics, prevalent in today's world of extremist propaganda. I value safety in my community/ country where my family can walk freely without fear.

There are some people who complain about this and that, blaming others for something, whereby others may not have control over, or just want to vent their frustration on others. Blame father. Blame mother. Blame government. Blame everyone except themselves. Why? It subconsciously make them feel better, relieve of any ounce of self responsibility and accountability. It is exactly this kind of negativity and blaming attitude that limit one's growth. Be grateful for what we have. Don't blame others. Work on it. Positivity breeds positivity and leads to growth and a happier life. That's why it is often said that it is important to practise gratitude. 🙏

Using AI gets output fast. Training AI gets output faster. Creating pipeline gets output even faster. Getting human out of the loop is the fastest.

Some hold off on starting until they've covered one more course or certification. It's a reasonable instinct, and it comes from an era when learning was slow and expensive. The old order was study first, then work. With AI, you can pick up a missing detail in the time it takes to ask a question, so the waiting period buys you very little now. Someone who jumps in a bit underprepared still ends up with working output sooner, and the details they pick up along the way tend to stick because they ran into them for real. Just start.

You may dislike him but never bet against the man who create a flying car. Crazy times. Great time to be living on same timeline as @elonmusk $TSLA

If I am the smartest person/ most knowledgeable in the room, I need to get out at times. Find a room where I am the bottom few in terms of knowledge and experience. It is uncomfortable but that's how I will learn faster.

Three AI myths I keep hearing. 1. AI gives you more free time. In theory, yes. In practice, the better you get at using it, the busier you become. AI replies in seconds, so now you're the bottleneck. You have to read fast and review fast, and domain knowledge is the only thing that tells you whether you're looking at slop or something that's actually done. Run 10 agents at once, which isn't hard, and your head is full before lunch. 2. AI can be learnt in a few days. You can learn to use it in a few days. Using it well takes longer. You need to understand prompt engineering, context engineering, what happens when memory runs out, and when the model is likely to hallucinate. Skip that and you get bad output and burn tokens for nothing. 3. AI isn't good enough for daily use. I disagree. Most of the time it's the human. Current models already know more than any single person, but they can only work with the context you give them. In the software realm, I'd say 99% of problems are solvable today. The physical realm is next, probably within a decade, and that's when our skillsets will really need to evolve. My humble 2 cents.

Trying to get my boss to buy DGX spark. Cleared 67% of the approval stage. My work is gonna exciting if I can get my hands on it.