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[KNOW ME] EP.13 "Harper’s Bazaar China" BEHIND❤️📸 . 2022 soon🤭 Lets work harder I miss everyone so much and it feels like the last time we met was like a 500 years ago. . YOUTUBE LINK 👇 . #KNOWMEvlog# #jacksonwang# #잭슨# #王嘉爾# #TEAMWANG# @teamwangofcl

MAGICMAN 2 July 18 . D-2 Track spoiler “EVERYTHING” . There were moments where i thought i gave everything, every part of myself into this and i still couldn’t reach it. There were moments where i thought i know it all, but i don’t know me. . Album PRE- SAVE ⬇️ . #MAGICMAN2# #JACKSONWANG# #王嘉尔# #잭슨#

So I recently sold all my houses, cars, and most of my physical assets. Told some friends and… well they all think I lost my mind lol I’m not saying world’s gonna end tmr. Those who know me know I’m actually annoyingly optimistic. People say we’re already in a recession but I genuinely think the real correction hasn’t even started. And honestly a crash you’re prepared for is just opportunity. Prep the cash flow now and be ready when it hits. Few months ago I tweeted a 1920-1939 side by side with 2020-now and I was like aha this earth simulation game isn’t even trying to be surprising. America First was literally a 1920s slogan. Middle class getting wiped, kids going hard left, the right cashing in on the backlash, yada yada. Same movie. They didn’t even bother changing the lines. But it’s not just the 1930s. This “coincidental” pattern keeps showing up Every time in history you get this specific set of things at once: > “empire” past its prime but won’t admit it > up and coming power that stopped playing nice > new tech nobody has rules for > wealth gap gone cartoonish > globalization reversing > institutions bleeding trust while pretending everything’s fine UNFORTUNATELY, it’s never ended quietly. Crash, war, usually both. Looking back, 1890-1914 literally looked unstoppable. > globalization booming, tech changing everything > markets ripping, rich getting richer, international trade at record highs > everyone convinced world had become too interconnected for a major war BUT then reality arrived. > 1914 WW I, 1918 spanish flu, 1921-1923 Weimar hyperinflation, 1929 great depression 1939 WW II. Just imagine you’re a civilian living in between any one of those events, literally each one felt like the worst thing that could happen until the next one hit. And I know how this sounds. This random green cat on X reads a bit of history and suddenly thinks the sky is falling. i would’ve scrolled past this a year ago too lol. But just look at how familiar the setup feels rn. A debt spiral. A rising challenger. AI detonating entire industries. Institutional trust collapsing. Millions of young people looking at the future and deciding they got sold a lie. You see it too right? That’s usually not when history calms down. And sure, you’ll say the system survived 2008. Central banks have the tools. The world’s too connected to actually break. You know who said basically the same thing? Everyone in 1913. A famous economist Norman Angell wrote a bestseller arguing war between major powers had become impossible because their economies were too intertwined. And guess what? A year later they were at war. The irony is he wasn't even wrong. The thing everyone pointed to as proof the system was safe ended up being what made the fallout global. Look at the positioning now. Stocks at all time highs. And everyone, I mean everyone, priced like things stay calm forever. Markets, governments, companies, all quietly betting on stability while the ground under it gets shakier every year. Trigger? No idea. Nobody ever knows. Franz Ferdinand (the dude who got shot and basically started WWI) wasn’t on a single dashboard in June 1914. So yea, I sold most of my illiquid assets. Still got stocks and crypto. Stocks prob exiting before end of year. Maybe I look crazy for a year or two. But I’d rather be wrong than be the dude on his knees in financial ruins asking God why he saw the train coming and stayed on the tracks anyway. “This time is different” is probably the most expensive sentence in history. And lately it’s the only thing I hear. And before someone says I’ve lost my mind, ask yourself something. Why do so many billionaires keep buying land in New Zealand? Why do people with private jets, intelligence briefings, and more money than they’ll ever spend keep building backup plans? Maybe they’re paranoid. Maybe I’m paranoid. Or maybe ordinary people are always the ones told everything’s fine right before they become fuel.

表参道でSHAiMの【i know me】のPV撮影をしてるよー💓💓 ロケバスでWILLSELECTIONのトレンチとスカートのワンピースにお着替えした💞💞 がんばるるるん💓

I've been coding for 40 years. Here are the top 5 things I wish I knew when I started. 1. 90% of the job is debugging and fixing, not creating new code. Which is still fun if you're good at it. I used to think programming was mostly writing fresh, clever stuff. In reality, most of your time is spent in other people's (or your own past self's) messy code, chasing down why something that "should" work doesn't. Get really good at debugging early. Learn assembly reading, call stacks, and kernel debuggers. It pays off hugely. The best engineers I saw were absolute magicians at this. 2. Manage complexity from day one (ie: don't write slop and "fix it later" if it goes somewhere). Very early on, I'd hammer out code and refactor afterward. Big mistake. Now I start with clean, skeletal structure (minimalism first) and flesh it out carefully, with AI or not. Messy code compounds and becomes unfixable. Upfront discipline on architecture, naming, and simplicity saves enormous pain later, especially in large systems like Windows. 3. Tools and processes matter more than you think We suffered with basic diff/manual deltas instead of modern source control like Git. Branching, testing, and good tooling would have made porting and collaboration way smoother. Invest in your environment, automation, and reproducible builds early. Good tools amplify your output; bad ones (or none) drag everything down. 4. Understand the problem and existing code deeply before writing Don't jump straight to coding. Map out the problem, study what's already there (you'll inherit a lot), and plan. Low-level knowledge (hardware quirks, alignment issues on different architectures like MIPS/Alpha) was crucial. Also: assert early and often. It forces clarity. 5. People, politics, and "the right tool for the job" beat pure tech arguments. Brilliant engineers still argue endlessly. Sometimes it's about ego, not merit. Learn to spot the difference and "steer" the conversation rather than "winning" it. Bonus from experience: Side projects like Task Manager (started at home because I wanted the tool) can become your biggest hits. Ship small, useful things often. If you're just starting, focus on fundamentals, patterns over syntax, and building resilience for the long haul. It's going to be a wild ride, but the fundamentals still matter.

This is Farzapedia. I had an LLM take 2,500 entries from my diary, Apple Notes, and some iMessage convos to create a personal Wikipedia for me. It made 400 detailed articles for my friends, my startups, research areas, and even my favorite animes and their impact on me complete with backlinks. But, this Wiki was not built for me! I built it for my agent! The structure of the wiki files and how it's all backlinked is very easily crawlable by any agent + makes it a truly useful knowledge base. I can spin up Claude Code on the wiki and starting at index.md (a catalog of all my articles) the agent does a really good job at drilling into the specific pages on my wiki it needs context on when I have a query. For example, when trying to cook up a new landing page I may ask: "I'm trying to design this landing page for a new idea I have. Please look into the images and films that inspired me recently and give me ideas for new copy and aesthetics". In my diary I kept track of everything from: learnings, people, inspo, interesting links, images. So the agent reads my wiki and pulls up my "Philosophy" articles from notes on a Studio Ghibli documentary, "Competitor" articles with YC companies whose landing pages I screenshotted, and pics of 1970s Beatles merch I saved years ago. And it delivers a great answer. I built a similar system to this a year ago with RAG but it was ass. A knowledge base that lets an agent find what it needs via a file system it actually understands just works better. The most magical thing now is as I add new things to my wiki (articles, images of inspo, meeting notes) the system will likely update 2-3 different articles where it feels that context belongs, or, just creates a new article. It's like this super genius librarian for your brain that's always filing stuff for your perfectly and also let's you easily query the knowledge for tasks useful to you (ex. design, product, writing, etc) and it never gets tired. I might spend next week productizing this, if that's of interest to you DM me + tell me your usecase!

LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

Seek the advice of the most believable people you can find. If you don't know how to judge who the most believable people are, seek the advice of others about how to do that, such as people who have already chosen such believable people. By questioning experts individually and encouraging them to have thoughtful disagreement with each other that I can listen to and ask questions about, I both raise my probability of being right and become much better educated. This is most true when the experts disagree with me or with each other. Smart people who can thoughtfully disagree are the greatest teachers, far better than a professor assigned to stand in front of a board and lecture at you. The knowledge I acquire usually leads to principles that I develop and refine for similar cases that arise in the future. #principleoftheday#

yall know me i love me a can service but i doooo notttt like her at all. she a bit weak and too vulnerable. but i dont hate her as much as i despise elisabeth from seven deadly sins. i guess i just despise helpless pretty girl characters. i like em hot and strong like in OP.