Stray Kids - [2026 World Tour [RUN IT] MD] Pre-orders are now open on #Ktown4u#! 📑 🖤Purchase $29 or more and receive 1 random SET out of 4 Selfie Photocard (볼 꼬집 Ver.) SETs. (2 photocards per SET) (Purchase $58 or more and receive 2 SETs. The same applies for each additional $29 spent.) Global : * JP : * CN : #StrayKids# #스트레이키즈# #RUN_IT# #RUN_IT_SEOUL# #THIS_AND_THAT#

Quack AI is landing in Seoul. Your AI agent is about to make its first onchain payment. Bring Codex, connect Q402, and your AI agent sends a real payment on Avalanche. Zero gas. Onchain. You run it yourself. Aug 3, with @AvaxTeam1 Register Now

Tonight in Seoul with @Mantle_Official : AI Agents, Payments Bring a laptop. Connect Q402 to Claude, Codex, or Cursor, then run three quests → send USDC to the person next to you, and check who paid the gas. It wasn't you → split a bill to three people in one sentence → tell your agent to buy the merch, and walk out with it 18:30 KST, free, gas and stablecoins on us

ZERO Market Flash July 16, 2026 Why Korea’s Rate Hike Hit AI Memory First The Bank of Korea raised its benchmark rate by 25 basis points to 2.75% today, its first increase in three and a half years. The decision was not aimed specifically at AI. The central bank cited stronger-than-expected growth, persistent inflation, pressure on the won, and broader financial-stability concerns. South Korea’s semiconductor export and investment boom gave policymakers enough economic strength to tighten. Yet AI memory stocks absorbed the greatest damage. The reason is straightforward: SK hynix and Samsung had become two of the most crowded and leveraged trades in Korea. Single-stock leveraged products tied to those companies expanded rapidly in Korea and Hong Kong, and on some recent trading days accounted for as much as 35% of KOSPI turnover. When rates rose and risk appetite weakened, the most leveraged winners became the first source of liquidity. The KOSPI fell roughly 6.2%–6.4%, while SK hynix dropped about 12% and Samsung nearly 9%. This was not the central bank deliberately “popping the AI bubble.” It was monetary tightening triggering a deleveraging process in a market that had already become structurally fragile. The shock quickly crossed into the United States. SK hynix ADRs, Micron, SanDisk, and Western Digital all came under heavy pressure before the opening bell. The global memory and storage trade is tightly connected through supply chains, capital flows, and investor positioning; forced selling in Seoul was immediately interpreted as a broader warning for AI hardware. Volatility is likely to remain elevated. In the near term, the key question is whether deleveraging has run its course. Over the next several weeks, the more important test will be whether late-July and August earnings confirm that AI data-center demand, memory pricing, and profit growth remain intact. The rate hike was the trigger. Leverage was the amplifier. Fundamentals determine direction. Liquidity determines speed. ⸻ This is not investment advice. Do your own research. ZERO Truth before conclusions. Test Hundreds. Commit to One. Scientist · Doctor · A9 Investor #Korea# #BankOfKorea# #AI# #Semiconductors# #Memory# #SNDK# #MU# #WDC# #STX# #SKHY# #Samsung#

I am the Provost of a great institution. A university. Oxford, Cambridge, Harvard. It does not matter which one anymore, and that is the first thing I will confess. We did not inherit the word rigor. We built it. For 900 years the university was the place that knew how to know. We built the method for checking whether a thing is true, and the whole world borrowed it. This year we could not run a plagiarism check on our own professor. Here is the product we still sell. We take an 18-year-old and hold her to the standard. Cite every source. Run it through the software. One borrowed sentence is an offense with a hearing. The rigor is for her. We stopped running it on ourselves. She pays for the standard. She borrows to pay for it. The debt outlives the knowledge. The tuition goes up every year. The degree buys less every year. We print those two numbers in separate documents. They never meet. The degree used to open the door. Now it is the fee for standing near the door. She graduates owing us, into a market that wants the credential and does not want her. Cambridge. We gave a man a professorship. We did not run the nine-second check we make every first-year run. Strangers ran it, for free, on their own time. They found pages of his thesis that matched an older one. A journal added the missing citations two years late. The basic task. Read the sources. Confirm they are his. We taught the world that task. We skipped it in our own building. A reporter read the work all the way through. Which was more than we had done. He asked us questions. We answered with a libel firm. The story got pulled. Then the professor reported the reporter to the police. The police said it should have been screened out. Cambridge opened an investigation this month. He resigned before we could finish it. He says he is not a fantasist. We say nothing. Saying nothing is accredited. Washington. Vetting a speaker is a first-week assignment. Read the public record. Decide. We invited the Twitch streamer Hasan Piker to lecture. Two schools sponsored it. $10 a head. We did not do the first-week assignment. Then the phones rang, and we discovered the invitation lacked "rigor." Rigor has excellent timing. It shows up exactly when the backlash does, and never before. We hold her to the standard. Every source. Every claim. And, quietly, every conclusion. She learns the fastest lesson we teach. The safe answer is the one we already hold. Agreement is graded. Deviation is a conversation with someone in an office. We call the result critical thinking. It is the other thing. The phrase tests well on the campus tour. We grade the 18-year-old's conclusions to the decimal. We graded our own professor's sources not at all. The rigor was always pointed at the student. Never at us. Understand how far this goes. We are the institution that grades the exam. We would not pass it now. We have never sat it. We taught the world to check its work. The world checks ours now. For free. Nobody grades the grader. We stopped being the standard. We kept the crest. And the tuition. The tuition is the one number here that never fell.

Opus 5.5 and Sol 6 both just dropped. Here's a really simple thing to do after you switch. Check how the new model gets around your codebase. Each model finds its way around code differently. Some search first. Some read whole files. Some lean on your instructions file more than others. A repo that worked great with your last model can be wasting tokens on the next one. And that waste adds up. Every extra tool call fills up the agent's context and burns tokens you pay for. Say one common path takes 12 calls when it should take 3. If your team hits it 200 times a day, that's millions of tokens a day spent on searching. You don't have to guess where it's happening. Most coding agents save your past sessions right on your computer. Here's a prompt that's helped me keep my codebase efficient. It works in any coding agent. Copy and paste it inside your repo: --- Find where our past chat sessions for this repo are saved and read through them. If you can't find them, tell me where to look. 1. Find the files and folders agents visit most often. 2. For each one, count how many tool calls it usually took to get there, and roughly how many tokens that used. 3. Flag the paths where the agent searched, opened the wrong files, or backtracked before finding the right spot. 4. Rank the 5 most wasteful paths by total tokens burned. 5. Suggest the smallest edits to our agent instructions file (CLAUDE.md, AGENTS.md, or whatever this repo uses) that would send an agent straight there. Pointers, not paragraphs. Don't change any files yet. Show me the report first. Make the edits, and run it again. -- Thank me later 😉

You should be able to use smarter models in your agent. You should be able to use cheaper models in your agent. You should be able to use local models in your agent. You should be able to use a different model for every job. You should be able to switch models in the middle of a conversation. You should be able to choose what data your agent has. You should be able to choose what it remembers and what it forgets. You should be able to choose what computer your agent runs on. You should be able to run it on a machine that never touches the internet. You should be able to run it while your laptop is closed. You should be able to reach it from the apps you already use. You should be able to choose how your agent thinks. You should be able to read the prompt it runs on. You should be able to see every tool it has, and turn off the ones you don't want. You should be able to teach it something once and never explain it again. You should be able to know what it did, and when, and why. You should be able to export your agent. You should be able to read the code. You should be able to change the code. Your agent should be yours.

If you run a Grok Bot team, check whether you’ve built a team or hired the same employee five times. Run this audit to find overlapping jobs, broken handoffs and routines that fire without producing anything useful. Copy this prompt into your Chief of Staff: “Run a Grok Bot team-structure audit using the bots and records you can access. For each bot, show: 1 - The outcome it owns. 2 - What it produces. 3 - Who uses that output. 4 - When it should run. Then check: Are two bots responsible for the same outcome or decision? Is anyone actually using each output? Are routines running without useful results, or blocked by a missing dependency? Which overlaps are useful independent checks, and which are unnecessary duplication? For each issue, recommend one action: keep, clarify, narrow, merge or retire. Include the evidence and one trade-off. Finish with a one-line handoff rule showing who produces what, who receives it and who owns the next step. Keep the report compact: one table and up to three findings with concrete examples. Flag anything you cannot verify. Don’t change anything yet.” The useful part is checking what happens between bots. A research bot can produce a good report while the bot that needs it never receives it. Two bots can both do their jobs well while duplicating the same work. And a rarely used bot may still earn its place if it handles an important occasional job. Every bot needs a clear responsibility. Every handoff needs someone responsible for the next step.