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Ever seen cherries labelled "J," "JJ" or "JJJ" in a Chinese supermarket? Here's what it means. In China, imported sweet cherries are called "che li zi," a transliteration of the English word "cherries." They're sold by size grade, and the number of J's tells you the diameter. Based on common labels on Chinese e-commerce platforms:
 J: about 26 to 28 mm
JJ: about 28 to 30 mm
JJJ: about 30 to 32 mm
4J: about 32 to 34 mm Each extra J adds roughly 2 mm, and can add quite a bit to the price. Right now is Chilean cherry season in China. Chile's summer is China's winter, so the first air shipments arrive in late October, with peak supply in December and January, just in time for New Year and Lunar New Year gifting. China is Chile's biggest cherry customer by far: according to 21st Century Business Herald, China has bought more than 90% of Chile's cherry exports for seven years in a row. Does anywhere near you grade fruit by size like this? 
#cherries# #Chile# #agriculture# In-depth reports at

Leopold Aschenbrenner’s complete Q2 performance before his newest trades release Monday Top tier: • SanDisk $SNDK: -62% • Micron $MU: -34% Second tier: • Bloom Energy $BE: -38% • Taiwan Semiconductor $TSM: -18% • Nebius $NBIS: -59% Third tier: • CoreWeave $CRWV: -49% • Core Scientific $CORZ: -45% • STMicroelectronics $STM: -39% • Applied Digital $APLD: -49% • Riot Platforms $RIOT: -37% • SharonAI $SHAZ: -74% • IREN $IREN: -61% Smaller positions: • T1 Energy $TE: -82% • WhiteFiber $WYFI: -76% • Babcock & Wilcox $BW: -69% • Keel Infrastructure $KEEL: -69% • Bitdeer $BTDR: -66% • HIVE Digital $HIVE: -58% • CleanSpark $CLSK: -56% • Cerebras $CBRS: -53% • Vishay $VSH: -52% • ProPetro $PUMP: -49% • Solaris Energy $SEI: -23% Options: • Bloom Energy $BE [Calls]: $44,100,000 • Taiwan Semiconductor $TSM [Calls]: $23,900,000 • Infosys $INFY [Puts]: $5,200,000 Summary: Every common stock position is red. His entire portfolio is down 46.8% from Q2 release to present day. His newest trades release Monday, November 16th, showing what he owned on September 30th, after he sold most of this to Citadel in late July

Judge: Ma’am, you need to leave this courtroom and return properly dressed. Woman: Properly dressed for what exactly? Walking up to a podium for two minutes? Judge: Properly dressed to appear before a court of law. This is not a gym, and it is not your living room. Woman: I’m here to handle my case, Your Honor, not walk a runway. I didn’t think yoga clothes were going to become the issue. Judge: The issue is respect. Respect for this court, for these proceedings, and for the people in this room. Woman: I do respect the court. I just don’t respect pretending workout clothes are somehow offensive. Judge: I’m not going to stand here and argue with you about basic courtroom decorum. Woman: I’m not arguing either, sir, but this is ridiculous. I’m fully covered, I’m on time, and I’m here for my hearing. Judge: And yet instead of listening, you’re challenging the court at every turn. Woman: Because nobody told me there was some invisible fashion rule attached to showing up. Judge: Common sense is not an invisible rule, ma’am. Woman: So now this is about common sense? Judge: One more word and I will hold you in contempt. Woman: … Judge: That’s what I thought. You may step out, change into something appropriate, and come back when you’re prepared to conduct yourself properly. Woman: So I’m being sent home over clothes? Judge: No, ma’am. You’re being sent out over your attitude and your clothes. Woman: …Understood. Judge: Good. Bailiff, escort her out. We’ll recall the case when she’s ready to respect the courtroom.

We're still so small but I'm loving the team we've got at Superlogical. They're so talented and kind and love their craft. It's a different era of software development and our expectations and practices reflect that. I want to share a bit about how we work. First, everyone is trusted to push to main. We do machine-aided code review pre-push and human plus more machine-aided code review post-push. Limited to no PRs. Culturally, we've all agreed that if we ever hire someone who we DON'T trust to push directly to main, then they probably aren't the right fit for the team (so far, not an issue). Some common sense here: if someone is stepping outside their lane or comfort zone, we'll open PRs. For example, I'm pretty trash at macOS/iOS UI, so I'll open PRs for UI-affecting functionality and ask for eyeballs/help. But for business logic and internals, I push to main. Second, everyone can cut a release. Our release process is fully automated and when you start a release (for any platform) it triggers a Slack notification and a 15 minute countdown. The 15 minute warning is a final gate that allows people to review plus collaborate on a changelog. During that 15 minutes, anyone in the company can pause the release rollout. After 15 minutes, the train leaves the station. You can't override the 15 minutes (except to pause). It gives everyone a chance to step in. Third, we avoid synchronization through fast remediation rather than through planning. The server and clients on various platforms ship features as fast as they can. We use post-hoc human review and AI-assistance to identify feature mismatches across products ("Windows has support for X and Mac doesn't." or "Mac and Windows differ in their behaviors for Y in this way."). This avoids expensive "sync" meetings and coordination. We trust that producers of features are mindful of what they're producing and we trust that adjacent product lines will self-synchronize quickly. In practice, we see feature parity across platforms within 24 to 48 hours. All of this works on mutual trust. It ONLY works with mutual trust. Everyone at all times has to trust that they've got each others back: 1. Push to main only works if you fix your mess, or if you don't, that someone else will quickly but that you'd do the same (and generally in equal amounts). 2. Anyone can deploy only works if you trust everyone to cut at the right time, with the right changelog, and that people will respect the 15 minute timer. 3. Sync through fast remediation only works if you trust other teams will pick up and integrate your work quickly, which only works if you would too. It also only works if you are fairly producing mismatches (e.g. not constantly). We've got a great team. I know we're still small and that these practices will change in many ways as we scale. But this is already a markedly different practice than what I would've done 15 years ago. In general, we are building from day 1 on a foundation of mutual respect, excellence, and trust. And it's been awesome.

I'm releasing all my project files for free, under Creative Commons. Since my disability makes it hard to make more art, this is a way for my art to continue to grow. Remix them and have fun, using your agents or your human brain. Download:

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And yeah @fermah_xyz started with a problem that sits deep inside the ZK stack. Zero knowledge applications can prove that a computation was done correctly, but producing those proofs at scale takes serious infrastructure: specialised hardware, several proving systems, workload scheduling, and reliability and cost controls. A team building a ZK rollup, zkVM, bridge or coprocessor cannot simply ask for "a proof" and move on. Fermah's first answer was a universal proof generation layer. Provers with GPUs and other hardware supply capacity, applications that need proofs create the demand, and Fermah matches the two. That marketplace is now called Froben. The problem behind the proof Running this in production exposed something bigger. A proof is one step in a longer sequence. Something has to trigger the work, the work has to run, the result has to be verified, and then the result usually has to start the next action. Fermah's argument is that blockchains are good at recording state and weak at starting what happens next. Take a lending position that crosses its liquidation threshold. The chain records the fact, but nothing forces the liquidation. A keeper, bot or backend still has to notice the condition, build the transaction and submit it. Consensus settles what happened, and a human run service still decides what happens after. Fermah calls the missing piece protocol agency: a protocol's ability to act on a verifiable condition without depending on an outside operator. What Kernel does Kernel is Fermah's Protocol Agency Engine, and its pitch is short: Define. Execute. Attest. A developer defines a workflow, Kernel runs it, and the result is published onchain with a cryptographic proof. Three design choices matter here: -Typed nodes. Workflows are built from a library of verified building blocks such as HTTP calls, onchain reads, price feeds and math functions. The type system validates how they connect before anything runs. -Sandboxed execution. Workflows run in an isolated environment built for untrusted code, and the same inputs always produce the same output. - AI where it helps, code where it counts. Fermah says AI agents can compose workflows, but deterministic code executes them, with no LLM in the execution path. Kernel can work as an oracle too. A smart contract requests a workflow, Kernel runs it, and the contract receives the result with a proof that the workflow ran exactly as written. Froben and Flashcast Fermah did not walk away from ZK. Froben still runs, now on top of Kernel, and its paying customers include ZKsync Era and Abstract. In Froben, a workflow defines the proving pipeline and Kernel schedules the work across the prover fleet. Flashcast Social shows a different use of the same engine. A user posts a question, and within seconds it becomes a live prediction market. Kernel then runs the resolution workflow and settles the market automatically when the event happens, with no committee and no dispute window. Proof generation and market resolution have little in common on the surface. What they share is a condition that has to produce an action, and that is the workload Kernel is built around. Fermah also lists data aggregation, pricing oracles and cross chain coordination as things Kernel can run. Where agents fit Fermah's latest thinking extends this to autonomous software agents. An agent working onchain needs to find available actions, check whether it is allowed to run them, pay, execute, and use the output as the input to its next step. Fermah describes this as action manifests, which make actions machine readable across discovery, eligibility, execution, payment and output. The shift in one line The old question was how to generate ZK proofs efficiently at scale. The new one is how a protocol can turn a verifiable condition into a complete, executable sequence. Proving is still part of the system, but it is now one workload among several that Kernel coordinates.

很多人都不知道 @ptsdshow NFT 的設計。 今天就來說說 3,333 張 NFT Tickets。 這些 Ticket 不只是拿來收藏，官方目前公布的機制裡，Ticket 的稀有度會影響未來 PTSD Token allocation multiplier。 也就是說，同一個 Ticket，因為 rarity 不同，對應的 allocation multiplier 也不同。 目前公布的倍率是 Common：1x Rare：2x Epic：3x Legendary：5x Golden：100x 這裡有一點要特別注意。 Multiplier 指的是 Token allocation 的分配倍率，不能直接理解成投資報酬率。 目 前官方也還沒有確認 Token launch date、base allocation、mint price 等細節，所以我覺得這部分還是要等後續正式資訊。 我自己看 NFT 的時候，會比較在意這種機制能不能跟整個內容 IP 連起來。 PTSD 的 Ticket 有自己的 rarity，同時又跟未來的內容權益和 allocation 機制產生關聯。 再加上官方提到未來可能有 drops 和 perks，這讓 Ticket 的定位不只停留在一張圖片。 當然，最後實際價值還是要看後續產品與內容怎麼發展。 目前階段，我會把它理解成一張連接 PTSD 世界觀與未來生態的 Ticket。 這個設計蠻值得繼續追。 #PTSD#