.@JonahJeng marks the highly anticipated international release of the Hong Kong crime thriller THE FURIOUS with an expansive appreciation of its action director Kensuke Sonomura, who has masterminded “some of the most idiosyncratic fight scenes in contemporary action cinema.” →

Zimbabwe, often considered an economic basket-case because of its history of farm seizures and hyperinflation, is enjoying an idiosyncratic boom. Register for free to find out why

Privacy in crypto will win in two forms: 1. Private money — encrypted assets 2. Private computation — encrypted smart contracts The second category is especially important for institutions because each one of them has its own idiosyncratic requirements—bespoke business logic, compliance rules, etc. For them, providing privacy to their users isn’t as simple as just encrypting everything. They need to be able to encode who can see what and under which conditions. That means privacy cannot just be binary. It must be programmable.

WOW: Bitcoin's correlation with tech has COLLAPSED. Over the last year, BTC has maintained a respectable relationship with high-duration tech: BTC / QQQ correlation: 0.44 BTC / IGV correlation: 0.40 But look underneath the hood. During the major tech-beta regimes last fall and again in Feb/March, Bitcoin's 20-day correlation with QQQ and IGV repeatedly lived around 0.60-0.70+. Today: BTC / QQQ 20D correlation: -0.05 BTC / IGV 20D correlation: +0.03 Essentially ZERO. Now look at what happened while that correlation disappeared. Since August 10: Bitcoin: +21.8% QQQ: -1.7% IGV: -3.0% The factor driving Bitcoin's marginal return appears to have changed. The market spent portions of the last year pricing BTC like an extremely volatile high-duration risk asset. Then the latest leg happened while software fell, Nasdaq fell, and Bitcoin ripped more than 20%. That is a completely different market structure. Bitcoin now has an idiosyncratic bid strong enough to rally without tech. If QQQ subsequently resumes its bull market while that Bitcoin-specific demand remains? You potentially go from one engine to two. BTC-specific capital keeps bidding Bitcoin. Then traditional risk-on liquidity comes back and starts bidding everything again. Correlation can re-expand because TECH CATCHES UP TO BITCOIN rather than Bitcoin needing Nasdaq to drag it higher. That is the regime change I'm watching. Bitcoin just rallied 22% while one of its historical macro transmission mechanisms effectively switched off. Something else is driving the bus now:

I used Grok 4.6 to build a 2D agent simulation of The Office. Here are notes and observations on 4.6: It is a huge step up from 4.5 - both in the quality of visual outputs and in the breadth of work: personality, features, and finding usable assets for this project. The editorial content and writing were good - I like the "feel" of this model. One of the biggest wins for 4.6 is speed. The throughput (~80 tok/s) and latency are so good that it feels Composer-adjacent - something @ericzakariasson called out in his writeup. That speed + cost + performance makes for a true "workhorse" model that's practical for both personal and work use. My goal was to simulate The Office through "agents" that behaved like the characters - including all of the neuroticism and idiosyncrasies that made the show so funny. I knew what I wanted artistically - I've built similar 2D apps with pixel art. I asked Grok 4.6 to research psychological modeling, pull character descriptions, find references for the cast, and assemble 2D sprites with the Universal LPC sprite sheet generator and some RPG tilesets I grabbed online. I rattled a brief into the @cursor_ai mobile app late one night at the Cursor office and woke up to an MVP. Then I spent a day or so spamming cloud agents until the simulation worked how I liked. The cool part is that Grok did the simulation, art, and research on its own: - Every couple of minutes, each person scores a few possible actions and picks one. They pathfind around desks, work, grab coffee, talk at the cooler, hide in the annex, or end up in a meeting. - Click someone and you can inspect traits, needs, mood, relationships, and whatever they're doing right now. You can also speed time up, force Michael to call a meeting, or roll a random office event. - Each character has Big Five scores that bias what they tend to do. Needs like work, social, caffeine, status, and comfort rise over time and fall when they do something about them. - Grok reconstructed the layout from Dunderpedia (Office Wiki) and online sources - Michael's office and the conference room on the north glass, Pam at reception, sales and accounting in the bullpen, the annex to the east, restrooms and the warehouse down the stairs. - Grok helped me find a few sprite sheet packs to implement. I was surprised at how accurate some of the character sprites were - from hair color to clothing. The combination of a performant model, cloud workspaces, and high throughput made for a really fun devex - the amazing folks at @cursor_ai and @SpaceXAI make my job easier everyday. A huge shout-out to @_Brian_Zhang, whose Notion hackathon project inspired this exploration.

"What if we're insufficiently optimistic?" Anish Acharya's case in eight parts, from a conversation with Jen Kha on AI: 1. Macro (03:34) - Prices for last-gen GPUs are RISING on a per-hour basis. That never happens in computing. It points to essentially infinite demand for intelligence and highly constrained supply. 2. Moats (05:30) - Network effects, scale, and brand are as good as they've ever been. "No amount of coding agents is gonna make Nike not Nike." The vulnerability is integration, the moat built on being painful to migrate away from. 3. Tokens (06:42) - For unbounded-upside work like sales and product, it's rational to pay almost any price for a model even one IQ point smarter. 4. Labs (10:37) - They're vertically integrating down into inference, not up into apps. Inference is one homogeneous workload at enormous scale. The app layer is a thousand idiosyncrasies of pricing and packaging. 5. Models (11:15) - They're not commodities. Some are literal and precise, some are creative, and organizations will need both. 6. Agents (15:20) - An agent is just a model in a loop with tools and memory. Coding loops already fix reported bugs end to end. Business loops come next, think the model that says "We need to open a branch in Tijuana." 7. Consumer (17:20) - We're in the DOS era of AI, so there's no app store for it yet. But the 99-cent app era is over, people are paying $200 a month, and the Birkin bag of software is coming. 8. Builders (35:05) - New business formation is skyrocketing. The 25-year-old who would've been a YouTube creator is now building software for their neighborhood: a mom-and-pop SaaS economy. YouTube: @illscience @jkhamehl

Accurately pricing derivatives when underlyings are closed is a universal source of edge in market making, from tier-1 firm graybox ETF trading to newer operations in 24-hour prediction markets and perpetual futures. Some widely applicable pricing strategies: Related futures and currency moves. When a derivative’s underlyings are closed, market makers use other instruments as proxies that have sufficient beta to the derivative. Examples for a US ETF on Japanese stocks: the USD/JPY pair trades 24/7 through a variety of FX ECNs, interdealer platforms, and futures exchanges. Nikkei 225 index futures also trade on multiple global derivatives exchanges during extended hours. Moves in these assets during the Japanese night will on average predict the opening prints of individual stocks listed on JPX. Home market index moves. An ETF moves with a non-trivial correlation factor to other US names simply because it’s a US-listed security. The effect is easily observable during heightened volatility. It’s a common industry saying that in a market-wide selloff, all correlations go to 1. Both narrow- and broad-based indexes of US names explain some of the signals in mid-frequency alphas. US index returns comprise a small but meaningful component of derivatives’ multifactor beta models. News. A key requirement for pricing derivatives on foreign stocks is processing local news and earnings releases that relate either to the particular stocks or relevant stocks in the same sector. Quantitative trading firms use automated translation tools to process local foreign-language news and suggest idiosyncratic adjustments to traditional factor models. Recent advances in LLM-based NLP have made sentiment analysis viable for blackbox trading systems to react instantaneously to news-based signals. Microstructure. High-frequency trading firms successfully and counterintuitively price derivatives by ignoring the underlyings’ characteristics. Firms extract short-term alphas from order book characteristics, recent returns, microprices, and other microstructure features. From the perspective of a reinforcement-learning non-linear model trainer, unlabeled feature sets and time series data result in ETFs, ADRs, and common stocks being treated as mathematically equivalent. Based on the above, here is a rough model of how large market-making firms predict mid-frequency returns when traditional underlying markets are closed: ΔP = β_FUT · ΔFUT + β_FX · ΔFX + β_IND · ΔIND + [News Term] These firms also heavily prepare for circumstances under which this model fails. FX and futures have idiosyncratic moves, home-market betas can break during regime shifts, news sentiments still have positives, and microstructure signals decay fast. The market shift underway in 24/7 traditional derivative and prediction contract trading will test the model’s longevity for market making and statistical arbitrage going forward.

Some thoughts on recent price action in Tech: Post-earnings action continues to be a lot better in internet & software. Investors there are still showing an appetite for buying names up post-earnings, especially where numbers accelerated and the narrative improved, even if just marginally. $PLTR +2%; $TEAM +2%; $TWLO +3.5%; $U +25bps; $UBER +4%; $SAP +1%; $MSFT +1%; $ABNB +4%; $TTWO +2% are a few of the names that printed solid earnings and have follow-through. This shift in flows out of semis started with $s flowing into hyperscalers a couple weeks ago and has now expanded. We even started to see some willingness to buy the dip on misses, with $DDOG +11% and $FIG +9% today, while something like $AKAM +6.5% didn’t stay down long. Compare that with the anemic follow-through in AI Semis: $ANET following its big beat, $STX, or $ALAB selling off despite the big Sept Q guide. The weaker prints like $INTC and $SNDK also continue to struggle to find a bid. There’s admittedly some cherry-picking in the attached table showing how stocks have reacted following prints, but in our defense, there are a lot of cherries. To put it succinctly: with the AI semi vibes & narrative remaining choppy and some of the more favored parts of the trade (memory, CPUs, storage, etc.) seemingly rangebound for the time being, investors appear increasingly comfortable putting $ to work in idiosyncratic ideas outside the space in names where the earnings narratives are moving in the right direction, numbers are beating, valuations have come down, names have underperformed YTD, and sentiment is tilted to the left. We thought some of those flows would also go to $NVDA, but even that is lagging. Today felt like that risk appetite broadened another notch outside AI semis, with investors willing to spread dollars further down the bench, buying misses and revisiting high-quality underperformers without an obvious near-term catalyst, as the $NFLX/ $SPOT moves illustrated.