SGP-0003 is a middle finger to every microstructure-sensitive application on Solana. It signals to every developer that the cost model for the chain may be upended at any time, even if no applications support it. How can you put your business logic on-chain in this world?

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Expect more English in my posts going forward. The logic is simple: Crypto is a global playing field. The core infra updates, raw API specs, and serious discussions on market microstructure—the primary sources are almost always in English. Code and markets speak the same language.

Architect is hiring for three lateral roles: options trading quantitative researcher, compute capital market sales, and structured derivatives specialist. Details on what we’re building and the new team members we’re looking for Quantitative Researcher: We’re building: automated high-frequency options trading strategies on legacy and new(er) exchanges What you’ll work on: volatility modeling, ML for parameter optimization, order placement simulation, new venue exploration Stack: Rust, Python, AWS, ClickHouse What we’re looking for: field experience in derivatives trading and research, strong system design instincts, multiple approaches to options pricing theory, bias towards shipping Nice to have: familiarity with the latest ML toolkits and exchange microstructure Location: your preference Compute Capital Markets Sales: We’re building: the first US-regulated exchange purpose-built for compute futures, options, swaps, forwards, and capacity, plus AI supply chain commodities What you’ll work on: building relationships and facilitating trades with neoclouds, lenders, insurance providers, inference companies, datacenters, AI labs, interdealer brokers, retail brokers, hedge funds, and asset managers What we’re looking for: network of established and startup AI companies, fluency with compute capacity pricing/structures, translation skills between finance and AI, aversion to slop, commitment to competitive markets for compute Nice to have: experience in compute procurement at a hyperscaler, neocloud, or lab Location: SF Structured Derivatives Specialist: We’re building: specialized contracts for trading and hedging compute, compute derivatives, and a range of physical commodities supporting the US datacenter build-out What you’ll work on: modeling and structuring OTC products with Architect’s traders/technologists and AI counterparties for compute, memory, inference, and energy trading What we’re looking for: experience trading OTC derivatives contracts at banks or trading firms, sell-side institutional network, enthusiasm for working at startups Nice to have: relevant FINRA/NFA certifications or passion for test-taking Location: your preference

AI has broadly become synonymous with LLMs, but most AI/ML models used in quantitative trading are decades old. Boosted trees and PCA still account for more alpha than anything built on transformers. More detail on several ML techniques in quant trading: Gradient boosted trees and random forests: A standard strategy in short-horizon trading is to start with an intuitive set of microstructure features and train a non-linear model on them. Random forests provide a baseline ensemble that’s hard to overfit, while gradient boosted models fit fainter structures at the risk of overfitting. Features can include microprices, characteristics of the present order book, recent returns of related instruments, momentum signals, and orderflow directionality. Pre-transformer natural language processing: Throughout the 2010s, NLP PhDs were a sought-after resource for quantitative hedge funds. A variety of techniques such as Loughran-McDonald finance-specific sentiment dictionaries, TF-IDF and bag-of-words classifiers, and LDA topic modeling were used to extract sentiment from news, earnings reports, SEC filings, and FOMC minutes. Transformers are more generalized tools for language processing but some of the older methods in the toolkit are still used for their simplicity, predictability, and cost-effectiveness. Dimensionality reduction: A statistical arbitrage researcher may have thousands of correlated instruments and only a few years of clean historical data, so the covariance matrix has more parameters than observations to estimate them from. Principal component analysis is the standard remedy. A handful of leading components explain most of the variation in a universe of equities, and hedge funds will frequently neutralize the leading factors and trade the residuals. Autoencoders generalize the same idea to nonlinear structure, but PCA persists because its output is stable, interpretable, and cheap to recompute. Computer vision: From around 2010 to 2017, hedge funds gained a large informational edge by building automated pipelines to extract alternative data from satellite imagery. The models were unremarkable by research standards, mainly derivatives of convolutional neural networks. Some of the most famous applications include counting parked cars in retail lots, gauging construction progress at industrial sites, estimating crop yields from near-infrared reflectance rather than visible light, and reading crude inventory off the shadow cast by a floating tank roof. The trade worked best during a time when imagery was scarce and the engineering was novel. Today image-based alternative data is sold by third-party vendors for relatively inexpensive consumption. The large memory footprint, slow inference, and black-box nature of LLMs make them a poor fit for alpha generation at trading firms. Hand-tuned models based on intuitional feature selection are still the norm. For niche industries like quantitative finance, there is ample room for competition with generalized frontier models.

First Principles Ep. 5 with Paul Milgrom One of the most important auction designs in modern history was sold to the FCC on a 3.5-inch disk. Nobel Prize winner Paul Milgrom on auctions, price discovery, and how mechanism design became real-world infrastructure — from spectrum auctions to DeFi. Hosted by @Tim_Roughgarden with @skominers. 00:00 Intro: economics assumptions that are “just wrong” 02:19 Scott Kominers and Tim Roughgarden on the genius of Paul Milgrom 05:35 An intro to the  Glosten-Milgrom model: The paper that created entire fields of economics 07:48 The auction theory breakthroughs of the 1980s 17:15 Why market microstructure matters for DeFi 24:17 When math teaches economics something new 32:38 How theory became spectrum auction design 36:22 The floppy disk that helped convince the FCC 41:05 What changed when auctions moved online 45:28 The auction that reorganized television 1:07:30 What economics and computer science can learn from each other 1:13:22 Futures markets for compute 1:15:12 Paul Milgrom’s advice for builders

As someone who loves trading technicals I think learning about markets via technicals (like I did) is one of the worst ways to start It’s a rigid framework where grown men argue with each other about the exact Japanese name for a specific candlestick or a box they’ve drawn on an arbitrary time frame It doesn’t teach you the foundations - why markets move, different types of participants, microstructure, order types and their impact, perps vs spot, and all that stuff - market ‘plumbing’ as a category One of the biggest issues with being hyperfocused on technicals is that they don’t teach you principles and market effects Most technical setups can be decomposed into broad buckets which are well-established (trend, mean reversion, momentum, order flow / price impact, vol clustering etc.) A lot of technical analysis is an often unknowing attempt to map those broad market effects into a recognisable pattern But even a technical-first view is better served by understanding the underlying market effect first and then decomposing it, as opposed to focusing on the specific pattern without ever looking at what’s happening under the hood “This type of triangle tends to go up” is a lot less useful than “this type of flow tends to resolve higher over N time frame”, even if you use the same triangle to identify it Another example: if you’re drawing a support level and buying it, you’re assuming some version of buyers being more aggressive than sellers in that area over a given time frame and predicting a higher price as a result - but what does that mean? Shorts closing / taking profit, allowing for mean reversion? Aggressive sellers being absorbed by passive buyers? Some price insensitive buyer predictably stepping in at a value area? Sellers getting margin called and forcibly trading at bad prices/causing a dislocation? Clustering of orders creating some sort of imbalance? And so on. There’s definitely a risk of overthinking this stuff, and you can make money from charts alone But if you haven’t thought about the underlying market effects and ‘plumbing’ for your setups you’ll likely be stuck in rigid pattern matching that doesn’t generalise and isn’t subject to deeper investigation and more nuanced application Even if your main lens remains TA-focused, there is no harm in understanding the stuff you’re trading on a product level (eg perp contract specs, OI, funding, mark/last/index etc) and on a foundational level (why and how markets move) Especially now that you can jam this stuff into an LLM and keep saying “dumb it down” until you get it, no excuse not to do your homework This is something I really wish I did much earlier in my trading life, so hopefully it resonates with a fellow trader stuck in TA psychosis spending his mum’s credit card on a fourth Udemy candlestick course Anyway GM

No complexity. No accident. 10/10 was caused by irresponsible marketing campaigns by certain companies. On October 10, tens of billions of dollars were liquidated. As CEO of OKX, we observed clearly that the crypto market’s microstructure fundamentally changed after that day. Many industry participants believe the damage was more severe than the FTX collapse. Since then, there has been extensive discussion about why it happened and how to prevent a recurrence. The root causes are not difficult to identify. ⸻ What actually happened 1.Binance launched a temporary user-acquisition campaign offering 12% APY on USDe, while allowing USDe to be used as collateral with the same treatment as USDT and USDC, and without effective limits. 2.USDe is a tokenized hedge fund product. Ethena raises capital via a so-called “stablecoin,” deploys it into index arbitrage and algorithmic trading strategies, and tokenizes the resulting fund. The token can then be deposited on exchanges to earn yield. 3.USDe is fundamentally different from products such as BlackRock BUIDL and Franklin Templeton BENJI, which are tokenized money market funds with low-risk profiles. USDe, by contrast, embeds hedge-fund-level risk. This difference is structural, not cosmetic. 4.Binance users were encouraged to convert USDT and USDC into USDe to earn attractive yields, without sufficient emphasis on the underlying risks. From a user’s perspective, trading with USDe appeared no different from trading with traditional stablecoins—while the actual risk profile was materially higher. 5.Risk escalated further as users: •converted USDT/USDC into USDe, •used USDe as collateral to borrow USDT, •converted the borrowed USDT back into USDe, •and repeated the cycle. This leverage loop produced artificial APYs of 24%, 36%, and even 70%+, widely perceived as “low risk” simply because they were offered by a major platform. Systemic risk accumulated rapidly across the global crypto market. that point, even a small market shock was sufficient to trigger a collapse. When volatility hit, USDe depegged quickly. Cascading liquidations followed, and weaknesses in risk management around assets such as WETH and BNSOL further amplified the crash. Some tokens briefly traded near zero. The damage to global users and companies—including OKX customers—was severe, and recovery will take time. ⸻ Why this matters I am discussing the root cause, not assigning blame or launching an attack on Binance. Speaking openly about systemic risks is sometimes uncomfortable, but it is necessary if the industry is to mature responsibly. I expect there may be significant misinformation and coordinated FUD directed at OKX in the near future. Even so, speaking honestly about systemic risk is the right thing to do—and we will continue to do so. As the largest global platform, Binance has outsized influence—and corresponding responsibility—as an industry leader. Long-term trust in crypto cannot be built on short-term yield games, excessive leverage, or marketing practices that obscure risk. The industry needs leaders who prioritize market stability, transparency, and responsible innovation—not a winner-take-all mentality where criticism is treated as hostility. Crypto is still early. What we choose to normalize today will determine whether this industry earns lasting trust—or repeats the same mistakes again.

Hollow core had a good week in English. I spent it reading three Chinese write-ups from one forum in Shenzhen, and they are more specific about the technology than anything I have read on it this year. Same stage, 11 September, three speakers, all three write-ups published within half an hour of each other. The buyer first. Han Liuyan of China Mobile's research institute calls anti-resonant hollow core a complete disruption of optical communications, and says loss reduction is essentially finished and now sits far below the solid-core limit. Then he lists what is not finished. CO2 absorption badly hurts transmission and the two known fixes, sealing and positive pressure, are in his words neither an ultimate solution. The loss spectrum is rougher than solid core and degrades after transmission. Hollow-core fibres with different cladding unit counts have high connection loss and are not suited to being mixed at random. He had good news too: two years of monitoring on the Wuxi line, gas evenly diffused, fibre and splice loss stable, without purging. Then the standards body. Ao Li, a vice president of CAICT, listed five engineering problems. The first is the one I keep coming back to. A single preform draws less than 100 kilometres. Lifetime cannot yet be assessed. In live networks splice time is long and loss is high, and construction and maintenance norms do not exist yet. Then the toolmaker, which is the part nobody covers. Fujikura's Zhao Lin: hollow core has an internal microstructure, so splicing needs rotational alignment and the structure must be kept from collapsing. Conventional four and six-motor splicers align cladding or core only. Hollow core needs an eighth motor. That is a specialist machine, and it is not what is in the vans. Now put the preform number next to the volumes. CAICT puts global hollow-core demand at 800 thousand fibre-km in 2030, from a market it says is still at pilot stage. At under 100 km a preform, that is more than 8,000 preforms. Microsoft's 15,000 km is more than 150, and more again if that figure is route rather than fibre. That one line explains something I had filed as a curiosity: why the most motivated hollow-core buyer on earth stopped trying to scale the company it bought and hired Corning and Heraeus instead. Corning draws it in North Carolina. Then the clock. ITU-T opened its first hollow-core project in July, two months ago, and its scope covers interoperability, deployment and maintenance, which are precisely what the buyer and the standards body flagged. For calibration, the multicore items opened in March 2025 and are expected to publish in 2028. So here is what I take from it. Between now and a standard, the money in hollow core is not in the fibre design, because the design is the part they say is working. It is in preform and draw capacity, and in the splicer. Corning is paid on both new-fibre routes at once, drawing Microsoft's hollow core in Carolina and co-founding the four-core group in March alongside Sumitomo Electric, Fujikura and TeraHop. The disruptor is paying the incumbents to industrialise it. What would change my mind is a preform length. If anyone shows a draw well past 100 km at usable yield, five problems collapse to two and the timeline moves in. Until then I read hollow core as a datacentre and campus product, where runs are short and splices are few, and not yet as a long-haul one. $GLW $MSFT