finance-skills v10.0.0 - New skill: tradingview-mcp — headless TradingView market data. No desktop app, no login, no API key. - TA + multi-timeframe, BB squeeze & volume scans, futures, pre/post-market, backtests.

Introducing Korea Deleveraging-Pressure Dashboard. KOSPI has fallen sharply from its recent high, margin financing is contracting, and forced liquidations are running at extreme levels. The question for investors with Korea exposure is whether the deleveraging is complete or still in progress. This Play answers that question with daily-updated structural data. Deleveraging Pressure Index Nine sub-indicators synthesize into a single composite score that identifies the current phase of the deleveraging cycle. Three phases are distinguished: margin call trigger, forced liquidation dominance, and stabilization. The index updates daily as new KOFIA FreeSIS data arrives. Key Readings and Signals Credit margin balance, margin-to-deposit ratios, forced liquidation volumes, and index drawdown are tracked in a unified view. Each reading carries its historical percentile ranking and short-term momentum, making it possible to assess whether pressure is intensifying or fading. Signal Framework Three conditions must clear before a durable recovery: technical selling exhaustion, external catalyst resolution, and regulatory clarity. The Play monitors the first condition automatically and flags the remaining two for manual confirmation. Until all three signals turn green, rallies are classified as technical in nature. Historical Context Multi-year time series cover margin financing, forced liquidation volumes, outstanding receivables, and index levels across both KOSPI and KOSDAQ. Composite pressure history allows current readings to be compared against prior deleveraging episodes. Available to all paid clients on our full-stack research platform. Start here:

Deep|LLM: RSI Is the Most Important Variable, and Compute Is the Deepest Moat July 2026 produced the sharpest drawdown of this cycle for the AI trade. Semiconductors posted their worst month since 2002, and more than $1 trillion of AI-linked market value came off. The concern behind the move is straightforward: coding looks like a special case that is hard to replicate, enterprise adoption is running behind expectations, and the “next coding” that would justify hundreds of billions of dollars of capex has not yet appeared. We think the debate is framed around the wrong question. The market is evaluating AI through an application lens, looking for the next killer use case and checking whether current revenue covers capital expenditure. The industry’s own objective, however, is AGI, and the mechanism most labs point to on that path is recursive self-improvement (RSI). A more useful way to think about it is as a sequence of capability milestones, each with its own commercial payoff. Coding capability has already been monetized, in the form of this year’s first-half revenue inflection. The next milestone is continual learning, which lets models accumulate knowledge in deployment the way an employee does; combined with deployment mechanisms such as forward-deployed engineering, it will open up most of the enterprise opportunity. Full RSI, in which models participate in and accelerate their own development, sits furthest out and will determine the competitive structure that follows. Progress is visible at each stage, and in several places it is accelerating. We make three claims. First, the “context problem” the market worries about is a capability question rather than a use-case question, and it is what continual learning is designed to address. Once models can accumulate tacit organizational knowledge the way employees do, the search for a single “next coding-like” use case dissolves: enterprise use cases open up progressively. The TAM reference then shifts from a software budget measured in hundreds of billions to a labor market measured in tens of trillions. Second, early signals of RSI are already observable. Model iteration has moved from annual to monthly. GPT-5.5 took roughly one month from the end of pre-training to release. OpenAI cut prices by up to 80% three weeks after GPT-5.6 shipped, against a history in which price cuts followed months of inference optimization. Lab code is now written almost entirely by models, and AI’s measured contribution to internal R&D velocity rose from 5% to 15–20% over six months. Third, if RSI crosses its threshold, catch-up strategies built on distilling frontier model outputs are likely to become progressively harder, and compute becomes the least compressible constraint and the deepest competitive moat. When the frontier iterates monthly, a follower may still hold a fixed time lag, but it cannot readily replicate a frontier lab’s full learning, data, and experimentation stack. Native self-improvement capability requires proprietary experimental infrastructure and the compute to run it. This is also the view inside China’s leading labs: China has not hit a scaling wall, but does not yet have enough compute to reach one. Capex, on this reading, should not be dismissed as a bubble. The latest earnings season shows real current returns from AI-driven cloud growth, and beyond that, capex is what ensures a player has sufficient compute when the RSI threshold arrives. Strategic necessity is not the same thing as shareholder return, and we treat that distinction explicitly in a scenario framework. The variable that matters most is less “what is the next application” than when RSI arrives and who gets there first. Detailed Report

@FundaAI released this week an in-depth report into SpaceXAI's computing power. SpaceXAI plans to build 4 GW of compute capacity using Rubin racks next year and their Rubin ramp looks power-constrained rather than supply-constrained. Our colleague who worked on this piece might be the first person in the world to dive this deep into this topic. I highly recommend this read alongside the great pieces written by the talented @damnang2 and @vikramskr

Trade Skills v2.3.0 - additional pitfalls - expanded case studies - overnight index futures framework - parent order-flow classification framework Grateful to my friends for their feedback

$nok will acquire a NXP fab in Arizona, initially leasing a portion of the facility starting early 2027 before closing the acquisition of the entire site in Q1 2029. They will convert the site to InP production for optical components. AI & Cloud orders went from EUR 1B in Q1 to EUR 2.8B in Q2 as IP Networks design wins started converting to orders and, we suspect, customers knowing they need to get in the queue quick.

Weekly|Kimi K3 "DeepSeek Moment" Jitters, TSM and $ASML Beat-and-Raise, $ATI & the Starship Supply Chain, $INTC , $AXTI , $NBIS Semis got smoked this week, with the indexes closing out weekly losses on fears of another “DeepSeek moment” after Kimi K3’s release. We think that read gets K3 exactly backward. K3 is a 2.8-trillion-parameter model priced at $3/$15 per million tokens - in line with Claude Sonnet, not the cut-price disruption playbook - and it currently runs only at maximum reasoning effort, which means it very likely burns more compute per task than its US frontier peers. A Chinese lab scaling up aggressively and pricing at frontier levels is evidence that scaling still works and that demand for inference compute keeps climbing. That is not a bearish datapoint for the compute chain. The fundamentals told a story opposite to the tape. TSMC and ASML - the two most important prints of the season - both delivered beat-and-raise quarters. TSMC lifted its full-year 2026 USD revenue growth guidance to above 40%, raised CapEx again, and C.C. Wei described AI demand as getting “stronger and stronger” through 2029-30. ASML raised its 2026 revenue guide to EUR 43-45bn (midpoint 12% above consensus) and is expanding both EUV and immersion DUV capacity by 30% in 2027, with another 30% under study for 2028. Yes, TSMC’s 3Q gross margin guide came in light - but the culprit is a faster-than-expected 2nm ramp, which is the kind of margin problem you want to have. When the supply chain’s two most important companies are guiding like this while the market frets over a model release that actually reinforces the compute-demand thesis, we are comfortable treating this week’s selloff as positioning, not fundamentals. We stay constructive on the AI compute chain. This Week’s Reports Kimi K3 - scaling still works, and it is not cheap. K3 lands in the global top tier on coding, agents, and long-horizon tasks. However, prices at Claude Sonnet levels and runs only at max reasoning effort - more proof that frontier capability still costs frontier compute. TSM & ASML - the supply chain says demand is accelerating, not slowing. Both quarters were beat-and-raise: TSMC surprised on CapEx and lifted full-year growth to 40%+, while ASML guided 2026 revenue 12% above consensus with 30% EUV capacity expansion ahead. The 2nm-driven margin dilution at TSMC is noise next to the demand signal. ATI - the materials chokepoint of the Starship era. Superalloy content per Starship stack is 25-30x a Falcon 9, and specialty melting is the one layer SpaceX cannot vertically integrate - yet ATI trades at the lowest multiple of the Big Three melt names—one of the few structural public-market entries into the SpaceX supply chain. Premium Report Snapshot A portion of our research is reserved for Premium subscribers and is not distributed via Substack. Below is a snapshot of what Premium subscribers received this week beyond the Substack feed. ... Selected Weekly Expert Interviews A snapshot of the expert calls conducted for Premium subscribers this week. Full transcripts and takeaways are available on the FUNDA platform. ... Claude Deployment Spend, ROI and Governance Constraints Employee of AI Department, Aviva Canada Aviva’s Canadian Claude rollout is seeing strong consumption in coding and insurance workflows, but ROI remains uneven, and governance limits broader adoption. AI Agent usage Former Engineer, Google Google DeepMind’s Gemini app commercialization is framed as advancing through harness-system buildout, but consumer monetization, execution discipline, and coding competitiveness remain challenges. Detailed Report