Ask HN: What Are You Working On? (December 2025)

I'm building a session prep tool for tabletop RPG game masters. The idea is to make a narrative engine rather than another static wiki. Most existing tools are great for storing lore, but they don't help you run the story. I wanted something that supports the "create now, refine later" workflow — get ideas into structure fast, then refine as you play.

Core features: - interconnected world-building (NPCs, factions, locations) and story-building (situations, fronts, clocks) - Bidirectional linking — connecting a story hook to an NPC makes that hook visible from the NPC's view - Clock system with milestone consequences that can spawn or edit entities - Situations fire different consequences based on outcome (players engaged vs. ignored the hook) - Material waste detection — flags under-connected content so you know what's prepped but unused.

The main workflow is mindmap-based. Each entity gets its own context layer showing direct relationships. (Soon available in demo version) Working on next: automatic player-facing content. As players complete situations, public notes from involved entities get published — so the GM doesn't have to maintain a separate campaign log.

Stack: TypeScript, Effect-TS, SolidJS, Cytoscape (graphs), Leaflet (maps)

The hosted version is rough — I've been using it to get early feedback from GM friends. Happy to hear thoughts from anyone who preps campaigns

Volatility regime models (Markov-switching GARCH, regime-switching stochastic volatility) are ubiquitous in finance. However, they share a fundamental limitation: regimes are identified ex post from return dynamics, providing no predictive power for regime transitions. The standard approach fits a Hidden Markov Model to returns, labels high and low volatility states, and estimates state transition probabilities that are essentially unconditional averages. This matters because the economic value of volatility timing depends entirely on predicting regime changes before they occur. A model that identifies regimes only after observing the returns is useless for trading volatility.

Existing research documents regime-dependent behavior but does not identify causal drivers of regime transitions. The papers on volatility forecasting factors, variance risk premium dynamics, and market instability from option flows dance around this question without directly addressing it. The recent work on causal ML in finance (double machine learning, causal forests) has focused primarily on equity return prediction rather than volatility states. The connection between options market variables and subsequent volatility regime transitions has not been rigorously established through causal methods.

We develop a causal framework for volatility regime prediction using option-implied variables as potential causes of regime transitions. The key insight is that options markets are forward-looking, so information embedded in the implied volatility surface, put-call ratios, option order flow, and term structure slopes may causally influence future realized volatility regimes rather than merely correlate with them.

Currently building a robust dataset.

Om Friday after Thanksgiving I spent half a day building a telegram bot that accepts an address and a list of Amazon links, and in turn orders the item (at a discount since it uses my Amazon credit card), and adds it to the above "family debts" spreadsheet.

I really like the idea of programmable, trusted lending like this, and feel like it could be extended to other groups that you implicitly trust.