Pubblicazioni
Ricerche su agenti IA, backtest e audit delle previsioni di mercato, e sull’architettura della piattaforma.
IA e business
Ten clients at $8,000 a month is $960,000 a year. The real profit arithmetic, six categories already working with the companies doing them, three pricing models, and a step-by-step from zero to first retainer.
10 min di lettura9 sezioni
IA e business
An AI agent runs a loop - observe, decide, act - and a chatbot does not. What that difference demands in scope, records and stop controls, where agents genuinely win, and four questions that evaluate one faster than any demo.
5 min di lettura6 sezioni
Insidie dei modelli
Regime shift is the most common way machine learning fails in production, and backtests average it away. A worked example with real numbers from our own board, and four habits that catch it before the results do.
5 min di lettura6 sezioni
IA e business
For most people AI changes the cost side of a household long before the income side. What genuinely moves - fees, comparison, gated expertise - what it puts at risk, and why "let AI trade for you" is the weakest version of the claim.
5 min di lettura6 sezioni
IA e business
AI removes steps for tasks where being wrong is cheap and visible, and adds a verification tax where being wrong is expensive and invisible. A single question sorts almost every case correctly.
5 min di lettura7 sezioni
Crypto e mercati
MEV is one of the few places where machine learning has a defensible job, because the task is simulation and classification rather than price prediction. But private order flow makes every public-data study a lower bound of unknown tightness.
6 min di lettura6 sezioni
Crypto e mercati
Most depegs that matter are plumbing wearing the costume of solvency. What machine learning can genuinely do with stablecoin data, why depeg prediction breaks on rare events, and how supply works as a regime descriptor rather than a signal.
6 min di lettura6 sezioni
Crypto e mercati
Funding rates and open interest describe how levered a market is and what that position costs to hold. They forecast the size of coming moves, not the direction - and the data is full of traps that quietly wreck studies. What AI can honestly extract.
6 min di lettura6 sezioni
Crypto e mercati
On-chain data is the only complete dataset in markets - no survivorship bias, no vendor filter. It is also built on unvalidated address labels and a handful of independent cycles. What AI can honestly do with a blockchain, and what it cannot.
7 min di lettura7 sezioni
Valutare le previsioni
The instinct to grade a forecast by whether it came true is the wrong test. Calibration - when a model says 70% it happens about 70% of the time - is the property that separates a real forecaster from a confident guesser, and it is measurable.
7 min di lettura7 sezioni
Crypto e mercati
Direction is the AI forecast everyone wants and a liquid market defeats. How far an asset can move is the one machine learning can make - and prove. Why sigma-root-t is wrong in both directions, and what coverage really tests.
7 min di lettura7 sezioni
Crypto e mercati
Everyone chases AI that predicts price. The place machine learning genuinely earns its keep is risk management — volatility, position sizing, drawdown, liquidation. Measurable, unglamorous, honest.
9 min di lettura7 sezioni
