Blog
Deep dives on post-quantum cryptography, quantitative finance, machine learning, and applied statistics.
Quantum computers will break RSA and ECDSA. NIST finalized the replacement algorithms in August 2024. Here is everything a developer needs to know to start migrating today.
You do not have to choose between classical and post-quantum cryptography. Hybrid encryption lets you use both simultaneously, protecting data against both classical and quantum attackers during the transition period.
The Black-Scholes formula is 50 years old and still powers trillions in daily derivatives volume. But most developer implementations miss the edge cases that matter in real trading systems.
Monte Carlo is the workhorse of modern risk management. Banks run millions of simulations nightly to compute VaR. Here is how to use the same techniques in your own applications without a quant team.
The Kelly Criterion is the mathematically optimal bet sizing formula. It maximizes long-run wealth growth while avoiding ruin. Most traders either ignore it or misapply it.
Most product teams run A/B tests incorrectly. They peek at results early, stop tests when they see significance, and ignore multiple comparisons. The result is a false positive rate far higher than they think.
Most teams building AI features spend 80% of their time on infrastructure and 20% on the actual product. A managed inference API flips that ratio. Here is when it makes sense and when it does not.
Every business with historical data has a forecasting problem. Most solve it with spreadsheets and gut feel. Here is how to do it properly with statistical and ML-based time series models.
QR code generation, PDF rendering, OCR, screenshot capture — every application eventually needs these. Here is why maintaining your own implementation is usually the wrong call.
The developer population outside the United States is growing faster than inside it. India alone produces 1.5 million engineering graduates per year. Here is why that matters for API businesses.